100_explorace.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ae66392c-614d-445c-bedd-841b9a6aac75",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', 100)\n",
    "pd.set_option('display.max_rows', 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "514cc305-4567-4608-b0d8-c36528a06445",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_parquet(os.path.join(\"data\",\"jizdenky.parquet\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "666c9c39-07e6-4575-926a-aa94d542d121",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(447224, 25)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1bcb74ec-4742-4073-83e1-1bb24b40c18e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_11260\\4259943038.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  df.groupby('den').size().sort_values(ascending=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "den\n",
       "so    62350\n",
       "út    62470\n",
       "st    62998\n",
       "pá    63513\n",
       "čt    64602\n",
       "ne    65641\n",
       "po    65650\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('den').size().sort_values(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "26007ec8-b103-4308-b296-646144d411fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_11260\\787208855.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  df.groupby([\"odkud\",\"kam\"])['cena'].max().nsmallest(200)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "odkud                          kam                                    \n",
       "Liberec                        Ústí nad Labem hlavní nádraží                145.0\n",
       "Ústí nad Labem hlavní nádraží  Liberec                                      145.0\n",
       "Praha hl.n.                    Tanvald                                      160.0\n",
       "Tanvald                        Praha hl.n.                                  160.0\n",
       "Liberec                        Pardubice hl.n.                              170.0\n",
       "Pardubice hl.n.                Liberec                                      170.0\n",
       "Praha hl.n.                    České Budějovice                             200.0\n",
       "České Budějovice               Praha hl.n.                                  200.0\n",
       "Ústí n.L.hl.n.                 Dresden-Neustadt                             310.0\n",
       "Brno                           Ostrava                                      369.0\n",
       "Ostrava                        Brno                                         369.0\n",
       "Praha-Smíchov                  Plzeň hl.n.                                  428.0\n",
       "České Budějovice               Plzeň hl.n.                                  428.0\n",
       "Znojmo                         Jihlava                                      456.0\n",
       "Brno hl.n.                     Praha hl.n.                                  499.0\n",
       "Praha hl.n.                    Brno hl.n.                                   525.0\n",
       "Praha-Holešovice               Brno hl.n.                                   545.0\n",
       "Brno                           Vídeň                                        669.0\n",
       "Vídeň                          Brno                                         669.0\n",
       "Praha-Dejvice                  Brno hl.n.                                   699.0\n",
       "Praha-Smíchov                  Brno hl.n.                                   699.0\n",
       "Plzeň hl.n.                    Slavonice                                    734.0\n",
       "Praha-Smíchov                  Ostrava hl.n.                                734.0\n",
       "                               Bratislava hl.st.                            757.0\n",
       "Brno hl.n.                     Praha-Holešovice                             762.0\n",
       "Krakov                         Praha                                        800.0\n",
       "Šťáhlavy                       Slavonice                                    811.0\n",
       "Slavonice                      Plzeň hl.n.                                  854.0\n",
       "Praha-Smíchov                  Wien Franz-Josefs-Bahnhof                    858.0\n",
       "Olomouc                        Przemysl                                     870.0\n",
       "Ostrava-Svinov                 Karlovy Vary                                 874.0\n",
       "Praha                          Krakov                                       890.0\n",
       "Praha hl.n.                    Wien Franz-Josefs-Bahnhof                    890.0\n",
       "Wien Franz-Josefs-Bahnhof      Praha hl.n.                                  890.0\n",
       "Praha                          Čop                                          899.0\n",
       "Čop                            Praha                                        899.0\n",
       "Brno                           Praha                                        918.0\n",
       "Karlovy Vary                   Ostrava-Svinov                               923.0\n",
       "Praha                          Bratislava                                   949.0\n",
       "Ostrava hl.n.                  Praha-Holešovice                             960.0\n",
       "Praha-Holešovice               Ostrava hl.n.                                960.0\n",
       "Ostrava                        Praha                                       1008.0\n",
       "Praha                          Ostrava                                     1008.0\n",
       "                               Brno                                        1088.0\n",
       "                               Vídeň                                       1149.0\n",
       "Vídeň                          Praha                                       1149.0\n",
       "Ostrava hl.n.                  Karlovy Vary dolní n.                       1208.0\n",
       "Praha-Smíchov                  Budapest-Nyugati pu                         1208.0\n",
       "Budapešť                       Praha                                       1249.0\n",
       "Praha                          Budapešť                                    1249.0\n",
       "Ústí n.L.hl.n.                 Dresden Hbf                                 1264.0\n",
       "Praha-Dejvice                  Budapest-Nyugati pu                         1266.0\n",
       "Praha-Libeň                    Ostrava hl.n.                               1269.0\n",
       "                               Ostrava-Svinov                              1269.0\n",
       "Ostrava-Svinov                 Gdansk Glowny                               1273.0\n",
       "Praha-Smíchov                  Warszawa Centralna                          1286.0\n",
       "Przemysl                       Olomouc                                     1290.0\n",
       "Praha-Dejvice                  Ostrava hl.n.                               1333.0\n",
       "Praha-Holešovice               Krakow Glowny                               1344.0\n",
       "Dresden Hbf                    Ústí n.L.hl.n.                              1393.0\n",
       "Praha hl.n.                    Berlin-Lichtenberg                          1393.0\n",
       "                               Bratislava hl.st.                           1393.0\n",
       "Ostrava hl.n.                  Karlovy Vary                                1401.0\n",
       "Praha hl.n.                    Wien Hbf                                    1406.0\n",
       "Wien Hbf (Bahnsteige 1-2)      Praha hl.n.                                 1406.0\n",
       "Karlovy Vary                   Ostrava hl.n.                               1407.0\n",
       "Kraków Główny                  Praha hl.n.                                 1419.0\n",
       "Bratislava                     Praha                                       1422.0\n",
       "Praha hl.n.                    Krakow Glowny                               1478.0\n",
       "Olomouc hl.n.                  Przemysl Gl.                                1484.0\n",
       "Przemysl Gl.                   Olomouc hl.n.                               1484.0\n",
       "Košice                         Praha                                       1499.0\n",
       "Praha                          Košice                                      1499.0\n",
       "Praha-Dejvice                  Berlin Hbf (tief)                           1522.0\n",
       "Wien Hbf                       Praha hl.n.                                 1545.0\n",
       "Zagreb Glavni kolodvor         Praha hl.n.                                 1548.0\n",
       "Praha-Dejvice                  Wien Hbf                                    1550.0\n",
       "Berlin Hbf                     Brno hl.n.                                  1574.0\n",
       "Krakow Glowny                  Praha hl.n.                                 1576.0\n",
       "Praha-Dejvice                  Bratislava hl.st.                           1586.0\n",
       "Ostrava hl.n.                  Praha-Libeň                                 1649.0\n",
       "Ostrava-Svinov                 Praha-Libeň                                 1649.0\n",
       "Budapest-Nyugati pu            Praha hl.n.                                 1664.0\n",
       "Ostrava hl.n.                  Praha hl.n.                                 1699.0\n",
       "Ostrava-Svinov                 Praha hl.n.                                 1699.0\n",
       "Praha-Dejvice                  Warszawa Centralna                          1703.0\n",
       "Praha hl.n.                    Ostrava hl.n.                               1759.0\n",
       "Wien Hbf                       Praha-Holešovice                            1803.0\n",
       "Praha hl.n.                    Kraków Główny                               1809.0\n",
       "Praha-Holešovice               Warszawa Centralna                          1860.0\n",
       "Bratislava hl.st.              Praha hl.n.                                 1876.0\n",
       "Bratislava-Petržalka           Wien Hbf                                    1876.0\n",
       "Praha hl.n.                    Ostrava-Svinov                              1879.0\n",
       "Košice                         Pardubice hl.n.                             1889.0\n",
       "Praha hl.n.                    Budapest-Nyugati pu                         1922.0\n",
       "Praha-Dejvice                  Zagreb Glavni kolodvor                      1937.0\n",
       "Flughafen Wien                 Praha hl.n.                                 1940.0\n",
       "Praha-Dejvice                  Ljubljana                                   2040.0\n",
       "Berlin Südkreuz                Hamburg Hbf                                 2064.0\n",
       "Praha hl.n.                    Bratislava-Petržalka                        2085.0\n",
       "Gdansk Glowny                  Ostrava hl.n.                               2103.0\n",
       "Ostrava hl.n.                  Gdansk Glowny                               2180.0\n",
       "Praha hl.n.                    Warszawa Wschodnia                          2205.0\n",
       "                               Budapest-Kelenföld                          2518.0\n",
       "Pardubice hl.n.                Košice                                      2599.0\n",
       "Budapest-Kelenföld             Praha hl.n.                                 2753.0\n",
       "Berlin Ostbahnhof              Praha hl.n.                                 2823.0\n",
       "Berlin Hbf                     Praha hl.n.                                 2915.0\n",
       "Berlin-Lichtenberg             Praha hl.n.                                 2915.0\n",
       "Praha-Dejvice                  Budapest-Kelenföld                          3008.0\n",
       "Brno hl.n.                     Ljubljana                                   3153.0\n",
       "Warszawa Centralna             Praha hl.n.                                 3245.0\n",
       "Berlin Gesundbrunnen           Praha hl.n.                                 3277.0\n",
       "Leipzig Hbf                    Praha hl.n.                                 3277.0\n",
       "Leipzig Hbf (tief)             Berlin Hbf (tief)                           3277.0\n",
       "                               Berlin Südkreuz                             3277.0\n",
       "Praha hl.n.                    Berlin Gesundbrunnen                        3277.0\n",
       "                               Berlin Hbf                                  3277.0\n",
       "                               Berlin Südkreuz                             3277.0\n",
       "Liberec                        Salzburg Hbf                                3541.0\n",
       "Ljubljana                      Praha hl.n.                                 3612.0\n",
       "Praha hl.n.                    Zagreb Glavni kolodvor                      3622.0\n",
       "Zürich HB                      České Budějovice                            3687.0\n",
       "Ljubljana                      Brno hl.n.                                  3787.0\n",
       "Praha hl.n.                    Ljubljana                                   3824.0\n",
       "                               Berlin Ostbahnhof                           4149.0\n",
       "Ljubljana                      Praha-Holešovice                            4174.0\n",
       "Praha hl.n.                    Warszawa Centralna                          4324.0\n",
       "Zagreb Glavni kolodvor         Praha-Holešovice                            5028.0\n",
       "Praha hl.n.                    Berlin Hbf (tief)                           5181.0\n",
       "Železná Ruda-Alžbětín          Praha-Smíchov                               5369.0\n",
       "Berlin Hbf                     Amsterdam Centraal                          5578.0\n",
       "Leipzig Hbf (tief)             Amsterdam Centraal                          5578.0\n",
       "München Hbf                    Praha hl.n.                                 6097.0\n",
       "Frankfurt(Main)Hbf             Amsterdam Centraal                          6215.0\n",
       "                               Bruxelles-Midi                              6244.0\n",
       "Amsterdam Zuid                 Praha hl.n.                                 6311.0\n",
       "Praha hl.n.                    Amsterdam Zuid                              6311.0\n",
       "Frankfurt Hbf (tief)           Praha hl.n.                                 6365.0\n",
       "Bruxelles-Central              Praha hl.n.                                 6411.0\n",
       "Praha-Smíchov                  Bruxelles-Midi                              6543.0\n",
       "Berlin Hbf (tief)              Brno hl.n.                                  6651.0\n",
       "České Budějovice               Zürich HB                                   6827.0\n",
       "Berlin Hbf                     Amsterdam Zuid                              6852.0\n",
       "Praha hl.n.                    Bern                                        6894.0\n",
       "Berlin Hbf                     Hamburg Hbf                                 7018.0\n",
       "Berlin Hbf (tief)              Hamburg Hbf                                 7018.0\n",
       "Praha hl.n.                    Bruxelles-Midi                              7322.0\n",
       "Bern                           Praha hl.n.                                 7332.0\n",
       "Berlin Hbf                     Bruxelles-Central                           7412.0\n",
       "                               Bruxelles-Midi                              7412.0\n",
       "Berlin Hbf (tief)              Praha hl.n.                                 7412.0\n",
       "Bruxelles-Midi                 Praha hl.n.                                 8001.0\n",
       "Berlin Hbf (tief)              Amsterdam Centraal                          8488.0\n",
       "Brno hl.n.                     Hamburg Hbf                                 9814.0\n",
       "Hamburg Hbf                    Brno hl.n.                                  9814.0\n",
       "Amsterdam Bijlmer ArenA        Praha hl.n.                                11442.0\n",
       "Amsterdam Centraal             Praha hl.n.                                11463.0\n",
       "Praha hl.n.                    Amsterdam Centraal                         11644.0\n",
       "Amsterdam Bijlmer ArenA        Amsterdam Bijlmer ArenA                        NaN\n",
       "                               Amsterdam Centraal                             NaN\n",
       "                               Amsterdam Zuid                                 NaN\n",
       "                               Berlin Betriebsbf Schöneweide                  NaN\n",
       "                               Berlin Gesundbrunnen                           NaN\n",
       "                               Berlin Hbf                                     NaN\n",
       "                               Berlin Hbf (tief)                              NaN\n",
       "                               Berlin Ostbahnhof                              NaN\n",
       "                               Berlin Südkreuz                                NaN\n",
       "                               Berlin Zoologischer Garten                     NaN\n",
       "                               Berlin-Grünau                                  NaN\n",
       "                               Berlin-Lichtenberg                             NaN\n",
       "                               Berlin-Lichterfelde Ost                        NaN\n",
       "                               Bern                                           NaN\n",
       "                               Bratislava                                     NaN\n",
       "                               Bratislava hl.st.                              NaN\n",
       "                               Bratislava-Petržalka                           NaN\n",
       "                               Brno                                           NaN\n",
       "                               Brno hl.n.                                     NaN\n",
       "                               Bruxelles-Central                              NaN\n",
       "                               Bruxelles-Midi                                 NaN\n",
       "                               Budapest-Deli pu                               NaN\n",
       "                               Budapest-Kelenföld                             NaN\n",
       "                               Budapest-Nyugati pu                            NaN\n",
       "                               Budapešť                                       NaN\n",
       "                               Dresden Hbf                                    NaN\n",
       "                               Dresden-Neustadt                               NaN\n",
       "                               Flughafen BER - Terminal 5 (Schönefeld)        NaN\n",
       "                               Flughafen Wien                                 NaN\n",
       "                               Gdansk Glowny                                  NaN\n",
       "                               Hamburg Hbf                                    NaN\n",
       "                               Hamburg-Harburg                                NaN\n",
       "                               Jihlava                                        NaN\n",
       "                               Karlovy Vary                                   NaN\n",
       "                               Karlovy Vary dolní n.                          NaN\n",
       "                               Karlovy Vary, , terminál                       NaN\n",
       "                               Košice                                         NaN\n",
       "                               Krakov                                         NaN\n",
       "                               Krakow Business Park                           NaN\n",
       "                               Krakow Glowny                                  NaN\n",
       "                               Krakow Mydlniki                                NaN\n",
       "Name: cena, dtype: float64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"odkud\",\"kam\"])['cena'].max().nsmallest(200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "831e38aa-5d1d-4527-bec7-8cfe0d9a7cd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_11260\\997885303.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  df[df['odkud'].str.contains('Brn')].groupby([\"odkud\",\"kam\"])['cena'].max().nlargest(200)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "odkud                    kam                                    \n",
       "Brno hl.n.               Hamburg Hbf                                9814.0\n",
       "                         Ljubljana                                  3153.0\n",
       "Brno                     Praha                                       918.0\n",
       "Brno hl.n.               Praha-Holešovice                            762.0\n",
       "Brno                     Vídeň                                       669.0\n",
       "Brno hl.n.               Praha hl.n.                                 499.0\n",
       "Brno                     Ostrava                                     369.0\n",
       "Amsterdam Bijlmer ArenA  Amsterdam Bijlmer ArenA                       NaN\n",
       "                         Amsterdam Centraal                            NaN\n",
       "                         Amsterdam Zuid                                NaN\n",
       "                         Berlin Betriebsbf Schöneweide                 NaN\n",
       "                         Berlin Gesundbrunnen                          NaN\n",
       "                         Berlin Hbf                                    NaN\n",
       "                         Berlin Hbf (tief)                             NaN\n",
       "                         Berlin Ostbahnhof                             NaN\n",
       "                         Berlin Südkreuz                               NaN\n",
       "                         Berlin Zoologischer Garten                    NaN\n",
       "                         Berlin-Grünau                                 NaN\n",
       "                         Berlin-Lichtenberg                            NaN\n",
       "                         Berlin-Lichterfelde Ost                       NaN\n",
       "                         Bern                                          NaN\n",
       "                         Bratislava                                    NaN\n",
       "                         Bratislava hl.st.                             NaN\n",
       "                         Bratislava-Petržalka                          NaN\n",
       "                         Brno                                          NaN\n",
       "                         Brno hl.n.                                    NaN\n",
       "                         Bruxelles-Central                             NaN\n",
       "                         Bruxelles-Midi                                NaN\n",
       "                         Budapest-Deli pu                              NaN\n",
       "                         Budapest-Kelenföld                            NaN\n",
       "                         Budapest-Nyugati pu                           NaN\n",
       "                         Budapešť                                      NaN\n",
       "                         Dresden Hbf                                   NaN\n",
       "                         Dresden-Neustadt                              NaN\n",
       "                         Flughafen BER - Terminal 5 (Schönefeld)       NaN\n",
       "                         Flughafen Wien                                NaN\n",
       "                         Gdansk Glowny                                 NaN\n",
       "                         Hamburg Hbf                                   NaN\n",
       "                         Hamburg-Harburg                               NaN\n",
       "                         Jihlava                                       NaN\n",
       "                         Karlovy Vary                                  NaN\n",
       "                         Karlovy Vary dolní n.                         NaN\n",
       "                         Karlovy Vary, , terminál                      NaN\n",
       "                         Košice                                        NaN\n",
       "                         Krakov                                        NaN\n",
       "                         Krakow Business Park                          NaN\n",
       "                         Krakow Glowny                                 NaN\n",
       "                         Krakow Mydlniki                               NaN\n",
       "                         Kraków Główny                                 NaN\n",
       "                         Liberec                                       NaN\n",
       "                         Ljubljana                                     NaN\n",
       "                         Olomouc                                       NaN\n",
       "                         Olomouc hl.n.                                 NaN\n",
       "                         Ostrava                                       NaN\n",
       "                         Ostrava hl.n.                                 NaN\n",
       "                         Ostrava-Svinov                                NaN\n",
       "                         Pardubice hl.n.                               NaN\n",
       "                         Plzeň hl.n.                                   NaN\n",
       "                         Praha                                         NaN\n",
       "                         Praha Masarykovo n.                           NaN\n",
       "                         Praha hl.n.                                   NaN\n",
       "                         Praha-Holešovice                              NaN\n",
       "                         Praha-Libeň                                   NaN\n",
       "                         Praha-Smíchov                                 NaN\n",
       "                         Przemysl                                      NaN\n",
       "                         Przemysl Gl.                                  NaN\n",
       "                         Salzburg Hbf                                  NaN\n",
       "                         Slavonice                                     NaN\n",
       "                         Tanvald                                       NaN\n",
       "                         Vídeň                                         NaN\n",
       "                         Warszawa Centralna                            NaN\n",
       "                         Warszawa Wschodnia                            NaN\n",
       "                         Wien Franz-Josefs-Bahnhof                     NaN\n",
       "                         Wien Hbf                                      NaN\n",
       "                         Wien Stadlau                                  NaN\n",
       "                         Wien Stadlau Frachtenbahnhof                  NaN\n",
       "                         Zagreb Glavni kolodvor                        NaN\n",
       "                         Zürich HB                                     NaN\n",
       "                         Ústí n.L.hl.n.                                NaN\n",
       "                         Ústí nad Labem hlavní nádraží                 NaN\n",
       "                         České Budějovice                              NaN\n",
       "                         Čop                                           NaN\n",
       "Amsterdam Centraal       Amsterdam Bijlmer ArenA                       NaN\n",
       "                         Amsterdam Centraal                            NaN\n",
       "                         Amsterdam Zuid                                NaN\n",
       "                         Berlin Betriebsbf Schöneweide                 NaN\n",
       "                         Berlin Gesundbrunnen                          NaN\n",
       "                         Berlin Hbf                                    NaN\n",
       "                         Berlin Hbf (tief)                             NaN\n",
       "                         Berlin Ostbahnhof                             NaN\n",
       "                         Berlin Südkreuz                               NaN\n",
       "                         Berlin Zoologischer Garten                    NaN\n",
       "                         Berlin-Grünau                                 NaN\n",
       "                         Berlin-Lichtenberg                            NaN\n",
       "                         Berlin-Lichterfelde Ost                       NaN\n",
       "                         Bern                                          NaN\n",
       "                         Bratislava                                    NaN\n",
       "                         Bratislava hl.st.                             NaN\n",
       "                         Bratislava-Petržalka                          NaN\n",
       "                         Brno                                          NaN\n",
       "                         Brno hl.n.                                    NaN\n",
       "                         Bruxelles-Central                             NaN\n",
       "                         Bruxelles-Midi                                NaN\n",
       "                         Budapest-Deli pu                              NaN\n",
       "                         Budapest-Kelenföld                            NaN\n",
       "                         Budapest-Nyugati pu                           NaN\n",
       "                         Budapešť                                      NaN\n",
       "                         Dresden Hbf                                   NaN\n",
       "                         Dresden-Neustadt                              NaN\n",
       "                         Flughafen BER - Terminal 5 (Schönefeld)       NaN\n",
       "                         Flughafen Wien                                NaN\n",
       "                         Gdansk Glowny                                 NaN\n",
       "                         Hamburg Hbf                                   NaN\n",
       "                         Hamburg-Harburg                               NaN\n",
       "                         Jihlava                                       NaN\n",
       "                         Karlovy Vary                                  NaN\n",
       "                         Karlovy Vary dolní n.                         NaN\n",
       "                         Karlovy Vary, , terminál                      NaN\n",
       "                         Košice                                        NaN\n",
       "                         Krakov                                        NaN\n",
       "                         Krakow Business Park                          NaN\n",
       "                         Krakow Glowny                                 NaN\n",
       "                         Krakow Mydlniki                               NaN\n",
       "                         Kraków Główny                                 NaN\n",
       "                         Liberec                                       NaN\n",
       "                         Ljubljana                                     NaN\n",
       "                         Olomouc                                       NaN\n",
       "                         Olomouc hl.n.                                 NaN\n",
       "                         Ostrava                                       NaN\n",
       "                         Ostrava hl.n.                                 NaN\n",
       "                         Ostrava-Svinov                                NaN\n",
       "                         Pardubice hl.n.                               NaN\n",
       "                         Plzeň hl.n.                                   NaN\n",
       "                         Praha                                         NaN\n",
       "                         Praha Masarykovo n.                           NaN\n",
       "                         Praha hl.n.                                   NaN\n",
       "                         Praha-Holešovice                              NaN\n",
       "                         Praha-Libeň                                   NaN\n",
       "                         Praha-Smíchov                                 NaN\n",
       "                         Przemysl                                      NaN\n",
       "                         Przemysl Gl.                                  NaN\n",
       "                         Salzburg Hbf                                  NaN\n",
       "                         Slavonice                                     NaN\n",
       "                         Tanvald                                       NaN\n",
       "                         Vídeň                                         NaN\n",
       "                         Warszawa Centralna                            NaN\n",
       "                         Warszawa Wschodnia                            NaN\n",
       "                         Wien Franz-Josefs-Bahnhof                     NaN\n",
       "                         Wien Hbf                                      NaN\n",
       "                         Wien Stadlau                                  NaN\n",
       "                         Wien Stadlau Frachtenbahnhof                  NaN\n",
       "                         Zagreb Glavni kolodvor                        NaN\n",
       "                         Zürich HB                                     NaN\n",
       "                         Ústí n.L.hl.n.                                NaN\n",
       "                         Ústí nad Labem hlavní nádraží                 NaN\n",
       "                         České Budějovice                              NaN\n",
       "                         Čop                                           NaN\n",
       "Amsterdam Zuid           Amsterdam Bijlmer ArenA                       NaN\n",
       "                         Amsterdam Centraal                            NaN\n",
       "                         Amsterdam Zuid                                NaN\n",
       "                         Berlin Betriebsbf Schöneweide                 NaN\n",
       "                         Berlin Gesundbrunnen                          NaN\n",
       "                         Berlin Hbf                                    NaN\n",
       "                         Berlin Hbf (tief)                             NaN\n",
       "                         Berlin Ostbahnhof                             NaN\n",
       "                         Berlin Südkreuz                               NaN\n",
       "                         Berlin Zoologischer Garten                    NaN\n",
       "                         Berlin-Grünau                                 NaN\n",
       "                         Berlin-Lichtenberg                            NaN\n",
       "                         Berlin-Lichterfelde Ost                       NaN\n",
       "                         Bern                                          NaN\n",
       "                         Bratislava                                    NaN\n",
       "                         Bratislava hl.st.                             NaN\n",
       "                         Bratislava-Petržalka                          NaN\n",
       "                         Brno                                          NaN\n",
       "                         Brno hl.n.                                    NaN\n",
       "                         Bruxelles-Central                             NaN\n",
       "                         Bruxelles-Midi                                NaN\n",
       "                         Budapest-Deli pu                              NaN\n",
       "                         Budapest-Kelenföld                            NaN\n",
       "                         Budapest-Nyugati pu                           NaN\n",
       "                         Budapešť                                      NaN\n",
       "                         Dresden Hbf                                   NaN\n",
       "                         Dresden-Neustadt                              NaN\n",
       "                         Flughafen BER - Terminal 5 (Schönefeld)       NaN\n",
       "                         Flughafen Wien                                NaN\n",
       "                         Gdansk Glowny                                 NaN\n",
       "                         Hamburg Hbf                                   NaN\n",
       "                         Hamburg-Harburg                               NaN\n",
       "                         Jihlava                                       NaN\n",
       "                         Karlovy Vary                                  NaN\n",
       "                         Karlovy Vary dolní n.                         NaN\n",
       "                         Karlovy Vary, , terminál                      NaN\n",
       "                         Košice                                        NaN\n",
       "                         Krakov                                        NaN\n",
       "                         Krakow Business Park                          NaN\n",
       "                         Krakow Glowny                                 NaN\n",
       "                         Krakow Mydlniki                               NaN\n",
       "                         Kraków Główny                                 NaN\n",
       "                         Liberec                                       NaN\n",
       "Name: cena, dtype: float64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['odkud'].str.contains('Brn')].groupby([\"odkud\",\"kam\"])['cena'].max().nlargest(200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "8b713832-0665-4e67-9ce9-fb63004d1a5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_11260\\192807131.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  df[df['odkud'].str.contains('Ostrav')].groupby([\"odkud\",\"kam\"])['cena'].max().nlargest(200)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "odkud                    kam                                    \n",
       "Ostrava hl.n.            Gdansk Glowny                              2180.0\n",
       "                         Praha hl.n.                                1699.0\n",
       "Ostrava-Svinov           Praha hl.n.                                1699.0\n",
       "Ostrava hl.n.            Praha-Libeň                                1649.0\n",
       "Ostrava-Svinov           Praha-Libeň                                1649.0\n",
       "Ostrava hl.n.            Karlovy Vary                               1401.0\n",
       "Ostrava-Svinov           Gdansk Glowny                              1273.0\n",
       "Ostrava hl.n.            Karlovy Vary dolní n.                      1208.0\n",
       "Ostrava                  Praha                                      1008.0\n",
       "Ostrava hl.n.            Praha-Holešovice                            960.0\n",
       "Ostrava-Svinov           Karlovy Vary                                874.0\n",
       "Ostrava                  Brno                                        369.0\n",
       "Amsterdam Bijlmer ArenA  Amsterdam Bijlmer ArenA                       NaN\n",
       "                         Amsterdam Centraal                            NaN\n",
       "                         Amsterdam Zuid                                NaN\n",
       "                         Berlin Betriebsbf Schöneweide                 NaN\n",
       "                         Berlin Gesundbrunnen                          NaN\n",
       "                         Berlin Hbf                                    NaN\n",
       "                         Berlin Hbf (tief)                             NaN\n",
       "                         Berlin Ostbahnhof                             NaN\n",
       "                         Berlin Südkreuz                               NaN\n",
       "                         Berlin Zoologischer Garten                    NaN\n",
       "                         Berlin-Grünau                                 NaN\n",
       "                         Berlin-Lichtenberg                            NaN\n",
       "                         Berlin-Lichterfelde Ost                       NaN\n",
       "                         Bern                                          NaN\n",
       "                         Bratislava                                    NaN\n",
       "                         Bratislava hl.st.                             NaN\n",
       "                         Bratislava-Petržalka                          NaN\n",
       "                         Brno                                          NaN\n",
       "                         Brno hl.n.                                    NaN\n",
       "                         Bruxelles-Central                             NaN\n",
       "                         Bruxelles-Midi                                NaN\n",
       "                         Budapest-Deli pu                              NaN\n",
       "                         Budapest-Kelenföld                            NaN\n",
       "                         Budapest-Nyugati pu                           NaN\n",
       "                         Budapešť                                      NaN\n",
       "                         Dresden Hbf                                   NaN\n",
       "                         Dresden-Neustadt                              NaN\n",
       "                         Flughafen BER - Terminal 5 (Schönefeld)       NaN\n",
       "                         Flughafen Wien                                NaN\n",
       "                         Gdansk Glowny                                 NaN\n",
       "                         Hamburg Hbf                                   NaN\n",
       "                         Hamburg-Harburg                               NaN\n",
       "                         Jihlava                                       NaN\n",
       "                         Karlovy Vary                                  NaN\n",
       "                         Karlovy Vary dolní n.                         NaN\n",
       "                         Karlovy Vary, , terminál                      NaN\n",
       "                         Košice                                        NaN\n",
       "                         Krakov                                        NaN\n",
       "                         Krakow Business Park                          NaN\n",
       "                         Krakow Glowny                                 NaN\n",
       "                         Krakow Mydlniki                               NaN\n",
       "                         Kraków Główny                                 NaN\n",
       "                         Liberec                                       NaN\n",
       "                         Ljubljana                                     NaN\n",
       "                         Olomouc                                       NaN\n",
       "                         Olomouc hl.n.                                 NaN\n",
       "                         Ostrava                                       NaN\n",
       "                         Ostrava hl.n.                                 NaN\n",
       "                         Ostrava-Svinov                                NaN\n",
       "                         Pardubice hl.n.                               NaN\n",
       "                         Plzeň hl.n.                                   NaN\n",
       "                         Praha                                         NaN\n",
       "                         Praha Masarykovo n.                           NaN\n",
       "                         Praha hl.n.                                   NaN\n",
       "                         Praha-Holešovice                              NaN\n",
       "                         Praha-Libeň                                   NaN\n",
       "                         Praha-Smíchov                                 NaN\n",
       "                         Przemysl                                      NaN\n",
       "                         Przemysl Gl.                                  NaN\n",
       "                         Salzburg Hbf                                  NaN\n",
       "                         Slavonice                                     NaN\n",
       "                         Tanvald                                       NaN\n",
       "                         Vídeň                                         NaN\n",
       "                         Warszawa Centralna                            NaN\n",
       "                         Warszawa Wschodnia                            NaN\n",
       "                         Wien Franz-Josefs-Bahnhof                     NaN\n",
       "                         Wien Hbf                                      NaN\n",
       "                         Wien Stadlau                                  NaN\n",
       "                         Wien Stadlau Frachtenbahnhof                  NaN\n",
       "                         Zagreb Glavni kolodvor                        NaN\n",
       "                         Zürich HB                                     NaN\n",
       "                         Ústí n.L.hl.n.                                NaN\n",
       "                         Ústí nad Labem hlavní nádraží                 NaN\n",
       "                         České Budějovice                              NaN\n",
       "                         Čop                                           NaN\n",
       "Amsterdam Centraal       Amsterdam Bijlmer ArenA                       NaN\n",
       "                         Amsterdam Centraal                            NaN\n",
       "                         Amsterdam Zuid                                NaN\n",
       "                         Berlin Betriebsbf Schöneweide                 NaN\n",
       "                         Berlin Gesundbrunnen                          NaN\n",
       "                         Berlin Hbf                                    NaN\n",
       "                         Berlin Hbf (tief)                             NaN\n",
       "                         Berlin Ostbahnhof                             NaN\n",
       "                         Berlin Südkreuz                               NaN\n",
       "                         Berlin Zoologischer Garten                    NaN\n",
       "                         Berlin-Grünau                                 NaN\n",
       "                         Berlin-Lichtenberg                            NaN\n",
       "                         Berlin-Lichterfelde Ost                       NaN\n",
       "                         Bern                                          NaN\n",
       "                         Bratislava                                    NaN\n",
       "                         Bratislava hl.st.                             NaN\n",
       "                         Bratislava-Petržalka                          NaN\n",
       "                         Brno                                          NaN\n",
       "                         Brno hl.n.                                    NaN\n",
       "                         Bruxelles-Central                             NaN\n",
       "                         Bruxelles-Midi                                NaN\n",
       "                         Budapest-Deli pu                              NaN\n",
       "                         Budapest-Kelenföld                            NaN\n",
       "                         Budapest-Nyugati pu                           NaN\n",
       "                         Budapešť                                      NaN\n",
       "                         Dresden Hbf                                   NaN\n",
       "                         Dresden-Neustadt                              NaN\n",
       "                         Flughafen BER - Terminal 5 (Schönefeld)       NaN\n",
       "                         Flughafen Wien                                NaN\n",
       "                         Gdansk Glowny                                 NaN\n",
       "                         Hamburg Hbf                                   NaN\n",
       "                         Hamburg-Harburg                               NaN\n",
       "                         Jihlava                                       NaN\n",
       "                         Karlovy Vary                                  NaN\n",
       "                         Karlovy Vary dolní n.                         NaN\n",
       "                         Karlovy Vary, , terminál                      NaN\n",
       "                         Košice                                        NaN\n",
       "                         Krakov                                        NaN\n",
       "                         Krakow Business Park                          NaN\n",
       "                         Krakow Glowny                                 NaN\n",
       "                         Krakow Mydlniki                               NaN\n",
       "                         Kraków Główny                                 NaN\n",
       "                         Liberec                                       NaN\n",
       "                         Ljubljana                                     NaN\n",
       "                         Olomouc                                       NaN\n",
       "                         Olomouc hl.n.                                 NaN\n",
       "                         Ostrava                                       NaN\n",
       "                         Ostrava hl.n.                                 NaN\n",
       "                         Ostrava-Svinov                                NaN\n",
       "                         Pardubice hl.n.                               NaN\n",
       "                         Plzeň hl.n.                                   NaN\n",
       "                         Praha                                         NaN\n",
       "                         Praha Masarykovo n.                           NaN\n",
       "                         Praha hl.n.                                   NaN\n",
       "                         Praha-Holešovice                              NaN\n",
       "                         Praha-Libeň                                   NaN\n",
       "                         Praha-Smíchov                                 NaN\n",
       "                         Przemysl                                      NaN\n",
       "                         Przemysl Gl.                                  NaN\n",
       "                         Salzburg Hbf                                  NaN\n",
       "                         Slavonice                                     NaN\n",
       "                         Tanvald                                       NaN\n",
       "                         Vídeň                                         NaN\n",
       "                         Warszawa Centralna                            NaN\n",
       "                         Warszawa Wschodnia                            NaN\n",
       "                         Wien Franz-Josefs-Bahnhof                     NaN\n",
       "                         Wien Hbf                                      NaN\n",
       "                         Wien Stadlau                                  NaN\n",
       "                         Wien Stadlau Frachtenbahnhof                  NaN\n",
       "                         Zagreb Glavni kolodvor                        NaN\n",
       "                         Zürich HB                                     NaN\n",
       "                         Ústí n.L.hl.n.                                NaN\n",
       "                         Ústí nad Labem hlavní nádraží                 NaN\n",
       "                         České Budějovice                              NaN\n",
       "                         Čop                                           NaN\n",
       "Amsterdam Zuid           Amsterdam Bijlmer ArenA                       NaN\n",
       "                         Amsterdam Centraal                            NaN\n",
       "                         Amsterdam Zuid                                NaN\n",
       "                         Berlin Betriebsbf Schöneweide                 NaN\n",
       "                         Berlin Gesundbrunnen                          NaN\n",
       "                         Berlin Hbf                                    NaN\n",
       "                         Berlin Hbf (tief)                             NaN\n",
       "                         Berlin Ostbahnhof                             NaN\n",
       "                         Berlin Südkreuz                               NaN\n",
       "                         Berlin Zoologischer Garten                    NaN\n",
       "                         Berlin-Grünau                                 NaN\n",
       "                         Berlin-Lichtenberg                            NaN\n",
       "                         Berlin-Lichterfelde Ost                       NaN\n",
       "                         Bern                                          NaN\n",
       "                         Bratislava                                    NaN\n",
       "                         Bratislava hl.st.                             NaN\n",
       "                         Bratislava-Petržalka                          NaN\n",
       "                         Brno                                          NaN\n",
       "                         Brno hl.n.                                    NaN\n",
       "                         Bruxelles-Central                             NaN\n",
       "                         Bruxelles-Midi                                NaN\n",
       "                         Budapest-Deli pu                              NaN\n",
       "                         Budapest-Kelenföld                            NaN\n",
       "                         Budapest-Nyugati pu                           NaN\n",
       "                         Budapešť                                      NaN\n",
       "                         Dresden Hbf                                   NaN\n",
       "                         Dresden-Neustadt                              NaN\n",
       "                         Flughafen BER - Terminal 5 (Schönefeld)       NaN\n",
       "                         Flughafen Wien                                NaN\n",
       "                         Gdansk Glowny                                 NaN\n",
       "                         Hamburg Hbf                                   NaN\n",
       "                         Hamburg-Harburg                               NaN\n",
       "                         Jihlava                                       NaN\n",
       "                         Karlovy Vary                                  NaN\n",
       "                         Karlovy Vary dolní n.                         NaN\n",
       "                         Karlovy Vary, , terminál                      NaN\n",
       "                         Košice                                        NaN\n",
       "                         Krakov                                        NaN\n",
       "Name: cena, dtype: float64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['odkud'].str.contains('Ostrav')].groupby([\"odkud\",\"kam\"])['cena'].max().nlargest(200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "9a4ff046-dbb4-4ca4-9f9e-aff19acd091f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_11260\\1906889071.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  df[df['odkud'].str.contains('Prah')].groupby([\"odkud\",\"kam\"])['cena'].min().nsmallest(200)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "odkud                    kam                                    \n",
       "Praha                    Brno                                         99.0\n",
       "Praha hl.n.              Ostrava hl.n.                                99.0\n",
       "                         Ostrava-Svinov                               99.0\n",
       "Praha-Libeň              Ostrava hl.n.                                99.0\n",
       "                         Ostrava-Svinov                               99.0\n",
       "Praha hl.n.              Tanvald                                     160.0\n",
       "                         České Budějovice                            200.0\n",
       "Praha                    Ostrava                                     249.0\n",
       "Praha hl.n.              Brno hl.n.                                  269.0\n",
       "Praha                    Bratislava                                  279.0\n",
       "Praha hl.n.              Kraków Główny                               279.0\n",
       "Praha                    Vídeň                                       299.0\n",
       "Praha-Smíchov            Plzeň hl.n.                                 350.0\n",
       "Praha hl.n.              Krakow Glowny                               361.0\n",
       "                         Berlin Hbf (tief)                           387.0\n",
       "                         Berlin Südkreuz                             387.0\n",
       "                         Wien Hbf                                    387.0\n",
       "Praha-Dejvice            Berlin Hbf (tief)                           387.0\n",
       "Praha                    Budapešť                                    399.0\n",
       "                         Košice                                      399.0\n",
       "Praha hl.n.              Bratislava hl.st.                           400.0\n",
       "Praha-Dejvice            Bratislava hl.st.                           400.0\n",
       "Praha hl.n.              Wien Franz-Josefs-Bahnhof                   439.0\n",
       "Praha                    Krakov                                      440.0\n",
       "Praha-Holešovice         Brno hl.n.                                  479.0\n",
       "Praha hl.n.              Warszawa Centralna                          490.0\n",
       "Praha                    Čop                                         499.0\n",
       "Praha-Smíchov            Brno hl.n.                                  603.0\n",
       "Praha hl.n.              Budapest-Nyugati pu                         606.0\n",
       "Praha-Smíchov            Bratislava hl.st.                           679.0\n",
       "Praha-Dejvice            Brno hl.n.                                  699.0\n",
       "                         Ostrava hl.n.                               699.0\n",
       "Praha-Smíchov            Wien Franz-Josefs-Bahnhof                   704.0\n",
       "                         Ostrava hl.n.                               734.0\n",
       "Praha hl.n.              Berlin Hbf                                  748.0\n",
       "Praha-Holešovice         Ostrava hl.n.                               769.0\n",
       "Praha hl.n.              Warszawa Wschodnia                          809.0\n",
       "Praha-Dejvice            Wien Hbf                                    871.0\n",
       "Praha hl.n.              Berlin Gesundbrunnen                        877.0\n",
       "                         Berlin-Lichtenberg                          903.0\n",
       "Praha-Dejvice            Budapest-Nyugati pu                        1034.0\n",
       "Praha-Holešovice         Warszawa Centralna                         1097.0\n",
       "Praha-Smíchov            Warszawa Centralna                         1105.0\n",
       "Praha-Holešovice         Krakow Glowny                              1123.0\n",
       "Praha-Dejvice            Warszawa Centralna                         1161.0\n",
       "Praha-Smíchov            Budapest-Nyugati pu                        1208.0\n",
       "Praha hl.n.              Zagreb Glavni kolodvor                     1342.0\n",
       "                         Bruxelles-Midi                             1364.0\n",
       "                         Ljubljana                                  1419.0\n",
       "                         Amsterdam Centraal                         1522.0\n",
       "Praha-Dejvice            Zagreb Glavni kolodvor                     1697.0\n",
       "                         Ljubljana                                  1800.0\n",
       "Praha hl.n.              Bratislava-Petržalka                       2085.0\n",
       "                         Bern                                       2387.0\n",
       "                         Budapest-Kelenföld                         2495.0\n",
       "Praha-Dejvice            Budapest-Kelenföld                         2974.0\n",
       "Praha hl.n.              Berlin Ostbahnhof                          4149.0\n",
       "                         Amsterdam Zuid                             5477.0\n",
       "Praha-Smíchov            Bruxelles-Midi                             6543.0\n",
       "Amsterdam Bijlmer ArenA  Amsterdam Bijlmer ArenA                       NaN\n",
       "                         Amsterdam Centraal                            NaN\n",
       "                         Amsterdam Zuid                                NaN\n",
       "                         Berlin Betriebsbf Schöneweide                 NaN\n",
       "                         Berlin Gesundbrunnen                          NaN\n",
       "                         Berlin Hbf                                    NaN\n",
       "                         Berlin Hbf (tief)                             NaN\n",
       "                         Berlin Ostbahnhof                             NaN\n",
       "                         Berlin Südkreuz                               NaN\n",
       "                         Berlin Zoologischer Garten                    NaN\n",
       "                         Berlin-Grünau                                 NaN\n",
       "                         Berlin-Lichtenberg                            NaN\n",
       "                         Berlin-Lichterfelde Ost                       NaN\n",
       "                         Bern                                          NaN\n",
       "                         Bratislava                                    NaN\n",
       "                         Bratislava hl.st.                             NaN\n",
       "                         Bratislava-Petržalka                          NaN\n",
       "                         Brno                                          NaN\n",
       "                         Brno hl.n.                                    NaN\n",
       "                         Bruxelles-Central                             NaN\n",
       "                         Bruxelles-Midi                                NaN\n",
       "                         Budapest-Deli pu                              NaN\n",
       "                         Budapest-Kelenföld                            NaN\n",
       "                         Budapest-Nyugati pu                           NaN\n",
       "                         Budapešť                                      NaN\n",
       "                         Dresden Hbf                                   NaN\n",
       "                         Dresden-Neustadt                              NaN\n",
       "                         Flughafen BER - Terminal 5 (Schönefeld)       NaN\n",
       "                         Flughafen Wien                                NaN\n",
       "                         Gdansk Glowny                                 NaN\n",
       "                         Hamburg Hbf                                   NaN\n",
       "                         Hamburg-Harburg                               NaN\n",
       "                         Jihlava                                       NaN\n",
       "                         Karlovy Vary                                  NaN\n",
       "                         Karlovy Vary dolní n.                         NaN\n",
       "                         Karlovy Vary, , terminál                      NaN\n",
       "                         Košice                                        NaN\n",
       "                         Krakov                                        NaN\n",
       "                         Krakow Business Park                          NaN\n",
       "                         Krakow Glowny                                 NaN\n",
       "                         Krakow Mydlniki                               NaN\n",
       "                         Kraków Główny                                 NaN\n",
       "                         Liberec                                       NaN\n",
       "                         Ljubljana                                     NaN\n",
       "                         Olomouc                                       NaN\n",
       "                         Olomouc hl.n.                                 NaN\n",
       "                         Ostrava                                       NaN\n",
       "                         Ostrava hl.n.                                 NaN\n",
       "                         Ostrava-Svinov                                NaN\n",
       "                         Pardubice hl.n.                               NaN\n",
       "                         Plzeň hl.n.                                   NaN\n",
       "                         Praha                                         NaN\n",
       "                         Praha Masarykovo n.                           NaN\n",
       "                         Praha hl.n.                                   NaN\n",
       "                         Praha-Holešovice                              NaN\n",
       "                         Praha-Libeň                                   NaN\n",
       "                         Praha-Smíchov                                 NaN\n",
       "                         Przemysl                                      NaN\n",
       "                         Przemysl Gl.                                  NaN\n",
       "                         Salzburg Hbf                                  NaN\n",
       "                         Slavonice                                     NaN\n",
       "                         Tanvald                                       NaN\n",
       "                         Vídeň                                         NaN\n",
       "                         Warszawa Centralna                            NaN\n",
       "                         Warszawa Wschodnia                            NaN\n",
       "                         Wien Franz-Josefs-Bahnhof                     NaN\n",
       "                         Wien Hbf                                      NaN\n",
       "                         Wien Stadlau                                  NaN\n",
       "                         Wien Stadlau Frachtenbahnhof                  NaN\n",
       "                         Zagreb Glavni kolodvor                        NaN\n",
       "                         Zürich HB                                     NaN\n",
       "                         Ústí n.L.hl.n.                                NaN\n",
       "                         Ústí nad Labem hlavní nádraží                 NaN\n",
       "                         České Budějovice                              NaN\n",
       "                         Čop                                           NaN\n",
       "Amsterdam Centraal       Amsterdam Bijlmer ArenA                       NaN\n",
       "                         Amsterdam Centraal                            NaN\n",
       "                         Amsterdam Zuid                                NaN\n",
       "                         Berlin Betriebsbf Schöneweide                 NaN\n",
       "                         Berlin Gesundbrunnen                          NaN\n",
       "                         Berlin Hbf                                    NaN\n",
       "                         Berlin Hbf (tief)                             NaN\n",
       "                         Berlin Ostbahnhof                             NaN\n",
       "                         Berlin Südkreuz                               NaN\n",
       "                         Berlin Zoologischer Garten                    NaN\n",
       "                         Berlin-Grünau                                 NaN\n",
       "                         Berlin-Lichtenberg                            NaN\n",
       "                         Berlin-Lichterfelde Ost                       NaN\n",
       "                         Bern                                          NaN\n",
       "                         Bratislava                                    NaN\n",
       "                         Bratislava hl.st.                             NaN\n",
       "                         Bratislava-Petržalka                          NaN\n",
       "                         Brno                                          NaN\n",
       "                         Brno hl.n.                                    NaN\n",
       "                         Bruxelles-Central                             NaN\n",
       "                         Bruxelles-Midi                                NaN\n",
       "                         Budapest-Deli pu                              NaN\n",
       "                         Budapest-Kelenföld                            NaN\n",
       "                         Budapest-Nyugati pu                           NaN\n",
       "                         Budapešť                                      NaN\n",
       "                         Dresden Hbf                                   NaN\n",
       "                         Dresden-Neustadt                              NaN\n",
       "                         Flughafen BER - Terminal 5 (Schönefeld)       NaN\n",
       "                         Flughafen Wien                                NaN\n",
       "                         Gdansk Glowny                                 NaN\n",
       "                         Hamburg Hbf                                   NaN\n",
       "                         Hamburg-Harburg                               NaN\n",
       "                         Jihlava                                       NaN\n",
       "                         Karlovy Vary                                  NaN\n",
       "                         Karlovy Vary dolní n.                         NaN\n",
       "                         Karlovy Vary, , terminál                      NaN\n",
       "                         Košice                                        NaN\n",
       "                         Krakov                                        NaN\n",
       "                         Krakow Business Park                          NaN\n",
       "                         Krakow Glowny                                 NaN\n",
       "                         Krakow Mydlniki                               NaN\n",
       "                         Kraków Główny                                 NaN\n",
       "                         Liberec                                       NaN\n",
       "                         Ljubljana                                     NaN\n",
       "                         Olomouc                                       NaN\n",
       "                         Olomouc hl.n.                                 NaN\n",
       "                         Ostrava                                       NaN\n",
       "                         Ostrava hl.n.                                 NaN\n",
       "                         Ostrava-Svinov                                NaN\n",
       "                         Pardubice hl.n.                               NaN\n",
       "                         Plzeň hl.n.                                   NaN\n",
       "                         Praha                                         NaN\n",
       "                         Praha Masarykovo n.                           NaN\n",
       "                         Praha hl.n.                                   NaN\n",
       "                         Praha-Holešovice                              NaN\n",
       "                         Praha-Libeň                                   NaN\n",
       "                         Praha-Smíchov                                 NaN\n",
       "                         Przemysl                                      NaN\n",
       "                         Przemysl Gl.                                  NaN\n",
       "                         Salzburg Hbf                                  NaN\n",
       "                         Slavonice                                     NaN\n",
       "                         Tanvald                                       NaN\n",
       "                         Vídeň                                         NaN\n",
       "                         Warszawa Centralna                            NaN\n",
       "                         Warszawa Wschodnia                            NaN\n",
       "                         Wien Franz-Josefs-Bahnhof                     NaN\n",
       "Name: cena, dtype: float64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['odkud'].str.contains('Prah')].groupby([\"odkud\",\"kam\"])['cena'].min().nsmallest(200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7ebc041b-0103-439e-9b4c-874a8a7a3256",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36278"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[df['prodejce'] == 'ARR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c9953e8a-66f6-40f0-b261-c40f69a5fecf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36278"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[df['prodejce'] == 'ARR'].drop_duplicates(subset=['odkud','kam','oscrapovano','odjezd']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d5105280-4978-4d71-a6fc-bd86185b0212",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>oscrapovano</th>\n",
       "      <th>prodejce</th>\n",
       "      <th>odkud</th>\n",
       "      <th>kam</th>\n",
       "      <th>odjezd</th>\n",
       "      <th>predstih</th>\n",
       "      <th>predstih_d</th>\n",
       "      <th>predstih_h</th>\n",
       "      <th>cena</th>\n",
       "      <th>prostredek</th>\n",
       "      <th>volnych_mist</th>\n",
       "      <th>obsazenost</th>\n",
       "      <th>jizdni_doba</th>\n",
       "      <th>vzdalenost</th>\n",
       "      <th>zpozdeni</th>\n",
       "      <th>cena_poznamka</th>\n",
       "      <th>den</th>\n",
       "      <th>prestupy</th>\n",
       "      <th>vlaky</th>\n",
       "      <th>mistenka_zdarma</th>\n",
       "      <th>nahradni_bus</th>\n",
       "      <th>volna_mista_economy</th>\n",
       "      <th>volna_mista_economy_plus</th>\n",
       "      <th>volna_mista_economy_business</th>\n",
       "      <th>volna_mista_premium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>133520</th>\n",
       "      <td>2024-11-07 19:13:44</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Ústí nad Labem hlavní nádraží</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-22 21:27:00</td>\n",
       "      <td>15 days 02:13:16</td>\n",
       "      <td>15</td>\n",
       "      <td>362.221111</td>\n",
       "      <td>145.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>39.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1335]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153192</th>\n",
       "      <td>2024-11-08 12:53:13</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Ústí nad Labem hlavní nádraží</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-22 21:27:00</td>\n",
       "      <td>14 days 08:33:47</td>\n",
       "      <td>14</td>\n",
       "      <td>344.563056</td>\n",
       "      <td>145.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>39.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1335]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220671</th>\n",
       "      <td>2024-11-09 19:27:48</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Ústí nad Labem hlavní nádraží</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-22 21:27:00</td>\n",
       "      <td>13 days 01:59:12</td>\n",
       "      <td>13</td>\n",
       "      <td>313.986667</td>\n",
       "      <td>145.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>39.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1335]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237276</th>\n",
       "      <td>2024-11-10 08:29:12</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Ústí nad Labem hlavní nádraží</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-22 21:27:00</td>\n",
       "      <td>12 days 12:57:48</td>\n",
       "      <td>12</td>\n",
       "      <td>300.963333</td>\n",
       "      <td>145.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>39.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1335]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245171</th>\n",
       "      <td>2024-11-10 12:25:46</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Ústí nad Labem hlavní nádraží</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-22 21:27:00</td>\n",
       "      <td>12 days 09:01:14</td>\n",
       "      <td>12</td>\n",
       "      <td>297.020556</td>\n",
       "      <td>145.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>39.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1335]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274347</th>\n",
       "      <td>2024-11-11 08:24:43</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Ústí nad Labem hlavní nádraží</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-22 21:27:00</td>\n",
       "      <td>11 days 13:02:17</td>\n",
       "      <td>11</td>\n",
       "      <td>277.038056</td>\n",
       "      <td>145.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>39.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1335]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318566</th>\n",
       "      <td>2024-11-12 12:25:47</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Ústí nad Labem hlavní nádraží</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-22 21:27:00</td>\n",
       "      <td>10 days 09:01:13</td>\n",
       "      <td>10</td>\n",
       "      <td>249.020278</td>\n",
       "      <td>145.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>39.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1335]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334538</th>\n",
       "      <td>2024-11-12 19:26:27</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Ústí nad Labem hlavní nádraží</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-22 21:27:00</td>\n",
       "      <td>10 days 02:00:33</td>\n",
       "      <td>10</td>\n",
       "      <td>242.009167</td>\n",
       "      <td>145.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>39.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1335]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348349</th>\n",
       "      <td>2024-11-13 06:29:57</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Ústí nad Labem hlavní nádraží</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-22 21:27:00</td>\n",
       "      <td>9 days 14:57:03</td>\n",
       "      <td>9</td>\n",
       "      <td>230.950833</td>\n",
       "      <td>145.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>39.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1335]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363648</th>\n",
       "      <td>2024-11-13 14:25:17</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Ústí nad Labem hlavní nádraží</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-22 21:27:00</td>\n",
       "      <td>9 days 07:01:43</td>\n",
       "      <td>9</td>\n",
       "      <td>223.028611</td>\n",
       "      <td>145.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>39.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1335]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372905</th>\n",
       "      <td>2024-11-13 18:24:54</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Ústí nad Labem hlavní nádraží</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-22 21:27:00</td>\n",
       "      <td>9 days 03:02:06</td>\n",
       "      <td>9</td>\n",
       "      <td>219.035000</td>\n",
       "      <td>145.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>39.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1335]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385034</th>\n",
       "      <td>2024-11-14 00:25:22</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Ústí nad Labem hlavní nádraží</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-22 21:27:00</td>\n",
       "      <td>8 days 21:01:38</td>\n",
       "      <td>8</td>\n",
       "      <td>213.027222</td>\n",
       "      <td>145.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>39.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1335]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389161</th>\n",
       "      <td>2024-11-14 06:30:20</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Ústí nad Labem hlavní nádraží</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-22 21:27:00</td>\n",
       "      <td>8 days 14:56:40</td>\n",
       "      <td>8</td>\n",
       "      <td>206.944444</td>\n",
       "      <td>145.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>39.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1335]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408970</th>\n",
       "      <td>2024-11-14 16:24:35</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Ústí nad Labem hlavní nádraží</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-22 21:27:00</td>\n",
       "      <td>8 days 05:02:25</td>\n",
       "      <td>8</td>\n",
       "      <td>197.040278</td>\n",
       "      <td>145.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>39.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1335]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417149</th>\n",
       "      <td>2024-11-14 19:29:37</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Ústí nad Labem hlavní nádraží</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-22 21:27:00</td>\n",
       "      <td>8 days 01:57:23</td>\n",
       "      <td>8</td>\n",
       "      <td>193.956389</td>\n",
       "      <td>145.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>39.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1335]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436830</th>\n",
       "      <td>2024-11-15 10:24:55</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Ústí nad Labem hlavní nádraží</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-22 21:27:00</td>\n",
       "      <td>7 days 11:02:05</td>\n",
       "      <td>7</td>\n",
       "      <td>179.034722</td>\n",
       "      <td>145.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>39.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1335]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440078</th>\n",
       "      <td>2024-11-15 12:23:06</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Ústí nad Labem hlavní nádraží</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-22 21:27:00</td>\n",
       "      <td>7 days 09:03:54</td>\n",
       "      <td>7</td>\n",
       "      <td>177.065000</td>\n",
       "      <td>145.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>39.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1335]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474064</th>\n",
       "      <td>2024-11-16 10:30:45</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Ústí nad Labem hlavní nádraží</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-22 21:27:00</td>\n",
       "      <td>6 days 10:56:15</td>\n",
       "      <td>6</td>\n",
       "      <td>154.937500</td>\n",
       "      <td>145.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>39.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1335]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491817</th>\n",
       "      <td>2024-11-16 18:28:03</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Ústí nad Labem hlavní nádraží</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-22 21:27:00</td>\n",
       "      <td>6 days 02:58:57</td>\n",
       "      <td>6</td>\n",
       "      <td>146.982500</td>\n",
       "      <td>145.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>39.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1335]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499856</th>\n",
       "      <td>2024-11-16 22:28:04</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Ústí nad Labem hlavní nádraží</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-22 21:27:00</td>\n",
       "      <td>5 days 22:58:56</td>\n",
       "      <td>5</td>\n",
       "      <td>142.982222</td>\n",
       "      <td>145.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>39.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1335]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               oscrapovano prodejce                          odkud      kam  \\\n",
       "133520 2024-11-07 19:13:44      ARR  Ústí nad Labem hlavní nádraží  Liberec   \n",
       "153192 2024-11-08 12:53:13      ARR  Ústí nad Labem hlavní nádraží  Liberec   \n",
       "220671 2024-11-09 19:27:48      ARR  Ústí nad Labem hlavní nádraží  Liberec   \n",
       "237276 2024-11-10 08:29:12      ARR  Ústí nad Labem hlavní nádraží  Liberec   \n",
       "245171 2024-11-10 12:25:46      ARR  Ústí nad Labem hlavní nádraží  Liberec   \n",
       "274347 2024-11-11 08:24:43      ARR  Ústí nad Labem hlavní nádraží  Liberec   \n",
       "318566 2024-11-12 12:25:47      ARR  Ústí nad Labem hlavní nádraží  Liberec   \n",
       "334538 2024-11-12 19:26:27      ARR  Ústí nad Labem hlavní nádraží  Liberec   \n",
       "348349 2024-11-13 06:29:57      ARR  Ústí nad Labem hlavní nádraží  Liberec   \n",
       "363648 2024-11-13 14:25:17      ARR  Ústí nad Labem hlavní nádraží  Liberec   \n",
       "372905 2024-11-13 18:24:54      ARR  Ústí nad Labem hlavní nádraží  Liberec   \n",
       "385034 2024-11-14 00:25:22      ARR  Ústí nad Labem hlavní nádraží  Liberec   \n",
       "389161 2024-11-14 06:30:20      ARR  Ústí nad Labem hlavní nádraží  Liberec   \n",
       "408970 2024-11-14 16:24:35      ARR  Ústí nad Labem hlavní nádraží  Liberec   \n",
       "417149 2024-11-14 19:29:37      ARR  Ústí nad Labem hlavní nádraží  Liberec   \n",
       "436830 2024-11-15 10:24:55      ARR  Ústí nad Labem hlavní nádraží  Liberec   \n",
       "440078 2024-11-15 12:23:06      ARR  Ústí nad Labem hlavní nádraží  Liberec   \n",
       "474064 2024-11-16 10:30:45      ARR  Ústí nad Labem hlavní nádraží  Liberec   \n",
       "491817 2024-11-16 18:28:03      ARR  Ústí nad Labem hlavní nádraží  Liberec   \n",
       "499856 2024-11-16 22:28:04      ARR  Ústí nad Labem hlavní nádraží  Liberec   \n",
       "\n",
       "                    odjezd         predstih  predstih_d  predstih_h   cena  \\\n",
       "133520 2024-11-22 21:27:00 15 days 02:13:16          15  362.221111  145.0   \n",
       "153192 2024-11-22 21:27:00 14 days 08:33:47          14  344.563056  145.0   \n",
       "220671 2024-11-22 21:27:00 13 days 01:59:12          13  313.986667  145.0   \n",
       "237276 2024-11-22 21:27:00 12 days 12:57:48          12  300.963333  145.0   \n",
       "245171 2024-11-22 21:27:00 12 days 09:01:14          12  297.020556  145.0   \n",
       "274347 2024-11-22 21:27:00 11 days 13:02:17          11  277.038056  145.0   \n",
       "318566 2024-11-22 21:27:00 10 days 09:01:13          10  249.020278  145.0   \n",
       "334538 2024-11-22 21:27:00 10 days 02:00:33          10  242.009167  145.0   \n",
       "348349 2024-11-22 21:27:00  9 days 14:57:03           9  230.950833  145.0   \n",
       "363648 2024-11-22 21:27:00  9 days 07:01:43           9  223.028611  145.0   \n",
       "372905 2024-11-22 21:27:00  9 days 03:02:06           9  219.035000  145.0   \n",
       "385034 2024-11-22 21:27:00  8 days 21:01:38           8  213.027222  145.0   \n",
       "389161 2024-11-22 21:27:00  8 days 14:56:40           8  206.944444  145.0   \n",
       "408970 2024-11-22 21:27:00  8 days 05:02:25           8  197.040278  145.0   \n",
       "417149 2024-11-22 21:27:00  8 days 01:57:23           8  193.956389  145.0   \n",
       "436830 2024-11-22 21:27:00  7 days 11:02:05           7  179.034722  145.0   \n",
       "440078 2024-11-22 21:27:00  7 days 09:03:54           7  177.065000  145.0   \n",
       "474064 2024-11-22 21:27:00  6 days 10:56:15           6  154.937500  145.0   \n",
       "491817 2024-11-22 21:27:00  6 days 02:58:57           6  146.982500  145.0   \n",
       "499856 2024-11-22 21:27:00  5 days 22:58:56           5  142.982222  145.0   \n",
       "\n",
       "       prostredek  volnych_mist obsazenost  jizdni_doba  vzdalenost zpozdeni  \\\n",
       "133520       vlak          39.0       None          NaN       113.0     None   \n",
       "153192       vlak          39.0       None          NaN       113.0     None   \n",
       "220671       vlak          39.0       None          NaN       113.0     None   \n",
       "237276       vlak          39.0       None          NaN       113.0     None   \n",
       "245171       vlak          39.0       None          NaN       113.0     None   \n",
       "274347       vlak          39.0       None          NaN       113.0     None   \n",
       "318566       vlak          39.0       None          NaN       113.0     None   \n",
       "334538       vlak          39.0       None          NaN       113.0     None   \n",
       "348349       vlak          39.0       None          NaN       113.0     None   \n",
       "363648       vlak          39.0       None          NaN       113.0     None   \n",
       "372905       vlak          39.0       None          NaN       113.0     None   \n",
       "385034       vlak          39.0       None          NaN       113.0     None   \n",
       "389161       vlak          39.0       None          NaN       113.0     None   \n",
       "408970       vlak          39.0       None          NaN       113.0     None   \n",
       "417149       vlak          39.0       None          NaN       113.0     None   \n",
       "436830       vlak          39.0       None          NaN       113.0     None   \n",
       "440078       vlak          39.0       None          NaN       113.0     None   \n",
       "474064       vlak          39.0       None          NaN       113.0     None   \n",
       "491817       vlak          39.0       None          NaN       113.0     None   \n",
       "499856       vlak          39.0       None          NaN       113.0     None   \n",
       "\n",
       "       cena_poznamka den  prestupy     vlaky mistenka_zdarma nahradni_bus  \\\n",
       "133520          None  pá         0  [R 1335]            None         None   \n",
       "153192          None  pá         0  [R 1335]            None         None   \n",
       "220671          None  pá         0  [R 1335]            None         None   \n",
       "237276          None  pá         0  [R 1335]            None         None   \n",
       "245171          None  pá         0  [R 1335]            None         None   \n",
       "274347          None  pá         0  [R 1335]            None         None   \n",
       "318566          None  pá         0  [R 1335]            None         None   \n",
       "334538          None  pá         0  [R 1335]            None         None   \n",
       "348349          None  pá         0  [R 1335]            None         None   \n",
       "363648          None  pá         0  [R 1335]            None         None   \n",
       "372905          None  pá         0  [R 1335]            None         None   \n",
       "385034          None  pá         0  [R 1335]            None         None   \n",
       "389161          None  pá         0  [R 1335]            None         None   \n",
       "408970          None  pá         0  [R 1335]            None         None   \n",
       "417149          None  pá         0  [R 1335]            None         None   \n",
       "436830          None  pá         0  [R 1335]            None         None   \n",
       "440078          None  pá         0  [R 1335]            None         None   \n",
       "474064          None  pá         0  [R 1335]            None         None   \n",
       "491817          None  pá         0  [R 1335]            None         None   \n",
       "499856          None  pá         0  [R 1335]            None         None   \n",
       "\n",
       "        volna_mista_economy  volna_mista_economy_plus  \\\n",
       "133520                  NaN                       NaN   \n",
       "153192                  NaN                       NaN   \n",
       "220671                  NaN                       NaN   \n",
       "237276                  NaN                       NaN   \n",
       "245171                  NaN                       NaN   \n",
       "274347                  NaN                       NaN   \n",
       "318566                  NaN                       NaN   \n",
       "334538                  NaN                       NaN   \n",
       "348349                  NaN                       NaN   \n",
       "363648                  NaN                       NaN   \n",
       "372905                  NaN                       NaN   \n",
       "385034                  NaN                       NaN   \n",
       "389161                  NaN                       NaN   \n",
       "408970                  NaN                       NaN   \n",
       "417149                  NaN                       NaN   \n",
       "436830                  NaN                       NaN   \n",
       "440078                  NaN                       NaN   \n",
       "474064                  NaN                       NaN   \n",
       "491817                  NaN                       NaN   \n",
       "499856                  NaN                       NaN   \n",
       "\n",
       "        volna_mista_economy_business  volna_mista_premium  \n",
       "133520                           NaN                  NaN  \n",
       "153192                           NaN                  NaN  \n",
       "220671                           NaN                  NaN  \n",
       "237276                           NaN                  NaN  \n",
       "245171                           NaN                  NaN  \n",
       "274347                           NaN                  NaN  \n",
       "318566                           NaN                  NaN  \n",
       "334538                           NaN                  NaN  \n",
       "348349                           NaN                  NaN  \n",
       "363648                           NaN                  NaN  \n",
       "372905                           NaN                  NaN  \n",
       "385034                           NaN                  NaN  \n",
       "389161                           NaN                  NaN  \n",
       "408970                           NaN                  NaN  \n",
       "417149                           NaN                  NaN  \n",
       "436830                           NaN                  NaN  \n",
       "440078                           NaN                  NaN  \n",
       "474064                           NaN                  NaN  \n",
       "491817                           NaN                  NaN  \n",
       "499856                           NaN                  NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['prodejce'] == 'ARR') & (df['odjezd'] == '2024-11-22 21:27:00')].head(20000).tail(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "259ed34d-4d04-4f3f-ba82-94db1e56bd95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>oscrapovano</th>\n",
       "      <th>prodejce</th>\n",
       "      <th>odkud</th>\n",
       "      <th>kam</th>\n",
       "      <th>odjezd</th>\n",
       "      <th>predstih</th>\n",
       "      <th>predstih_d</th>\n",
       "      <th>predstih_h</th>\n",
       "      <th>cena</th>\n",
       "      <th>prostredek</th>\n",
       "      <th>volnych_mist</th>\n",
       "      <th>obsazenost</th>\n",
       "      <th>jizdni_doba</th>\n",
       "      <th>vzdalenost</th>\n",
       "      <th>zpozdeni</th>\n",
       "      <th>cena_poznamka</th>\n",
       "      <th>den</th>\n",
       "      <th>prestupy</th>\n",
       "      <th>vlaky</th>\n",
       "      <th>mistenka_zdarma</th>\n",
       "      <th>nahradni_bus</th>\n",
       "      <th>volna_mista_economy</th>\n",
       "      <th>volna_mista_economy_plus</th>\n",
       "      <th>volna_mista_economy_business</th>\n",
       "      <th>volna_mista_premium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [oscrapovano, prodejce, odkud, kam, odjezd, predstih, predstih_d, predstih_h, cena, prostredek, volnych_mist, obsazenost, jizdni_doba, vzdalenost, zpozdeni, cena_poznamka, den, prestupy, vlaky, mistenka_zdarma, nahradni_bus, volna_mista_economy, volna_mista_economy_plus, volna_mista_economy_business, volna_mista_premium]\n",
       "Index: []"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['prestupy'].isnull()) & (df['oscrapovano'] >= '2024-11-11')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e0c87a49-fc2f-4f7b-8c72-c5df134fdf6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>oscrapovano</th>\n",
       "      <th>prodejce</th>\n",
       "      <th>odkud</th>\n",
       "      <th>kam</th>\n",
       "      <th>odjezd</th>\n",
       "      <th>predstih</th>\n",
       "      <th>predstih_d</th>\n",
       "      <th>predstih_h</th>\n",
       "      <th>cena</th>\n",
       "      <th>prostredek</th>\n",
       "      <th>volnych_mist</th>\n",
       "      <th>obsazenost</th>\n",
       "      <th>jizdni_doba</th>\n",
       "      <th>vzdalenost</th>\n",
       "      <th>zpozdeni</th>\n",
       "      <th>cena_poznamka</th>\n",
       "      <th>den</th>\n",
       "      <th>prestupy</th>\n",
       "      <th>vlaky</th>\n",
       "      <th>mistenka_zdarma</th>\n",
       "      <th>nahradni_bus</th>\n",
       "      <th>volna_mista_economy</th>\n",
       "      <th>volna_mista_economy_plus</th>\n",
       "      <th>volna_mista_economy_business</th>\n",
       "      <th>volna_mista_premium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>51940</th>\n",
       "      <td>2024-11-04 05:11:14</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Brno hl.n.</td>\n",
       "      <td>Hamburg Hbf</td>\n",
       "      <td>2024-11-27 10:22:00</td>\n",
       "      <td>23 days 05:10:46</td>\n",
       "      <td>23</td>\n",
       "      <td>557.179444</td>\n",
       "      <td>7265.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané nízké obsazení, Předpokládané n...</td>\n",
       "      <td>693.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 0]</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>3</td>\n",
       "      <td>[EC 275 Metropolitan, EC 103 Danubius, rjx 62 ...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256054</th>\n",
       "      <td>2024-11-10 18:06:51</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Ostrava</td>\n",
       "      <td>Praha</td>\n",
       "      <td>2024-11-22 06:52:00</td>\n",
       "      <td>11 days 12:45:09</td>\n",
       "      <td>11</td>\n",
       "      <td>276.752500</td>\n",
       "      <td>439.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>188.0</td>\n",
       "      <td>None</td>\n",
       "      <td>195.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105182</th>\n",
       "      <td>2024-11-06 17:32:30</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Zagreb Glavni kolodvor</td>\n",
       "      <td>2024-11-09 08:44:00</td>\n",
       "      <td>2 days 15:11:30</td>\n",
       "      <td>2</td>\n",
       "      <td>63.191667</td>\n",
       "      <td>1342.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané střední obsazení - doporučujeme...</td>\n",
       "      <td>723.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0]</td>\n",
       "      <td>None</td>\n",
       "      <td>so</td>\n",
       "      <td>2</td>\n",
       "      <td>[rj 75 Vindobona, D 157, D 213 Mimara]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422427</th>\n",
       "      <td>2024-11-14 23:57:38</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Budapest-Kelenföld</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2024-12-03 15:55:00</td>\n",
       "      <td>18 days 15:57:22</td>\n",
       "      <td>18</td>\n",
       "      <td>447.956111</td>\n",
       "      <td>2495.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané nízké obsazení, Téměr vyprodaná...</td>\n",
       "      <td>441.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>None</td>\n",
       "      <td>út</td>\n",
       "      <td>1</td>\n",
       "      <td>[rjx 12068, rj 372 Vindobona]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432779</th>\n",
       "      <td>2024-11-15 08:19:26</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2024-12-02 16:04:00</td>\n",
       "      <td>17 days 07:44:34</td>\n",
       "      <td>17</td>\n",
       "      <td>415.742778</td>\n",
       "      <td>199.0</td>\n",
       "      <td>None</td>\n",
       "      <td>56.0</td>\n",
       "      <td>None</td>\n",
       "      <td>204.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>po</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1260]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262054</th>\n",
       "      <td>2024-11-10 21:54:33</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Zagreb Glavni kolodvor</td>\n",
       "      <td>2024-11-30 10:44:00</td>\n",
       "      <td>19 days 12:49:27</td>\n",
       "      <td>19</td>\n",
       "      <td>468.824167</td>\n",
       "      <td>1342.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané vysoké obsazení - doporučujeme ...</td>\n",
       "      <td>702.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>None</td>\n",
       "      <td>so</td>\n",
       "      <td>1</td>\n",
       "      <td>[rj 257 Vindobona, D 159 Croatia]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271384</th>\n",
       "      <td>2024-11-11 07:40:28</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2024-11-14 18:12:00</td>\n",
       "      <td>3 days 10:31:32</td>\n",
       "      <td>3</td>\n",
       "      <td>82.525556</td>\n",
       "      <td>425.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané nízké obsazení]</td>\n",
       "      <td>202.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>[0]</td>\n",
       "      <td>None</td>\n",
       "      <td>čt</td>\n",
       "      <td>0</td>\n",
       "      <td>[EC 112 Silesia]</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287238</th>\n",
       "      <td>2024-11-11 17:43:06</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Warszawa Centralna</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2024-11-14 19:39:00</td>\n",
       "      <td>3 days 01:55:54</td>\n",
       "      <td>3</td>\n",
       "      <td>73.931667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané nízké obsazení, Téměr vyprodaná...</td>\n",
       "      <td>699.0</td>\n",
       "      <td>794.0</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>Zjistit cenu</td>\n",
       "      <td>čt</td>\n",
       "      <td>1</td>\n",
       "      <td>[EN 407 Chopin, IC 518 Ostravan]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89481</th>\n",
       "      <td>2024-11-05 21:51:48</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Pardubice hl.n.</td>\n",
       "      <td>Košice</td>\n",
       "      <td>2024-11-22 13:22:00</td>\n",
       "      <td>16 days 15:30:12</td>\n",
       "      <td>16</td>\n",
       "      <td>399.503333</td>\n",
       "      <td>782.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané vysoké obsazení - doporučujeme ...</td>\n",
       "      <td>451.0</td>\n",
       "      <td>601.0</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>1</td>\n",
       "      <td>[EC 143 Ostravan, Ex 611 TATRAN]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220403</th>\n",
       "      <td>2024-11-09 19:20:23</td>\n",
       "      <td>LE</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>2024-11-25 11:00:00</td>\n",
       "      <td>15 days 15:39:37</td>\n",
       "      <td>15</td>\n",
       "      <td>375.660278</td>\n",
       "      <td>419.0</td>\n",
       "      <td>None</td>\n",
       "      <td>50.0</td>\n",
       "      <td>None</td>\n",
       "      <td>204.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>po</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1257]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74908</th>\n",
       "      <td>2024-11-05 07:37:38</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Bratislava hl.st.</td>\n",
       "      <td>2024-11-15 12:44:00</td>\n",
       "      <td>10 days 05:06:22</td>\n",
       "      <td>10</td>\n",
       "      <td>245.106111</td>\n",
       "      <td>490.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané vysoké obsazení - doporučujeme ...</td>\n",
       "      <td>260.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>1</td>\n",
       "      <td>[rj 79 Vindobona, EC 131 Báthory]</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59514</th>\n",
       "      <td>2024-11-04 12:51:10</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Brno hl.n.</td>\n",
       "      <td>Hamburg Hbf</td>\n",
       "      <td>2024-11-10 07:39:00</td>\n",
       "      <td>5 days 18:47:50</td>\n",
       "      <td>5</td>\n",
       "      <td>138.797222</td>\n",
       "      <td>1574.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané střední obsazení - doporučujeme...</td>\n",
       "      <td>644.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0]</td>\n",
       "      <td>None</td>\n",
       "      <td>ne</td>\n",
       "      <td>2</td>\n",
       "      <td>[EC 282 Metropolitan Slovenská strela, EC 174 ...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99929</th>\n",
       "      <td>2024-11-06 10:57:36</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Brno hl.n.</td>\n",
       "      <td>Hamburg Hbf</td>\n",
       "      <td>2024-11-14 15:34:00</td>\n",
       "      <td>8 days 04:36:24</td>\n",
       "      <td>8</td>\n",
       "      <td>196.606667</td>\n",
       "      <td>8638.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané vysoké obsazení - doporučujeme ...</td>\n",
       "      <td>908.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 0]</td>\n",
       "      <td>None</td>\n",
       "      <td>čt</td>\n",
       "      <td>3</td>\n",
       "      <td>[R 825 Moravan, Os 2355, IC 296 Wienerwald, IC...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204177</th>\n",
       "      <td>2024-11-09 13:48:36</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2024-11-18 09:10:00</td>\n",
       "      <td>8 days 19:21:24</td>\n",
       "      <td>8</td>\n",
       "      <td>211.356667</td>\n",
       "      <td>504.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané nízké obsazení]</td>\n",
       "      <td>189.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>[0]</td>\n",
       "      <td>None</td>\n",
       "      <td>po</td>\n",
       "      <td>0</td>\n",
       "      <td>[IC 510 Ostravan]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252269</th>\n",
       "      <td>2024-11-10 16:02:57</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Bruxelles-Midi</td>\n",
       "      <td>2025-01-10 09:35:00</td>\n",
       "      <td>60 days 17:32:03</td>\n",
       "      <td>60</td>\n",
       "      <td>1457.534167</td>\n",
       "      <td>6543.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané nízké obsazení, Předpokládané n...</td>\n",
       "      <td>720.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0]</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>2</td>\n",
       "      <td>[EC 358 Bavorský expres, ICE 26, ICE 10 ICE In...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70148</th>\n",
       "      <td>2024-11-04 23:38:29</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Berlin Hbf (tief)</td>\n",
       "      <td>2024-11-16 12:28:00</td>\n",
       "      <td>11 days 12:49:31</td>\n",
       "      <td>11</td>\n",
       "      <td>276.825278</td>\n",
       "      <td>903.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané střední obsazení - doporučujeme...</td>\n",
       "      <td>255.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>[0]</td>\n",
       "      <td>None</td>\n",
       "      <td>so</td>\n",
       "      <td>0</td>\n",
       "      <td>[EC 378 Berliner]</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374880</th>\n",
       "      <td>2024-11-13 18:58:09</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Brno hl.n.</td>\n",
       "      <td>2025-01-10 10:08:00</td>\n",
       "      <td>57 days 15:09:51</td>\n",
       "      <td>57</td>\n",
       "      <td>1383.164167</td>\n",
       "      <td>415.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané nízké obsazení]</td>\n",
       "      <td>212.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>[0]</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 979 Vysočina]</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297746</th>\n",
       "      <td>2024-11-11 22:31:14</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Tanvald</td>\n",
       "      <td>2024-11-12 17:57:00</td>\n",
       "      <td>0 days 19:25:46</td>\n",
       "      <td>0</td>\n",
       "      <td>19.429444</td>\n",
       "      <td>160.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>31.0</td>\n",
       "      <td>None</td>\n",
       "      <td>154.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>út</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1150]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63716</th>\n",
       "      <td>2024-11-04 17:22:53</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Brno hl.n.</td>\n",
       "      <td>2024-11-09 06:06:00</td>\n",
       "      <td>4 days 12:43:07</td>\n",
       "      <td>4</td>\n",
       "      <td>108.718611</td>\n",
       "      <td>305.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané nízké obsazení]</td>\n",
       "      <td>224.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>[0]</td>\n",
       "      <td>None</td>\n",
       "      <td>so</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 975 Vysočina]</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6636</th>\n",
       "      <td>2024-11-01 12:51:52</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Brno hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2024-11-08 06:39:00</td>\n",
       "      <td>6 days 17:47:08</td>\n",
       "      <td>6</td>\n",
       "      <td>161.785556</td>\n",
       "      <td>329.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané nízké obsazení]</td>\n",
       "      <td>154.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>[0]</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[rj 574 Brněnský drak]</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311857</th>\n",
       "      <td>2024-11-12 09:33:28</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Budapest-Nyugati pu</td>\n",
       "      <td>2024-11-14 07:44:00</td>\n",
       "      <td>1 days 22:10:32</td>\n",
       "      <td>1</td>\n",
       "      <td>46.175556</td>\n",
       "      <td>903.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané nízké obsazení]</td>\n",
       "      <td>404.0</td>\n",
       "      <td>610.0</td>\n",
       "      <td>[0]</td>\n",
       "      <td>None</td>\n",
       "      <td>čt</td>\n",
       "      <td>0</td>\n",
       "      <td>[EC 275 Metropolitan]</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422050</th>\n",
       "      <td>2024-11-14 23:52:03</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Bratislava hl.st.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2024-11-26 05:58:00</td>\n",
       "      <td>11 days 06:05:57</td>\n",
       "      <td>11</td>\n",
       "      <td>270.099167</td>\n",
       "      <td>400.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané nízké obsazení]</td>\n",
       "      <td>253.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>[0]</td>\n",
       "      <td>None</td>\n",
       "      <td>út</td>\n",
       "      <td>0</td>\n",
       "      <td>[EC 282 Metropolitan Slovenská strela]</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18937</th>\n",
       "      <td>2024-11-02 04:44:43</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Praha-Holešovice</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>2024-11-07 00:40:00</td>\n",
       "      <td>4 days 19:55:17</td>\n",
       "      <td>4</td>\n",
       "      <td>115.921389</td>\n",
       "      <td>825.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané nízké obsazení, Předpokládané n...</td>\n",
       "      <td>399.0</td>\n",
       "      <td>427.0</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>None</td>\n",
       "      <td>čt</td>\n",
       "      <td>1</td>\n",
       "      <td>[NJ 457 nightjet, R 1101 RegioJet /R 201101/]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152053</th>\n",
       "      <td>2024-11-08 11:48:36</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Wien Hbf</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2024-11-17 23:35:00</td>\n",
       "      <td>9 days 11:46:24</td>\n",
       "      <td>9</td>\n",
       "      <td>227.773333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané střední obsazení - doporučujeme...</td>\n",
       "      <td>389.0</td>\n",
       "      <td>404.0</td>\n",
       "      <td>[0, 0, 0]</td>\n",
       "      <td>Zjistit cenu</td>\n",
       "      <td>ne</td>\n",
       "      <td>2</td>\n",
       "      <td>[EN 406 Chopin, Ex 1136 Gepard Express/, R 876...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>2024-10-31 23:36:00</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>Salzburg Hbf</td>\n",
       "      <td>2024-11-02 05:01:00</td>\n",
       "      <td>1 days 05:25:00</td>\n",
       "      <td>1</td>\n",
       "      <td>29.416667</td>\n",
       "      <td>1662.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané vysoké obsazení - doporučujeme ...</td>\n",
       "      <td>592.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 0, 0]</td>\n",
       "      <td>None</td>\n",
       "      <td>so</td>\n",
       "      <td>4</td>\n",
       "      <td>[TL 6600/, R 1181/, EC 275 Metropolitan, EC 10...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215600</th>\n",
       "      <td>2024-11-09 18:21:50</td>\n",
       "      <td>LE</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>2025-01-13 18:58:00</td>\n",
       "      <td>65 days 00:36:10</td>\n",
       "      <td>65</td>\n",
       "      <td>1560.602778</td>\n",
       "      <td>199.0</td>\n",
       "      <td>None</td>\n",
       "      <td>67.0</td>\n",
       "      <td>None</td>\n",
       "      <td>213.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>po</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1263]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383502</th>\n",
       "      <td>2024-11-13 23:51:56</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Bratislava hl.st.</td>\n",
       "      <td>2024-11-29 11:44:00</td>\n",
       "      <td>15 days 11:52:04</td>\n",
       "      <td>15</td>\n",
       "      <td>371.867778</td>\n",
       "      <td>632.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané vysoké obsazení - doporučujeme ...</td>\n",
       "      <td>258.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>[0]</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[EC 279 Metropolitan]</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229121</th>\n",
       "      <td>2024-11-10 05:30:42</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2024-11-10 06:02:00</td>\n",
       "      <td>0 days 00:31:18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.521667</td>\n",
       "      <td>569.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané nízké obsazení]</td>\n",
       "      <td>212.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>[0]</td>\n",
       "      <td>None</td>\n",
       "      <td>ne</td>\n",
       "      <td>0</td>\n",
       "      <td>[IC 546 Ostravan]</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204841</th>\n",
       "      <td>2024-11-09 13:57:29</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Berlin Hbf (tief)</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2024-12-05 15:16:00</td>\n",
       "      <td>26 days 01:18:31</td>\n",
       "      <td>26</td>\n",
       "      <td>625.308611</td>\n",
       "      <td>748.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané nízké obsazení]</td>\n",
       "      <td>248.0</td>\n",
       "      <td>397.0</td>\n",
       "      <td>[0]</td>\n",
       "      <td>None</td>\n",
       "      <td>čt</td>\n",
       "      <td>0</td>\n",
       "      <td>[EC 177 Berliner]</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86878</th>\n",
       "      <td>2024-11-05 18:56:38</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Bruxelles-Midi</td>\n",
       "      <td>2024-11-14 18:05:00</td>\n",
       "      <td>8 days 23:08:22</td>\n",
       "      <td>8</td>\n",
       "      <td>215.139444</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Téměr vyprodaná místa k sezení, Téměr vyproda...</td>\n",
       "      <td>903.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>Zjistit cenu</td>\n",
       "      <td>čt</td>\n",
       "      <td>1</td>\n",
       "      <td>[ES 452 European Sleeper, THA 9316]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353308</th>\n",
       "      <td>2024-11-13 09:50:11</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Bratislava hl.st.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2024-11-26 21:58:00</td>\n",
       "      <td>13 days 12:07:49</td>\n",
       "      <td>13</td>\n",
       "      <td>324.130278</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Téměr vyprodaná místa k sezení]</td>\n",
       "      <td>615.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>[0]</td>\n",
       "      <td>Cena v dalším kroku</td>\n",
       "      <td>út</td>\n",
       "      <td>0</td>\n",
       "      <td>[EN 476 Metropol /IC 576]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187813</th>\n",
       "      <td>2024-11-09 06:05:43</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>2025-01-07 08:22:00</td>\n",
       "      <td>59 days 02:16:17</td>\n",
       "      <td>59</td>\n",
       "      <td>1418.271389</td>\n",
       "      <td>425.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané nízké obsazení]</td>\n",
       "      <td>206.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>[0]</td>\n",
       "      <td>None</td>\n",
       "      <td>út</td>\n",
       "      <td>0</td>\n",
       "      <td>[EC 141 Ostravan]</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260238</th>\n",
       "      <td>2024-11-10 19:16:29</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Brno</td>\n",
       "      <td>Ostrava</td>\n",
       "      <td>2024-11-14 12:02:00</td>\n",
       "      <td>3 days 16:45:31</td>\n",
       "      <td>3</td>\n",
       "      <td>88.758611</td>\n",
       "      <td>269.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>268.0</td>\n",
       "      <td>None</td>\n",
       "      <td>129.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>čt</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481566</th>\n",
       "      <td>2024-11-16 14:11:06</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Brno</td>\n",
       "      <td>2024-11-29 06:15:00</td>\n",
       "      <td>12 days 16:03:54</td>\n",
       "      <td>12</td>\n",
       "      <td>304.065000</td>\n",
       "      <td>249.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>127.0</td>\n",
       "      <td>None</td>\n",
       "      <td>151.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381264</th>\n",
       "      <td>2024-11-13 22:18:45</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Vídeň</td>\n",
       "      <td>Brno</td>\n",
       "      <td>2024-11-15 22:48:00</td>\n",
       "      <td>2 days 00:29:15</td>\n",
       "      <td>2</td>\n",
       "      <td>48.487500</td>\n",
       "      <td>429.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>74.0</td>\n",
       "      <td>None</td>\n",
       "      <td>182.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459346</th>\n",
       "      <td>2024-11-15 22:24:28</td>\n",
       "      <td>ARR</td>\n",
       "      <td>České Budějovice</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2024-12-04 12:33:00</td>\n",
       "      <td>18 days 14:08:32</td>\n",
       "      <td>18</td>\n",
       "      <td>446.142222</td>\n",
       "      <td>200.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>39.0</td>\n",
       "      <td>None</td>\n",
       "      <td>201.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1167]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189931</th>\n",
       "      <td>2024-11-09 07:39:45</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Budapest-Kelenföld</td>\n",
       "      <td>2024-11-11 22:03:00</td>\n",
       "      <td>2 days 14:23:15</td>\n",
       "      <td>2</td>\n",
       "      <td>62.387500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané nízké obsazení, Předpokládané n...</td>\n",
       "      <td>599.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 0, 0]</td>\n",
       "      <td>Zjistit cenu</td>\n",
       "      <td>po</td>\n",
       "      <td>4</td>\n",
       "      <td>[IC 577 Metropol, Ex 1135 Gepard Express /Ex 1...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202951</th>\n",
       "      <td>2024-11-09 13:30:59</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>2024-11-10 12:19:00</td>\n",
       "      <td>0 days 22:48:01</td>\n",
       "      <td>0</td>\n",
       "      <td>22.800278</td>\n",
       "      <td>609.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané vysoké obsazení - doporučujeme ...</td>\n",
       "      <td>209.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>[0]</td>\n",
       "      <td>None</td>\n",
       "      <td>ne</td>\n",
       "      <td>0</td>\n",
       "      <td>[EC 143 Ostravan]</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244516</th>\n",
       "      <td>2024-11-10 12:09:20</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Krakov</td>\n",
       "      <td>2024-11-10 20:50:00</td>\n",
       "      <td>0 days 08:40:40</td>\n",
       "      <td>0</td>\n",
       "      <td>8.677778</td>\n",
       "      <td>800.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>92.0</td>\n",
       "      <td>None</td>\n",
       "      <td>468.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>ne</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40739</th>\n",
       "      <td>2024-11-03 09:47:44</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Praha-Holešovice</td>\n",
       "      <td>Ljubljana</td>\n",
       "      <td>2024-11-28 00:40:00</td>\n",
       "      <td>24 days 14:52:16</td>\n",
       "      <td>24</td>\n",
       "      <td>590.871111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané střední obsazení - doporučujeme...</td>\n",
       "      <td>800.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>Cena v dalším kroku</td>\n",
       "      <td>čt</td>\n",
       "      <td>1</td>\n",
       "      <td>[NJ 457 nightjet, EC 151 Emona]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               oscrapovano prodejce               odkud  \\\n",
       "51940  2024-11-04 05:11:14       ČD          Brno hl.n.   \n",
       "256054 2024-11-10 18:06:51       RJ             Ostrava   \n",
       "105182 2024-11-06 17:32:30       ČD         Praha hl.n.   \n",
       "422427 2024-11-14 23:57:38       ČD  Budapest-Kelenföld   \n",
       "432779 2024-11-15 08:19:26       LE      Ostrava-Svinov   \n",
       "262054 2024-11-10 21:54:33       ČD         Praha hl.n.   \n",
       "271384 2024-11-11 07:40:28       ČD       Ostrava hl.n.   \n",
       "287238 2024-11-11 17:43:06       ČD  Warszawa Centralna   \n",
       "89481  2024-11-05 21:51:48       ČD     Pardubice hl.n.   \n",
       "220403 2024-11-09 19:20:23       LE         Praha hl.n.   \n",
       "74908  2024-11-05 07:37:38       ČD         Praha hl.n.   \n",
       "59514  2024-11-04 12:51:10       ČD          Brno hl.n.   \n",
       "99929  2024-11-06 10:57:36       ČD          Brno hl.n.   \n",
       "204177 2024-11-09 13:48:36       ČD       Ostrava hl.n.   \n",
       "252269 2024-11-10 16:02:57       ČD         Praha hl.n.   \n",
       "70148  2024-11-04 23:38:29       ČD         Praha hl.n.   \n",
       "374880 2024-11-13 18:58:09       ČD         Praha hl.n.   \n",
       "297746 2024-11-11 22:31:14      ARR         Praha hl.n.   \n",
       "63716  2024-11-04 17:22:53       ČD         Praha hl.n.   \n",
       "6636   2024-11-01 12:51:52       ČD          Brno hl.n.   \n",
       "311857 2024-11-12 09:33:28       ČD         Praha hl.n.   \n",
       "422050 2024-11-14 23:52:03       ČD   Bratislava hl.st.   \n",
       "18937  2024-11-02 04:44:43       ČD    Praha-Holešovice   \n",
       "152053 2024-11-08 11:48:36       ČD            Wien Hbf   \n",
       "277    2024-10-31 23:36:00       ČD             Liberec   \n",
       "215600 2024-11-09 18:21:50       LE         Praha hl.n.   \n",
       "383502 2024-11-13 23:51:56       ČD         Praha hl.n.   \n",
       "229121 2024-11-10 05:30:42       ČD       Ostrava hl.n.   \n",
       "204841 2024-11-09 13:57:29       ČD   Berlin Hbf (tief)   \n",
       "86878  2024-11-05 18:56:38       ČD         Praha hl.n.   \n",
       "353308 2024-11-13 09:50:11       ČD   Bratislava hl.st.   \n",
       "187813 2024-11-09 06:05:43       ČD         Praha hl.n.   \n",
       "260238 2024-11-10 19:16:29       RJ                Brno   \n",
       "481566 2024-11-16 14:11:06       RJ               Praha   \n",
       "381264 2024-11-13 22:18:45       RJ               Vídeň   \n",
       "459346 2024-11-15 22:24:28      ARR    České Budějovice   \n",
       "189931 2024-11-09 07:39:45       ČD         Praha hl.n.   \n",
       "202951 2024-11-09 13:30:59       ČD         Praha hl.n.   \n",
       "244516 2024-11-10 12:09:20       RJ               Praha   \n",
       "40739  2024-11-03 09:47:44       ČD    Praha-Holešovice   \n",
       "\n",
       "                           kam              odjezd         predstih  \\\n",
       "51940              Hamburg Hbf 2024-11-27 10:22:00 23 days 05:10:46   \n",
       "256054                   Praha 2024-11-22 06:52:00 11 days 12:45:09   \n",
       "105182  Zagreb Glavni kolodvor 2024-11-09 08:44:00  2 days 15:11:30   \n",
       "422427             Praha hl.n. 2024-12-03 15:55:00 18 days 15:57:22   \n",
       "432779             Praha hl.n. 2024-12-02 16:04:00 17 days 07:44:34   \n",
       "262054  Zagreb Glavni kolodvor 2024-11-30 10:44:00 19 days 12:49:27   \n",
       "271384             Praha hl.n. 2024-11-14 18:12:00  3 days 10:31:32   \n",
       "287238             Praha hl.n. 2024-11-14 19:39:00  3 days 01:55:54   \n",
       "89481                   Košice 2024-11-22 13:22:00 16 days 15:30:12   \n",
       "220403          Ostrava-Svinov 2024-11-25 11:00:00 15 days 15:39:37   \n",
       "74908        Bratislava hl.st. 2024-11-15 12:44:00 10 days 05:06:22   \n",
       "59514              Hamburg Hbf 2024-11-10 07:39:00  5 days 18:47:50   \n",
       "99929              Hamburg Hbf 2024-11-14 15:34:00  8 days 04:36:24   \n",
       "204177             Praha hl.n. 2024-11-18 09:10:00  8 days 19:21:24   \n",
       "252269          Bruxelles-Midi 2025-01-10 09:35:00 60 days 17:32:03   \n",
       "70148        Berlin Hbf (tief) 2024-11-16 12:28:00 11 days 12:49:31   \n",
       "374880              Brno hl.n. 2025-01-10 10:08:00 57 days 15:09:51   \n",
       "297746                 Tanvald 2024-11-12 17:57:00  0 days 19:25:46   \n",
       "63716               Brno hl.n. 2024-11-09 06:06:00  4 days 12:43:07   \n",
       "6636               Praha hl.n. 2024-11-08 06:39:00  6 days 17:47:08   \n",
       "311857     Budapest-Nyugati pu 2024-11-14 07:44:00  1 days 22:10:32   \n",
       "422050             Praha hl.n. 2024-11-26 05:58:00 11 days 06:05:57   \n",
       "18937            Ostrava hl.n. 2024-11-07 00:40:00  4 days 19:55:17   \n",
       "152053             Praha hl.n. 2024-11-17 23:35:00  9 days 11:46:24   \n",
       "277               Salzburg Hbf 2024-11-02 05:01:00  1 days 05:25:00   \n",
       "215600           Ostrava hl.n. 2025-01-13 18:58:00 65 days 00:36:10   \n",
       "383502       Bratislava hl.st. 2024-11-29 11:44:00 15 days 11:52:04   \n",
       "229121             Praha hl.n. 2024-11-10 06:02:00  0 days 00:31:18   \n",
       "204841             Praha hl.n. 2024-12-05 15:16:00 26 days 01:18:31   \n",
       "86878           Bruxelles-Midi 2024-11-14 18:05:00  8 days 23:08:22   \n",
       "353308             Praha hl.n. 2024-11-26 21:58:00 13 days 12:07:49   \n",
       "187813           Ostrava hl.n. 2025-01-07 08:22:00 59 days 02:16:17   \n",
       "260238                 Ostrava 2024-11-14 12:02:00  3 days 16:45:31   \n",
       "481566                    Brno 2024-11-29 06:15:00 12 days 16:03:54   \n",
       "381264                    Brno 2024-11-15 22:48:00  2 days 00:29:15   \n",
       "459346             Praha hl.n. 2024-12-04 12:33:00 18 days 14:08:32   \n",
       "189931      Budapest-Kelenföld 2024-11-11 22:03:00  2 days 14:23:15   \n",
       "202951           Ostrava hl.n. 2024-11-10 12:19:00  0 days 22:48:01   \n",
       "244516                  Krakov 2024-11-10 20:50:00  0 days 08:40:40   \n",
       "40739                Ljubljana 2024-11-28 00:40:00 24 days 14:52:16   \n",
       "\n",
       "        predstih_d   predstih_h    cena prostredek  volnych_mist  \\\n",
       "51940           23   557.179444  7265.0       vlak           NaN   \n",
       "256054          11   276.752500   439.0       vlak         188.0   \n",
       "105182           2    63.191667  1342.0       vlak           NaN   \n",
       "422427          18   447.956111  2495.0       vlak           NaN   \n",
       "432779          17   415.742778   199.0       None          56.0   \n",
       "262054          19   468.824167  1342.0       vlak           NaN   \n",
       "271384           3    82.525556   425.0       vlak           NaN   \n",
       "287238           3    73.931667     NaN       vlak           NaN   \n",
       "89481           16   399.503333   782.0       vlak           NaN   \n",
       "220403          15   375.660278   419.0       None          50.0   \n",
       "74908           10   245.106111   490.0       vlak           NaN   \n",
       "59514            5   138.797222  1574.0       vlak           NaN   \n",
       "99929            8   196.606667  8638.0       vlak           NaN   \n",
       "204177           8   211.356667   504.0       vlak           NaN   \n",
       "252269          60  1457.534167  6543.0       vlak           NaN   \n",
       "70148           11   276.825278   903.0       vlak           NaN   \n",
       "374880          57  1383.164167   415.0       vlak           NaN   \n",
       "297746           0    19.429444   160.0       vlak          31.0   \n",
       "63716            4   108.718611   305.0       vlak           NaN   \n",
       "6636             6   161.785556   329.0       vlak           NaN   \n",
       "311857           1    46.175556   903.0       vlak           NaN   \n",
       "422050          11   270.099167   400.0       vlak           NaN   \n",
       "18937            4   115.921389   825.0       vlak           NaN   \n",
       "152053           9   227.773333     NaN       vlak           NaN   \n",
       "277              1    29.416667  1662.0       vlak           NaN   \n",
       "215600          65  1560.602778   199.0       None          67.0   \n",
       "383502          15   371.867778   632.0       vlak           NaN   \n",
       "229121           0     0.521667   569.0       vlak           NaN   \n",
       "204841          26   625.308611   748.0       vlak           NaN   \n",
       "86878            8   215.139444     NaN       vlak           NaN   \n",
       "353308          13   324.130278     NaN       vlak           NaN   \n",
       "187813          59  1418.271389   425.0       vlak           NaN   \n",
       "260238           3    88.758611   269.0       vlak         268.0   \n",
       "481566          12   304.065000   249.0       vlak         127.0   \n",
       "381264           2    48.487500   429.0       vlak          74.0   \n",
       "459346          18   446.142222   200.0       vlak          39.0   \n",
       "189931           2    62.387500     NaN       vlak           NaN   \n",
       "202951           0    22.800278   609.0       vlak           NaN   \n",
       "244516           0     8.677778   800.0       vlak          92.0   \n",
       "40739           24   590.871111     NaN       vlak           NaN   \n",
       "\n",
       "                                               obsazenost  jizdni_doba  \\\n",
       "51940   [Předpokládané nízké obsazení, Předpokládané n...        693.0   \n",
       "256054                                               None        195.0   \n",
       "105182  [Předpokládané střední obsazení - doporučujeme...        723.0   \n",
       "422427  [Předpokládané nízké obsazení, Téměr vyprodaná...        441.0   \n",
       "432779                                               None        204.0   \n",
       "262054  [Předpokládané vysoké obsazení - doporučujeme ...        702.0   \n",
       "271384                     [Předpokládané nízké obsazení]        202.0   \n",
       "287238  [Předpokládané nízké obsazení, Téměr vyprodaná...        699.0   \n",
       "89481   [Předpokládané vysoké obsazení - doporučujeme ...        451.0   \n",
       "220403                                               None        204.0   \n",
       "74908   [Předpokládané vysoké obsazení - doporučujeme ...        260.0   \n",
       "59514   [Předpokládané střední obsazení - doporučujeme...        644.0   \n",
       "99929   [Předpokládané vysoké obsazení - doporučujeme ...        908.0   \n",
       "204177                     [Předpokládané nízké obsazení]        189.0   \n",
       "252269  [Předpokládané nízké obsazení, Předpokládané n...        720.0   \n",
       "70148   [Předpokládané střední obsazení - doporučujeme...        255.0   \n",
       "374880                     [Předpokládané nízké obsazení]        212.0   \n",
       "297746                                               None        154.0   \n",
       "63716                      [Předpokládané nízké obsazení]        224.0   \n",
       "6636                       [Předpokládané nízké obsazení]        154.0   \n",
       "311857                     [Předpokládané nízké obsazení]        404.0   \n",
       "422050                     [Předpokládané nízké obsazení]        253.0   \n",
       "18937   [Předpokládané nízké obsazení, Předpokládané n...        399.0   \n",
       "152053  [Předpokládané střední obsazení - doporučujeme...        389.0   \n",
       "277     [Předpokládané vysoké obsazení - doporučujeme ...        592.0   \n",
       "215600                                               None        213.0   \n",
       "383502  [Předpokládané vysoké obsazení - doporučujeme ...        258.0   \n",
       "229121                     [Předpokládané nízké obsazení]        212.0   \n",
       "204841                     [Předpokládané nízké obsazení]        248.0   \n",
       "86878   [Téměr vyprodaná místa k sezení, Téměr vyproda...        903.0   \n",
       "353308                   [Téměr vyprodaná místa k sezení]        615.0   \n",
       "187813                     [Předpokládané nízké obsazení]        206.0   \n",
       "260238                                               None        129.0   \n",
       "481566                                               None        151.0   \n",
       "381264                                               None        182.0   \n",
       "459346                                               None        201.0   \n",
       "189931  [Předpokládané nízké obsazení, Předpokládané n...        599.0   \n",
       "202951  [Předpokládané vysoké obsazení - doporučujeme ...        209.0   \n",
       "244516                                               None        468.0   \n",
       "40739   [Předpokládané střední obsazení - doporučujeme...        800.0   \n",
       "\n",
       "        vzdalenost         zpozdeni        cena_poznamka den  prestupy  \\\n",
       "51940          NaN     [0, 0, 0, 0]                 None  st         3   \n",
       "256054         NaN             None                 None  pá         0   \n",
       "105182         NaN        [0, 0, 0]                 None  so         2   \n",
       "422427         NaN           [0, 0]                 None  út         1   \n",
       "432779       351.0             None                 None  po         0   \n",
       "262054         NaN           [0, 0]                 None  so         1   \n",
       "271384       356.0              [0]                 None  čt         0   \n",
       "287238       794.0           [0, 0]         Zjistit cenu  čt         1   \n",
       "89481        601.0           [0, 0]                 None  pá         1   \n",
       "220403       351.0             None                 None  po         0   \n",
       "74908        396.0           [0, 0]                 None  pá         1   \n",
       "59514          NaN        [0, 0, 0]                 None  ne         2   \n",
       "99929          NaN     [0, 0, 0, 0]                 None  čt         3   \n",
       "204177       356.0              [0]                 None  po         0   \n",
       "252269         NaN        [0, 0, 0]                 None  pá         2   \n",
       "70148        386.0              [0]                 None  so         0   \n",
       "374880       257.0              [0]                 None  pá         0   \n",
       "297746       133.0             None                 None  út         0   \n",
       "63716        257.0              [0]                 None  so         0   \n",
       "6636         255.0              [0]                 None  pá         0   \n",
       "311857       610.0              [0]                 None  čt         0   \n",
       "422050       396.0              [0]                 None  út         0   \n",
       "18937        427.0           [0, 0]                 None  čt         1   \n",
       "152053       404.0        [0, 0, 0]         Zjistit cenu  ne         2   \n",
       "277            NaN  [0, 0, 0, 0, 0]                 None  so         4   \n",
       "215600       356.0             None                 None  po         0   \n",
       "383502       396.0              [0]                 None  pá         0   \n",
       "229121       356.0              [0]                 None  ne         0   \n",
       "204841       397.0              [0]                 None  čt         0   \n",
       "86878          NaN           [0, 0]         Zjistit cenu  čt         1   \n",
       "353308       396.0              [0]  Cena v dalším kroku  út         0   \n",
       "187813       356.0              [0]                 None  út         0   \n",
       "260238         NaN             None                 None  čt         0   \n",
       "481566         NaN             None                 None  pá         0   \n",
       "381264         NaN             None                 None  pá         0   \n",
       "459346       187.0             None                 None  st         0   \n",
       "189931         NaN  [0, 0, 0, 0, 0]         Zjistit cenu  po         4   \n",
       "202951       356.0              [0]                 None  ne         0   \n",
       "244516         NaN             None                 None  ne         0   \n",
       "40739          NaN           [0, 0]  Cena v dalším kroku  čt         1   \n",
       "\n",
       "                                                    vlaky mistenka_zdarma  \\\n",
       "51940   [EC 275 Metropolitan, EC 103 Danubius, rjx 62 ...            None   \n",
       "256054                                               None            None   \n",
       "105182             [rj 75 Vindobona, D 157, D 213 Mimara]            None   \n",
       "422427                      [rjx 12068, rj 372 Vindobona]            None   \n",
       "432779                                           [LE1260]            None   \n",
       "262054                  [rj 257 Vindobona, D 159 Croatia]            None   \n",
       "271384                                   [EC 112 Silesia]            True   \n",
       "287238                   [EN 407 Chopin, IC 518 Ostravan]            None   \n",
       "89481                    [EC 143 Ostravan, Ex 611 TATRAN]            None   \n",
       "220403                                           [LE1257]            None   \n",
       "74908                   [rj 79 Vindobona, EC 131 Báthory]            True   \n",
       "59514   [EC 282 Metropolitan Slovenská strela, EC 174 ...            None   \n",
       "99929   [R 825 Moravan, Os 2355, IC 296 Wienerwald, IC...            None   \n",
       "204177                                  [IC 510 Ostravan]            None   \n",
       "252269  [EC 358 Bavorský expres, ICE 26, ICE 10 ICE In...            None   \n",
       "70148                                   [EC 378 Berliner]            True   \n",
       "374880                                   [R 979 Vysočina]            True   \n",
       "297746                                           [R 1150]            None   \n",
       "63716                                    [R 975 Vysočina]            True   \n",
       "6636                               [rj 574 Brněnský drak]            True   \n",
       "311857                              [EC 275 Metropolitan]            True   \n",
       "422050             [EC 282 Metropolitan Slovenská strela]            True   \n",
       "18937       [NJ 457 nightjet, R 1101 RegioJet /R 201101/]            None   \n",
       "152053  [EN 406 Chopin, Ex 1136 Gepard Express/, R 876...            None   \n",
       "277     [TL 6600/, R 1181/, EC 275 Metropolitan, EC 10...            None   \n",
       "215600                                           [LE1263]            None   \n",
       "383502                              [EC 279 Metropolitan]            True   \n",
       "229121                                  [IC 546 Ostravan]            True   \n",
       "204841                                  [EC 177 Berliner]            True   \n",
       "86878                 [ES 452 European Sleeper, THA 9316]            None   \n",
       "353308                          [EN 476 Metropol /IC 576]            None   \n",
       "187813                                  [EC 141 Ostravan]            True   \n",
       "260238                                               None            None   \n",
       "481566                                               None            None   \n",
       "381264                                               None            None   \n",
       "459346                                           [R 1167]            None   \n",
       "189931  [IC 577 Metropol, Ex 1135 Gepard Express /Ex 1...            None   \n",
       "202951                                  [EC 143 Ostravan]            True   \n",
       "244516                                               None            None   \n",
       "40739                     [NJ 457 nightjet, EC 151 Emona]            None   \n",
       "\n",
       "       nahradni_bus  volna_mista_economy  volna_mista_economy_plus  \\\n",
       "51940          None                  NaN                       NaN   \n",
       "256054         None                  NaN                       NaN   \n",
       "105182         None                  NaN                       NaN   \n",
       "422427         None                  NaN                       NaN   \n",
       "432779         None                 21.0                      17.0   \n",
       "262054         None                  NaN                       NaN   \n",
       "271384         None                  NaN                       NaN   \n",
       "287238         None                  NaN                       NaN   \n",
       "89481          None                  NaN                       NaN   \n",
       "220403         None                 21.0                      11.0   \n",
       "74908          None                  NaN                       NaN   \n",
       "59514          None                  NaN                       NaN   \n",
       "99929          None                  NaN                       NaN   \n",
       "204177         None                  NaN                       NaN   \n",
       "252269         None                  NaN                       NaN   \n",
       "70148          None                  NaN                       NaN   \n",
       "374880         None                  NaN                       NaN   \n",
       "297746         None                  NaN                       NaN   \n",
       "63716          True                  NaN                       NaN   \n",
       "6636           None                  NaN                       NaN   \n",
       "311857         None                  NaN                       NaN   \n",
       "422050         None                  NaN                       NaN   \n",
       "18937          None                  NaN                       NaN   \n",
       "152053         None                  NaN                       NaN   \n",
       "277            None                  NaN                       NaN   \n",
       "215600         None                 21.0                      21.0   \n",
       "383502         None                  NaN                       NaN   \n",
       "229121         None                  NaN                       NaN   \n",
       "204841         None                  NaN                       NaN   \n",
       "86878          None                  NaN                       NaN   \n",
       "353308         None                  NaN                       NaN   \n",
       "187813         None                  NaN                       NaN   \n",
       "260238         None                  NaN                       NaN   \n",
       "481566         None                  NaN                       NaN   \n",
       "381264         None                  NaN                       NaN   \n",
       "459346         None                  NaN                       NaN   \n",
       "189931         None                  NaN                       NaN   \n",
       "202951         None                  NaN                       NaN   \n",
       "244516         None                  NaN                       NaN   \n",
       "40739          None                  NaN                       NaN   \n",
       "\n",
       "        volna_mista_economy_business  volna_mista_premium  \n",
       "51940                            NaN                  NaN  \n",
       "256054                           NaN                  NaN  \n",
       "105182                           NaN                  NaN  \n",
       "422427                           NaN                  NaN  \n",
       "432779                          12.0                  6.0  \n",
       "262054                           NaN                  NaN  \n",
       "271384                           NaN                  NaN  \n",
       "287238                           NaN                  NaN  \n",
       "89481                            NaN                  NaN  \n",
       "220403                          16.0                  2.0  \n",
       "74908                            NaN                  NaN  \n",
       "59514                            NaN                  NaN  \n",
       "99929                            NaN                  NaN  \n",
       "204177                           NaN                  NaN  \n",
       "252269                           NaN                  NaN  \n",
       "70148                            NaN                  NaN  \n",
       "374880                           NaN                  NaN  \n",
       "297746                           NaN                  NaN  \n",
       "63716                            NaN                  NaN  \n",
       "6636                             NaN                  NaN  \n",
       "311857                           NaN                  NaN  \n",
       "422050                           NaN                  NaN  \n",
       "18937                            NaN                  NaN  \n",
       "152053                           NaN                  NaN  \n",
       "277                              NaN                  NaN  \n",
       "215600                          19.0                  6.0  \n",
       "383502                           NaN                  NaN  \n",
       "229121                           NaN                  NaN  \n",
       "204841                           NaN                  NaN  \n",
       "86878                            NaN                  NaN  \n",
       "353308                           NaN                  NaN  \n",
       "187813                           NaN                  NaN  \n",
       "260238                           NaN                  NaN  \n",
       "481566                           NaN                  NaN  \n",
       "381264                           NaN                  NaN  \n",
       "459346                           NaN                  NaN  \n",
       "189931                           NaN                  NaN  \n",
       "202951                           NaN                  NaN  \n",
       "244516                           NaN                  NaN  \n",
       "40739                            NaN                  NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "58def34c-82b3-4077-8d46-0a48ab3b8c60",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_11260\\3731602829.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  df.groupby(['prodejce',pd.Grouper(key='oscrapovano',freq='D')]).size()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "prodejce  oscrapovano\n",
       "ARR       2024-10-31         0\n",
       "          2024-11-01         0\n",
       "          2024-11-02         0\n",
       "          2024-11-03         0\n",
       "          2024-11-04         0\n",
       "          2024-11-05         0\n",
       "          2024-11-06         0\n",
       "          2024-11-07      2229\n",
       "          2024-11-08      7128\n",
       "          2024-11-09      3320\n",
       "          2024-11-10      3157\n",
       "          2024-11-11      3367\n",
       "          2024-11-12      3323\n",
       "          2024-11-13      3632\n",
       "          2024-11-14      3072\n",
       "          2024-11-15      3552\n",
       "          2024-11-16      3498\n",
       "LE        2024-10-31         0\n",
       "          2024-11-01         0\n",
       "          2024-11-02         0\n",
       "          2024-11-03         0\n",
       "          2024-11-04         0\n",
       "          2024-11-05         0\n",
       "          2024-11-06         0\n",
       "          2024-11-07       332\n",
       "          2024-11-08      1573\n",
       "          2024-11-09      1873\n",
       "          2024-11-10      1755\n",
       "          2024-11-11      1697\n",
       "          2024-11-12      1889\n",
       "          2024-11-13      1886\n",
       "          2024-11-14      1981\n",
       "          2024-11-15      2063\n",
       "          2024-11-16      2002\n",
       "RJ        2024-10-31         0\n",
       "          2024-11-01         0\n",
       "          2024-11-02         0\n",
       "          2024-11-03         0\n",
       "          2024-11-04         0\n",
       "          2024-11-05         0\n",
       "          2024-11-06      1793\n",
       "          2024-11-07      1033\n",
       "          2024-11-08      5743\n",
       "          2024-11-09      7629\n",
       "          2024-11-10      7099\n",
       "          2024-11-11      3987\n",
       "          2024-11-12      7103\n",
       "          2024-11-13      7852\n",
       "          2024-11-14      6297\n",
       "          2024-11-15      5897\n",
       "          2024-11-16      7182\n",
       "ČD        2024-10-31      1325\n",
       "          2024-11-01     13717\n",
       "          2024-11-02     19225\n",
       "          2024-11-03     15819\n",
       "          2024-11-04     20915\n",
       "          2024-11-05     21095\n",
       "          2024-11-06     21009\n",
       "          2024-11-07     20398\n",
       "          2024-11-08     21999\n",
       "          2024-11-09     23636\n",
       "          2024-11-10     21435\n",
       "          2024-11-11     21862\n",
       "          2024-11-12     22390\n",
       "          2024-11-13     21706\n",
       "          2024-11-14     21600\n",
       "          2024-11-15     21842\n",
       "          2024-11-16     22307\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['prodejce',pd.Grouper(key='oscrapovano',freq='D')]).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ca25d095-36cc-4b0d-b6f0-1b3374e91cb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_11260\\2040972483.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  df.groupby(['odkud','kam'])['cena'].max().nlargest(10)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "odkud                    kam               \n",
       "Praha hl.n.              Amsterdam Centraal    11644.0\n",
       "Amsterdam Centraal       Praha hl.n.           11463.0\n",
       "Amsterdam Bijlmer ArenA  Praha hl.n.           11442.0\n",
       "Brno hl.n.               Hamburg Hbf            9814.0\n",
       "Hamburg Hbf              Brno hl.n.             9814.0\n",
       "Berlin Hbf (tief)        Amsterdam Centraal     8488.0\n",
       "Bruxelles-Midi           Praha hl.n.            8001.0\n",
       "Berlin Hbf               Bruxelles-Central      7412.0\n",
       "                         Bruxelles-Midi         7412.0\n",
       "Berlin Hbf (tief)        Praha hl.n.            7412.0\n",
       "Name: cena, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['odkud','kam'])['cena'].max().nlargest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7a09a503-e42a-437c-90d3-44296f3e4648",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_11260\\646767245.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  df.groupby(['odkud','kam'])['cena'].min().nlargest(20)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "odkud                    kam               \n",
       "Berlin Hbf               Hamburg Hbf           7018.0\n",
       "                         Bruxelles-Central     6814.0\n",
       "Praha-Smíchov            Bruxelles-Midi        6543.0\n",
       "Bruxelles-Central        Praha hl.n.           6375.0\n",
       "Frankfurt(Main)Hbf       Bruxelles-Midi        6244.0\n",
       "Amsterdam Zuid           Praha hl.n.           6228.0\n",
       "Frankfurt Hbf (tief)     Praha hl.n.           6215.0\n",
       "Frankfurt(Main)Hbf       Amsterdam Centraal    6215.0\n",
       "Amsterdam Bijlmer ArenA  Praha hl.n.           6195.0\n",
       "München Hbf              Praha hl.n.           6097.0\n",
       "Berlin Hbf (tief)        Amsterdam Centraal    5578.0\n",
       "Leipzig Hbf (tief)       Amsterdam Centraal    5578.0\n",
       "Praha hl.n.              Amsterdam Zuid        5477.0\n",
       "Železná Ruda-Alžbětín    Praha-Smíchov         5369.0\n",
       "Zagreb Glavni kolodvor   Praha-Holešovice      4899.0\n",
       "Praha hl.n.              Berlin Ostbahnhof     4149.0\n",
       "Leipzig Hbf              Praha hl.n.           3277.0\n",
       "Leipzig Hbf (tief)       Berlin Hbf (tief)     3277.0\n",
       "                         Berlin Südkreuz       3277.0\n",
       "Ljubljana                Praha-Holešovice      3142.0\n",
       "Name: cena, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['odkud','kam'])['cena'].min().nlargest(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4b230613-25f8-47ac-830c-fc1932938d25",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_11260\\3724199744.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  df[df['odkud'].str.contains('Praha',na=False)].groupby('kam')['cena'].max().nlargest(20)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "kam\n",
       "Amsterdam Centraal        11644.0\n",
       "Bruxelles-Midi             7322.0\n",
       "Bern                       6894.0\n",
       "Amsterdam Zuid             6311.0\n",
       "Berlin Hbf (tief)          5181.0\n",
       "Warszawa Centralna         4324.0\n",
       "Berlin Ostbahnhof          4149.0\n",
       "Ljubljana                  3824.0\n",
       "Zagreb Glavni kolodvor     3622.0\n",
       "Berlin Gesundbrunnen       3277.0\n",
       "Berlin Hbf                 3277.0\n",
       "Berlin Südkreuz            3277.0\n",
       "Budapest-Kelenföld         3008.0\n",
       "Warszawa Wschodnia         2205.0\n",
       "Bratislava-Petržalka       2085.0\n",
       "Budapest-Nyugati pu        1922.0\n",
       "Ostrava-Svinov             1879.0\n",
       "Kraków Główny              1809.0\n",
       "Ostrava hl.n.              1759.0\n",
       "Bratislava hl.st.          1586.0\n",
       "Name: cena, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['odkud'].str.contains('Praha',na=False)].groupby('kam')['cena'].max().nlargest(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "130feca7-cb64-47b9-8da9-f462d9cdb43d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['kam'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cd3b2de8-4a00-416b-a2cf-9ef7de12cfe0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_11260\\432856156.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  df[df['odkud'].str.contains('Praha')].groupby(['kam','prodejce'])['cena'].min().nsmallest(40)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "kam                        prodejce\n",
       "Brno                       RJ            99.0\n",
       "Ostrava hl.n.              LE            99.0\n",
       "Ostrava-Svinov             LE            99.0\n",
       "Tanvald                    ARR          160.0\n",
       "České Budějovice           ARR          200.0\n",
       "Ostrava                    RJ           249.0\n",
       "Brno hl.n.                 ČD           269.0\n",
       "Bratislava                 RJ           279.0\n",
       "Kraków Główny              LE           279.0\n",
       "Ostrava hl.n.              ČD           289.0\n",
       "Ostrava-Svinov             ČD           289.0\n",
       "Vídeň                      RJ           299.0\n",
       "Plzeň hl.n.                ČD           350.0\n",
       "Krakow Glowny              ČD           361.0\n",
       "Berlin Hbf (tief)          ČD           387.0\n",
       "Berlin Südkreuz            ČD           387.0\n",
       "Wien Hbf                   ČD           387.0\n",
       "Budapešť                   RJ           399.0\n",
       "Košice                     RJ           399.0\n",
       "Bratislava hl.st.          ČD           400.0\n",
       "Wien Franz-Josefs-Bahnhof  ČD           439.0\n",
       "Krakov                     RJ           440.0\n",
       "Warszawa Centralna         ČD           490.0\n",
       "Čop                        RJ           499.0\n",
       "Budapest-Nyugati pu        ČD           606.0\n",
       "Berlin Hbf                 ČD           748.0\n",
       "Warszawa Wschodnia         ČD           809.0\n",
       "Berlin Gesundbrunnen       ČD           877.0\n",
       "Berlin-Lichtenberg         ČD           903.0\n",
       "Zagreb Glavni kolodvor     ČD          1342.0\n",
       "Bruxelles-Midi             ČD          1364.0\n",
       "Ljubljana                  ČD          1419.0\n",
       "Amsterdam Centraal         ČD          1522.0\n",
       "Bratislava-Petržalka       ČD          2085.0\n",
       "Bern                       ČD          2387.0\n",
       "Budapest-Kelenföld         ČD          2495.0\n",
       "Berlin Ostbahnhof          ČD          4149.0\n",
       "Amsterdam Zuid             ČD          5477.0\n",
       "Amsterdam Bijlmer ArenA    ARR            NaN\n",
       "                           LE             NaN\n",
       "Name: cena, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['odkud'].str.contains('Praha')].groupby(['kam','prodejce'])['cena'].min().nsmallest(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "472cd4b2-8496-4c43-bddf-16ed6167bc33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='predstih_d', ylabel='cena'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['odkud'].str.contains('Praha') & df['kam'].str.contains('Buda') & (df['prodejce'] == 'ČD') & (df['prestupy'] == 0)].plot.scatter(x='predstih_d',y='cena')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d0b2a270-e0a5-4d32-b5d0-9960b29047d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_11260\\1571294427.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  df[df['volnych_mist'].notnull()].groupby('prodejce').size()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "prodejce\n",
       "ARR    36224\n",
       "LE     16555\n",
       "RJ     61615\n",
       "ČD         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['volnych_mist'].notnull()].groupby('prodejce').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5af8ad80-e0a9-4da2-82f9-05e4d472709b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='predstih_d', ylabel='volnych_mist'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['volnych_mist'].notnull() & (df['kam'] == 'Ostrava')].plot.scatter(x='predstih_d',y='volnych_mist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "436ccb12-f2fd-4038-8a48-defe4f898234",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>oscrapovano</th>\n",
       "      <th>prodejce</th>\n",
       "      <th>odkud</th>\n",
       "      <th>kam</th>\n",
       "      <th>odjezd</th>\n",
       "      <th>predstih</th>\n",
       "      <th>predstih_d</th>\n",
       "      <th>predstih_h</th>\n",
       "      <th>cena</th>\n",
       "      <th>prostredek</th>\n",
       "      <th>volnych_mist</th>\n",
       "      <th>obsazenost</th>\n",
       "      <th>jizdni_doba</th>\n",
       "      <th>vzdalenost</th>\n",
       "      <th>zpozdeni</th>\n",
       "      <th>cena_poznamka</th>\n",
       "      <th>den</th>\n",
       "      <th>prestupy</th>\n",
       "      <th>vlaky</th>\n",
       "      <th>mistenka_zdarma</th>\n",
       "      <th>nahradni_bus</th>\n",
       "      <th>volna_mista_economy</th>\n",
       "      <th>volna_mista_economy_plus</th>\n",
       "      <th>volna_mista_economy_business</th>\n",
       "      <th>volna_mista_premium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>168266</th>\n",
       "      <td>2024-11-08 21:35:26</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2024-12-01 15:57:00</td>\n",
       "      <td>22 days 18:21:34</td>\n",
       "      <td>22</td>\n",
       "      <td>546.359444</td>\n",
       "      <td>999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>34.0</td>\n",
       "      <td>None</td>\n",
       "      <td>211.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>ne</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1260]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207111</th>\n",
       "      <td>2024-11-09 14:23:36</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2024-12-01 16:04:00</td>\n",
       "      <td>22 days 01:40:24</td>\n",
       "      <td>22</td>\n",
       "      <td>529.673333</td>\n",
       "      <td>999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>32.0</td>\n",
       "      <td>None</td>\n",
       "      <td>204.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>ne</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1260]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207109</th>\n",
       "      <td>2024-11-09 14:23:36</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2024-12-01 15:57:00</td>\n",
       "      <td>22 days 01:33:24</td>\n",
       "      <td>22</td>\n",
       "      <td>529.556667</td>\n",
       "      <td>999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>32.0</td>\n",
       "      <td>None</td>\n",
       "      <td>211.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>ne</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1260]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168267</th>\n",
       "      <td>2024-11-08 21:35:26</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2024-12-01 16:04:00</td>\n",
       "      <td>22 days 18:28:34</td>\n",
       "      <td>22</td>\n",
       "      <td>546.476111</td>\n",
       "      <td>999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>34.0</td>\n",
       "      <td>None</td>\n",
       "      <td>204.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>ne</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1260]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311156</th>\n",
       "      <td>2024-11-12 08:22:15</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2024-12-01 16:04:00</td>\n",
       "      <td>19 days 07:41:45</td>\n",
       "      <td>19</td>\n",
       "      <td>463.695833</td>\n",
       "      <td>999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>33.0</td>\n",
       "      <td>None</td>\n",
       "      <td>204.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>ne</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1260]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311157</th>\n",
       "      <td>2024-11-12 08:22:15</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2024-12-01 15:57:00</td>\n",
       "      <td>19 days 07:34:45</td>\n",
       "      <td>19</td>\n",
       "      <td>463.579167</td>\n",
       "      <td>999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>33.0</td>\n",
       "      <td>None</td>\n",
       "      <td>211.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>ne</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1260]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289886</th>\n",
       "      <td>2024-11-11 18:17:56</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2024-12-01 16:04:00</td>\n",
       "      <td>19 days 21:46:04</td>\n",
       "      <td>19</td>\n",
       "      <td>477.767778</td>\n",
       "      <td>999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>33.0</td>\n",
       "      <td>None</td>\n",
       "      <td>204.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>ne</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1260]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289881</th>\n",
       "      <td>2024-11-11 18:17:56</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2024-12-01 15:57:00</td>\n",
       "      <td>19 days 21:39:04</td>\n",
       "      <td>19</td>\n",
       "      <td>477.651111</td>\n",
       "      <td>999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>33.0</td>\n",
       "      <td>None</td>\n",
       "      <td>211.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>ne</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1260]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207092</th>\n",
       "      <td>2024-11-09 14:22:43</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2024-11-24 16:04:00</td>\n",
       "      <td>15 days 01:41:17</td>\n",
       "      <td>15</td>\n",
       "      <td>361.688056</td>\n",
       "      <td>1099.0</td>\n",
       "      <td>None</td>\n",
       "      <td>34.0</td>\n",
       "      <td>None</td>\n",
       "      <td>204.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>ne</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1260]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207091</th>\n",
       "      <td>2024-11-09 14:22:43</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2024-11-24 15:57:00</td>\n",
       "      <td>15 days 01:34:17</td>\n",
       "      <td>15</td>\n",
       "      <td>361.571389</td>\n",
       "      <td>1099.0</td>\n",
       "      <td>None</td>\n",
       "      <td>34.0</td>\n",
       "      <td>None</td>\n",
       "      <td>211.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>ne</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1260]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               oscrapovano prodejce           odkud          kam  \\\n",
       "168266 2024-11-08 21:35:26       LE   Ostrava hl.n.  Praha hl.n.   \n",
       "207111 2024-11-09 14:23:36       LE  Ostrava-Svinov  Praha hl.n.   \n",
       "207109 2024-11-09 14:23:36       LE   Ostrava hl.n.  Praha hl.n.   \n",
       "168267 2024-11-08 21:35:26       LE  Ostrava-Svinov  Praha hl.n.   \n",
       "311156 2024-11-12 08:22:15       LE  Ostrava-Svinov  Praha hl.n.   \n",
       "311157 2024-11-12 08:22:15       LE   Ostrava hl.n.  Praha hl.n.   \n",
       "289886 2024-11-11 18:17:56       LE  Ostrava-Svinov  Praha hl.n.   \n",
       "289881 2024-11-11 18:17:56       LE   Ostrava hl.n.  Praha hl.n.   \n",
       "207092 2024-11-09 14:22:43       LE  Ostrava-Svinov  Praha hl.n.   \n",
       "207091 2024-11-09 14:22:43       LE   Ostrava hl.n.  Praha hl.n.   \n",
       "\n",
       "                    odjezd         predstih  predstih_d  predstih_h    cena  \\\n",
       "168266 2024-12-01 15:57:00 22 days 18:21:34          22  546.359444   999.0   \n",
       "207111 2024-12-01 16:04:00 22 days 01:40:24          22  529.673333   999.0   \n",
       "207109 2024-12-01 15:57:00 22 days 01:33:24          22  529.556667   999.0   \n",
       "168267 2024-12-01 16:04:00 22 days 18:28:34          22  546.476111   999.0   \n",
       "311156 2024-12-01 16:04:00 19 days 07:41:45          19  463.695833   999.0   \n",
       "311157 2024-12-01 15:57:00 19 days 07:34:45          19  463.579167   999.0   \n",
       "289886 2024-12-01 16:04:00 19 days 21:46:04          19  477.767778   999.0   \n",
       "289881 2024-12-01 15:57:00 19 days 21:39:04          19  477.651111   999.0   \n",
       "207092 2024-11-24 16:04:00 15 days 01:41:17          15  361.688056  1099.0   \n",
       "207091 2024-11-24 15:57:00 15 days 01:34:17          15  361.571389  1099.0   \n",
       "\n",
       "       prostredek  volnych_mist obsazenost  jizdni_doba  vzdalenost zpozdeni  \\\n",
       "168266       None          34.0       None        211.0       356.0     None   \n",
       "207111       None          32.0       None        204.0       351.0     None   \n",
       "207109       None          32.0       None        211.0       356.0     None   \n",
       "168267       None          34.0       None        204.0       351.0     None   \n",
       "311156       None          33.0       None        204.0       351.0     None   \n",
       "311157       None          33.0       None        211.0       356.0     None   \n",
       "289886       None          33.0       None        204.0       351.0     None   \n",
       "289881       None          33.0       None        211.0       356.0     None   \n",
       "207092       None          34.0       None        204.0       351.0     None   \n",
       "207091       None          34.0       None        211.0       356.0     None   \n",
       "\n",
       "       cena_poznamka den  prestupy     vlaky mistenka_zdarma nahradni_bus  \\\n",
       "168266          None  ne         0  [LE1260]            None         None   \n",
       "207111          None  ne         0  [LE1260]            None         None   \n",
       "207109          None  ne         0  [LE1260]            None         None   \n",
       "168267          None  ne         0  [LE1260]            None         None   \n",
       "311156          None  ne         0  [LE1260]            None         None   \n",
       "311157          None  ne         0  [LE1260]            None         None   \n",
       "289886          None  ne         0  [LE1260]            None         None   \n",
       "289881          None  ne         0  [LE1260]            None         None   \n",
       "207092          None  ne         0  [LE1260]            None         None   \n",
       "207091          None  ne         0  [LE1260]            None         None   \n",
       "\n",
       "        volna_mista_economy  volna_mista_economy_plus  \\\n",
       "168266                  3.0                      14.0   \n",
       "207111                  3.0                      14.0   \n",
       "207109                  3.0                      14.0   \n",
       "168267                  3.0                      14.0   \n",
       "311156                  2.0                      14.0   \n",
       "311157                  2.0                      14.0   \n",
       "289886                  2.0                      14.0   \n",
       "289881                  2.0                      14.0   \n",
       "207092                  0.0                      14.0   \n",
       "207091                  0.0                      14.0   \n",
       "\n",
       "        volna_mista_economy_business  volna_mista_premium  \n",
       "168266                          12.0                  5.0  \n",
       "207111                          12.0                  3.0  \n",
       "207109                          12.0                  3.0  \n",
       "168267                          12.0                  5.0  \n",
       "311156                          12.0                  5.0  \n",
       "311157                          12.0                  5.0  \n",
       "289886                          12.0                  5.0  \n",
       "289881                          12.0                  5.0  \n",
       "207092                          17.0                  3.0  \n",
       "207091                          17.0                  3.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['volna_mista_economy'] < 5].sort_values(by='predstih_d',ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4b189879-336e-4858-9fe4-d51cdeac7ba6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>oscrapovano</th>\n",
       "      <th>prodejce</th>\n",
       "      <th>odkud</th>\n",
       "      <th>kam</th>\n",
       "      <th>odjezd</th>\n",
       "      <th>predstih</th>\n",
       "      <th>predstih_d</th>\n",
       "      <th>predstih_h</th>\n",
       "      <th>cena</th>\n",
       "      <th>prostredek</th>\n",
       "      <th>volnych_mist</th>\n",
       "      <th>obsazenost</th>\n",
       "      <th>jizdni_doba</th>\n",
       "      <th>vzdalenost</th>\n",
       "      <th>zpozdeni</th>\n",
       "      <th>cena_poznamka</th>\n",
       "      <th>den</th>\n",
       "      <th>prestupy</th>\n",
       "      <th>vlaky</th>\n",
       "      <th>mistenka_zdarma</th>\n",
       "      <th>nahradni_bus</th>\n",
       "      <th>volna_mista_economy</th>\n",
       "      <th>volna_mista_economy_plus</th>\n",
       "      <th>volna_mista_economy_business</th>\n",
       "      <th>volna_mista_premium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>114395</th>\n",
       "      <td>2024-11-06 21:53:34</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Košice</td>\n",
       "      <td>2024-12-21 14:47:00</td>\n",
       "      <td>44 days 16:53:26</td>\n",
       "      <td>44</td>\n",
       "      <td>1072.890556</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vlak</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>505.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>so</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114398</th>\n",
       "      <td>2024-11-06 21:53:34</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Košice</td>\n",
       "      <td>2024-12-21 07:47:00</td>\n",
       "      <td>44 days 09:53:26</td>\n",
       "      <td>44</td>\n",
       "      <td>1065.890556</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vlak</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>505.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>so</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112851</th>\n",
       "      <td>2024-11-06 21:45:06</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Brno</td>\n",
       "      <td>Praha</td>\n",
       "      <td>2024-12-21 10:09:00</td>\n",
       "      <td>44 days 12:23:54</td>\n",
       "      <td>44</td>\n",
       "      <td>1068.398333</td>\n",
       "      <td>339.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>2.0</td>\n",
       "      <td>None</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>so</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342632</th>\n",
       "      <td>2024-11-13 00:10:57</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Brno</td>\n",
       "      <td>2024-12-26 09:16:00</td>\n",
       "      <td>43 days 09:05:03</td>\n",
       "      <td>43</td>\n",
       "      <td>1041.084167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vlak</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>čt</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338322</th>\n",
       "      <td>2024-11-12 22:15:20</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Brno</td>\n",
       "      <td>2024-12-26 09:16:00</td>\n",
       "      <td>43 days 11:00:40</td>\n",
       "      <td>43</td>\n",
       "      <td>1043.011111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vlak</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>čt</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111897</th>\n",
       "      <td>2024-11-06 21:42:49</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Brno</td>\n",
       "      <td>2024-12-20 11:16:00</td>\n",
       "      <td>43 days 13:33:11</td>\n",
       "      <td>43</td>\n",
       "      <td>1045.553056</td>\n",
       "      <td>399.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>4.0</td>\n",
       "      <td>None</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114382</th>\n",
       "      <td>2024-11-06 21:53:31</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Košice</td>\n",
       "      <td>2024-12-20 21:44:00</td>\n",
       "      <td>43 days 23:50:29</td>\n",
       "      <td>43</td>\n",
       "      <td>1055.841389</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vlak</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>504.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114383</th>\n",
       "      <td>2024-11-06 21:53:31</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Košice</td>\n",
       "      <td>2024-12-20 14:47:00</td>\n",
       "      <td>43 days 16:53:29</td>\n",
       "      <td>43</td>\n",
       "      <td>1048.891389</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vlak</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>505.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114384</th>\n",
       "      <td>2024-11-06 21:53:31</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Košice</td>\n",
       "      <td>2024-12-20 07:47:00</td>\n",
       "      <td>43 days 09:53:29</td>\n",
       "      <td>43</td>\n",
       "      <td>1041.891389</td>\n",
       "      <td>1079.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>505.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111845</th>\n",
       "      <td>2024-11-06 21:42:44</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Brno</td>\n",
       "      <td>2024-12-18 15:16:00</td>\n",
       "      <td>41 days 17:33:16</td>\n",
       "      <td>41</td>\n",
       "      <td>1001.554444</td>\n",
       "      <td>399.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>3.0</td>\n",
       "      <td>None</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               oscrapovano prodejce  odkud     kam              odjezd  \\\n",
       "114395 2024-11-06 21:53:34       RJ  Praha  Košice 2024-12-21 14:47:00   \n",
       "114398 2024-11-06 21:53:34       RJ  Praha  Košice 2024-12-21 07:47:00   \n",
       "112851 2024-11-06 21:45:06       RJ   Brno   Praha 2024-12-21 10:09:00   \n",
       "342632 2024-11-13 00:10:57       RJ  Praha    Brno 2024-12-26 09:16:00   \n",
       "338322 2024-11-12 22:15:20       RJ  Praha    Brno 2024-12-26 09:16:00   \n",
       "111897 2024-11-06 21:42:49       RJ  Praha    Brno 2024-12-20 11:16:00   \n",
       "114382 2024-11-06 21:53:31       RJ  Praha  Košice 2024-12-20 21:44:00   \n",
       "114383 2024-11-06 21:53:31       RJ  Praha  Košice 2024-12-20 14:47:00   \n",
       "114384 2024-11-06 21:53:31       RJ  Praha  Košice 2024-12-20 07:47:00   \n",
       "111845 2024-11-06 21:42:44       RJ  Praha    Brno 2024-12-18 15:16:00   \n",
       "\n",
       "               predstih  predstih_d   predstih_h    cena prostredek  \\\n",
       "114395 44 days 16:53:26          44  1072.890556     NaN       vlak   \n",
       "114398 44 days 09:53:26          44  1065.890556     NaN       vlak   \n",
       "112851 44 days 12:23:54          44  1068.398333   339.0       vlak   \n",
       "342632 43 days 09:05:03          43  1041.084167     NaN       vlak   \n",
       "338322 43 days 11:00:40          43  1043.011111     NaN       vlak   \n",
       "111897 43 days 13:33:11          43  1045.553056   399.0       vlak   \n",
       "114382 43 days 23:50:29          43  1055.841389     NaN       vlak   \n",
       "114383 43 days 16:53:29          43  1048.891389     NaN       vlak   \n",
       "114384 43 days 09:53:29          43  1041.891389  1079.0       vlak   \n",
       "111845 41 days 17:33:16          41  1001.554444   399.0       vlak   \n",
       "\n",
       "        volnych_mist obsazenost  jizdni_doba  vzdalenost zpozdeni  \\\n",
       "114395           0.0       None        505.0         NaN     None   \n",
       "114398           0.0       None        505.0         NaN     None   \n",
       "112851           2.0       None        150.0         NaN     None   \n",
       "342632           0.0       None        150.0         NaN     None   \n",
       "338322           0.0       None        150.0         NaN     None   \n",
       "111897           4.0       None        150.0         NaN     None   \n",
       "114382           0.0       None        504.0         NaN     None   \n",
       "114383           0.0       None        505.0         NaN     None   \n",
       "114384           1.0       None        505.0         NaN     None   \n",
       "111845           3.0       None        150.0         NaN     None   \n",
       "\n",
       "       cena_poznamka den  prestupy vlaky mistenka_zdarma nahradni_bus  \\\n",
       "114395          None  so         0  None            None         None   \n",
       "114398          None  so         0  None            None         None   \n",
       "112851          None  so         0  None            None         None   \n",
       "342632          None  čt         0  None            None         None   \n",
       "338322          None  čt         0  None            None         None   \n",
       "111897          None  pá         0  None            None         None   \n",
       "114382          None  pá         0  None            None         None   \n",
       "114383          None  pá         0  None            None         None   \n",
       "114384          None  pá         0  None            None         None   \n",
       "111845          None  st         0  None            None         None   \n",
       "\n",
       "        volna_mista_economy  volna_mista_economy_plus  \\\n",
       "114395                  NaN                       NaN   \n",
       "114398                  NaN                       NaN   \n",
       "112851                  NaN                       NaN   \n",
       "342632                  NaN                       NaN   \n",
       "338322                  NaN                       NaN   \n",
       "111897                  NaN                       NaN   \n",
       "114382                  NaN                       NaN   \n",
       "114383                  NaN                       NaN   \n",
       "114384                  NaN                       NaN   \n",
       "111845                  NaN                       NaN   \n",
       "\n",
       "        volna_mista_economy_business  volna_mista_premium  \n",
       "114395                           NaN                  NaN  \n",
       "114398                           NaN                  NaN  \n",
       "112851                           NaN                  NaN  \n",
       "342632                           NaN                  NaN  \n",
       "338322                           NaN                  NaN  \n",
       "111897                           NaN                  NaN  \n",
       "114382                           NaN                  NaN  \n",
       "114383                           NaN                  NaN  \n",
       "114384                           NaN                  NaN  \n",
       "111845                           NaN                  NaN  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['volnych_mist'] < 5].sort_values(by='predstih_d',ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "89e55a9a-a026-4692-91f3-5350d9f93213",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='odjezd'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(df[df['volnych_mist'] < 5].groupby(pd.Grouper(key='odjezd',freq='d')).size() / df[df['volnych_mist'].notnull()].groupby(pd.Grouper(key='odjezd',freq='d')).size()).plot.bar(figsize=(25,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "07bdf619-656e-44af-84a3-8bac02ba133a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>oscrapovano</th>\n",
       "      <th>prodejce</th>\n",
       "      <th>odkud</th>\n",
       "      <th>kam</th>\n",
       "      <th>odjezd</th>\n",
       "      <th>predstih</th>\n",
       "      <th>predstih_d</th>\n",
       "      <th>predstih_h</th>\n",
       "      <th>cena</th>\n",
       "      <th>prostredek</th>\n",
       "      <th>volnych_mist</th>\n",
       "      <th>obsazenost</th>\n",
       "      <th>jizdni_doba</th>\n",
       "      <th>vzdalenost</th>\n",
       "      <th>zpozdeni</th>\n",
       "      <th>cena_poznamka</th>\n",
       "      <th>den</th>\n",
       "      <th>prestupy</th>\n",
       "      <th>vlaky</th>\n",
       "      <th>mistenka_zdarma</th>\n",
       "      <th>nahradni_bus</th>\n",
       "      <th>volna_mista_economy</th>\n",
       "      <th>volna_mista_economy_plus</th>\n",
       "      <th>volna_mista_economy_business</th>\n",
       "      <th>volna_mista_premium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>202290</th>\n",
       "      <td>2024-11-09 12:25:36</td>\n",
       "      <td>LE</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>2024-11-29 20:00:00</td>\n",
       "      <td>20 days 07:34:24</td>\n",
       "      <td>20</td>\n",
       "      <td>487.573333</td>\n",
       "      <td>419.0</td>\n",
       "      <td>None</td>\n",
       "      <td>52.0</td>\n",
       "      <td>None</td>\n",
       "      <td>204.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1263]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244597</th>\n",
       "      <td>2024-11-10 12:11:58</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Ostrava</td>\n",
       "      <td>2024-11-14 17:50:00</td>\n",
       "      <td>4 days 05:38:02</td>\n",
       "      <td>4</td>\n",
       "      <td>101.633889</td>\n",
       "      <td>439.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>116.0</td>\n",
       "      <td>None</td>\n",
       "      <td>191.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>čt</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160252</th>\n",
       "      <td>2024-11-08 15:43:08</td>\n",
       "      <td>LE</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>2024-11-09 13:00:00</td>\n",
       "      <td>0 days 21:16:52</td>\n",
       "      <td>0</td>\n",
       "      <td>21.281111</td>\n",
       "      <td>559.0</td>\n",
       "      <td>None</td>\n",
       "      <td>35.0</td>\n",
       "      <td>None</td>\n",
       "      <td>204.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>so</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1259]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415526</th>\n",
       "      <td>2024-11-14 19:07:08</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Ostrava</td>\n",
       "      <td>2024-11-17 07:50:00</td>\n",
       "      <td>2 days 12:42:52</td>\n",
       "      <td>2</td>\n",
       "      <td>60.714444</td>\n",
       "      <td>439.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>159.0</td>\n",
       "      <td>None</td>\n",
       "      <td>190.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>ne</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439723</th>\n",
       "      <td>2024-11-15 12:13:44</td>\n",
       "      <td>LE</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>2024-11-18 13:00:00</td>\n",
       "      <td>3 days 00:46:16</td>\n",
       "      <td>3</td>\n",
       "      <td>72.771111</td>\n",
       "      <td>419.0</td>\n",
       "      <td>None</td>\n",
       "      <td>39.0</td>\n",
       "      <td>None</td>\n",
       "      <td>204.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>po</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1259]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328873</th>\n",
       "      <td>2024-11-12 18:07:38</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Ostrava</td>\n",
       "      <td>2024-11-23 17:50:00</td>\n",
       "      <td>10 days 23:42:22</td>\n",
       "      <td>10</td>\n",
       "      <td>263.706111</td>\n",
       "      <td>439.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>223.0</td>\n",
       "      <td>None</td>\n",
       "      <td>191.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>so</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397352</th>\n",
       "      <td>2024-11-14 10:22:07</td>\n",
       "      <td>LE</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>2024-11-19 16:00:00</td>\n",
       "      <td>5 days 05:37:53</td>\n",
       "      <td>5</td>\n",
       "      <td>125.631389</td>\n",
       "      <td>299.0</td>\n",
       "      <td>None</td>\n",
       "      <td>36.0</td>\n",
       "      <td>None</td>\n",
       "      <td>204.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>út</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE413]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469928</th>\n",
       "      <td>2024-11-16 08:11:19</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Ostrava</td>\n",
       "      <td>2024-11-19 15:50:00</td>\n",
       "      <td>3 days 07:38:41</td>\n",
       "      <td>3</td>\n",
       "      <td>79.644722</td>\n",
       "      <td>369.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>178.0</td>\n",
       "      <td>None</td>\n",
       "      <td>191.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>út</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368806</th>\n",
       "      <td>2024-11-13 16:26:43</td>\n",
       "      <td>LE</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>2024-12-14 23:00:00</td>\n",
       "      <td>31 days 06:33:17</td>\n",
       "      <td>31</td>\n",
       "      <td>750.554722</td>\n",
       "      <td>199.0</td>\n",
       "      <td>None</td>\n",
       "      <td>67.0</td>\n",
       "      <td>None</td>\n",
       "      <td>211.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>so</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1265]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495969</th>\n",
       "      <td>2024-11-16 19:18:37</td>\n",
       "      <td>LE</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>2024-12-01 20:00:00</td>\n",
       "      <td>15 days 00:41:23</td>\n",
       "      <td>15</td>\n",
       "      <td>360.689722</td>\n",
       "      <td>559.0</td>\n",
       "      <td>None</td>\n",
       "      <td>31.0</td>\n",
       "      <td>None</td>\n",
       "      <td>211.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>ne</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1263]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               oscrapovano prodejce        odkud             kam  \\\n",
       "202290 2024-11-09 12:25:36       LE  Praha hl.n.  Ostrava-Svinov   \n",
       "244597 2024-11-10 12:11:58       RJ        Praha         Ostrava   \n",
       "160252 2024-11-08 15:43:08       LE  Praha hl.n.  Ostrava-Svinov   \n",
       "415526 2024-11-14 19:07:08       RJ        Praha         Ostrava   \n",
       "439723 2024-11-15 12:13:44       LE  Praha hl.n.  Ostrava-Svinov   \n",
       "328873 2024-11-12 18:07:38       RJ        Praha         Ostrava   \n",
       "397352 2024-11-14 10:22:07       LE  Praha hl.n.  Ostrava-Svinov   \n",
       "469928 2024-11-16 08:11:19       RJ        Praha         Ostrava   \n",
       "368806 2024-11-13 16:26:43       LE  Praha hl.n.   Ostrava hl.n.   \n",
       "495969 2024-11-16 19:18:37       LE  Praha hl.n.   Ostrava hl.n.   \n",
       "\n",
       "                    odjezd         predstih  predstih_d  predstih_h   cena  \\\n",
       "202290 2024-11-29 20:00:00 20 days 07:34:24          20  487.573333  419.0   \n",
       "244597 2024-11-14 17:50:00  4 days 05:38:02           4  101.633889  439.0   \n",
       "160252 2024-11-09 13:00:00  0 days 21:16:52           0   21.281111  559.0   \n",
       "415526 2024-11-17 07:50:00  2 days 12:42:52           2   60.714444  439.0   \n",
       "439723 2024-11-18 13:00:00  3 days 00:46:16           3   72.771111  419.0   \n",
       "328873 2024-11-23 17:50:00 10 days 23:42:22          10  263.706111  439.0   \n",
       "397352 2024-11-19 16:00:00  5 days 05:37:53           5  125.631389  299.0   \n",
       "469928 2024-11-19 15:50:00  3 days 07:38:41           3   79.644722  369.0   \n",
       "368806 2024-12-14 23:00:00 31 days 06:33:17          31  750.554722  199.0   \n",
       "495969 2024-12-01 20:00:00 15 days 00:41:23          15  360.689722  559.0   \n",
       "\n",
       "       prostredek  volnych_mist obsazenost  jizdni_doba  vzdalenost zpozdeni  \\\n",
       "202290       None          52.0       None        204.0       351.0     None   \n",
       "244597       vlak         116.0       None        191.0         NaN     None   \n",
       "160252       None          35.0       None        204.0       351.0     None   \n",
       "415526       vlak         159.0       None        190.0         NaN     None   \n",
       "439723       None          39.0       None        204.0       351.0     None   \n",
       "328873       vlak         223.0       None        191.0         NaN     None   \n",
       "397352       None          36.0       None        204.0       351.0     None   \n",
       "469928       vlak         178.0       None        191.0         NaN     None   \n",
       "368806       None          67.0       None        211.0       356.0     None   \n",
       "495969       None          31.0       None        211.0       356.0     None   \n",
       "\n",
       "       cena_poznamka den  prestupy     vlaky mistenka_zdarma nahradni_bus  \\\n",
       "202290          None  pá         0  [LE1263]            None         None   \n",
       "244597          None  čt         0      None            None         None   \n",
       "160252          None  so         0  [LE1259]            None         None   \n",
       "415526          None  ne         0      None            None         None   \n",
       "439723          None  po         0  [LE1259]            None         None   \n",
       "328873          None  so         0      None            None         None   \n",
       "397352          None  út         0   [LE413]            None         None   \n",
       "469928          None  út         0      None            None         None   \n",
       "368806          None  so         0  [LE1265]            None         None   \n",
       "495969          None  ne         0  [LE1263]            None         None   \n",
       "\n",
       "        volna_mista_economy  volna_mista_economy_plus  \\\n",
       "202290                 21.0                      14.0   \n",
       "244597                  NaN                       NaN   \n",
       "160252                 21.0                       6.0   \n",
       "415526                  NaN                       NaN   \n",
       "439723                 21.0                      13.0   \n",
       "328873                  NaN                       NaN   \n",
       "397352                 21.0                      10.0   \n",
       "469928                  NaN                       NaN   \n",
       "368806                 21.0                      21.0   \n",
       "495969                 21.0                       5.0   \n",
       "\n",
       "        volna_mista_economy_business  volna_mista_premium  \n",
       "202290                          14.0                  3.0  \n",
       "244597                           NaN                  NaN  \n",
       "160252                           8.0                  0.0  \n",
       "415526                           NaN                  NaN  \n",
       "439723                           5.0                  0.0  \n",
       "328873                           NaN                  NaN  \n",
       "397352                           4.0                  1.0  \n",
       "469928                           NaN                  NaN  \n",
       "368806                          19.0                  6.0  \n",
       "495969                           5.0                  0.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['prodejce'].isin(['LE','RJ'])) & (df['odkud'].str.contains('Praha')) & (df['kam'].str.contains('Ostrava')) & (df['volnych_mist'].notnull())].sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4aee91dc-366a-48dd-b0d7-0e914af9388b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_11260\\2090071924.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  df[df['predstih_h'] <= 4].groupby('den')['volnych_mist'].median()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "den\n",
       "ne    23.0\n",
       "po    56.0\n",
       "pá    28.0\n",
       "so    59.0\n",
       "st    59.0\n",
       "út    86.0\n",
       "čt    36.0\n",
       "Name: volnych_mist, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['predstih_h'] <= 4].groupby('den')['volnych_mist'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "52eb1bbe-4e89-481c-9118-0be0631df35c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='predstih_d', ylabel='cena'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['odkud'].str.contains('Praha') & df['kam'].str.contains('Buda') & (df['prestupy'] == 0)].plot.scatter(x='predstih_d',y='cena')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "87b4553a-4c44-45a7-aedc-9dd12788894b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35             Praha hl.n.\n",
       "69        Praha-Holešovice\n",
       "112            Hamburg Hbf\n",
       "32624            Ljubljana\n",
       "60476      Hamburg-Harburg\n",
       "109455               Praha\n",
       "113319               Vídeň\n",
       "141122             Ostrava\n",
       "498224         Praha-Libeň\n",
       "Name: kam, dtype: category\n",
       "Categories (75, object): ['Amsterdam Bijlmer ArenA', 'Amsterdam Centraal', 'Amsterdam Zuid', 'Berlin Betriebsbf Schöneweide', ..., 'Ústí n.L.hl.n.', 'Ústí nad Labem hlavní nádraží', 'České Budějovice', 'Čop']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['odkud'].str.contains('Brn')]['kam'].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d746a88d-9ede-4feb-8754-0fd9307d816d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>oscrapovano</th>\n",
       "      <th>prodejce</th>\n",
       "      <th>odkud</th>\n",
       "      <th>kam</th>\n",
       "      <th>odjezd</th>\n",
       "      <th>predstih</th>\n",
       "      <th>predstih_d</th>\n",
       "      <th>predstih_h</th>\n",
       "      <th>cena</th>\n",
       "      <th>prostredek</th>\n",
       "      <th>volnych_mist</th>\n",
       "      <th>obsazenost</th>\n",
       "      <th>jizdni_doba</th>\n",
       "      <th>vzdalenost</th>\n",
       "      <th>zpozdeni</th>\n",
       "      <th>cena_poznamka</th>\n",
       "      <th>den</th>\n",
       "      <th>prestupy</th>\n",
       "      <th>vlaky</th>\n",
       "      <th>mistenka_zdarma</th>\n",
       "      <th>nahradni_bus</th>\n",
       "      <th>volna_mista_economy</th>\n",
       "      <th>volna_mista_economy_plus</th>\n",
       "      <th>volna_mista_economy_business</th>\n",
       "      <th>volna_mista_premium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>2024-10-31 23:32:19</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Brno hl.n.</td>\n",
       "      <td>Hamburg Hbf</td>\n",
       "      <td>2024-11-01 15:22:00</td>\n",
       "      <td>0 days 15:49:41</td>\n",
       "      <td>0</td>\n",
       "      <td>15.828056</td>\n",
       "      <td>8571.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané střední obsazení - doporučujeme...</td>\n",
       "      <td>920.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0]</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>2</td>\n",
       "      <td>[rj 79 Vindobona, IC 296 Wienerwald, ICE 1080]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>2024-10-31 23:32:19</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Brno hl.n.</td>\n",
       "      <td>Hamburg Hbf</td>\n",
       "      <td>2024-11-01 12:22:00</td>\n",
       "      <td>0 days 12:49:41</td>\n",
       "      <td>0</td>\n",
       "      <td>12.828056</td>\n",
       "      <td>8643.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané vysoké obsazení - doporučujeme ...</td>\n",
       "      <td>704.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 0, 0]</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>4</td>\n",
       "      <td>[EC 277 Metropolitan, EC 105 Polonia, rjx 64 r...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>2024-10-31 23:32:19</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Brno hl.n.</td>\n",
       "      <td>Hamburg Hbf</td>\n",
       "      <td>2024-11-01 18:22:00</td>\n",
       "      <td>0 days 18:49:41</td>\n",
       "      <td>0</td>\n",
       "      <td>18.828056</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané střední obsazení - doporučujeme...</td>\n",
       "      <td>865.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 0]</td>\n",
       "      <td>Zjistit cenu</td>\n",
       "      <td>pá</td>\n",
       "      <td>3</td>\n",
       "      <td>[EC 281 Metropolitan, EC 107 Porta Moravica, N...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>2024-10-31 23:32:19</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Brno hl.n.</td>\n",
       "      <td>Hamburg Hbf</td>\n",
       "      <td>2024-11-01 17:22:00</td>\n",
       "      <td>0 days 17:49:41</td>\n",
       "      <td>0</td>\n",
       "      <td>17.828056</td>\n",
       "      <td>2429.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané nízké obsazení, Předpokládané n...</td>\n",
       "      <td>925.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>1</td>\n",
       "      <td>[rj 371 Vindobona, NJ 490 nightjet]</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>2024-10-31 23:32:19</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Brno hl.n.</td>\n",
       "      <td>Hamburg Hbf</td>\n",
       "      <td>2024-11-01 15:39:00</td>\n",
       "      <td>0 days 16:06:41</td>\n",
       "      <td>0</td>\n",
       "      <td>16.111389</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané vysoké obsazení - doporučujeme ...</td>\n",
       "      <td>992.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 0]</td>\n",
       "      <td>Zjistit cenu</td>\n",
       "      <td>pá</td>\n",
       "      <td>3</td>\n",
       "      <td>[EC 276 Metropolitan, EC 458 Canopus, ICE 1500...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502068</th>\n",
       "      <td>2024-11-16 23:58:23</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Brno hl.n.</td>\n",
       "      <td>Hamburg Hbf</td>\n",
       "      <td>2024-12-13 07:39:00</td>\n",
       "      <td>26 days 07:40:37</td>\n",
       "      <td>26</td>\n",
       "      <td>631.676944</td>\n",
       "      <td>2296.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané střední obsazení - doporučujeme...</td>\n",
       "      <td>644.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0]</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>2</td>\n",
       "      <td>[EC 282 Metropolitan Slovenská strela, EC 174 ...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502069</th>\n",
       "      <td>2024-11-16 23:58:23</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Brno hl.n.</td>\n",
       "      <td>Hamburg Hbf</td>\n",
       "      <td>2024-12-13 05:39:00</td>\n",
       "      <td>26 days 05:40:37</td>\n",
       "      <td>26</td>\n",
       "      <td>629.676944</td>\n",
       "      <td>4747.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané střední obsazení - doporučujeme...</td>\n",
       "      <td>631.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0]</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>2</td>\n",
       "      <td>[IC 576 Metropol, EC 176 Berliner, ICE 1600]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502070</th>\n",
       "      <td>2024-11-16 23:58:23</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Brno hl.n.</td>\n",
       "      <td>Hamburg Hbf</td>\n",
       "      <td>2024-12-13 03:10:00</td>\n",
       "      <td>26 days 03:11:37</td>\n",
       "      <td>26</td>\n",
       "      <td>627.193611</td>\n",
       "      <td>1574.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané nízké obsazení, Předpokládané n...</td>\n",
       "      <td>661.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0]</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>2</td>\n",
       "      <td>[R 876 Svitava, EC 178 Berliner, ICE 602]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502071</th>\n",
       "      <td>2024-11-16 23:58:23</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Brno hl.n.</td>\n",
       "      <td>Hamburg Hbf</td>\n",
       "      <td>2024-12-15 17:22:00</td>\n",
       "      <td>28 days 17:23:37</td>\n",
       "      <td>28</td>\n",
       "      <td>689.393611</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané střední obsazení - doporučujeme...</td>\n",
       "      <td>925.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>Zjistit cenu</td>\n",
       "      <td>ne</td>\n",
       "      <td>1</td>\n",
       "      <td>[rj 371 Vindobona, NJ 490 nightjet]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502072</th>\n",
       "      <td>2024-11-16 23:58:23</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Brno hl.n.</td>\n",
       "      <td>Hamburg Hbf</td>\n",
       "      <td>2024-12-13 11:39:00</td>\n",
       "      <td>26 days 11:40:37</td>\n",
       "      <td>26</td>\n",
       "      <td>635.676944</td>\n",
       "      <td>1574.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané vysoké obsazení - doporučujeme ...</td>\n",
       "      <td>630.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>1</td>\n",
       "      <td>[EC 172 Hungaria, ICE 504]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8424 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               oscrapovano prodejce       odkud          kam  \\\n",
       "112    2024-10-31 23:32:19       ČD  Brno hl.n.  Hamburg Hbf   \n",
       "113    2024-10-31 23:32:19       ČD  Brno hl.n.  Hamburg Hbf   \n",
       "114    2024-10-31 23:32:19       ČD  Brno hl.n.  Hamburg Hbf   \n",
       "115    2024-10-31 23:32:19       ČD  Brno hl.n.  Hamburg Hbf   \n",
       "116    2024-10-31 23:32:19       ČD  Brno hl.n.  Hamburg Hbf   \n",
       "...                    ...      ...         ...          ...   \n",
       "502068 2024-11-16 23:58:23       ČD  Brno hl.n.  Hamburg Hbf   \n",
       "502069 2024-11-16 23:58:23       ČD  Brno hl.n.  Hamburg Hbf   \n",
       "502070 2024-11-16 23:58:23       ČD  Brno hl.n.  Hamburg Hbf   \n",
       "502071 2024-11-16 23:58:23       ČD  Brno hl.n.  Hamburg Hbf   \n",
       "502072 2024-11-16 23:58:23       ČD  Brno hl.n.  Hamburg Hbf   \n",
       "\n",
       "                    odjezd         predstih  predstih_d  predstih_h    cena  \\\n",
       "112    2024-11-01 15:22:00  0 days 15:49:41           0   15.828056  8571.0   \n",
       "113    2024-11-01 12:22:00  0 days 12:49:41           0   12.828056  8643.0   \n",
       "114    2024-11-01 18:22:00  0 days 18:49:41           0   18.828056     NaN   \n",
       "115    2024-11-01 17:22:00  0 days 17:49:41           0   17.828056  2429.0   \n",
       "116    2024-11-01 15:39:00  0 days 16:06:41           0   16.111389     NaN   \n",
       "...                    ...              ...         ...         ...     ...   \n",
       "502068 2024-12-13 07:39:00 26 days 07:40:37          26  631.676944  2296.0   \n",
       "502069 2024-12-13 05:39:00 26 days 05:40:37          26  629.676944  4747.0   \n",
       "502070 2024-12-13 03:10:00 26 days 03:11:37          26  627.193611  1574.0   \n",
       "502071 2024-12-15 17:22:00 28 days 17:23:37          28  689.393611     NaN   \n",
       "502072 2024-12-13 11:39:00 26 days 11:40:37          26  635.676944  1574.0   \n",
       "\n",
       "       prostredek  volnych_mist  \\\n",
       "112          vlak           NaN   \n",
       "113          vlak           NaN   \n",
       "114          vlak           NaN   \n",
       "115          vlak           NaN   \n",
       "116          vlak           NaN   \n",
       "...           ...           ...   \n",
       "502068       vlak           NaN   \n",
       "502069       vlak           NaN   \n",
       "502070       vlak           NaN   \n",
       "502071       vlak           NaN   \n",
       "502072       vlak           NaN   \n",
       "\n",
       "                                               obsazenost  jizdni_doba  \\\n",
       "112     [Předpokládané střední obsazení - doporučujeme...        920.0   \n",
       "113     [Předpokládané vysoké obsazení - doporučujeme ...        704.0   \n",
       "114     [Předpokládané střední obsazení - doporučujeme...        865.0   \n",
       "115     [Předpokládané nízké obsazení, Předpokládané n...        925.0   \n",
       "116     [Předpokládané vysoké obsazení - doporučujeme ...        992.0   \n",
       "...                                                   ...          ...   \n",
       "502068  [Předpokládané střední obsazení - doporučujeme...        644.0   \n",
       "502069  [Předpokládané střední obsazení - doporučujeme...        631.0   \n",
       "502070  [Předpokládané nízké obsazení, Předpokládané n...        661.0   \n",
       "502071  [Předpokládané střední obsazení - doporučujeme...        925.0   \n",
       "502072  [Předpokládané vysoké obsazení - doporučujeme ...        630.0   \n",
       "\n",
       "        vzdalenost         zpozdeni cena_poznamka den  prestupy  \\\n",
       "112            NaN        [0, 0, 0]          None  pá         2   \n",
       "113            NaN  [0, 0, 0, 0, 0]          None  pá         4   \n",
       "114            NaN     [0, 0, 0, 0]  Zjistit cenu  pá         3   \n",
       "115            NaN           [0, 0]          None  pá         1   \n",
       "116            NaN     [0, 0, 0, 0]  Zjistit cenu  pá         3   \n",
       "...            ...              ...           ...  ..       ...   \n",
       "502068         NaN        [0, 0, 0]          None  pá         2   \n",
       "502069         NaN        [0, 0, 0]          None  pá         2   \n",
       "502070         NaN        [0, 0, 0]          None  pá         2   \n",
       "502071         NaN           [0, 0]  Zjistit cenu  ne         1   \n",
       "502072         NaN           [0, 0]          None  pá         1   \n",
       "\n",
       "                                                    vlaky mistenka_zdarma  \\\n",
       "112        [rj 79 Vindobona, IC 296 Wienerwald, ICE 1080]            None   \n",
       "113     [EC 277 Metropolitan, EC 105 Polonia, rjx 64 r...            None   \n",
       "114     [EC 281 Metropolitan, EC 107 Porta Moravica, N...            None   \n",
       "115                   [rj 371 Vindobona, NJ 490 nightjet]            True   \n",
       "116     [EC 276 Metropolitan, EC 458 Canopus, ICE 1500...            None   \n",
       "...                                                   ...             ...   \n",
       "502068  [EC 282 Metropolitan Slovenská strela, EC 174 ...            None   \n",
       "502069       [IC 576 Metropol, EC 176 Berliner, ICE 1600]            None   \n",
       "502070          [R 876 Svitava, EC 178 Berliner, ICE 602]            None   \n",
       "502071                [rj 371 Vindobona, NJ 490 nightjet]            None   \n",
       "502072                         [EC 172 Hungaria, ICE 504]            None   \n",
       "\n",
       "       nahradni_bus  volna_mista_economy  volna_mista_economy_plus  \\\n",
       "112            None                  NaN                       NaN   \n",
       "113            None                  NaN                       NaN   \n",
       "114            None                  NaN                       NaN   \n",
       "115            None                  NaN                       NaN   \n",
       "116            None                  NaN                       NaN   \n",
       "...             ...                  ...                       ...   \n",
       "502068         None                  NaN                       NaN   \n",
       "502069         None                  NaN                       NaN   \n",
       "502070         None                  NaN                       NaN   \n",
       "502071         None                  NaN                       NaN   \n",
       "502072         None                  NaN                       NaN   \n",
       "\n",
       "        volna_mista_economy_business  volna_mista_premium  \n",
       "112                              NaN                  NaN  \n",
       "113                              NaN                  NaN  \n",
       "114                              NaN                  NaN  \n",
       "115                              NaN                  NaN  \n",
       "116                              NaN                  NaN  \n",
       "...                              ...                  ...  \n",
       "502068                           NaN                  NaN  \n",
       "502069                           NaN                  NaN  \n",
       "502070                           NaN                  NaN  \n",
       "502071                           NaN                  NaN  \n",
       "502072                           NaN                  NaN  \n",
       "\n",
       "[8424 rows x 25 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['odkud'].str.contains('Brn') & df['kam'].str.contains('Hamb')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c9165bf2-b4c4-4805-801c-bad0b8c27083",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['odkud'].str.contains('Prah') & df['kam'].str.contains('Brn')]['cena'].plot.hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fb7dd4c2-85d7-472e-a84a-ecd8794e5251",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "predstih_d\n",
       "-1     419.0\n",
       " 0     359.0\n",
       " 1     359.0\n",
       " 2     359.0\n",
       " 3     359.0\n",
       " 4     359.0\n",
       " 5     339.0\n",
       " 6     359.0\n",
       " 7     339.0\n",
       " 8     339.0\n",
       " 9     329.0\n",
       " 10    339.0\n",
       " 11    329.0\n",
       " 12    329.0\n",
       " 13    339.0\n",
       " 14    339.0\n",
       " 15    329.0\n",
       " 16    329.0\n",
       " 17    329.0\n",
       " 18    339.0\n",
       " 19    329.0\n",
       " 20    339.0\n",
       " 21    329.0\n",
       " 22    329.0\n",
       " 23    339.0\n",
       " 24    329.0\n",
       " 25    329.0\n",
       " 26    339.0\n",
       " 27    329.0\n",
       " 28    339.0\n",
       " 29    329.0\n",
       " 30    329.0\n",
       " 31    339.0\n",
       " 32    339.0\n",
       " 33    329.0\n",
       " 34    329.0\n",
       " 35    359.0\n",
       " 36    359.0\n",
       " 37    359.0\n",
       " 38    339.0\n",
       " 39    359.0\n",
       " 40    339.0\n",
       " 41    329.0\n",
       " 42    339.0\n",
       " 43    329.0\n",
       " 44    339.0\n",
       " 45    329.0\n",
       " 46    329.0\n",
       " 47    344.0\n",
       " 48    359.0\n",
       " 49    329.0\n",
       " 50    329.0\n",
       " 51    359.0\n",
       " 52    359.0\n",
       " 53    329.0\n",
       " 54    329.0\n",
       " 55    359.0\n",
       " 56    329.0\n",
       " 57    415.0\n",
       " 58    329.0\n",
       " 59    329.0\n",
       " 60    359.0\n",
       " 61      NaN\n",
       " 62      NaN\n",
       " 63      NaN\n",
       " 64      NaN\n",
       " 65      NaN\n",
       " 66      NaN\n",
       " 67      NaN\n",
       " 68      NaN\n",
       " 69      NaN\n",
       " 70      NaN\n",
       " 71      NaN\n",
       " 73      NaN\n",
       " 74      NaN\n",
       " 75      NaN\n",
       " 76      NaN\n",
       " 77      NaN\n",
       " 78      NaN\n",
       " 79      NaN\n",
       " 80      NaN\n",
       " 81      NaN\n",
       " 82      NaN\n",
       " 83      NaN\n",
       " 84      NaN\n",
       " 85      NaN\n",
       " 86      NaN\n",
       " 87      NaN\n",
       " 88      NaN\n",
       " 89      NaN\n",
       "Name: cena, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['odkud'].str.contains('Prah') & df['kam'].str.contains('Brn')].groupby('predstih_d')['cena'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "94d21e99-a095-44b0-971b-630e55c823e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_11260\\2553821891.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  df[df['odkud'].str.contains('Brno')].groupby(['kam','prodejce'])['cena'].min().nsmallest(50)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "kam                            prodejce\n",
       "Praha                          RJ            99.0\n",
       "Vídeň                          RJ           149.0\n",
       "Ostrava                        RJ           189.0\n",
       "Praha hl.n.                    ČD           269.0\n",
       "Praha-Holešovice               ČD           479.0\n",
       "Hamburg Hbf                    ČD           645.0\n",
       "Ljubljana                      ČD          1109.0\n",
       "Amsterdam Bijlmer ArenA        ARR            NaN\n",
       "                               LE             NaN\n",
       "                               RJ             NaN\n",
       "                               ČD             NaN\n",
       "Amsterdam Centraal             ARR            NaN\n",
       "                               LE             NaN\n",
       "                               RJ             NaN\n",
       "                               ČD             NaN\n",
       "Amsterdam Zuid                 ARR            NaN\n",
       "                               LE             NaN\n",
       "                               RJ             NaN\n",
       "                               ČD             NaN\n",
       "Berlin Betriebsbf Schöneweide  ARR            NaN\n",
       "                               LE             NaN\n",
       "                               RJ             NaN\n",
       "                               ČD             NaN\n",
       "Berlin Gesundbrunnen           ARR            NaN\n",
       "                               LE             NaN\n",
       "                               RJ             NaN\n",
       "                               ČD             NaN\n",
       "Berlin Hbf                     ARR            NaN\n",
       "                               LE             NaN\n",
       "                               RJ             NaN\n",
       "                               ČD             NaN\n",
       "Berlin Hbf (tief)              ARR            NaN\n",
       "                               LE             NaN\n",
       "                               RJ             NaN\n",
       "                               ČD             NaN\n",
       "Berlin Ostbahnhof              ARR            NaN\n",
       "                               LE             NaN\n",
       "                               RJ             NaN\n",
       "                               ČD             NaN\n",
       "Berlin Südkreuz                ARR            NaN\n",
       "                               LE             NaN\n",
       "                               RJ             NaN\n",
       "                               ČD             NaN\n",
       "Berlin Zoologischer Garten     ARR            NaN\n",
       "                               LE             NaN\n",
       "                               RJ             NaN\n",
       "                               ČD             NaN\n",
       "Berlin-Grünau                  ARR            NaN\n",
       "                               LE             NaN\n",
       "                               RJ             NaN\n",
       "Name: cena, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['odkud'].str.contains('Brno')].groupby(['kam','prodejce'])['cena'].min().nsmallest(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "59a6f751-3b5a-470b-b669-82041781bff3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_11260\\1571294427.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  df[df['volnych_mist'].notnull()].groupby('prodejce').size()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "prodejce\n",
       "ARR    36224\n",
       "LE     16555\n",
       "RJ     61615\n",
       "ČD         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['volnych_mist'].notnull()].groupby('prodejce').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "09fa64c9-5d20-4b5b-b6eb-0bd1ca5b3c7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['predstih_i'] = df['predstih_h'].apply(lambda x: round(x,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9a870aa6-118b-4949-8f68-9a62795a3edf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "predstih_i\n",
       "-1.0            NaN\n",
       " 0.0      26.333333\n",
       " 1.0      51.267442\n",
       " 2.0      88.140000\n",
       " 3.0      61.140845\n",
       " 4.0      87.350649\n",
       " 5.0      56.689189\n",
       " 6.0     104.500000\n",
       " 7.0      72.188679\n",
       " 8.0      92.405405\n",
       " 9.0      84.297872\n",
       " 10.0     84.704225\n",
       " 11.0     98.969697\n",
       " 12.0    106.600000\n",
       " 13.0     65.071429\n",
       " 14.0     98.409836\n",
       " 15.0     81.035088\n",
       " 16.0     74.228571\n",
       " 17.0     54.426230\n",
       " 18.0     75.100000\n",
       "Name: volnych_mist, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['odkud'].str.contains('Praha') & (df['kam'].str.contains('Ostrava'))].groupby('predstih_i')['volnych_mist'].mean().head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8c35a81f-5b7b-4eb7-8ac8-9fd577c0798b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "predstih_d\n",
       "-1      537.0\n",
       " 0      435.0\n",
       " 1      425.0\n",
       " 2      419.0\n",
       " 3      400.0\n",
       " 4      419.0\n",
       " 5      435.0\n",
       " 6      425.0\n",
       " 7      425.0\n",
       " 8      439.0\n",
       " 9      387.0\n",
       " 10     369.0\n",
       " 11     369.0\n",
       " 12     399.0\n",
       " 13     399.0\n",
       " 14     400.0\n",
       " 15     399.0\n",
       " 16     425.0\n",
       " 17     400.0\n",
       " 18     400.0\n",
       " 19     399.0\n",
       " 20     399.0\n",
       " 21     425.0\n",
       " 22     419.0\n",
       " 23     425.0\n",
       " 24     435.0\n",
       " 25     419.0\n",
       " 26     469.0\n",
       " 27     460.0\n",
       " 28     435.0\n",
       " 29     439.0\n",
       " 30     460.0\n",
       " 31     568.0\n",
       " 32     399.0\n",
       " 33     419.0\n",
       " 34     459.0\n",
       " 35     464.0\n",
       " 36     400.0\n",
       " 37     439.0\n",
       " 38     490.0\n",
       " 39     469.0\n",
       " 40     402.0\n",
       " 41     479.0\n",
       " 42     499.0\n",
       " 43     404.0\n",
       " 44     510.0\n",
       " 45     393.5\n",
       " 46     429.0\n",
       " 47     464.5\n",
       " 48     415.0\n",
       " 49     479.0\n",
       " 50     460.0\n",
       " 51     529.0\n",
       " 52     415.0\n",
       " 53     439.0\n",
       " 54     400.0\n",
       " 55     469.0\n",
       " 56     400.0\n",
       " 57     425.0\n",
       " 58     400.0\n",
       " 59     439.0\n",
       " 60     606.0\n",
       " 61     748.0\n",
       " 62     606.0\n",
       " 63     606.0\n",
       " 64     477.5\n",
       " 65     606.0\n",
       " 66     606.0\n",
       " 67    1211.0\n",
       " 68     748.0\n",
       " 69     606.0\n",
       " 70     748.0\n",
       " 71     387.0\n",
       " 72     606.0\n",
       " 73     606.0\n",
       " 74     748.0\n",
       " 75     606.0\n",
       " 76     606.0\n",
       " 77     606.0\n",
       " 78     606.0\n",
       " 79     697.0\n",
       " 80     748.0\n",
       " 81     516.0\n",
       " 82     748.0\n",
       " 83     516.0\n",
       " 84     387.0\n",
       " 85     516.0\n",
       " 86     606.0\n",
       " 87     606.0\n",
       " 88     645.0\n",
       " 89     748.0\n",
       " 90    1264.0\n",
       " 91       NaN\n",
       " 92     319.0\n",
       " 93       NaN\n",
       "Name: cena, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('predstih_d')['cena'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6272b01d-3322-4806-b0c2-58be42729bcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>oscrapovano</th>\n",
       "      <th>prodejce</th>\n",
       "      <th>odkud</th>\n",
       "      <th>kam</th>\n",
       "      <th>odjezd</th>\n",
       "      <th>predstih</th>\n",
       "      <th>predstih_d</th>\n",
       "      <th>predstih_h</th>\n",
       "      <th>cena</th>\n",
       "      <th>prostredek</th>\n",
       "      <th>volnych_mist</th>\n",
       "      <th>obsazenost</th>\n",
       "      <th>jizdni_doba</th>\n",
       "      <th>vzdalenost</th>\n",
       "      <th>zpozdeni</th>\n",
       "      <th>cena_poznamka</th>\n",
       "      <th>den</th>\n",
       "      <th>prestupy</th>\n",
       "      <th>vlaky</th>\n",
       "      <th>mistenka_zdarma</th>\n",
       "      <th>nahradni_bus</th>\n",
       "      <th>volna_mista_economy</th>\n",
       "      <th>volna_mista_economy_plus</th>\n",
       "      <th>volna_mista_economy_business</th>\n",
       "      <th>volna_mista_premium</th>\n",
       "      <th>predstih_i</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>147728</th>\n",
       "      <td>2024-11-08 11:08:13</td>\n",
       "      <td>LE</td>\n",
       "      <td>Praha-Libeň</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>2024-11-08 11:07:00</td>\n",
       "      <td>-1 days +23:58:47</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.020278</td>\n",
       "      <td>999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>13.0</td>\n",
       "      <td>None</td>\n",
       "      <td>197.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1257]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348737</th>\n",
       "      <td>2024-11-13 07:32:04</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Bruxelles-Midi</td>\n",
       "      <td>2024-11-13 07:23:00</td>\n",
       "      <td>-1 days +23:50:56</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.151111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané střední obsazení - doporučujeme...</td>\n",
       "      <td>732.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 6, 0]</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>2</td>\n",
       "      <td>[EC 360 Bavorský expres, ICE 28, ICE 12 ICE In...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83754</th>\n",
       "      <td>2024-11-05 16:52:54</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Budapest-Kelenföld</td>\n",
       "      <td>2024-11-05 16:44:00</td>\n",
       "      <td>-1 days +23:51:06</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.148333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané střední obsazení - doporučujeme...</td>\n",
       "      <td>438.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>None</td>\n",
       "      <td>út</td>\n",
       "      <td>1</td>\n",
       "      <td>[rj 373 Vindobona, rjx 12261]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425178</th>\n",
       "      <td>2024-11-15 05:31:51</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Bruxelles-Midi</td>\n",
       "      <td>2024-11-15 05:23:00</td>\n",
       "      <td>-1 days +23:51:09</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.147500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané nízké obsazení, Předpokládané n...</td>\n",
       "      <td>732.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0]</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>2</td>\n",
       "      <td>[EC 362 Bavorský expres, ICE 228, ICE 14 ICE I...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233645</th>\n",
       "      <td>2024-11-10 07:31:00</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Brno hl.n.</td>\n",
       "      <td>Ljubljana</td>\n",
       "      <td>2024-11-10 07:22:00</td>\n",
       "      <td>-1 days +23:51:00</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.150000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané nízké obsazení, Předpokládané n...</td>\n",
       "      <td>576.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0]</td>\n",
       "      <td>None</td>\n",
       "      <td>ne</td>\n",
       "      <td>2</td>\n",
       "      <td>[rj 71 Vindobona, D 17879, D 317]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40967</th>\n",
       "      <td>2024-11-03 09:54:26</td>\n",
       "      <td>ČD</td>\n",
       "      <td>České Budějovice</td>\n",
       "      <td>Zürich HB</td>\n",
       "      <td>2025-02-03 10:05:00</td>\n",
       "      <td>92 days 00:10:34</td>\n",
       "      <td>92</td>\n",
       "      <td>2208.176111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Téměr vyprodaná místa k sezení, Téměr vyproda...</td>\n",
       "      <td>617.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 0, 0]</td>\n",
       "      <td>Zjistit cenu</td>\n",
       "      <td>po</td>\n",
       "      <td>4</td>\n",
       "      <td>[Sp 3803 Vltava Dunaj, rjx 862, R 5728, IR 327...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2208.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40973</th>\n",
       "      <td>2024-11-03 09:54:26</td>\n",
       "      <td>ČD</td>\n",
       "      <td>München Hbf Gl.27-36</td>\n",
       "      <td>Zürich HB</td>\n",
       "      <td>2025-02-04 12:54:00</td>\n",
       "      <td>93 days 02:59:34</td>\n",
       "      <td>93</td>\n",
       "      <td>2234.992778</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Téměr vyprodaná místa k sezení, Téměr vyproda...</td>\n",
       "      <td>623.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 0, 0]</td>\n",
       "      <td>Zjistit cenu</td>\n",
       "      <td>út</td>\n",
       "      <td>4</td>\n",
       "      <td>[R 658 Bezdrev, IC 566 Západní expres, Sp 5282...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40968</th>\n",
       "      <td>2024-11-03 09:54:26</td>\n",
       "      <td>ČD</td>\n",
       "      <td>České Budějovice</td>\n",
       "      <td>Zürich HB</td>\n",
       "      <td>2025-02-04 10:05:00</td>\n",
       "      <td>93 days 00:10:34</td>\n",
       "      <td>93</td>\n",
       "      <td>2232.176111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Téměr vyprodaná místa k sezení, Téměr vyproda...</td>\n",
       "      <td>617.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 0, 0]</td>\n",
       "      <td>Zjistit cenu</td>\n",
       "      <td>út</td>\n",
       "      <td>4</td>\n",
       "      <td>[Sp 3803 Vltava Dunaj, rjx 862, R 5728, IR 327...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40959</th>\n",
       "      <td>2024-11-03 09:54:26</td>\n",
       "      <td>ČD</td>\n",
       "      <td>České Budějovice</td>\n",
       "      <td>Zürich HB</td>\n",
       "      <td>2025-02-04 12:05:00</td>\n",
       "      <td>93 days 02:10:34</td>\n",
       "      <td>93</td>\n",
       "      <td>2234.176111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Téměr vyprodaná místa k sezení, Téměr vyproda...</td>\n",
       "      <td>555.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>Zjistit cenu</td>\n",
       "      <td>út</td>\n",
       "      <td>1</td>\n",
       "      <td>[EC 333 Jižní expres, rjx 166]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2234.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40958</th>\n",
       "      <td>2024-11-03 09:54:26</td>\n",
       "      <td>ČD</td>\n",
       "      <td>České Budějovice</td>\n",
       "      <td>Zürich HB</td>\n",
       "      <td>2025-02-04 14:05:00</td>\n",
       "      <td>93 days 04:10:34</td>\n",
       "      <td>93</td>\n",
       "      <td>2236.176111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Téměr vyprodaná místa k sezení, Téměr vyproda...</td>\n",
       "      <td>555.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>Zjistit cenu</td>\n",
       "      <td>út</td>\n",
       "      <td>1</td>\n",
       "      <td>[Sp 3805 Vltava Dunaj, rjx 168]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2236.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>447224 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               oscrapovano prodejce                 odkud                 kam  \\\n",
       "147728 2024-11-08 11:08:13       LE           Praha-Libeň      Ostrava-Svinov   \n",
       "348737 2024-11-13 07:32:04       ČD           Praha hl.n.      Bruxelles-Midi   \n",
       "83754  2024-11-05 16:52:54       ČD           Praha hl.n.  Budapest-Kelenföld   \n",
       "425178 2024-11-15 05:31:51       ČD           Praha hl.n.      Bruxelles-Midi   \n",
       "233645 2024-11-10 07:31:00       ČD            Brno hl.n.           Ljubljana   \n",
       "...                    ...      ...                   ...                 ...   \n",
       "40967  2024-11-03 09:54:26       ČD      České Budějovice           Zürich HB   \n",
       "40973  2024-11-03 09:54:26       ČD  München Hbf Gl.27-36           Zürich HB   \n",
       "40968  2024-11-03 09:54:26       ČD      České Budějovice           Zürich HB   \n",
       "40959  2024-11-03 09:54:26       ČD      České Budějovice           Zürich HB   \n",
       "40958  2024-11-03 09:54:26       ČD      České Budějovice           Zürich HB   \n",
       "\n",
       "                    odjezd          predstih  predstih_d   predstih_h   cena  \\\n",
       "147728 2024-11-08 11:07:00 -1 days +23:58:47          -1    -0.020278  999.0   \n",
       "348737 2024-11-13 07:23:00 -1 days +23:50:56          -1    -0.151111    NaN   \n",
       "83754  2024-11-05 16:44:00 -1 days +23:51:06          -1    -0.148333    NaN   \n",
       "425178 2024-11-15 05:23:00 -1 days +23:51:09          -1    -0.147500    NaN   \n",
       "233645 2024-11-10 07:22:00 -1 days +23:51:00          -1    -0.150000    NaN   \n",
       "...                    ...               ...         ...          ...    ...   \n",
       "40967  2025-02-03 10:05:00  92 days 00:10:34          92  2208.176111    NaN   \n",
       "40973  2025-02-04 12:54:00  93 days 02:59:34          93  2234.992778    NaN   \n",
       "40968  2025-02-04 10:05:00  93 days 00:10:34          93  2232.176111    NaN   \n",
       "40959  2025-02-04 12:05:00  93 days 02:10:34          93  2234.176111    NaN   \n",
       "40958  2025-02-04 14:05:00  93 days 04:10:34          93  2236.176111    NaN   \n",
       "\n",
       "       prostredek  volnych_mist  \\\n",
       "147728       None          13.0   \n",
       "348737       vlak           NaN   \n",
       "83754        vlak           NaN   \n",
       "425178       vlak           NaN   \n",
       "233645       vlak           NaN   \n",
       "...           ...           ...   \n",
       "40967        vlak           NaN   \n",
       "40973        vlak           NaN   \n",
       "40968        vlak           NaN   \n",
       "40959        vlak           NaN   \n",
       "40958        vlak           NaN   \n",
       "\n",
       "                                               obsazenost  jizdni_doba  \\\n",
       "147728                                               None        197.0   \n",
       "348737  [Předpokládané střední obsazení - doporučujeme...        732.0   \n",
       "83754   [Předpokládané střední obsazení - doporučujeme...        438.0   \n",
       "425178  [Předpokládané nízké obsazení, Předpokládané n...        732.0   \n",
       "233645  [Předpokládané nízké obsazení, Předpokládané n...        576.0   \n",
       "...                                                   ...          ...   \n",
       "40967   [Téměr vyprodaná místa k sezení, Téměr vyproda...        617.0   \n",
       "40973   [Téměr vyprodaná místa k sezení, Téměr vyproda...        623.0   \n",
       "40968   [Téměr vyprodaná místa k sezení, Téměr vyproda...        617.0   \n",
       "40959   [Téměr vyprodaná místa k sezení, Téměr vyproda...        555.0   \n",
       "40958   [Téměr vyprodaná místa k sezení, Téměr vyproda...        555.0   \n",
       "\n",
       "        vzdalenost         zpozdeni cena_poznamka den  prestupy  \\\n",
       "147728       346.0             None          None  pá         0   \n",
       "348737         NaN        [0, 6, 0]          None  st         2   \n",
       "83754          NaN           [0, 0]          None  út         1   \n",
       "425178         NaN        [0, 1, 0]          None  pá         2   \n",
       "233645         NaN        [0, 1, 0]          None  ne         2   \n",
       "...            ...              ...           ...  ..       ...   \n",
       "40967          NaN  [0, 0, 0, 0, 0]  Zjistit cenu  po         4   \n",
       "40973          NaN  [0, 0, 0, 0, 0]  Zjistit cenu  út         4   \n",
       "40968          NaN  [0, 0, 0, 0, 0]  Zjistit cenu  út         4   \n",
       "40959          NaN           [0, 0]  Zjistit cenu  út         1   \n",
       "40958          NaN           [0, 0]  Zjistit cenu  út         1   \n",
       "\n",
       "                                                    vlaky mistenka_zdarma  \\\n",
       "147728                                           [LE1257]            None   \n",
       "348737  [EC 360 Bavorský expres, ICE 28, ICE 12 ICE In...            None   \n",
       "83754                       [rj 373 Vindobona, rjx 12261]            None   \n",
       "425178  [EC 362 Bavorský expres, ICE 228, ICE 14 ICE I...            None   \n",
       "233645                  [rj 71 Vindobona, D 17879, D 317]            None   \n",
       "...                                                   ...             ...   \n",
       "40967   [Sp 3803 Vltava Dunaj, rjx 862, R 5728, IR 327...            None   \n",
       "40973   [R 658 Bezdrev, IC 566 Západní expres, Sp 5282...            None   \n",
       "40968   [Sp 3803 Vltava Dunaj, rjx 862, R 5728, IR 327...            None   \n",
       "40959                      [EC 333 Jižní expres, rjx 166]            None   \n",
       "40958                     [Sp 3805 Vltava Dunaj, rjx 168]            None   \n",
       "\n",
       "       nahradni_bus  volna_mista_economy  volna_mista_economy_plus  \\\n",
       "147728         None                  2.0                       2.0   \n",
       "348737         None                  NaN                       NaN   \n",
       "83754          None                  NaN                       NaN   \n",
       "425178         None                  NaN                       NaN   \n",
       "233645         None                  NaN                       NaN   \n",
       "...             ...                  ...                       ...   \n",
       "40967          None                  NaN                       NaN   \n",
       "40973          None                  NaN                       NaN   \n",
       "40968          None                  NaN                       NaN   \n",
       "40959          None                  NaN                       NaN   \n",
       "40958          None                  NaN                       NaN   \n",
       "\n",
       "        volna_mista_economy_business  volna_mista_premium  predstih_i  \n",
       "147728                           9.0                  0.0        -0.0  \n",
       "348737                           NaN                  NaN        -0.0  \n",
       "83754                            NaN                  NaN        -0.0  \n",
       "425178                           NaN                  NaN        -0.0  \n",
       "233645                           NaN                  NaN        -0.0  \n",
       "...                              ...                  ...         ...  \n",
       "40967                            NaN                  NaN      2208.0  \n",
       "40973                            NaN                  NaN      2235.0  \n",
       "40968                            NaN                  NaN      2232.0  \n",
       "40959                            NaN                  NaN      2234.0  \n",
       "40958                            NaN                  NaN      2236.0  \n",
       "\n",
       "[447224 rows x 26 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('predstih_d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4af8cbf5-1607-4098-be3b-e2b7b5bf9574",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='predstih_d'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['odkud'].str.contains('Prah') & df['kam'].str.contains('Ostrava')].groupby('predstih_d')['volnych_mist'].median().plot.bar(figsize=(25,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4ed1e5f3-7ace-4ec2-b8c0-def129af2ded",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='predstih_d'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['odkud'].str.contains('Prah') & df['kam'].str.contains('Bratisla')].groupby('predstih_d')['cena'].median().plot.bar(figsize=(25,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "33426771-87b7-4954-8f28-57d510160d34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='predstih_d'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAB8kAAAHECAYAAAC6FBvsAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAABGP0lEQVR4nO3de5ic890/8M/sbg6b80Gzm5WVrEda/BKliao4JCWiKg3y1KG04SpK4xShWjyt1EOitKRCozyKUqEHoahTHdLmCsqqCEWiCQnJCprmQGxO398fnsxjc9AuI7Pjfr2ua64rc9/fmX3PZHbv7z3vue/JpZRSAAAAAAAAAEAGlBU7AAAAAAAAAABsKUpyAAAAAAAAADJDSQ4AAAAAAABAZijJAQAAAAAAAMgMJTkAAAAAAAAAmaEkBwAAAAAAACAzlOQAAAAAAAAAZIaSHAAAAAAAAIDMqCh2gA9j3bp1sXDhwujYsWPkcrlixwEAAAAAAACgyFJKsXz58qipqYmyss0fL16SJfnChQujtra22DEAAAAAAAAAaGEWLFgQvXr12uz6kizJO3bsGBHvPbhOnToVOQ0AAAAAAAAAxbZs2bKora3N98mbU5Il+fpTrHfq1ElJDgAAAAAAAEDev/rK7s2fiB0AAAAAAAAAPmGU5AAAAAAAAABkhpIcAAAAAAAAgMxQkgMAAAAAAACQGUpyAAAAAAAAADJDSQ4AAAAAAABAZijJAQAAAAAAAMgMJTkAAAAAAAAAmaEkBwAAAAAAACAzlOQAAAAAAAAAZIaSHAAAAAAAAIDMUJIDAAAAAAAAkBlKcgAAAAAAAAAyQ0kOAAAAAAAAQGYoyQEAAAAAAADIjIpiBwAAAACgNPT53t0Fvb+XLzqwoPcXUfiMER9PTmhJSuH3RsbC8be8MGQsnCy+JkshY4Q5EJ9sjiQHAAAAAAAAIDOU5AAAAAAAAABkhpIcAAAAAAAAgMxQkgMAAAAAAACQGUpyAAAAAAAAADJDSQ4AAAAAAABAZijJAQAAAAAAAMgMJTkAAAAAAAAAmaEkBwAAAAAAACAzlOQAAAAAAAAAZIaSHAAAAAAAAIDMUJIDAAAAAAAAkBlKcgAAAAAAAAAyQ0kOAAAAAAAAQGYoyQEAAAAAAADIDCU5AAAAAAAAAJmhJAcAAAAAAAAgM5TkAAAAAAAAAGSGkhwAAAAAAACAzFCSAwAAAAAAAJAZSnIAAAAAAAAAMkNJDgAAAAAAAEBmKMkBAAAAAAAAyAwlOQAAAAAAAACZoSQHAAAAAAAAIDOU5AAAAAAAAABkhpIcAAAAAAAAgMxQkgMAAAAAAACQGUpyAAAAAAAAADJDSQ4AAAAAAABAZijJAQAAAAAAAMgMJTkAAAAAAAAAmaEkBwAAAAAAACAzlOQAAAAAAAAAZIaSHAAAAAAAAIDMaFZJvmbNmviv//qvqKuri8rKyth2223j/PPPj3Xr1uXHpJRi3LhxUVNTE5WVlTFkyJB47rnnmtxPY2NjnHLKKbHVVltF+/btY8SIEfHqq68W5hEBAAAAAAAAwGY0qyT/0Y9+FFdddVVcccUV8fzzz8fFF18cl1xySUyaNCk/5uKLL45LL700rrjiinjiiSeiuro69ttvv1i+fHl+zJgxY2Lq1Klxyy23xPTp02PFihUxfPjwWLt2beEeGQAAAAAAAABsoKI5gx999NE46KCD4sADD4yIiD59+sSUKVPiySefjIj3jiKfOHFinHvuuTFy5MiIiLjhhhuiqqoqbr755jjhhBNi6dKlce2118aNN94YQ4cOjYiIm266KWpra+OPf/xj7L///oV8fAAAAAAAAACQ16wjyffcc8948MEHY/bs2RERMXPmzJg+fXp8+ctfjoiIefPmRUNDQwwbNix/mzZt2sTgwYNjxowZERFRX18fq1evbjKmpqYm+vXrlx8DAAAAAAAAAB+HZh1J/t3vfjeWLl0a22+/fZSXl8fatWvjwgsvjK997WsREdHQ0BAREVVVVU1uV1VVFa+88kp+TOvWraNr164bjVl/+w01NjZGY2Nj/vqyZcuaExsAAAAAAAAAIqKZR5LfeuutcdNNN8XNN98cTz31VNxwww3x4x//OG644YYm43K5XJPrKaWNlm3og8ZMmDAhOnfunL/U1tY2JzYAAAAAAAAAREQzS/LvfOc78b3vfS+OOOKI6N+/f3zjG9+I008/PSZMmBAREdXV1RERGx0Rvnjx4vzR5dXV1bFq1apYsmTJZsds6Oyzz46lS5fmLwsWLGhObAAAAAAAAACIiGaW5O+8806UlTW9SXl5eaxbty4iIurq6qK6ujoeeOCB/PpVq1bFtGnTYtCgQRERMWDAgGjVqlWTMYsWLYpnn302P2ZDbdq0iU6dOjW5AAAAAAAAAEBzNes7yb/yla/EhRdeGNtss038v//3/+Kvf/1rXHrppfHNb34zIt47zfqYMWNi/Pjx0bdv3+jbt2+MHz8+2rVrF0ceeWRERHTu3DmOPfbYOOOMM6J79+7RrVu3OPPMM6N///4xdOjQwj9CAAAAAAAAAPhfzSrJJ02aFN///vdj9OjRsXjx4qipqYkTTjghfvCDH+THnHXWWbFy5coYPXp0LFmyJHbbbbe4//77o2PHjvkxl112WVRUVMRhhx0WK1eujH333Teuv/76KC8vL9wjAwAAAAAAAIANNKsk79ixY0ycODEmTpy42TG5XC7GjRsX48aN2+yYtm3bxqRJk2LSpEnN+fEAAAAAAAAA8JE06zvJAQAAAAAAAKCUKckBAAAAAAAAyAwlOQAAAAAAAACZoSQHAAAAAAAAIDOU5AAAAAAAAABkhpIcAAAAAAAAgMxQkgMAAAAAAACQGUpyAAAAAAAAADJDSQ4AAAAAAABAZijJAQAAAAAAAMgMJTkAAAAAAAAAmaEkBwAAAAAAACAzlOQAAAAAAAAAZIaSHAAAAAAAAIDMUJIDAAAAAAAAkBlKcgAAAAAAAAAyQ0kOAAAAAAAAQGYoyQEAAAAAAADIDCU5AAAAAAAAAJmhJAcAAAAAAAAgM5TkAAAAAAAAAGSGkhwAAAAAAACAzFCSAwAAAAAAAJAZSnIAAAAAAAAAMkNJDgAAAAAAAEBmKMkBAAAAAAAAyAwlOQAAAAAAAACZoSQHAAAAAAAAIDOU5AAAAAAAAABkhpIcAAAAAAAAgMxQkgMAAAAAAACQGUpyAAAAAAAAADJDSQ4AAAAAAABAZijJAQAAAAAAAMgMJTkAAAAAAAAAmVFR7AAAAAAAAJ9Ufb53d0Hv7+WLDizo/QHAllLobWJE4beLpZCxVLT059KR5AAAAAAAAABkhpIcAAAAAAAAgMxQkgMAAAAAAACQGUpyAAAAAAAAADJDSQ4AAAAAAABAZijJAQAAAAAAAMgMJTkAAAAAAAAAmaEkBwAAAAAAACAzlOQAAAAAAAAAZIaSHAAAAAAAAIDMUJIDAAAAAAAAkBlKcgAAAAAAAAAyQ0kOAAAAAAAAQGYoyQEAAAAAAADIDCU5AAAAAAAAAJmhJAcAAAAAAAAgM5TkAAAAAAAAAGSGkhwAAAAAAACAzFCSAwAAAAAAAJAZSnIAAAAAAAAAMkNJDgAAAAAAAEBmKMkBAAAAAAAAyAwlOQAAAAAAAACZoSQHAAAAAAAAIDOU5AAAAAAAAABkhpIcAAAAAAAAgMxQkgMAAAAAAACQGUpyAAAAAAAAADJDSQ4AAAAAAABAZijJAQAAAAAAAMgMJTkAAAAAAAAAmaEkBwAAAAAAACAzlOQAAAAAAAAAZIaSHAAAAAAAAIDMUJIDAAAAAAAAkBnNLslfe+21+PrXvx7du3ePdu3axc477xz19fX59SmlGDduXNTU1ERlZWUMGTIknnvuuSb30djYGKecckpstdVW0b59+xgxYkS8+uqrH/3RAAAAAAAAAMAHaFZJvmTJkthjjz2iVatWcc8998Tf/va3+MlPfhJdunTJj7n44ovj0ksvjSuuuCKeeOKJqK6ujv322y+WL1+eHzNmzJiYOnVq3HLLLTF9+vRYsWJFDB8+PNauXVuwBwYAAAAAAAAAG6pozuAf/ehHUVtbG9ddd11+WZ8+ffL/TinFxIkT49xzz42RI0dGRMQNN9wQVVVVcfPNN8cJJ5wQS5cujWuvvTZuvPHGGDp0aERE3HTTTVFbWxt//OMfY//99y/AwwIAAAAAAACAjTXrSPLf//73MXDgwDj00EOjR48escsuu8Q111yTXz9v3rxoaGiIYcOG5Ze1adMmBg8eHDNmzIiIiPr6+li9enWTMTU1NdGvX7/8mA01NjbGsmXLmlwAAAAAAAAAoLmaVZLPnTs3Jk+eHH379o377rsvTjzxxDj11FPjl7/8ZURENDQ0REREVVVVk9tVVVXl1zU0NETr1q2ja9eumx2zoQkTJkTnzp3zl9ra2ubEBgAAAAAAAICIaObp1tetWxcDBw6M8ePHR0TELrvsEs8991xMnjw5Ro0alR+Xy+Wa3C6ltNGyDX3QmLPPPjvGjh2bv75s2TJFOQAAAPCJ0ed7dxf8Pl++6MCC3yeFUQr/36WQMaLwOf3eAAClwBzoo2vWkeQ9e/aMHXfcscmyHXbYIebPnx8REdXV1RERGx0Rvnjx4vzR5dXV1bFq1apYsmTJZsdsqE2bNtGpU6cmFwAAAAAAAABormaV5HvssUe8+OKLTZbNnj07evfuHRERdXV1UV1dHQ888EB+/apVq2LatGkxaNCgiIgYMGBAtGrVqsmYRYsWxbPPPpsfAwAAAAAAAAAfh2adbv3000+PQYMGxfjx4+Owww6Lv/zlL3H11VfH1VdfHRHvnWZ9zJgxMX78+Ojbt2/07ds3xo8fH+3atYsjjzwyIiI6d+4cxx57bJxxxhnRvXv36NatW5x55pnRv3//GDp0aOEfIQAAAAAAAAD8r2aV5LvuumtMnTo1zj777Dj//POjrq4uJk6cGEcddVR+zFlnnRUrV66M0aNHx5IlS2K33XaL+++/Pzp27Jgfc9lll0VFRUUcdthhsXLlyth3333j+uuvj/Ly8sI9MgAAAAAAAADYQLNK8oiI4cOHx/Dhwze7PpfLxbhx42LcuHGbHdO2bduYNGlSTJo0qbk/HgAAAAAAAAA+tGZ9JzkAAAAAAAAAlDIlOQAAAAAAAACZoSQHAAAAAAAAIDOU5AAAAAAAAABkhpIcAAAAAAAAgMxQkgMAAAAAAACQGUpyAAAAAAAAADJDSQ4AAAAAAABAZijJAQAAAAAAAMgMJTkAAAAAAAAAmaEkBwAAAAAAACAzlOQAAAAAAAAAZIaSHAAAAAAAAIDMUJIDAAAAAAAAkBlKcgAAAAAAAAAyQ0kOAAAAAAAAQGYoyQEAAAAAAADIDCU5AAAAAAAAAJmhJAcAAAAAAAAgM5TkAAAAAAAAAGSGkhwAAAAAAACAzFCSAwAAAAAAAJAZSnIAAAAAAAAAMkNJDgAAAAAAAEBmKMkBAAAAAAAAyAwlOQAAAAAAAACZoSQHAAAAAAAAIDOU5AAAAAAAAABkhpIcAAAAAAAAgMxQkgMAAAAAAACQGUpyAAAAAAAAADJDSQ4AAAAAAABAZijJAQAAAAAAAMgMJTkAAAAAAAAAmVFR7ABbQp/v3V3Q+3v5ogMLen8R2cwYUficpZAxIpv/36WQMSKbr8lSyBiRzddkKWSM8JosFBkLJ4uvyVLIGOE1WSgyFk4WX5OlkDEiu69JAAAAtgxHkgMAAAAAAACQGUpyAAAAAAAAADJDSQ4AAAAAAABAZijJAQAAAAAAAMgMJTkAAAAAAAAAmaEkBwAAAAAAACAzlOQAAAAAAAAAZIaSHAAAAAAAAIDMUJIDAAAAAAAAkBlKcgAAAAAAAAAyQ0kOAAAAAAAAQGYoyQEAAAAAAADIDCU5AAAAAAAAAJmhJAcAAAAAAAAgM5TkAAAAAAAAAGSGkhwAAAAAAACAzFCSAwAAAAAAAJAZSnIAAAAAAAAAMkNJDgAAAAAAAEBmKMkBAAAAAAAAyAwlOQAAAAAAAACZoSQHAAAAAAAAIDOU5AAAAAAAAABkhpIcAAAAAAAAgMxQkgMAAAAAAACQGUpyAAAAAAAAADJDSQ4AAAAAAABAZijJAQAAAAAAAMgMJTkAAAAAAAAAmaEkBwAAAAAAACAzlOQAAAAAAAAAZIaSHAAAAAAAAIDMUJIDAAAAAAAAkBlKcgAAAAAAAAAyQ0kOAAAAAAAAQGZ8pJJ8woQJkcvlYsyYMfllKaUYN25c1NTURGVlZQwZMiSee+65JrdrbGyMU045Jbbaaqto3759jBgxIl599dWPEgUAAAAAAAAA/qUPXZI/8cQTcfXVV8dOO+3UZPnFF18cl156aVxxxRXxxBNPRHV1dey3336xfPny/JgxY8bE1KlT45Zbbonp06fHihUrYvjw4bF27doP/0gAAAAAAAAA4F/4UCX5ihUr4qijjoprrrkmunbtml+eUoqJEyfGueeeGyNHjox+/frFDTfcEO+8807cfPPNERGxdOnSuPbaa+MnP/lJDB06NHbZZZe46aabYtasWfHHP/6xMI8KAAAAAAAAADbhQ5XkJ510Uhx44IExdOjQJsvnzZsXDQ0NMWzYsPyyNm3axODBg2PGjBkREVFfXx+rV69uMqampib69euXHwMAAAAAAAAAH4eK5t7glltuiaeeeiqeeOKJjdY1NDRERERVVVWT5VVVVfHKK6/kx7Ru3brJEejrx6y//YYaGxujsbExf33ZsmXNjQ0AAAAAAAAAzTuSfMGCBXHaaafFTTfdFG3btt3suFwu1+R6SmmjZRv6oDETJkyIzp075y+1tbXNiQ0AAAAAAAAAEdHMkry+vj4WL14cAwYMiIqKiqioqIhp06bF5ZdfHhUVFfkjyDc8Inzx4sX5ddXV1bFq1apYsmTJZsds6Oyzz46lS5fmLwsWLGhObAAAAAAAAACIiGaW5Pvuu2/MmjUrnn766fxl4MCBcdRRR8XTTz8d2267bVRXV8cDDzyQv82qVati2rRpMWjQoIiIGDBgQLRq1arJmEWLFsWzzz6bH7OhNm3aRKdOnZpcAAAAAAAAAKC5mvWd5B07dox+/fo1Wda+ffvo3r17fvmYMWNi/Pjx0bdv3+jbt2+MHz8+2rVrF0ceeWRERHTu3DmOPfbYOOOMM6J79+7RrVu3OPPMM6N///4xdOjQAj0sAAAAAAAAANhYs0ryf8dZZ50VK1eujNGjR8eSJUtit912i/vvvz86duyYH3PZZZdFRUVFHHbYYbFy5crYd9994/rrr4/y8vJCxwEAAAAAAACAvI9ckj/yyCNNrudyuRg3blyMGzdus7dp27ZtTJo0KSZNmvRRfzwAAAAAAAAA/Nua9Z3kAAAAAAAAAFDKlOQAAAAAAAAAZIaSHAAAAAAAAIDMUJIDAAAAAAAAkBlKcgAAAAAAAAAyQ0kOAAAAAAAAQGYoyQEAAAAAAADIDCU5AAAAAAAAAJmhJAcAAAAAAAAgM5TkAAAAAAAAAGSGkhwAAAAAAACAzFCSAwAAAAAAAJAZSnIAAAAAAAAAMkNJDgAAAAAAAEBmKMkBAAAAAAAAyAwlOQAAAAAAAACZoSQHAAAAAAAAIDOU5AAAAAAAAABkhpIcAAAAAAAAgMxQkgMAAAAAAACQGUpyAAAAAAAAADJDSQ4AAAAAAABAZijJAQAAAAAAAMgMJTkAAAAAAAAAmaEkBwAAAAAAACAzlOQAAAAAAAAAZIaSHAAAAAAAAIDMUJIDAAAAAAAAkBlKcgAAAAAAAAAyQ0kOAAAAAAAAQGYoyQEAAAAAAADIDCU5AAAAAAAAAJmhJAcAAAAAAAAgM5TkAAAAAAAAAGSGkhwAAAAAAACAzFCSAwAAAAAAAJAZSnIAAAAAAAAAMkNJDgAAAAAAAEBmKMkBAAAAAAAAyAwlOQAAAAAAAACZoSQHAAAAAAAAIDOU5AAAAAAAAABkhpIcAAAAAAAAgMxQkgMAAAAAAACQGUpyAAAAAAAAADJDSQ4AAAAAAABAZijJAQAAAAAAAMgMJTkAAAAAAAAAmaEkBwAAAAAAACAzlOQAAAAAAAAAZIaSHAAAAAAAAIDMUJIDAAAAAAAAkBlKcgAAAAAAAAAyQ0kOAAAAAAAAQGYoyQEAAAAAAADIDCU5AAAAAAAAAJmhJAcAAAAAAAAgM5TkAAAAAAAAAGSGkhwAAAAAAACAzFCSAwAAAAAAAJAZSnIAAAAAAAAAMkNJDgAAAAAAAEBmKMkBAAAAAAAAyAwlOQAAAAAAAACZoSQHAAAAAAAAIDOU5AAAAAAAAABkhpIcAAAAAAAAgMxQkgMAAAAAAACQGUpyAAAAAAAAADJDSQ4AAAAAAABAZijJAQAAAAAAAMiMZpXkEyZMiF133TU6duwYPXr0iIMPPjhefPHFJmNSSjFu3LioqamJysrKGDJkSDz33HNNxjQ2NsYpp5wSW221VbRv3z5GjBgRr7766kd/NAAAAAAAAADwAZpVkk+bNi1OOumkeOyxx+KBBx6INWvWxLBhw+Ltt9/Oj7n44ovj0ksvjSuuuCKeeOKJqK6ujv322y+WL1+eHzNmzJiYOnVq3HLLLTF9+vRYsWJFDB8+PNauXVu4RwYAAAAAAAAAG6hozuB77723yfXrrrsuevToEfX19bH33ntHSikmTpwY5557bowcOTIiIm644YaoqqqKm2++OU444YRYunRpXHvttXHjjTfG0KFDIyLipptuitra2vjjH/8Y+++/f4EeGgAAAAAAAAA09ZG+k3zp0qUREdGtW7eIiJg3b140NDTEsGHD8mPatGkTgwcPjhkzZkRERH19faxevbrJmJqamujXr19+zIYaGxtj2bJlTS4AAAAAAAAA0FwfuiRPKcXYsWNjzz33jH79+kVERENDQ0REVFVVNRlbVVWVX9fQ0BCtW7eOrl27bnbMhiZMmBCdO3fOX2praz9sbAAAAAAAAAAy7EOX5CeffHI888wzMWXKlI3W5XK5JtdTShst29AHjTn77LNj6dKl+cuCBQs+bGwAAAAAAAAAMuxDleSnnHJK/P73v4+HH344evXqlV9eXV0dEbHREeGLFy/OH11eXV0dq1atiiVLlmx2zIbatGkTnTp1anIBAAAAAAAAgOZqVkmeUoqTTz45brvttnjooYeirq6uyfq6urqorq6OBx54IL9s1apVMW3atBg0aFBERAwYMCBatWrVZMyiRYvi2WefzY8BAAAAAAAAgI9DRXMGn3TSSXHzzTfHHXfcER07dswfMd65c+eorKyMXC4XY8aMifHjx0ffvn2jb9++MX78+GjXrl0ceeSR+bHHHntsnHHGGdG9e/fo1q1bnHnmmdG/f/8YOnRo4R8hAAAAAAAAAPyvZpXkkydPjoiIIUOGNFl+3XXXxTHHHBMREWeddVasXLkyRo8eHUuWLInddtst7r///ujYsWN+/GWXXRYVFRVx2GGHxcqVK2PfffeN66+/PsrLyz/aowEAAAAAAACAD9Cskjyl9C/H5HK5GDduXIwbN26zY9q2bRuTJk2KSZMmNefHAwAAAAAAAMBH0qzvJAcAAAAAAACAUqYkBwAAAAAAACAzlOQAAAAAAAAAZIaSHAAAAAAAAIDMUJIDAAAAAAAAkBlKcgAAAAAAAAAyQ0kOAAAAAAAAQGYoyQEAAAAAAADIDCU5AAAAAAAAAJmhJAcAAAAAAAAgM5TkAAAAAAAAAGSGkhwAAAAAAACAzFCSAwAAAAAAAJAZSnIAAAAAAAAAMkNJDgAAAAAAAEBmKMkBAAAAAAAAyAwlOQAAAAAAAACZoSQHAAAAAAAAIDOU5AAAAAAAAABkhpIcAAAAAAAAgMxQkgMAAAAAAACQGUpyAAAAAAAAADJDSQ4AAAAAAABAZijJAQAAAAAAAMgMJTkAAAAAAAAAmaEkBwAAAAAAACAzlOQAAAAAAAAAZIaSHAAAAAAAAIDMUJIDAAAAAAAAkBlKcgAAAAAAAAAyQ0kOAAAAAAAAQGYoyQEAAAAAAADIDCU5AAAAAAAAAJmhJAcAAAAAAAAgM5TkAAAAAAAAAGSGkhwAAAAAAACAzFCSAwAAAAAAAJAZSnIAAAAAAAAAMkNJDgAAAAAAAEBmKMkBAAAAAAAAyAwlOQAAAAAAAACZoSQHAAAAAAAAIDOU5AAAAAAAAABkhpIcAAAAAAAAgMxQkgMAAAAAAACQGUpyAAAAAAAAADJDSQ4AAAAAAABAZijJAQAAAAAAAMgMJTkAAAAAAAAAmaEkBwAAAAAAACAzlOQAAAAAAAAAZIaSHAAAAAAAAIDMUJIDAAAAAAAAkBlKcgAAAAAAAAAyQ0kOAAAAAAAAQGYoyQEAAAAAAADIDCU5AAAAAAAAAJmhJAcAAAAAAAAgM5TkAAAAAAAAAGSGkhwAAAAAAACAzFCSAwAAAAAAAJAZSnIAAAAAAAAAMkNJDgAAAAAAAEBmKMkBAAAAAAAAyAwlOQAAAAAAAACZoSQHAAAAAAAAIDOU5AAAAAAAAABkhpIcAAAAAAAAgMxQkgMAAAAAAACQGUpyAAAAAAAAADKjqCX5z372s6irq4u2bdvGgAED4s9//nMx4wAAAAAAAADwCVe0kvzWW2+NMWPGxLnnnht//etfY6+99ooDDjgg5s+fX6xIAAAAAAAAAHzCFa0kv/TSS+PYY4+N4447LnbYYYeYOHFi1NbWxuTJk4sVCQAAAAAAAIBPuKKU5KtWrYr6+voYNmxYk+XDhg2LGTNmFCMSAAAAAAAAABlQUYwf+uabb8batWujqqqqyfKqqqpoaGjYaHxjY2M0Njbmry9dujQiIpYtW/Zv/bx1je98hLQb+3d/bnNkMWNE4XOWQsaIbP5/l0LGiGy+JkshY0Q2X5OlkDHCa7JQZCycLL4mSyFjhNdkochYOFl8TZZCxohsviZLIWOE12ShyFg4WXxNlkLGiGy+JkshY4TXZKHIWDhZfE2WQsaIbL4mSyFjhNfkvzMmpfSB43LpX434GCxcuDC23nrrmDFjRuy+++755RdeeGHceOON8cILLzQZP27cuPjhD3+4pWMCAAAAAAAAUGIWLFgQvXr12uz6ohxJvtVWW0V5eflGR40vXrx4o6PLIyLOPvvsGDt2bP76unXr4h//+Ed07949crlcQTItW7YsamtrY8GCBdGpU6eC3GehyVg4pZBTxsIohYwRpZFTxsKQsXBKIaeMhVEKGSNKI6eMhSFj4ZRCThkLoxQyRpRGThkLQ8bCKYWcMhZGKWSMKI2cMhaGjIVTCjllLIxSyBhRGjllLIyPI2NKKZYvXx41NTUfOK4oJXnr1q1jwIAB8cADD8QhhxySX/7AAw/EQQcdtNH4Nm3aRJs2bZos69Kly8eSrVOnTi32hbKejIVTCjllLIxSyBhRGjllLAwZC6cUcspYGKWQMaI0cspYGDIWTinklLEwSiFjRGnklLEwZCycUsgpY2GUQsaI0sgpY2HIWDilkFPGwiiFjBGlkVPGwih0xs6dO//LMUUpySMixo4dG9/4xjdi4MCBsfvuu8fVV18d8+fPjxNPPLFYkQAAAAAAAAD4hCtaSX744YfHW2+9Feeff34sWrQo+vXrF3/4wx+id+/exYoEAAAAAAAAwCdc0UryiIjRo0fH6NGjixkhr02bNnHeeedtdFr3lkTGwimFnDIWRilkjCiNnDIWhoyFUwo5ZSyMUsgYURo5ZSwMGQunFHLKWBilkDGiNHLKWBgyFk4p5JSxMEohY0Rp5JSxMGQsnFLIKWNhlELGiNLIKWNhFDNjLqWUtvhPBQAAAAAAAIAiKCt2AAAAAAAAAADYUpTkAAAAAAAAAGSGkhwAAAAAAACAzFCSAwAAAAAAAJAZSnJowVJKxY4AAAAAAAAAnygVxQ5QLK+++mpMnjw5ZsyYEQ0NDZHL5aKqqioGDRoUJ554YtTW1hY7IkSbNm1i5syZscMOOxQ7CrRIixYtismTJ8f06dNj0aJFUV5eHnV1dXHwwQfHMcccE+Xl5cWOCABbxNtvvx0333zzRvs3e+yxR3zta1+L9u3bFzviv/T666/Hz3/+8/jBD35Q7Cjx6quvRpcuXaJDhw5Nlq9evToeffTR2HvvvYuU7D1vvfVWPPPMM/HZz342unXrFm+++WZce+210djYGIceemiL3X/Ydttt47777ou+ffsWO8omrV69Ou6+++6YM2dO9OzZMw455JCi/+68+uqr0bZt29hqq60iIuLPf/5zXHXVVTF//vzo3bt3nHTSSbH77rsXNeNPfvKT+OpXvxq9e/cuao5/5c4774wnn3wyvvSlL8Xuu+8eDz30UPz4xz+OdevWxciRI+Nb3/pWsSPGypUrY8qUKZvcv9l3332LHQ/4GLSk+U+EOdDHpSXPgVri/CeiNOZAEbbdUOpyKYOHqk6fPj0OOOCAqK2tjWHDhkVVVVWklGLx4sXxwAMPxIIFC+Kee+6JPfbYo9hRP9CCBQvivPPOi1/84hdFy7By5cqor6+Pbt26xY477thk3bvvvhu//vWvY9SoUUVK93+ef/75eOyxx2L33XeP7bffPl544YX46U9/Go2NjfH1r3899tlnn6LmGzt27CaX//SnP42vf/3r0b1794iIuPTSS7dkrA+0ZMmSuOGGG/ITqKOPPrroHy7561//Gl26dIm6urqIiLjpppti8uTJ+cnTySefHEcccURRM0ZEnHLKKXHYYYfFXnvtVewoH2jSpEnx5JNPxoEHHhiHHXZY3HjjjTFhwoT8m0jnn39+VFQU77NWTz75ZAwdOjTq6uqisrIyHn/88TjqqKNi1apVcd9998UOO+wQ9913X3Ts2LFoGdcr9eKiJe2022H/+Nhpb55S2GEvldIiQnHxUf3tb3+L/fbbL955550YPHhwk/2badOmRfv27eP+++/faL7e0sycOTM+97nPxdq1a4uWYdGiRXHQQQdFfX195HK5OOqoo+LKK6/Mb3def/31qKmpKWrGv/zlLzFs2LBYtmxZdOnSJR544IE49NBDo6KiIlJK8dprr8X06dPjc5/7XNEyXn755ZtcPnbs2DjrrLOiuro6IiJOPfXULRlrI4MGDYo//OEP0aVLl3jjjTdi3333jRdffDF69+4dCxYsiB49esSMGTNi6623LmrG73//+3HAAQfEHXfcESNHjozhw4fHDjvsELNnz4677rorbrvtthg+fHjRMpaVlUVZWVl88YtfjOOOOy4OOeSQaN26ddHybMpVV10Vp5xySnz2s5+NOXPmxM9+9rP49re/HYcffniUl5fHL3/5y5gwYUKcdtppRcv40ksvxdChQ2PFihXRunXraGhoiC9/+cvx5ptvxpNPPhkjR46Mm2++uaj7YOvZvyk8890Pr5TmvJvSEuY/EeZAhVIKc6BSmP+sz9nS50Clsu0u9e12RMvadntv8qNrUe+vpAwaOHBgGjNmzGbXjxkzJg0cOHALJvpwnn766VRWVla0n//iiy+m3r17p1wul8rKytLgwYPTwoUL8+sbGhqKmm+9e+65J7Vu3Tp169YttW3bNt1zzz3pU5/6VBo6dGjad999U0VFRXrwwQeLmjGXy6Wdd945DRkypMkll8ulXXfdNQ0ZMiR98YtfLGrGnj17pjfffDOllNLcuXNTdXV1qq6uTvvtt1/q1atX6ty5c3r++eeLmnGXXXZJDz30UEoppWuuuSZVVlamU089NU2ePDmNGTMmdejQIV177bVFzZhSyv/O9O3bN1100UVp0aJFxY60kfPPPz917Ngx/ed//meqrq5OF110UerevXu64IIL0vjx49OnPvWp9IMf/KCoGffYY480bty4/PUbb7wx7bbbbimllP7xj3+knXfeOZ166qnFipf33HPPpZqamtSlS5d00EEHpW9961vp+OOPTwcddFDq0qVL2nrrrdNzzz1X7JgfqNjbm5RSWrhwYdp1111TWVlZKi8vT6NGjUrLly/Pr28J25zHH388de7cOeVyudS1a9f05JNPprq6utS3b9+03XbbpcrKylRfX1/UjCml9NOf/nSTl/Ly8nT22WfnrxfT7rvvnpYsWZJSSmnx4sWpf//+qXXr1qlv376pbdu2aZtttkmvvvpq0TP+4Q9/SCmldPvtt6eysrI0YsSI9N3vfjcdcsghqVWrVunOO+8sasZcLpfKy8vT0KFD0y233JIaGxuLmmdzJk+enCoqKtKAAQNSp06d0k033ZQ6duyYjjvuuHTCCSekysrKNHHixKJmnDNnTurdu3fq3r176tmzZ8rlcunAAw9Mu+22WyovL0+HHnpoWr16ddHyDRkyJB1xxBGb/D9ubGxMX/va19KQIUOKkKypmTNnfuDl1ltvLfrf8lGjRqUvfOEL6YknnkgPPPBAGjhwYBowYED6xz/+kVJ6b3uTy+WKmnHo0KHpuOOOS8uWLUuXXHJJ6tWrVzruuOPy64899th08MEHFzHhe39/evXqlfr06dPkksvl0tZbb5369OmT6urqippxfc7XX389pZTS8ccfn3beeef83PzNN99MgwYNSt/85jeLGTF17NgxzZs3L6WU0m677ZYuuuiiJusnTZqUdtlllyIk+z+5XC5dd9116aCDDkqtWrVK3bt3T6eddlqaNWtWUXO93w477JCuvvrqlFJKDz30UGrbtm268sor8+uvu+66tMMOOxQrXkoppQMOOCCdcMIJae3atSmllCZMmJAOOOCAlFJKs2fPTn369EnnnXdeERO+x/5NYZjvFk5Ln/OWwvwnJXOgQimFOVApzH9SKo05UClsuz8J2+2UWsa223uThdHS3l/JZEnetm3b9MILL2x2/fPPP5/atm27BRNt2h133PGBl8suu6yov3QHH3xwGj58eHrjjTfSnDlz0le+8pVUV1eXXnnllZRSy/ijkNJ7k/pzzz03pZTSlClTUteuXdM555yTX3/OOeek/fbbr1jxUkopjR8/PtXV1W1U1ldUVLSYjdT7J1BHHHFEGjJkSHr77bdTSim9++67afjw4emrX/1qMSOmdu3a5V9/u+yyS/r5z3/eZP2vfvWrtOOOOxYjWhO5XC798Y9/TKeddlraaqutUqtWrdKIESPSnXfemZ9UFdu2226bfve736WU3puElJeXp5tuuim//rbbbkvbbbddseKllFKqrKxMf//73/PX165dm1q1apUaGhpSSindf//9qaampljx8kqhuCiFnXY77IVjp70wSmGHvRRKi5QUF4VQWVn5gXPGWbNmpcrKyi2YaNPWf1Awl8ttdFm/vNjbm5qamvT444/nr7/77rvpoIMOSjvvvHN66623WsQ+TteuXdPf/va3lFJKq1atSmVlZU0yP/XUU2nrrbcuVryUUkrf+ta30s4775zPuV5L2r9Jqen25tOf/nS66667mqx/+OGHU58+fYoRLa9z585p5syZKaWUevTokf/3ei+99FJq165dMaLlvf95fP3119OPfvSjtP3226eysrK06667pquvvjotW7asqBkrKyvz+4oppdSqVasm28N58+YV/Xls165dmj17dv56Y2NjatWqVf7D6rfffnvRX48p2b8pFPPdwmnpc95SmP+kZA5UKKUwByqF+U9KpTEHKoVtdylst1MqjW239yYLo6W9v5LJkryuri794he/2Oz6X/ziF0V/czilD55EvX8yVSw9evRIzzzzTJNlo0ePTttss036+9//3iImTyml1KlTpzRnzpyU0nslWkVFRZNPy8yaNStVVVUVK17eX/7yl/TpT386nXHGGWnVqlUppZY7gdpUof/YY4+lXr16FSNaXvfu3dOTTz6ZUnrv9fn00083Wf/SSy+1mDeI1z+Xq1atSrfeemvaf//9U3l5eaqpqUnnnHNO/jVbLJt6E+nZZ5/NX3/55ZeLPhHt3bt3mj59ev76woULUy6XS++8805K6b03ulrCB55KobgohZ12O+yFY6e9MEphh70USouUFBeFUFNTk26//fbNrp86dWqL+ODYVlttla699tr08ssvb/Jy9913F/1vefv27Zv8X6eU0urVq9PBBx+cdtppp/TMM8+0iIzrS4uUUurQoUOTDw6+8sorLWIONHXq1FRbW5smTZqUX9aStjUpvfd3cvHixSml9/6Wb5jt5ZdfTm3atClGtLwRI0ak733veymllPbff/+NzvZyzTXXpL59+xYjWt77tzfv96c//SkdffTRqX379ql9+/ZFSPZ/evXqlf70pz+llFJ67bXXUi6XS3fffXd+/SOPPFL0/dmampom71UsWbIk5XK5/LZ67ty5RX89pmT/ppAZzXcLo6XPeUth/pOSOVAhtfQ5UCnMf1IqjTlQKWy7S2G7nVJpbLu9N1kYLe39lbIte3L3luHMM8+ME088MU4++eS444474rHHHovHH3887rjjjjj55JPj29/+dpx11lnFjhk9e/aM3/3ud7Fu3bpNXp566qmi5lu5cuVG36dx5ZVXxogRI2Lw4MExe/bsIiXbvLKysmjbtm106dIlv6xjx46xdOnS4oX6X7vuumvU19fHG2+8EQMHDoxZs2ZFLpcrdqwm1udpbGyMqqqqJuuqqqrijTfeKEasvAMOOCAmT54cERGDBw+O3/72t03W//rXv47tttuuGNE2q1WrVnHYYYfFvffeG3Pnzo3jjz8+fvWrX8VnPvOZouaqrq6Ov/3tbxERMWfOnFi7dm3+ekTEc889Fz169ChWvIiIOPjgg+PEE0+Me++9Nx5++OE46qijYvDgwVFZWRkRES+++GLRvz8pIqJr164xZ86cza5/6aWXomvXrlsw0ca6d+8e11xzTcybN2+jy9y5c+Ouu+4qar6IiKVLlzZ5ntq0aRO//e1vo0+fPvHFL34xFi9eXMR071m1alX+9deqVato165d/vv7It57nt96661ixcv7+c9/Huedd17sv//+ccUVVxQ7zmat3+b885//jLq6uibr6urqYtGiRcWIlTd48OCYMmVKRETssssu8cgjjzRZ//DDD7eIv0Hr9ejRI84666x4/vnn45FHHokdd9wxTj/99OjZs2exo0X37t3jlVdeiYiIhQsXxpo1a2L+/Pn59a+88kp069atWPEiIqJLly6xfPny/PV33nkn1qxZk//O3Z122qmor8njjz8+jj766Pjxj38cM2fOjIaGhnj99ddj5syZ8eMf/zi++c1vxgknnFC0fOsNGDAgFi5cGL17997kZeutt46UUlEzbrvttvHMM880WVZRURG/+c1vYtttty3qdx6uV1tbG3Pnzs1fv+WWW5r8Li9atKjJ9qdYDj744Hj00Udj6tSpccABB0RDQ0OxI23SMcccEyNHjozVq1fn/xatt2jRoib7jsVw0UUXxTXXXBNHH3107LnnnnHuuefGN77xjRg/fnwcffTRcfLJJ8c555xT1Iyb22/da6+94vrrr4+FCxfGZZddtoVTNXXQQQfFscceGxdeeGEccsghMWrUqDjjjDPi3nvvjfvuuy9OOeWUGDZsWFEz7rfffjF27Nh44YUXYt68eXHiiSfGzjvvHB07doyIiPnz5xd9HyzC/k0hme8WXkuc85bC/CfCHKiQSmEO1NLnPxGlMQcqhW13KWy3I0pj2+29ycJoce+vbLE6voW55ZZb0m677ZYqKiryn0ipqKhIu+22W7r11luLHS+llNJXvvKV9P3vf3+z659++uminr5h1113Tb/85S83ue6kk05KXbp0KfonZ1JKaaeddkr33HNP/vqsWbOafKfBn//85xZx5oD3mzJlSqqqqkplZWUt6lOG/fv3T7vsskvq0KFDuu2225qsnzZtWtE/hfTaa6+lPn36pL333juNHTs2VVZWpj333DMdf/zxae+9906tW7ducrRAsWzuSIv11q1bl+6///4tmGhj5557bvrUpz6VjjvuuFRXV5fOPvvstM0226TJkyenq666KtXW1qbTTz+9qBmXL1+eDjvssPzf8UGDBqW5c+fm1993333p17/+dRETvue8885LnTt3Tpdcckl6+umn06JFi1JDQ0N6+umn0yWXXJK6du2afvjDHxY14/7775/++7//e7Pri729SSml/v37p9/+9rcbLV//yfZtttmm6Nuc7bffvslZNu666678mQ1Sahln3Hi/V199Ne2zzz7pS1/6Ulq0aFGL+2T7l7/85XTIIYekrl275r8Lcb1HH3206GeB+dvf/pa6d++eRo0alf77v/87dejQIX39619PF154YRo1alRq06ZNuu6664qasays7AO3N0uXLs2f5ryYTjrppNS3b990wQUXpM9//vPp6KOPTttvv32655570r333pv69+9f9NONHn300Wnw4MHp+eefT3Pnzk2HH354k9OLPvLII6m2traICVO66KKL8t/nVVZWlv+0fc+ePdOPfvSjomZb77bbbks33njjZtf/4x//SNdff/0WTLSxs846Kw0bNmyT61avXp1GjBhR9O3NuHHj0pQpUza7/pxzzkkjR47cgok+2Lp169L48eNTdXV1Ki8vbzHbmpRSOuaYY5pcNpw7nnnmmWn//fcvUrr/89JLL6UjjjgidezYMf/+RatWrdKgQYPS1KlTix3vX+7ftAQrVqxIxx13XOrXr1868cQT06pVq9Ill1ySWrdunXK5XBoyZEjRH8Prr7+evvCFL+T/jvfp0yc99dRT+fW/+c1v0uWXX17EhO+xf1MY5ruF09LnvKUw/0nJHOjj0FLnQKUy/0mp5c+BSmHbXQrb7ZRKY9vtvcnCaGnvr2S2JF9v1apVaeHChWnhwoX5U1y3FH/605+alLsbWrFiRXrkkUe2YKKmxo8fn/+ugE359re/XfQ/XCmlNHny5I1OW/V+55xzTjr22GO3YKJ/z4IFC9Ltt9+eVqxYUewoKaX3JqLvv9x7771N1p955pnpiCOOKFK6/7NkyZL03e9+N+24446pbdu2qXXr1ql3797pyCOPTE888USx46WUUurTp0/+9CEt1Zo1a9IFF1yQhg8fnv/esSlTpqTa2trUvXv3dMwxx7SY1+bKlSvT8uXLix3jA7X04qIUdtr/nR32Ym9zSm2HPSU77R9VS99hL4XSIiXFRaHNnTs3zZgxI82YMaPJh8f496xevTotXbp0s+vXrFmTXn755S2YqPnefvvt9O677xY7xkaefPLJNHHixPx39pWCFStWpJUrVxY7Rt66detSQ0NDi3z/olStXLmyRXz1yPvNnj17ow/3tzT2bz66UprvHn744S12vptS6cx5N7Ru3bpiR2hic3Og9Tlb8hxofcaWPAe69NJLW/wcaP3z2NLmPyk1nQNt6ru1i23DbXdL+/1u6dvtlEpj2+3DRIWxqfdX3v+1BVv6/ZVcSi3gfC4AkAHz5s3Ln2arurp6o1PqsXlr1qyJd955Jzp16rTJ9WvXro1XX301evfuvYWT/fveeeedKC8vjzZt2hQ7ykbq6+tj+vTpMWrUqBZxmq1/5e23347y8vJo27ZtsaNERERKKRYvXhzr1q2LrbbaKlq1alXsSJ8I7777bqxevTp/qrqWYM6cOdHY2Bjbb7/9Rl87BABZY//m42O++8nXunXrmDlzZuywww7FjvKBSiGnjIVRChkjSiNnS81ou/3ReG+ysFrK+yve2QGALaSurm6jCeiCBQvivPPOi1/84hdFSvWvtYSMFRUVm52ERrz3PcY//OEPW/Tz+NZbbxX9edycAQMGxIABAyKiZfx//yv/+Mc/WlTGXC4XVVVVTZaVwvPY0jO2bds22rZt26Jy9u3bd5PLW0LGlStXRn19fXTr1i123HHHJuvefffd+PWvfx2jRo0qUrr/Uwo5ZSyMUsgYURo5ZSwMGQvn+eefj8ceeywGDRoUu+++e7zwwgtx8cUXR2NjY3z961+PffbZp9gR8xl333332H777eOFF16In/70py0y46BBg+Izn/lMyWQ8//zzW1TGiJb9/z127NhNLl+7dm1cdNFF0b1794iIuPTSS7dkrI2UQk4ZC6MUMkaURs5SyPh+dXV10aVLl7jhhhtizpw5UVNTE6NGjYra2tpiR9vIkiVL8jl79uwZRx99dNFzbvje5IYZjznmmBZXkLfE53G99e+vFDujI8kBoIhmzpwZn/vc52Lt2rXFjrJZMhZGKWSMKI2cMhZGKWSMKI2cxc44e/bsGDZsWMyfPz9yuVzstddeMWXKlOjZs2dERLz++utRU1NT9OewFHLKmJ2MpZJTRhlbUsaIiHvvvTcOOuig6NChQ7zzzjsxderUGDVqVHz2s5+NlFJMmzYt7rvvvqKWkjJmJ2Mp5CwrK4vPfvaz0aVLlybLp02bFgMHDoz27dtHLpeLhx56qCj51iuFnDIWRilkjCiNnKWQsaamJmbNmhXdu3ePefPmxR577BEppejfv388//zzsXz58njsscdi++23L1rGTeUcNGhQRESLyinjJzOjkhwAPka///3vP3D93Llz44wzzijqm10yFkYpZIwojZwyFkYpZIwojZwtPeMhhxwSa9asieuuuy7++c9/xtixY+PZZ5+NRx55JLbZZpsWU6yUQk4Zs5OxVHLKKGNLyhgRMWjQoNhnn33iggsuiFtuuSVGjx4d3/72t+PCCy+MiIhzzz03nnjiibj//vtllFHOiJgwYUJcc8018T//8z9NivpWrVrFzJkzNzprRLGUQk4ZC6MUMkaURs5SyFhWVhYNDQ3Ro0eP+NrXvhYNDQ1x9913R7t27aKxsTG++tWvRtu2beM3v/mNnDJmM+MW+/ZzAMigXC6XysrKUi6X2+ylrKxMRhnllDGTGUslZ0vP2KNHj/TMM880WTZ69Oi0zTbbpL///e+poaGh6M9hSqWRU8bCKIWMKZVGThkLQ8bC6dSpU5ozZ05KKaW1a9emioqKVF9fn18/a9asVFVVVax4KSUZC6UUMqZUGjn/8pe/pE9/+tPpjDPOSKtWrUoppVRRUZGee+65oubaUCnklLEwSiFjSqWRs6VnzOVy6fXXX08ppVRXV5cefPDBJusfe+yx1KtXr2JEa6IUcspYGC0tY9mWqeIBIJt69uwZv/vd72LdunWbvDz11FPFjihjhjJGlEZOGbOTMaI0crb0jCtXroyKioomy6688soYMWJEDB48OGbPnl2kZE2VQk4ZC6MUMkaURk4ZC0PGj0dZWVm0bdu2yWlmO3bsGEuXLi1eqA3IWBilkDGi5ebcddddo76+Pt54440YOHBgzJo1K3K5XFEzbUop5JSxMEohY0Rp5CyFjOvzNDY2RlVVVZN1VVVV8cYbbxQj1kZKIaeMhdGSMirJAeBjNGDAgA8sT3K5XKQif/OJjIVRChkjSiOnjIVRChkjSiNnS8+4/fbbx5NPPrnR8kmTJsVBBx0UI0aMKEKqjZVCThkLoxQyRpRGThkLQ8bC6dOnT7z00kv5648++mhss802+esLFizIf496schYGKWQMaJ0cnbo0CFuuOGGOPvss2O//fYr+lcnbE4p5JSxMEohY0Rp5GzpGffdd9/43Oc+F8uWLdvoQ3fz58+PrbbaqkjJmiqFnDIWRkvKWPGvhwAAH9Z3vvOdePvttze7frvttouHH354CybamIyFUQoZI0ojp4yFUQoZI0ojZ0vPeMghh8SUKVPiG9/4xkbrrrjiili3bl1cddVVRUjWVCnklLEwSiFjRGnklLEwZCycb3/7203e/O/Xr1+T9ffcc0+T72UtBhkLoxQyRpROzvWOOOKI2HPPPaO+vj569+5d7DibVQo5ZSyMUsgYURo5W2LG8847r8n1du3aNbl+5513xl577bUlI21SKeSUsTBaWsZcKvZhGQAAAAAAAACwhTjdOgAAAAAAAACZoSQHAAAAAAAAIDOU5AAAAAAAAABkhpIcAAAAAAAAgMxQkgMAAEAL16dPn5g4ceKHvv31118fXbp0yV8fN25c7Lzzzh851/sNGTIkxowZU9D7BAAAgI+DkhwAAAA+QTZVqB9++OExe/bs4gQCAACAFqai2AEAAAAgC1atWhWtW7cuys+urKyMysrKovxsAAAAaGkcSQ4AAAAfwpAhQ+Lkk0+Ok08+Obp06RLdu3eP//qv/4qUUkS8d0T3BRdcEMccc0x07tw5jj/++IiImDFjRuy9995RWVkZtbW1ceqpp8bbb7+dv9/FixfHV77ylaisrIy6urr41a9+tdHPHjduXGyzzTbRpk2bqKmpiVNPPTWf6ZVXXonTTz89crlc5HK5iNj4dOvr3XjjjdGnT5/o3LlzHHHEEbF8+fJ/67G//fbbMWrUqOjQoUP07NkzfvKTnzTruQMAAIBiUpIDAADAh3TDDTdERUVFPP7443H55ZfHZZddFv/zP/+TX3/JJZdEv379or6+Pr7//e/HrFmzYv/994+RI0fGM888E7feemtMnz49Tj755PxtjjnmmHj55ZfjoYceit/+9rfxs5/9LBYvXpxf/9vf/jYuu+yy+PnPfx5z5syJ22+/Pfr37x8REbfddlv06tUrzj///Fi0aFEsWrRos9n//ve/x+233x533XVX3HXXXTFt2rS46KKL/q3H/Z3vfCcefvjhmDp1atx///3xyCOPRH19fXOfPgAAACgKp1sHAACAD6m2tjYuu+yyyOVy8ZnPfCZmzZoVl112Wf6o8X322SfOPPPM/PhRo0bFkUceGWPGjImIiL59+8bll18egwcPjsmTJ8f8+fPjnnvuicceeyx22223iIi49tprY4cddsjfx/z586O6ujqGDh0arVq1im222SY+//nPR0REt27dory8PDp27BjV1dUfmH3dunVx/fXXR8eOHSMi4hvf+EY8+OCDceGFF37g7VasWBHXXntt/PKXv4z99tsvIt77sECvXr2a8cwBAABA8TiSHAAAAD6kL3zhC/lTmkdE7L777jFnzpxYu3ZtREQMHDiwyfj6+vq4/vrro0OHDvnL/vvvH+vWrYt58+bF888/HxUVFU1ut/322zc5Vfqhhx4aK1eujG233TaOP/74mDp1aqxZs6bZ2fv06ZMvyCMievbs2eSI9c35+9//HqtWrYrdd989v6xbt27xmc98ptkZAAAAoBiU5AAAAPAxad++fZPr69atixNOOCGefvrp/GXmzJkxZ86c+I//+I/895m/v3jfUG1tbbz44otx5ZVXRmVlZYwePTr23nvvWL16dbOytWrVqsn1XC4X69at+5e3W58RAAAASpWSHAAAAD6kxx57bKPrffv2jfLy8k2O/9znPhfPPfdcbLfddhtdWrduHTvssEOsWbMmnnzyyfxtXnzxxfjnP//Z5H4qKytjxIgRcfnll8cjjzwSjz76aMyaNSsiIlq3bp0/kv3jsN1220WrVq2aPPYlS5bE7NmzP7afCQAAAIWkJAcAAIAPacGCBTF27Nh48cUXY8qUKTFp0qQ47bTTNjv+u9/9bjz66KNx0kknxdNPPx1z5syJ3//+93HKKadERMRnPvOZ+NKXvhTHH398PP7441FfXx/HHXdcVFZW5u/j+uuvj2uvvTaeffbZmDt3btx4441RWVkZvXv3joj3TqP+pz/9KV577bV48803C/6YO3ToEMcee2x85zvfiQcffDCeffbZOOaYY6KszFsMAAAAlAZ7sAAAAPAhjRo1KlauXBmf//zn46STTopTTjklvvWtb212/E477RTTpk2LOXPmxF577RW77LJLfP/734+ePXvmx1x33XVRW1sbgwcPjpEjR8a3vvWt6NGjR359ly5d4pprrok99tgjdtppp3jwwQfjzjvvjO7du0dExPnnnx8vv/xy/Md//Ed86lOf+lge9yWXXBJ77713jBgxIoYOHRp77rlnDBgw4GP5WQAAAFBoueTLxAAAAKDZhgwZEjvvvHNMnDix2FEAAACAZnAkOQAAAAAAAACZoSQHAAAA8ubPnx8dOnTY7GX+/PnFjggAAAAfidOtAwAAAHlr1qyJl19+ebPr+/TpExUVFVsuEAAAABSYkhwAAAAAAACAzHC6dQAAAAAAAAAyQ0kOAAAAAAAAQGYoyQEAAAAAAADIDCU5AAAAAAAAAJmhJAcAAAAAAAAgM5TkAAAAAAAAAGSGkhwAAAAAAACAzFCSAwAAAAAAAJAZ/x8hTKMuflzkkgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 2500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['odkud'].str.contains('Prah') & df['kam'].str.contains('Čop')].groupby('predstih_d')['cena'].min().plot.bar(figsize=(25,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "52230147-668e-4e5c-8f64-c965fead3c02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='predstih_d'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['odkud'].str.contains('Prah') & df['kam'].str.contains('Wien')].groupby('predstih_d')['cena'].min().plot.bar(figsize=(25,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "70ada11f-8047-4c10-bfc0-95a6dc708c49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='predstih_d'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['odkud'].str.contains('Prah') & df['kam'].str.contains('Kra')].groupby('predstih_d')['cena'].min().plot.bar(figsize=(25,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ab53ce5f-fe4c-4545-85b8-fa8b4eb2cf31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='predstih_d'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['odkud'].str.contains('Prah') & df['kam'].str.contains('Buda')].groupby('predstih_d')['cena'].min().plot.bar(figsize=(25,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "70a263f3-8824-449c-8d63-38b81230f551",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_11260\\3516424537.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  df[df['odkud'].str.contains('Brno')].groupby('kam')['cena'].min().nsmallest(10)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "kam\n",
       "Praha                        99.0\n",
       "Vídeň                       149.0\n",
       "Ostrava                     189.0\n",
       "Praha hl.n.                 269.0\n",
       "Praha-Holešovice            479.0\n",
       "Hamburg Hbf                 645.0\n",
       "Ljubljana                  1109.0\n",
       "Amsterdam Bijlmer ArenA       NaN\n",
       "Amsterdam Centraal            NaN\n",
       "Amsterdam Zuid                NaN\n",
       "Name: cena, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['odkud'].str.contains('Brno')].groupby('kam')['cena'].min().nsmallest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b2e7d716-0a71-4f5d-a2ef-86d134267718",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_11260\\1741108948.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  df[df['odkud'].str.contains('Ostrava')].groupby('kam')['cena'].min().nsmallest(10)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "kam\n",
       "Praha hl.n.                  99.0\n",
       "Praha-Libeň                  99.0\n",
       "Brno                        189.0\n",
       "Praha                       249.0\n",
       "Karlovy Vary                514.0\n",
       "Gdansk Glowny               645.0\n",
       "Praha-Holešovice            825.0\n",
       "Karlovy Vary dolní n.      1173.0\n",
       "Amsterdam Bijlmer ArenA       NaN\n",
       "Amsterdam Centraal            NaN\n",
       "Name: cena, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['odkud'].str.contains('Ostrava')].groupby('kam')['cena'].min().nsmallest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d8dd2332-5ec8-4bbe-847e-743352505649",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>oscrapovano</th>\n",
       "      <th>prodejce</th>\n",
       "      <th>odkud</th>\n",
       "      <th>kam</th>\n",
       "      <th>odjezd</th>\n",
       "      <th>predstih</th>\n",
       "      <th>predstih_d</th>\n",
       "      <th>predstih_h</th>\n",
       "      <th>cena</th>\n",
       "      <th>prostredek</th>\n",
       "      <th>volnych_mist</th>\n",
       "      <th>obsazenost</th>\n",
       "      <th>jizdni_doba</th>\n",
       "      <th>vzdalenost</th>\n",
       "      <th>zpozdeni</th>\n",
       "      <th>cena_poznamka</th>\n",
       "      <th>den</th>\n",
       "      <th>prestupy</th>\n",
       "      <th>vlaky</th>\n",
       "      <th>mistenka_zdarma</th>\n",
       "      <th>nahradni_bus</th>\n",
       "      <th>volna_mista_economy</th>\n",
       "      <th>volna_mista_economy_plus</th>\n",
       "      <th>volna_mista_economy_business</th>\n",
       "      <th>volna_mista_premium</th>\n",
       "      <th>predstih_i</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>306781</th>\n",
       "      <td>2024-11-12 06:24:50</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-01-23 17:06:00</td>\n",
       "      <td>72 days 10:41:10</td>\n",
       "      <td>72</td>\n",
       "      <td>1738.686111</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>67.0</td>\n",
       "      <td>None</td>\n",
       "      <td>201.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>čt</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1739.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237216</th>\n",
       "      <td>2024-11-10 08:22:03</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-01-07 17:06:00</td>\n",
       "      <td>58 days 08:43:57</td>\n",
       "      <td>58</td>\n",
       "      <td>1400.732500</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>67.0</td>\n",
       "      <td>None</td>\n",
       "      <td>201.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>út</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1401.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228738</th>\n",
       "      <td>2024-11-10 00:23:21</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-01-20 17:06:00</td>\n",
       "      <td>71 days 16:42:39</td>\n",
       "      <td>71</td>\n",
       "      <td>1720.710833</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>64.0</td>\n",
       "      <td>None</td>\n",
       "      <td>201.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>po</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1721.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277263</th>\n",
       "      <td>2024-11-11 10:16:25</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2024-12-04 18:57:00</td>\n",
       "      <td>23 days 08:40:35</td>\n",
       "      <td>23</td>\n",
       "      <td>560.676389</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>64.0</td>\n",
       "      <td>None</td>\n",
       "      <td>211.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>561.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150269</th>\n",
       "      <td>2024-11-08 11:21:54</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha-Libeň</td>\n",
       "      <td>2025-01-11 17:06:00</td>\n",
       "      <td>64 days 05:44:06</td>\n",
       "      <td>64</td>\n",
       "      <td>1541.735000</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>67.0</td>\n",
       "      <td>None</td>\n",
       "      <td>194.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>so</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1542.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150268</th>\n",
       "      <td>2024-11-08 11:21:54</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-01-11 17:06:00</td>\n",
       "      <td>64 days 05:44:06</td>\n",
       "      <td>64</td>\n",
       "      <td>1541.735000</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>67.0</td>\n",
       "      <td>None</td>\n",
       "      <td>201.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>so</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1542.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292992</th>\n",
       "      <td>2024-11-11 19:17:08</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-01-17 17:06:00</td>\n",
       "      <td>66 days 21:48:52</td>\n",
       "      <td>66</td>\n",
       "      <td>1605.814444</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>62.0</td>\n",
       "      <td>None</td>\n",
       "      <td>201.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1606.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292991</th>\n",
       "      <td>2024-11-11 19:17:08</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-01-17 17:13:00</td>\n",
       "      <td>66 days 21:55:52</td>\n",
       "      <td>66</td>\n",
       "      <td>1605.931111</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>62.0</td>\n",
       "      <td>None</td>\n",
       "      <td>194.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1606.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228745</th>\n",
       "      <td>2024-11-10 00:23:21</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-01-20 17:13:00</td>\n",
       "      <td>71 days 16:49:39</td>\n",
       "      <td>71</td>\n",
       "      <td>1720.827500</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>64.0</td>\n",
       "      <td>None</td>\n",
       "      <td>194.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>po</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1721.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185002</th>\n",
       "      <td>2024-11-09 00:24:56</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-01-10 17:06:00</td>\n",
       "      <td>62 days 16:41:04</td>\n",
       "      <td>62</td>\n",
       "      <td>1504.684444</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>67.0</td>\n",
       "      <td>None</td>\n",
       "      <td>201.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1505.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326114</th>\n",
       "      <td>2024-11-12 16:18:52</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-01-25 17:13:00</td>\n",
       "      <td>74 days 00:54:08</td>\n",
       "      <td>74</td>\n",
       "      <td>1776.902222</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>66.0</td>\n",
       "      <td>None</td>\n",
       "      <td>194.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>so</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1777.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282699</th>\n",
       "      <td>2024-11-11 14:17:03</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-02-07 17:06:00</td>\n",
       "      <td>88 days 02:48:57</td>\n",
       "      <td>88</td>\n",
       "      <td>2114.815833</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>66.0</td>\n",
       "      <td>None</td>\n",
       "      <td>201.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326113</th>\n",
       "      <td>2024-11-12 16:18:52</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-01-25 17:06:00</td>\n",
       "      <td>74 days 00:47:08</td>\n",
       "      <td>74</td>\n",
       "      <td>1776.785556</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>66.0</td>\n",
       "      <td>None</td>\n",
       "      <td>201.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>so</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1777.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343459</th>\n",
       "      <td>2024-11-13 00:24:10</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-02-05 17:06:00</td>\n",
       "      <td>84 days 16:41:50</td>\n",
       "      <td>84</td>\n",
       "      <td>2032.697222</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>66.0</td>\n",
       "      <td>None</td>\n",
       "      <td>201.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2033.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343458</th>\n",
       "      <td>2024-11-13 00:24:10</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-02-05 17:13:00</td>\n",
       "      <td>84 days 16:48:50</td>\n",
       "      <td>84</td>\n",
       "      <td>2032.813889</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>66.0</td>\n",
       "      <td>None</td>\n",
       "      <td>194.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2033.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224919</th>\n",
       "      <td>2024-11-09 22:23:46</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-02-01 17:13:00</td>\n",
       "      <td>83 days 18:49:14</td>\n",
       "      <td>83</td>\n",
       "      <td>2010.820556</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>67.0</td>\n",
       "      <td>None</td>\n",
       "      <td>194.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>so</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2011.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282696</th>\n",
       "      <td>2024-11-11 14:17:03</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-02-07 17:13:00</td>\n",
       "      <td>88 days 02:55:57</td>\n",
       "      <td>88</td>\n",
       "      <td>2114.932500</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>66.0</td>\n",
       "      <td>None</td>\n",
       "      <td>194.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184992</th>\n",
       "      <td>2024-11-09 00:24:56</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-01-10 17:13:00</td>\n",
       "      <td>62 days 16:48:04</td>\n",
       "      <td>62</td>\n",
       "      <td>1504.801111</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>67.0</td>\n",
       "      <td>None</td>\n",
       "      <td>194.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1505.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451769</th>\n",
       "      <td>2024-11-15 18:23:39</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-01-20 17:13:00</td>\n",
       "      <td>65 days 22:49:21</td>\n",
       "      <td>65</td>\n",
       "      <td>1582.822500</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>64.0</td>\n",
       "      <td>None</td>\n",
       "      <td>194.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>po</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1583.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405240</th>\n",
       "      <td>2024-11-14 14:24:32</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-02-11 17:06:00</td>\n",
       "      <td>89 days 02:41:28</td>\n",
       "      <td>89</td>\n",
       "      <td>2138.691111</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>67.0</td>\n",
       "      <td>None</td>\n",
       "      <td>201.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>út</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2139.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302091</th>\n",
       "      <td>2024-11-12 00:23:23</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-01-30 17:06:00</td>\n",
       "      <td>79 days 16:42:37</td>\n",
       "      <td>79</td>\n",
       "      <td>1912.710278</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>66.0</td>\n",
       "      <td>None</td>\n",
       "      <td>201.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>čt</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1913.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443755</th>\n",
       "      <td>2024-11-15 14:20:22</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-02-12 06:58:00</td>\n",
       "      <td>88 days 16:37:38</td>\n",
       "      <td>88</td>\n",
       "      <td>2128.627222</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>66.0</td>\n",
       "      <td>None</td>\n",
       "      <td>209.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE412]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2129.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443751</th>\n",
       "      <td>2024-11-15 14:20:22</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-02-12 14:58:00</td>\n",
       "      <td>89 days 00:37:38</td>\n",
       "      <td>89</td>\n",
       "      <td>2136.627222</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>66.0</td>\n",
       "      <td>None</td>\n",
       "      <td>209.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1260]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2137.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443750</th>\n",
       "      <td>2024-11-15 14:20:22</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-02-12 15:05:00</td>\n",
       "      <td>89 days 00:44:38</td>\n",
       "      <td>89</td>\n",
       "      <td>2136.743889</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>66.0</td>\n",
       "      <td>None</td>\n",
       "      <td>202.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1260]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2137.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443747</th>\n",
       "      <td>2024-11-15 14:20:22</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-02-12 17:13:00</td>\n",
       "      <td>89 days 02:52:38</td>\n",
       "      <td>89</td>\n",
       "      <td>2138.877222</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>67.0</td>\n",
       "      <td>None</td>\n",
       "      <td>194.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2139.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443745</th>\n",
       "      <td>2024-11-15 14:20:22</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-02-12 07:05:00</td>\n",
       "      <td>88 days 16:44:38</td>\n",
       "      <td>88</td>\n",
       "      <td>2128.743889</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>66.0</td>\n",
       "      <td>None</td>\n",
       "      <td>202.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE412]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2129.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260421</th>\n",
       "      <td>2024-11-10 19:22:03</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2024-12-04 19:04:00</td>\n",
       "      <td>23 days 23:41:57</td>\n",
       "      <td>23</td>\n",
       "      <td>575.699167</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>64.0</td>\n",
       "      <td>None</td>\n",
       "      <td>204.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>576.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359521</th>\n",
       "      <td>2024-11-13 12:15:59</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2024-12-03 18:57:00</td>\n",
       "      <td>20 days 06:41:01</td>\n",
       "      <td>20</td>\n",
       "      <td>486.683611</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>67.0</td>\n",
       "      <td>None</td>\n",
       "      <td>211.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>út</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>487.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359519</th>\n",
       "      <td>2024-11-13 12:15:59</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2024-12-03 19:04:00</td>\n",
       "      <td>20 days 06:48:01</td>\n",
       "      <td>20</td>\n",
       "      <td>486.800278</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>67.0</td>\n",
       "      <td>None</td>\n",
       "      <td>204.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>út</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>487.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285948</th>\n",
       "      <td>2024-11-11 16:19:06</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-02-05 17:13:00</td>\n",
       "      <td>86 days 00:53:54</td>\n",
       "      <td>86</td>\n",
       "      <td>2064.898333</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>66.0</td>\n",
       "      <td>None</td>\n",
       "      <td>194.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2065.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277262</th>\n",
       "      <td>2024-11-11 10:16:25</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2024-12-04 19:04:00</td>\n",
       "      <td>23 days 08:47:35</td>\n",
       "      <td>23</td>\n",
       "      <td>560.793056</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>64.0</td>\n",
       "      <td>None</td>\n",
       "      <td>204.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>561.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356352</th>\n",
       "      <td>2024-11-13 10:22:00</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-02-10 17:13:00</td>\n",
       "      <td>89 days 06:51:00</td>\n",
       "      <td>89</td>\n",
       "      <td>2142.850000</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>66.0</td>\n",
       "      <td>None</td>\n",
       "      <td>194.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>po</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405239</th>\n",
       "      <td>2024-11-14 14:24:32</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-02-11 17:13:00</td>\n",
       "      <td>89 days 02:48:28</td>\n",
       "      <td>89</td>\n",
       "      <td>2138.807778</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>67.0</td>\n",
       "      <td>None</td>\n",
       "      <td>194.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>út</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2139.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260420</th>\n",
       "      <td>2024-11-10 19:22:03</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2024-12-04 18:57:00</td>\n",
       "      <td>23 days 23:34:57</td>\n",
       "      <td>23</td>\n",
       "      <td>575.582500</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>64.0</td>\n",
       "      <td>None</td>\n",
       "      <td>211.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>576.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237214</th>\n",
       "      <td>2024-11-10 08:22:03</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-01-07 17:13:00</td>\n",
       "      <td>58 days 08:50:57</td>\n",
       "      <td>58</td>\n",
       "      <td>1400.849167</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>67.0</td>\n",
       "      <td>None</td>\n",
       "      <td>194.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>út</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1401.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302086</th>\n",
       "      <td>2024-11-12 00:23:23</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-01-30 17:13:00</td>\n",
       "      <td>79 days 16:49:37</td>\n",
       "      <td>79</td>\n",
       "      <td>1912.826944</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>66.0</td>\n",
       "      <td>None</td>\n",
       "      <td>194.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>čt</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1913.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477914</th>\n",
       "      <td>2024-11-16 12:21:32</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-02-05 17:06:00</td>\n",
       "      <td>81 days 04:44:28</td>\n",
       "      <td>81</td>\n",
       "      <td>1948.741111</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>66.0</td>\n",
       "      <td>None</td>\n",
       "      <td>201.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1949.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477913</th>\n",
       "      <td>2024-11-16 12:21:32</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-02-05 17:13:00</td>\n",
       "      <td>81 days 04:51:28</td>\n",
       "      <td>81</td>\n",
       "      <td>1948.857778</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>66.0</td>\n",
       "      <td>None</td>\n",
       "      <td>194.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1949.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150245</th>\n",
       "      <td>2024-11-08 11:21:54</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-01-11 17:13:00</td>\n",
       "      <td>64 days 05:51:06</td>\n",
       "      <td>64</td>\n",
       "      <td>1541.851667</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>67.0</td>\n",
       "      <td>None</td>\n",
       "      <td>194.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>so</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1542.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443756</th>\n",
       "      <td>2024-11-15 14:20:22</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-02-12 17:06:00</td>\n",
       "      <td>89 days 02:45:38</td>\n",
       "      <td>89</td>\n",
       "      <td>2138.760556</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>67.0</td>\n",
       "      <td>None</td>\n",
       "      <td>201.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2139.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150244</th>\n",
       "      <td>2024-11-08 11:21:54</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>Praha-Libeň</td>\n",
       "      <td>2025-01-11 17:13:00</td>\n",
       "      <td>64 days 05:51:06</td>\n",
       "      <td>64</td>\n",
       "      <td>1541.851667</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>67.0</td>\n",
       "      <td>None</td>\n",
       "      <td>187.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>so</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1542.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420436</th>\n",
       "      <td>2024-11-14 22:19:42</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-01-25 17:06:00</td>\n",
       "      <td>71 days 18:46:18</td>\n",
       "      <td>71</td>\n",
       "      <td>1722.771667</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>66.0</td>\n",
       "      <td>None</td>\n",
       "      <td>201.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>so</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1723.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376229</th>\n",
       "      <td>2024-11-13 19:18:00</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2024-12-03 19:04:00</td>\n",
       "      <td>19 days 23:46:00</td>\n",
       "      <td>19</td>\n",
       "      <td>479.766667</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>67.0</td>\n",
       "      <td>None</td>\n",
       "      <td>204.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>út</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376230</th>\n",
       "      <td>2024-11-13 19:18:00</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2024-12-03 18:57:00</td>\n",
       "      <td>19 days 23:39:00</td>\n",
       "      <td>19</td>\n",
       "      <td>479.650000</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>67.0</td>\n",
       "      <td>None</td>\n",
       "      <td>211.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>út</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392773</th>\n",
       "      <td>2024-11-14 08:18:51</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2024-12-03 19:04:00</td>\n",
       "      <td>19 days 10:45:09</td>\n",
       "      <td>19</td>\n",
       "      <td>466.752500</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>67.0</td>\n",
       "      <td>None</td>\n",
       "      <td>204.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>út</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>467.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439831</th>\n",
       "      <td>2024-11-15 12:17:58</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-01-08 17:06:00</td>\n",
       "      <td>54 days 04:48:02</td>\n",
       "      <td>54</td>\n",
       "      <td>1300.800556</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>66.0</td>\n",
       "      <td>None</td>\n",
       "      <td>201.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1301.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470275</th>\n",
       "      <td>2024-11-16 08:19:51</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-01-10 17:06:00</td>\n",
       "      <td>55 days 08:46:09</td>\n",
       "      <td>55</td>\n",
       "      <td>1328.769167</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>67.0</td>\n",
       "      <td>None</td>\n",
       "      <td>201.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1329.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458931</th>\n",
       "      <td>2024-11-15 22:18:34</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-01-21 17:06:00</td>\n",
       "      <td>66 days 18:47:26</td>\n",
       "      <td>66</td>\n",
       "      <td>1602.790556</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>67.0</td>\n",
       "      <td>None</td>\n",
       "      <td>201.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>út</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1603.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451758</th>\n",
       "      <td>2024-11-15 18:23:39</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava hl.n.</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-01-20 17:06:00</td>\n",
       "      <td>65 days 22:42:21</td>\n",
       "      <td>65</td>\n",
       "      <td>1582.705833</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>64.0</td>\n",
       "      <td>None</td>\n",
       "      <td>201.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>po</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1583.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458932</th>\n",
       "      <td>2024-11-15 22:18:34</td>\n",
       "      <td>LE</td>\n",
       "      <td>Ostrava-Svinov</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-01-21 17:13:00</td>\n",
       "      <td>66 days 18:54:26</td>\n",
       "      <td>66</td>\n",
       "      <td>1602.907222</td>\n",
       "      <td>99.0</td>\n",
       "      <td>None</td>\n",
       "      <td>67.0</td>\n",
       "      <td>None</td>\n",
       "      <td>194.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>út</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE1262]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1603.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               oscrapovano prodejce           odkud          kam  \\\n",
       "306781 2024-11-12 06:24:50       LE   Ostrava hl.n.  Praha hl.n.   \n",
       "237216 2024-11-10 08:22:03       LE   Ostrava hl.n.  Praha hl.n.   \n",
       "228738 2024-11-10 00:23:21       LE   Ostrava hl.n.  Praha hl.n.   \n",
       "277263 2024-11-11 10:16:25       LE   Ostrava hl.n.  Praha hl.n.   \n",
       "150269 2024-11-08 11:21:54       LE   Ostrava hl.n.  Praha-Libeň   \n",
       "150268 2024-11-08 11:21:54       LE   Ostrava hl.n.  Praha hl.n.   \n",
       "292992 2024-11-11 19:17:08       LE   Ostrava hl.n.  Praha hl.n.   \n",
       "292991 2024-11-11 19:17:08       LE  Ostrava-Svinov  Praha hl.n.   \n",
       "228745 2024-11-10 00:23:21       LE  Ostrava-Svinov  Praha hl.n.   \n",
       "185002 2024-11-09 00:24:56       LE   Ostrava hl.n.  Praha hl.n.   \n",
       "326114 2024-11-12 16:18:52       LE  Ostrava-Svinov  Praha hl.n.   \n",
       "282699 2024-11-11 14:17:03       LE   Ostrava hl.n.  Praha hl.n.   \n",
       "326113 2024-11-12 16:18:52       LE   Ostrava hl.n.  Praha hl.n.   \n",
       "343459 2024-11-13 00:24:10       LE   Ostrava hl.n.  Praha hl.n.   \n",
       "343458 2024-11-13 00:24:10       LE  Ostrava-Svinov  Praha hl.n.   \n",
       "224919 2024-11-09 22:23:46       LE  Ostrava-Svinov  Praha hl.n.   \n",
       "282696 2024-11-11 14:17:03       LE  Ostrava-Svinov  Praha hl.n.   \n",
       "184992 2024-11-09 00:24:56       LE  Ostrava-Svinov  Praha hl.n.   \n",
       "451769 2024-11-15 18:23:39       LE  Ostrava-Svinov  Praha hl.n.   \n",
       "405240 2024-11-14 14:24:32       LE   Ostrava hl.n.  Praha hl.n.   \n",
       "302091 2024-11-12 00:23:23       LE   Ostrava hl.n.  Praha hl.n.   \n",
       "443755 2024-11-15 14:20:22       LE   Ostrava hl.n.  Praha hl.n.   \n",
       "443751 2024-11-15 14:20:22       LE   Ostrava hl.n.  Praha hl.n.   \n",
       "443750 2024-11-15 14:20:22       LE  Ostrava-Svinov  Praha hl.n.   \n",
       "443747 2024-11-15 14:20:22       LE  Ostrava-Svinov  Praha hl.n.   \n",
       "443745 2024-11-15 14:20:22       LE  Ostrava-Svinov  Praha hl.n.   \n",
       "260421 2024-11-10 19:22:03       LE  Ostrava-Svinov  Praha hl.n.   \n",
       "359521 2024-11-13 12:15:59       LE   Ostrava hl.n.  Praha hl.n.   \n",
       "359519 2024-11-13 12:15:59       LE  Ostrava-Svinov  Praha hl.n.   \n",
       "285948 2024-11-11 16:19:06       LE  Ostrava-Svinov  Praha hl.n.   \n",
       "277262 2024-11-11 10:16:25       LE  Ostrava-Svinov  Praha hl.n.   \n",
       "356352 2024-11-13 10:22:00       LE  Ostrava-Svinov  Praha hl.n.   \n",
       "405239 2024-11-14 14:24:32       LE  Ostrava-Svinov  Praha hl.n.   \n",
       "260420 2024-11-10 19:22:03       LE   Ostrava hl.n.  Praha hl.n.   \n",
       "237214 2024-11-10 08:22:03       LE  Ostrava-Svinov  Praha hl.n.   \n",
       "302086 2024-11-12 00:23:23       LE  Ostrava-Svinov  Praha hl.n.   \n",
       "477914 2024-11-16 12:21:32       LE   Ostrava hl.n.  Praha hl.n.   \n",
       "477913 2024-11-16 12:21:32       LE  Ostrava-Svinov  Praha hl.n.   \n",
       "150245 2024-11-08 11:21:54       LE  Ostrava-Svinov  Praha hl.n.   \n",
       "443756 2024-11-15 14:20:22       LE   Ostrava hl.n.  Praha hl.n.   \n",
       "150244 2024-11-08 11:21:54       LE  Ostrava-Svinov  Praha-Libeň   \n",
       "420436 2024-11-14 22:19:42       LE   Ostrava hl.n.  Praha hl.n.   \n",
       "376229 2024-11-13 19:18:00       LE  Ostrava-Svinov  Praha hl.n.   \n",
       "376230 2024-11-13 19:18:00       LE   Ostrava hl.n.  Praha hl.n.   \n",
       "392773 2024-11-14 08:18:51       LE  Ostrava-Svinov  Praha hl.n.   \n",
       "439831 2024-11-15 12:17:58       LE   Ostrava hl.n.  Praha hl.n.   \n",
       "470275 2024-11-16 08:19:51       LE   Ostrava hl.n.  Praha hl.n.   \n",
       "458931 2024-11-15 22:18:34       LE   Ostrava hl.n.  Praha hl.n.   \n",
       "451758 2024-11-15 18:23:39       LE   Ostrava hl.n.  Praha hl.n.   \n",
       "458932 2024-11-15 22:18:34       LE  Ostrava-Svinov  Praha hl.n.   \n",
       "\n",
       "                    odjezd         predstih  predstih_d   predstih_h  cena  \\\n",
       "306781 2025-01-23 17:06:00 72 days 10:41:10          72  1738.686111  99.0   \n",
       "237216 2025-01-07 17:06:00 58 days 08:43:57          58  1400.732500  99.0   \n",
       "228738 2025-01-20 17:06:00 71 days 16:42:39          71  1720.710833  99.0   \n",
       "277263 2024-12-04 18:57:00 23 days 08:40:35          23   560.676389  99.0   \n",
       "150269 2025-01-11 17:06:00 64 days 05:44:06          64  1541.735000  99.0   \n",
       "150268 2025-01-11 17:06:00 64 days 05:44:06          64  1541.735000  99.0   \n",
       "292992 2025-01-17 17:06:00 66 days 21:48:52          66  1605.814444  99.0   \n",
       "292991 2025-01-17 17:13:00 66 days 21:55:52          66  1605.931111  99.0   \n",
       "228745 2025-01-20 17:13:00 71 days 16:49:39          71  1720.827500  99.0   \n",
       "185002 2025-01-10 17:06:00 62 days 16:41:04          62  1504.684444  99.0   \n",
       "326114 2025-01-25 17:13:00 74 days 00:54:08          74  1776.902222  99.0   \n",
       "282699 2025-02-07 17:06:00 88 days 02:48:57          88  2114.815833  99.0   \n",
       "326113 2025-01-25 17:06:00 74 days 00:47:08          74  1776.785556  99.0   \n",
       "343459 2025-02-05 17:06:00 84 days 16:41:50          84  2032.697222  99.0   \n",
       "343458 2025-02-05 17:13:00 84 days 16:48:50          84  2032.813889  99.0   \n",
       "224919 2025-02-01 17:13:00 83 days 18:49:14          83  2010.820556  99.0   \n",
       "282696 2025-02-07 17:13:00 88 days 02:55:57          88  2114.932500  99.0   \n",
       "184992 2025-01-10 17:13:00 62 days 16:48:04          62  1504.801111  99.0   \n",
       "451769 2025-01-20 17:13:00 65 days 22:49:21          65  1582.822500  99.0   \n",
       "405240 2025-02-11 17:06:00 89 days 02:41:28          89  2138.691111  99.0   \n",
       "302091 2025-01-30 17:06:00 79 days 16:42:37          79  1912.710278  99.0   \n",
       "443755 2025-02-12 06:58:00 88 days 16:37:38          88  2128.627222  99.0   \n",
       "443751 2025-02-12 14:58:00 89 days 00:37:38          89  2136.627222  99.0   \n",
       "443750 2025-02-12 15:05:00 89 days 00:44:38          89  2136.743889  99.0   \n",
       "443747 2025-02-12 17:13:00 89 days 02:52:38          89  2138.877222  99.0   \n",
       "443745 2025-02-12 07:05:00 88 days 16:44:38          88  2128.743889  99.0   \n",
       "260421 2024-12-04 19:04:00 23 days 23:41:57          23   575.699167  99.0   \n",
       "359521 2024-12-03 18:57:00 20 days 06:41:01          20   486.683611  99.0   \n",
       "359519 2024-12-03 19:04:00 20 days 06:48:01          20   486.800278  99.0   \n",
       "285948 2025-02-05 17:13:00 86 days 00:53:54          86  2064.898333  99.0   \n",
       "277262 2024-12-04 19:04:00 23 days 08:47:35          23   560.793056  99.0   \n",
       "356352 2025-02-10 17:13:00 89 days 06:51:00          89  2142.850000  99.0   \n",
       "405239 2025-02-11 17:13:00 89 days 02:48:28          89  2138.807778  99.0   \n",
       "260420 2024-12-04 18:57:00 23 days 23:34:57          23   575.582500  99.0   \n",
       "237214 2025-01-07 17:13:00 58 days 08:50:57          58  1400.849167  99.0   \n",
       "302086 2025-01-30 17:13:00 79 days 16:49:37          79  1912.826944  99.0   \n",
       "477914 2025-02-05 17:06:00 81 days 04:44:28          81  1948.741111  99.0   \n",
       "477913 2025-02-05 17:13:00 81 days 04:51:28          81  1948.857778  99.0   \n",
       "150245 2025-01-11 17:13:00 64 days 05:51:06          64  1541.851667  99.0   \n",
       "443756 2025-02-12 17:06:00 89 days 02:45:38          89  2138.760556  99.0   \n",
       "150244 2025-01-11 17:13:00 64 days 05:51:06          64  1541.851667  99.0   \n",
       "420436 2025-01-25 17:06:00 71 days 18:46:18          71  1722.771667  99.0   \n",
       "376229 2024-12-03 19:04:00 19 days 23:46:00          19   479.766667  99.0   \n",
       "376230 2024-12-03 18:57:00 19 days 23:39:00          19   479.650000  99.0   \n",
       "392773 2024-12-03 19:04:00 19 days 10:45:09          19   466.752500  99.0   \n",
       "439831 2025-01-08 17:06:00 54 days 04:48:02          54  1300.800556  99.0   \n",
       "470275 2025-01-10 17:06:00 55 days 08:46:09          55  1328.769167  99.0   \n",
       "458931 2025-01-21 17:06:00 66 days 18:47:26          66  1602.790556  99.0   \n",
       "451758 2025-01-20 17:06:00 65 days 22:42:21          65  1582.705833  99.0   \n",
       "458932 2025-01-21 17:13:00 66 days 18:54:26          66  1602.907222  99.0   \n",
       "\n",
       "       prostredek  volnych_mist obsazenost  jizdni_doba  vzdalenost zpozdeni  \\\n",
       "306781       None          67.0       None        201.0       356.0     None   \n",
       "237216       None          67.0       None        201.0       356.0     None   \n",
       "228738       None          64.0       None        201.0       356.0     None   \n",
       "277263       None          64.0       None        211.0       356.0     None   \n",
       "150269       None          67.0       None        194.0       351.0     None   \n",
       "150268       None          67.0       None        201.0       356.0     None   \n",
       "292992       None          62.0       None        201.0       356.0     None   \n",
       "292991       None          62.0       None        194.0       351.0     None   \n",
       "228745       None          64.0       None        194.0       351.0     None   \n",
       "185002       None          67.0       None        201.0       356.0     None   \n",
       "326114       None          66.0       None        194.0       351.0     None   \n",
       "282699       None          66.0       None        201.0       356.0     None   \n",
       "326113       None          66.0       None        201.0       356.0     None   \n",
       "343459       None          66.0       None        201.0       356.0     None   \n",
       "343458       None          66.0       None        194.0       351.0     None   \n",
       "224919       None          67.0       None        194.0       351.0     None   \n",
       "282696       None          66.0       None        194.0       351.0     None   \n",
       "184992       None          67.0       None        194.0       351.0     None   \n",
       "451769       None          64.0       None        194.0       351.0     None   \n",
       "405240       None          67.0       None        201.0       356.0     None   \n",
       "302091       None          66.0       None        201.0       356.0     None   \n",
       "443755       None          66.0       None        209.0       356.0     None   \n",
       "443751       None          66.0       None        209.0       356.0     None   \n",
       "443750       None          66.0       None        202.0       351.0     None   \n",
       "443747       None          67.0       None        194.0       351.0     None   \n",
       "443745       None          66.0       None        202.0       351.0     None   \n",
       "260421       None          64.0       None        204.0       351.0     None   \n",
       "359521       None          67.0       None        211.0       356.0     None   \n",
       "359519       None          67.0       None        204.0       351.0     None   \n",
       "285948       None          66.0       None        194.0       351.0     None   \n",
       "277262       None          64.0       None        204.0       351.0     None   \n",
       "356352       None          66.0       None        194.0       351.0     None   \n",
       "405239       None          67.0       None        194.0       351.0     None   \n",
       "260420       None          64.0       None        211.0       356.0     None   \n",
       "237214       None          67.0       None        194.0       351.0     None   \n",
       "302086       None          66.0       None        194.0       351.0     None   \n",
       "477914       None          66.0       None        201.0       356.0     None   \n",
       "477913       None          66.0       None        194.0       351.0     None   \n",
       "150245       None          67.0       None        194.0       351.0     None   \n",
       "443756       None          67.0       None        201.0       356.0     None   \n",
       "150244       None          67.0       None        187.0       346.0     None   \n",
       "420436       None          66.0       None        201.0       356.0     None   \n",
       "376229       None          67.0       None        204.0       351.0     None   \n",
       "376230       None          67.0       None        211.0       356.0     None   \n",
       "392773       None          67.0       None        204.0       351.0     None   \n",
       "439831       None          66.0       None        201.0       356.0     None   \n",
       "470275       None          67.0       None        201.0       356.0     None   \n",
       "458931       None          67.0       None        201.0       356.0     None   \n",
       "451758       None          64.0       None        201.0       356.0     None   \n",
       "458932       None          67.0       None        194.0       351.0     None   \n",
       "\n",
       "       cena_poznamka den  prestupy     vlaky mistenka_zdarma nahradni_bus  \\\n",
       "306781          None  čt         0  [LE1262]            None         None   \n",
       "237216          None  út         0  [LE1262]            None         None   \n",
       "228738          None  po         0  [LE1262]            None         None   \n",
       "277263          None  st         0  [LE1262]            None         None   \n",
       "150269          None  so         0  [LE1262]            None         None   \n",
       "150268          None  so         0  [LE1262]            None         None   \n",
       "292992          None  pá         0  [LE1262]            None         None   \n",
       "292991          None  pá         0  [LE1262]            None         None   \n",
       "228745          None  po         0  [LE1262]            None         None   \n",
       "185002          None  pá         0  [LE1262]            None         None   \n",
       "326114          None  so         0  [LE1262]            None         None   \n",
       "282699          None  pá         0  [LE1262]            None         None   \n",
       "326113          None  so         0  [LE1262]            None         None   \n",
       "343459          None  st         0  [LE1262]            None         None   \n",
       "343458          None  st         0  [LE1262]            None         None   \n",
       "224919          None  so         0  [LE1262]            None         None   \n",
       "282696          None  pá         0  [LE1262]            None         None   \n",
       "184992          None  pá         0  [LE1262]            None         None   \n",
       "451769          None  po         0  [LE1262]            None         None   \n",
       "405240          None  út         0  [LE1262]            None         None   \n",
       "302091          None  čt         0  [LE1262]            None         None   \n",
       "443755          None  st         0   [LE412]            None         None   \n",
       "443751          None  st         0  [LE1260]            None         None   \n",
       "443750          None  st         0  [LE1260]            None         None   \n",
       "443747          None  st         0  [LE1262]            None         None   \n",
       "443745          None  st         0   [LE412]            None         None   \n",
       "260421          None  st         0  [LE1262]            None         None   \n",
       "359521          None  út         0  [LE1262]            None         None   \n",
       "359519          None  út         0  [LE1262]            None         None   \n",
       "285948          None  st         0  [LE1262]            None         None   \n",
       "277262          None  st         0  [LE1262]            None         None   \n",
       "356352          None  po         0  [LE1262]            None         None   \n",
       "405239          None  út         0  [LE1262]            None         None   \n",
       "260420          None  st         0  [LE1262]            None         None   \n",
       "237214          None  út         0  [LE1262]            None         None   \n",
       "302086          None  čt         0  [LE1262]            None         None   \n",
       "477914          None  st         0  [LE1262]            None         None   \n",
       "477913          None  st         0  [LE1262]            None         None   \n",
       "150245          None  so         0  [LE1262]            None         None   \n",
       "443756          None  st         0  [LE1262]            None         None   \n",
       "150244          None  so         0  [LE1262]            None         None   \n",
       "420436          None  so         0  [LE1262]            None         None   \n",
       "376229          None  út         0  [LE1262]            None         None   \n",
       "376230          None  út         0  [LE1262]            None         None   \n",
       "392773          None  út         0  [LE1262]            None         None   \n",
       "439831          None  st         0  [LE1262]            None         None   \n",
       "470275          None  pá         0  [LE1262]            None         None   \n",
       "458931          None  út         0  [LE1262]            None         None   \n",
       "451758          None  po         0  [LE1262]            None         None   \n",
       "458932          None  út         0  [LE1262]            None         None   \n",
       "\n",
       "        volna_mista_economy  volna_mista_economy_plus  \\\n",
       "306781                 21.0                      21.0   \n",
       "237216                 21.0                      21.0   \n",
       "228738                 21.0                      19.0   \n",
       "277263                 21.0                      20.0   \n",
       "150269                 21.0                      21.0   \n",
       "150268                 21.0                      21.0   \n",
       "292992                 21.0                      21.0   \n",
       "292991                 21.0                      21.0   \n",
       "228745                 21.0                      19.0   \n",
       "185002                 21.0                      21.0   \n",
       "326114                 21.0                      21.0   \n",
       "282699                 21.0                      21.0   \n",
       "326113                 21.0                      21.0   \n",
       "343459                 21.0                      21.0   \n",
       "343458                 21.0                      21.0   \n",
       "224919                 21.0                      21.0   \n",
       "282696                 21.0                      21.0   \n",
       "184992                 21.0                      21.0   \n",
       "451769                 21.0                      19.0   \n",
       "405240                 21.0                      21.0   \n",
       "302091                 21.0                      21.0   \n",
       "443755                 21.0                      21.0   \n",
       "443751                 21.0                      21.0   \n",
       "443750                 21.0                      21.0   \n",
       "443747                 21.0                      21.0   \n",
       "443745                 21.0                      21.0   \n",
       "260421                 21.0                      20.0   \n",
       "359521                 21.0                      21.0   \n",
       "359519                 21.0                      21.0   \n",
       "285948                 21.0                      21.0   \n",
       "277262                 21.0                      20.0   \n",
       "356352                 21.0                      21.0   \n",
       "405239                 21.0                      21.0   \n",
       "260420                 21.0                      20.0   \n",
       "237214                 21.0                      21.0   \n",
       "302086                 21.0                      21.0   \n",
       "477914                 21.0                      21.0   \n",
       "477913                 21.0                      21.0   \n",
       "150245                 21.0                      21.0   \n",
       "443756                 21.0                      21.0   \n",
       "150244                 21.0                      21.0   \n",
       "420436                 21.0                      21.0   \n",
       "376229                 21.0                      21.0   \n",
       "376230                 21.0                      21.0   \n",
       "392773                 21.0                      21.0   \n",
       "439831                 21.0                      20.0   \n",
       "470275                 21.0                      21.0   \n",
       "458931                 21.0                      21.0   \n",
       "451758                 21.0                      19.0   \n",
       "458932                 21.0                      21.0   \n",
       "\n",
       "        volna_mista_economy_business  volna_mista_premium  predstih_i  \n",
       "306781                          19.0                  6.0      1739.0  \n",
       "237216                          19.0                  6.0      1401.0  \n",
       "228738                          19.0                  5.0      1721.0  \n",
       "277263                          18.0                  5.0       561.0  \n",
       "150269                          19.0                  6.0      1542.0  \n",
       "150268                          19.0                  6.0      1542.0  \n",
       "292992                          15.0                  5.0      1606.0  \n",
       "292991                          15.0                  5.0      1606.0  \n",
       "228745                          19.0                  5.0      1721.0  \n",
       "185002                          19.0                  6.0      1505.0  \n",
       "326114                          19.0                  5.0      1777.0  \n",
       "282699                          19.0                  5.0      2115.0  \n",
       "326113                          19.0                  5.0      1777.0  \n",
       "343459                          18.0                  6.0      2033.0  \n",
       "343458                          18.0                  6.0      2033.0  \n",
       "224919                          19.0                  6.0      2011.0  \n",
       "282696                          19.0                  5.0      2115.0  \n",
       "184992                          19.0                  6.0      1505.0  \n",
       "451769                          19.0                  5.0      1583.0  \n",
       "405240                          19.0                  6.0      2139.0  \n",
       "302091                          18.0                  6.0      1913.0  \n",
       "443755                          18.0                  6.0      2129.0  \n",
       "443751                          18.0                  6.0      2137.0  \n",
       "443750                          18.0                  6.0      2137.0  \n",
       "443747                          19.0                  6.0      2139.0  \n",
       "443745                          18.0                  6.0      2129.0  \n",
       "260421                          18.0                  5.0       576.0  \n",
       "359521                          19.0                  6.0       487.0  \n",
       "359519                          19.0                  6.0       487.0  \n",
       "285948                          18.0                  6.0      2065.0  \n",
       "277262                          18.0                  5.0       561.0  \n",
       "356352                          18.0                  6.0      2143.0  \n",
       "405239                          19.0                  6.0      2139.0  \n",
       "260420                          18.0                  5.0       576.0  \n",
       "237214                          19.0                  6.0      1401.0  \n",
       "302086                          18.0                  6.0      1913.0  \n",
       "477914                          18.0                  6.0      1949.0  \n",
       "477913                          18.0                  6.0      1949.0  \n",
       "150245                          19.0                  6.0      1542.0  \n",
       "443756                          19.0                  6.0      2139.0  \n",
       "150244                          19.0                  6.0      1542.0  \n",
       "420436                          19.0                  5.0      1723.0  \n",
       "376229                          19.0                  6.0       480.0  \n",
       "376230                          19.0                  6.0       480.0  \n",
       "392773                          19.0                  6.0       467.0  \n",
       "439831                          19.0                  6.0      1301.0  \n",
       "470275                          19.0                  6.0      1329.0  \n",
       "458931                          19.0                  6.0      1603.0  \n",
       "451758                          19.0                  5.0      1583.0  \n",
       "458932                          19.0                  6.0      1603.0  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['odkud'].str.contains('Ostrava') & df['kam'].str.contains('Prah')].sort_values(by='cena').head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "31a54905-fcaf-47c9-a1eb-1fc2793b0780",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_11260\\70359749.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  df[df['odkud'].str.contains('Ostrava') & df['kam'].str.contains('Prah') & (df['predstih_h'] < 6)].groupby('prodejce')['cena'].max()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "prodejce\n",
       "ARR       NaN\n",
       "LE     1699.0\n",
       "RJ     1008.0\n",
       "ČD      825.0\n",
       "Name: cena, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['odkud'].str.contains('Ostrava') & df['kam'].str.contains('Prah') & (df['predstih_h'] < 6)].groupby('prodejce')['cena'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1ede086d-ad34-491c-9bbf-6730ec84d6a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_11260\\174816457.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  df[df['odkud'].str.contains('Ostrava') & df['kam'].str.contains('Prah') & (df['predstih_d'] < 1)].groupby('prodejce')['cena'].median()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "prodejce\n",
       "ARR      NaN\n",
       "LE     639.0\n",
       "RJ     439.0\n",
       "ČD     569.0\n",
       "Name: cena, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['odkud'].str.contains('Ostrava') & df['kam'].str.contains('Prah') & (df['predstih_d'] < 1)].groupby('prodejce')['cena'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ae3cf954-9bb3-42fe-b759-b79b21bacddf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_11260\\1998096608.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  df[df['odkud'].str.contains('Ostrava') & df['kam'].str.contains('Prah')].groupby('prodejce')['cena'].median()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "prodejce\n",
       "ARR      NaN\n",
       "LE     429.0\n",
       "RJ     369.0\n",
       "ČD     504.0\n",
       "Name: cena, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['odkud'].str.contains('Ostrava') & df['kam'].str.contains('Prah')].groupby('prodejce')['cena'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6753b11d-13a8-4f88-99d6-5f5d15e410eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_11260\\1185396162.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  df[df['odkud'].str.contains('Ostrava') & df['kam'].str.contains('Prah')].groupby('prodejce')['cena'].min()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "prodejce\n",
       "ARR      NaN\n",
       "LE      99.0\n",
       "RJ     249.0\n",
       "ČD     289.0\n",
       "Name: cena, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['odkud'].str.contains('Ostrava') & df['kam'].str.contains('Prah')].groupby('prodejce')['cena'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e02b970b-08d6-4f8c-87a6-61c3be8beb21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>oscrapovano</th>\n",
       "      <th>prodejce</th>\n",
       "      <th>odkud</th>\n",
       "      <th>kam</th>\n",
       "      <th>odjezd</th>\n",
       "      <th>predstih</th>\n",
       "      <th>predstih_d</th>\n",
       "      <th>predstih_h</th>\n",
       "      <th>cena</th>\n",
       "      <th>prostredek</th>\n",
       "      <th>volnych_mist</th>\n",
       "      <th>obsazenost</th>\n",
       "      <th>jizdni_doba</th>\n",
       "      <th>vzdalenost</th>\n",
       "      <th>zpozdeni</th>\n",
       "      <th>cena_poznamka</th>\n",
       "      <th>den</th>\n",
       "      <th>prestupy</th>\n",
       "      <th>vlaky</th>\n",
       "      <th>mistenka_zdarma</th>\n",
       "      <th>nahradni_bus</th>\n",
       "      <th>volna_mista_economy</th>\n",
       "      <th>volna_mista_economy_plus</th>\n",
       "      <th>volna_mista_economy_business</th>\n",
       "      <th>volna_mista_premium</th>\n",
       "      <th>predstih_i</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>310465</th>\n",
       "      <td>2024-11-12 08:14:27</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Brno</td>\n",
       "      <td>Praha</td>\n",
       "      <td>2024-11-13 05:03:00</td>\n",
       "      <td>0 days 20:48:33</td>\n",
       "      <td>0</td>\n",
       "      <td>20.809167</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>232.0</td>\n",
       "      <td>None</td>\n",
       "      <td>154.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178262</th>\n",
       "      <td>2024-11-08 22:46:48</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Brno</td>\n",
       "      <td>Praha</td>\n",
       "      <td>2024-11-10 05:03:00</td>\n",
       "      <td>1 days 06:16:12</td>\n",
       "      <td>1</td>\n",
       "      <td>30.270000</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>295.0</td>\n",
       "      <td>None</td>\n",
       "      <td>154.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>ne</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465859</th>\n",
       "      <td>2024-11-16 06:12:07</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Brno</td>\n",
       "      <td>Praha</td>\n",
       "      <td>2024-11-17 05:03:00</td>\n",
       "      <td>0 days 22:50:53</td>\n",
       "      <td>0</td>\n",
       "      <td>22.848056</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>315.0</td>\n",
       "      <td>None</td>\n",
       "      <td>154.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>ne</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295632</th>\n",
       "      <td>2024-11-11 22:06:33</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Brno</td>\n",
       "      <td>Praha</td>\n",
       "      <td>2024-11-13 05:03:00</td>\n",
       "      <td>1 days 06:56:27</td>\n",
       "      <td>1</td>\n",
       "      <td>30.940833</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>237.0</td>\n",
       "      <td>None</td>\n",
       "      <td>154.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392046</th>\n",
       "      <td>2024-11-14 08:07:06</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Brno</td>\n",
       "      <td>Praha</td>\n",
       "      <td>2024-11-17 05:03:00</td>\n",
       "      <td>2 days 20:55:54</td>\n",
       "      <td>2</td>\n",
       "      <td>68.931667</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>323.0</td>\n",
       "      <td>None</td>\n",
       "      <td>154.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>ne</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248352</th>\n",
       "      <td>2024-11-10 14:07:18</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Brno</td>\n",
       "      <td>Praha</td>\n",
       "      <td>2024-11-17 05:03:00</td>\n",
       "      <td>6 days 14:55:42</td>\n",
       "      <td>6</td>\n",
       "      <td>158.928333</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>312.0</td>\n",
       "      <td>None</td>\n",
       "      <td>154.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>ne</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>159.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400513</th>\n",
       "      <td>2024-11-14 12:09:20</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Brno</td>\n",
       "      <td>Praha</td>\n",
       "      <td>2024-11-17 05:03:00</td>\n",
       "      <td>2 days 16:53:40</td>\n",
       "      <td>2</td>\n",
       "      <td>64.894444</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>321.0</td>\n",
       "      <td>None</td>\n",
       "      <td>154.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>ne</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232105</th>\n",
       "      <td>2024-11-10 06:12:39</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Brno</td>\n",
       "      <td>Praha</td>\n",
       "      <td>2024-11-16 05:03:00</td>\n",
       "      <td>5 days 22:50:21</td>\n",
       "      <td>5</td>\n",
       "      <td>142.839167</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>274.0</td>\n",
       "      <td>None</td>\n",
       "      <td>154.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>so</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232117</th>\n",
       "      <td>2024-11-10 06:12:46</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Brno</td>\n",
       "      <td>Praha</td>\n",
       "      <td>2024-11-17 05:03:00</td>\n",
       "      <td>6 days 22:50:14</td>\n",
       "      <td>6</td>\n",
       "      <td>166.837222</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>312.0</td>\n",
       "      <td>None</td>\n",
       "      <td>154.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>ne</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>167.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196739</th>\n",
       "      <td>2024-11-09 10:14:00</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Brno</td>\n",
       "      <td>Praha</td>\n",
       "      <td>2024-11-12 05:03:00</td>\n",
       "      <td>2 days 18:49:00</td>\n",
       "      <td>2</td>\n",
       "      <td>66.816667</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>247.0</td>\n",
       "      <td>None</td>\n",
       "      <td>154.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>út</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228065</th>\n",
       "      <td>2024-11-10 00:14:50</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Brno</td>\n",
       "      <td>Praha</td>\n",
       "      <td>2024-11-17 05:03:00</td>\n",
       "      <td>7 days 04:48:10</td>\n",
       "      <td>7</td>\n",
       "      <td>172.802778</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>312.0</td>\n",
       "      <td>None</td>\n",
       "      <td>154.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>ne</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>173.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295590</th>\n",
       "      <td>2024-11-11 22:06:30</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Brno</td>\n",
       "      <td>Praha</td>\n",
       "      <td>2024-11-12 05:03:00</td>\n",
       "      <td>0 days 06:56:30</td>\n",
       "      <td>0</td>\n",
       "      <td>6.941667</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>174.0</td>\n",
       "      <td>None</td>\n",
       "      <td>154.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>út</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380190</th>\n",
       "      <td>2024-11-13 22:13:02</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Brno</td>\n",
       "      <td>Praha</td>\n",
       "      <td>2024-11-17 05:03:00</td>\n",
       "      <td>3 days 06:49:58</td>\n",
       "      <td>3</td>\n",
       "      <td>78.832778</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>328.0</td>\n",
       "      <td>None</td>\n",
       "      <td>154.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>ne</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477102</th>\n",
       "      <td>2024-11-16 12:09:36</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Brno</td>\n",
       "      <td>Praha</td>\n",
       "      <td>2024-11-17 05:03:00</td>\n",
       "      <td>0 days 16:53:24</td>\n",
       "      <td>0</td>\n",
       "      <td>16.890000</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>308.0</td>\n",
       "      <td>None</td>\n",
       "      <td>154.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>ne</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490481</th>\n",
       "      <td>2024-11-16 18:11:53</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Brno</td>\n",
       "      <td>Praha</td>\n",
       "      <td>2024-11-17 05:03:00</td>\n",
       "      <td>0 days 10:51:07</td>\n",
       "      <td>0</td>\n",
       "      <td>10.851944</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>300.0</td>\n",
       "      <td>None</td>\n",
       "      <td>154.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>ne</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224343</th>\n",
       "      <td>2024-11-09 22:12:42</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Brno</td>\n",
       "      <td>Praha</td>\n",
       "      <td>2024-11-15 05:03:00</td>\n",
       "      <td>5 days 06:50:18</td>\n",
       "      <td>5</td>\n",
       "      <td>126.838333</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>276.0</td>\n",
       "      <td>None</td>\n",
       "      <td>154.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>127.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314264</th>\n",
       "      <td>2024-11-12 10:08:03</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Brno</td>\n",
       "      <td>Praha</td>\n",
       "      <td>2024-11-15 05:03:00</td>\n",
       "      <td>2 days 18:54:57</td>\n",
       "      <td>2</td>\n",
       "      <td>66.915833</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>252.0</td>\n",
       "      <td>None</td>\n",
       "      <td>154.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143412</th>\n",
       "      <td>2024-11-08 07:53:52</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Brno</td>\n",
       "      <td>Praha</td>\n",
       "      <td>2024-11-17 05:03:00</td>\n",
       "      <td>8 days 21:09:08</td>\n",
       "      <td>8</td>\n",
       "      <td>213.152222</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>315.0</td>\n",
       "      <td>None</td>\n",
       "      <td>154.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>ne</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>213.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248323</th>\n",
       "      <td>2024-11-10 14:07:15</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Brno</td>\n",
       "      <td>Praha</td>\n",
       "      <td>2024-11-16 05:03:00</td>\n",
       "      <td>5 days 14:55:45</td>\n",
       "      <td>5</td>\n",
       "      <td>134.929167</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>268.0</td>\n",
       "      <td>None</td>\n",
       "      <td>154.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>so</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248298</th>\n",
       "      <td>2024-11-10 14:07:09</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Brno</td>\n",
       "      <td>Praha</td>\n",
       "      <td>2024-11-15 05:03:00</td>\n",
       "      <td>4 days 14:55:51</td>\n",
       "      <td>4</td>\n",
       "      <td>110.930833</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>276.0</td>\n",
       "      <td>None</td>\n",
       "      <td>154.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>111.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               oscrapovano prodejce odkud    kam              odjezd  \\\n",
       "310465 2024-11-12 08:14:27       RJ  Brno  Praha 2024-11-13 05:03:00   \n",
       "178262 2024-11-08 22:46:48       RJ  Brno  Praha 2024-11-10 05:03:00   \n",
       "465859 2024-11-16 06:12:07       RJ  Brno  Praha 2024-11-17 05:03:00   \n",
       "295632 2024-11-11 22:06:33       RJ  Brno  Praha 2024-11-13 05:03:00   \n",
       "392046 2024-11-14 08:07:06       RJ  Brno  Praha 2024-11-17 05:03:00   \n",
       "248352 2024-11-10 14:07:18       RJ  Brno  Praha 2024-11-17 05:03:00   \n",
       "400513 2024-11-14 12:09:20       RJ  Brno  Praha 2024-11-17 05:03:00   \n",
       "232105 2024-11-10 06:12:39       RJ  Brno  Praha 2024-11-16 05:03:00   \n",
       "232117 2024-11-10 06:12:46       RJ  Brno  Praha 2024-11-17 05:03:00   \n",
       "196739 2024-11-09 10:14:00       RJ  Brno  Praha 2024-11-12 05:03:00   \n",
       "228065 2024-11-10 00:14:50       RJ  Brno  Praha 2024-11-17 05:03:00   \n",
       "295590 2024-11-11 22:06:30       RJ  Brno  Praha 2024-11-12 05:03:00   \n",
       "380190 2024-11-13 22:13:02       RJ  Brno  Praha 2024-11-17 05:03:00   \n",
       "477102 2024-11-16 12:09:36       RJ  Brno  Praha 2024-11-17 05:03:00   \n",
       "490481 2024-11-16 18:11:53       RJ  Brno  Praha 2024-11-17 05:03:00   \n",
       "224343 2024-11-09 22:12:42       RJ  Brno  Praha 2024-11-15 05:03:00   \n",
       "314264 2024-11-12 10:08:03       RJ  Brno  Praha 2024-11-15 05:03:00   \n",
       "143412 2024-11-08 07:53:52       RJ  Brno  Praha 2024-11-17 05:03:00   \n",
       "248323 2024-11-10 14:07:15       RJ  Brno  Praha 2024-11-16 05:03:00   \n",
       "248298 2024-11-10 14:07:09       RJ  Brno  Praha 2024-11-15 05:03:00   \n",
       "\n",
       "              predstih  predstih_d  predstih_h  cena prostredek  volnych_mist  \\\n",
       "310465 0 days 20:48:33           0   20.809167  99.0       vlak         232.0   \n",
       "178262 1 days 06:16:12           1   30.270000  99.0       vlak         295.0   \n",
       "465859 0 days 22:50:53           0   22.848056  99.0       vlak         315.0   \n",
       "295632 1 days 06:56:27           1   30.940833  99.0       vlak         237.0   \n",
       "392046 2 days 20:55:54           2   68.931667  99.0       vlak         323.0   \n",
       "248352 6 days 14:55:42           6  158.928333  99.0       vlak         312.0   \n",
       "400513 2 days 16:53:40           2   64.894444  99.0       vlak         321.0   \n",
       "232105 5 days 22:50:21           5  142.839167  99.0       vlak         274.0   \n",
       "232117 6 days 22:50:14           6  166.837222  99.0       vlak         312.0   \n",
       "196739 2 days 18:49:00           2   66.816667  99.0       vlak         247.0   \n",
       "228065 7 days 04:48:10           7  172.802778  99.0       vlak         312.0   \n",
       "295590 0 days 06:56:30           0    6.941667  99.0       vlak         174.0   \n",
       "380190 3 days 06:49:58           3   78.832778  99.0       vlak         328.0   \n",
       "477102 0 days 16:53:24           0   16.890000  99.0       vlak         308.0   \n",
       "490481 0 days 10:51:07           0   10.851944  99.0       vlak         300.0   \n",
       "224343 5 days 06:50:18           5  126.838333  99.0       vlak         276.0   \n",
       "314264 2 days 18:54:57           2   66.915833  99.0       vlak         252.0   \n",
       "143412 8 days 21:09:08           8  213.152222  99.0       vlak         315.0   \n",
       "248323 5 days 14:55:45           5  134.929167  99.0       vlak         268.0   \n",
       "248298 4 days 14:55:51           4  110.930833  99.0       vlak         276.0   \n",
       "\n",
       "       obsazenost  jizdni_doba  vzdalenost zpozdeni cena_poznamka den  \\\n",
       "310465       None        154.0         NaN     None          None  st   \n",
       "178262       None        154.0         NaN     None          None  ne   \n",
       "465859       None        154.0         NaN     None          None  ne   \n",
       "295632       None        154.0         NaN     None          None  st   \n",
       "392046       None        154.0         NaN     None          None  ne   \n",
       "248352       None        154.0         NaN     None          None  ne   \n",
       "400513       None        154.0         NaN     None          None  ne   \n",
       "232105       None        154.0         NaN     None          None  so   \n",
       "232117       None        154.0         NaN     None          None  ne   \n",
       "196739       None        154.0         NaN     None          None  út   \n",
       "228065       None        154.0         NaN     None          None  ne   \n",
       "295590       None        154.0         NaN     None          None  út   \n",
       "380190       None        154.0         NaN     None          None  ne   \n",
       "477102       None        154.0         NaN     None          None  ne   \n",
       "490481       None        154.0         NaN     None          None  ne   \n",
       "224343       None        154.0         NaN     None          None  pá   \n",
       "314264       None        154.0         NaN     None          None  pá   \n",
       "143412       None        154.0         NaN     None          None  ne   \n",
       "248323       None        154.0         NaN     None          None  so   \n",
       "248298       None        154.0         NaN     None          None  pá   \n",
       "\n",
       "        prestupy vlaky mistenka_zdarma nahradni_bus  volna_mista_economy  \\\n",
       "310465         0  None            None         None                  NaN   \n",
       "178262         0  None            None         None                  NaN   \n",
       "465859         0  None            None         None                  NaN   \n",
       "295632         0  None            None         None                  NaN   \n",
       "392046         0  None            None         None                  NaN   \n",
       "248352         0  None            None         None                  NaN   \n",
       "400513         0  None            None         None                  NaN   \n",
       "232105         0  None            None         None                  NaN   \n",
       "232117         0  None            None         None                  NaN   \n",
       "196739         0  None            None         None                  NaN   \n",
       "228065         0  None            None         None                  NaN   \n",
       "295590         0  None            None         None                  NaN   \n",
       "380190         0  None            None         None                  NaN   \n",
       "477102         0  None            None         None                  NaN   \n",
       "490481         0  None            None         None                  NaN   \n",
       "224343         0  None            None         None                  NaN   \n",
       "314264         0  None            None         None                  NaN   \n",
       "143412         0  None            None         None                  NaN   \n",
       "248323         0  None            None         None                  NaN   \n",
       "248298         0  None            None         None                  NaN   \n",
       "\n",
       "        volna_mista_economy_plus  volna_mista_economy_business  \\\n",
       "310465                       NaN                           NaN   \n",
       "178262                       NaN                           NaN   \n",
       "465859                       NaN                           NaN   \n",
       "295632                       NaN                           NaN   \n",
       "392046                       NaN                           NaN   \n",
       "248352                       NaN                           NaN   \n",
       "400513                       NaN                           NaN   \n",
       "232105                       NaN                           NaN   \n",
       "232117                       NaN                           NaN   \n",
       "196739                       NaN                           NaN   \n",
       "228065                       NaN                           NaN   \n",
       "295590                       NaN                           NaN   \n",
       "380190                       NaN                           NaN   \n",
       "477102                       NaN                           NaN   \n",
       "490481                       NaN                           NaN   \n",
       "224343                       NaN                           NaN   \n",
       "314264                       NaN                           NaN   \n",
       "143412                       NaN                           NaN   \n",
       "248323                       NaN                           NaN   \n",
       "248298                       NaN                           NaN   \n",
       "\n",
       "        volna_mista_premium  predstih_i  \n",
       "310465                  NaN        21.0  \n",
       "178262                  NaN        30.0  \n",
       "465859                  NaN        23.0  \n",
       "295632                  NaN        31.0  \n",
       "392046                  NaN        69.0  \n",
       "248352                  NaN       159.0  \n",
       "400513                  NaN        65.0  \n",
       "232105                  NaN       143.0  \n",
       "232117                  NaN       167.0  \n",
       "196739                  NaN        67.0  \n",
       "228065                  NaN       173.0  \n",
       "295590                  NaN         7.0  \n",
       "380190                  NaN        79.0  \n",
       "477102                  NaN        17.0  \n",
       "490481                  NaN        11.0  \n",
       "224343                  NaN       127.0  \n",
       "314264                  NaN        67.0  \n",
       "143412                  NaN       213.0  \n",
       "248323                  NaN       135.0  \n",
       "248298                  NaN       111.0  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['odkud'].str.contains('Brn') & df['kam'].str.contains('Prah')].sort_values(by='cena').head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a7ec2c3e-fd82-40de-9d17-730071722351",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>oscrapovano</th>\n",
       "      <th>prodejce</th>\n",
       "      <th>odkud</th>\n",
       "      <th>kam</th>\n",
       "      <th>odjezd</th>\n",
       "      <th>predstih</th>\n",
       "      <th>predstih_d</th>\n",
       "      <th>predstih_h</th>\n",
       "      <th>cena</th>\n",
       "      <th>prostredek</th>\n",
       "      <th>volnych_mist</th>\n",
       "      <th>obsazenost</th>\n",
       "      <th>jizdni_doba</th>\n",
       "      <th>vzdalenost</th>\n",
       "      <th>zpozdeni</th>\n",
       "      <th>cena_poznamka</th>\n",
       "      <th>den</th>\n",
       "      <th>prestupy</th>\n",
       "      <th>vlaky</th>\n",
       "      <th>mistenka_zdarma</th>\n",
       "      <th>nahradni_bus</th>\n",
       "      <th>volna_mista_economy</th>\n",
       "      <th>volna_mista_economy_plus</th>\n",
       "      <th>volna_mista_economy_business</th>\n",
       "      <th>volna_mista_premium</th>\n",
       "      <th>predstih_i</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>375304</th>\n",
       "      <td>2024-11-13 19:07:06</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Brno</td>\n",
       "      <td>2024-11-14 20:56:00</td>\n",
       "      <td>1 days 01:48:54</td>\n",
       "      <td>1</td>\n",
       "      <td>25.815000</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>251.0</td>\n",
       "      <td>None</td>\n",
       "      <td>157.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>čt</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371762</th>\n",
       "      <td>2024-11-13 18:09:24</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Brno</td>\n",
       "      <td>2024-11-13 20:56:00</td>\n",
       "      <td>0 days 02:46:36</td>\n",
       "      <td>0</td>\n",
       "      <td>2.776667</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>307.0</td>\n",
       "      <td>None</td>\n",
       "      <td>157.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288906</th>\n",
       "      <td>2024-11-11 18:09:17</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Brno</td>\n",
       "      <td>2024-11-12 20:56:00</td>\n",
       "      <td>1 days 02:46:43</td>\n",
       "      <td>1</td>\n",
       "      <td>26.778611</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>283.0</td>\n",
       "      <td>None</td>\n",
       "      <td>157.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>út</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236349</th>\n",
       "      <td>2024-11-10 08:12:27</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Brno</td>\n",
       "      <td>2024-11-12 20:56:00</td>\n",
       "      <td>2 days 12:43:33</td>\n",
       "      <td>2</td>\n",
       "      <td>60.725833</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>302.0</td>\n",
       "      <td>None</td>\n",
       "      <td>157.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>út</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205777</th>\n",
       "      <td>2024-11-09 14:12:00</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Brno</td>\n",
       "      <td>2024-11-13 20:56:00</td>\n",
       "      <td>4 days 06:44:00</td>\n",
       "      <td>4</td>\n",
       "      <td>102.733333</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>299.0</td>\n",
       "      <td>None</td>\n",
       "      <td>157.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>103.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210158</th>\n",
       "      <td>2024-11-09 16:06:31</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Brno</td>\n",
       "      <td>2024-11-13 20:56:00</td>\n",
       "      <td>4 days 04:49:29</td>\n",
       "      <td>4</td>\n",
       "      <td>100.824722</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>299.0</td>\n",
       "      <td>None</td>\n",
       "      <td>157.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292461</th>\n",
       "      <td>2024-11-11 19:05:45</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Brno</td>\n",
       "      <td>2024-11-13 20:56:00</td>\n",
       "      <td>2 days 01:50:15</td>\n",
       "      <td>2</td>\n",
       "      <td>49.837500</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>288.0</td>\n",
       "      <td>None</td>\n",
       "      <td>157.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157105</th>\n",
       "      <td>2024-11-08 14:59:11</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Brno</td>\n",
       "      <td>2024-11-11 20:56:00</td>\n",
       "      <td>3 days 05:56:49</td>\n",
       "      <td>3</td>\n",
       "      <td>77.946944</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>286.0</td>\n",
       "      <td>None</td>\n",
       "      <td>157.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>po</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240140</th>\n",
       "      <td>2024-11-10 10:04:57</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Brno</td>\n",
       "      <td>2024-11-14 20:56:00</td>\n",
       "      <td>4 days 10:51:03</td>\n",
       "      <td>4</td>\n",
       "      <td>106.850833</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>270.0</td>\n",
       "      <td>None</td>\n",
       "      <td>157.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>čt</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111056</th>\n",
       "      <td>2024-11-06 21:41:12</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Brno</td>\n",
       "      <td>2024-11-11 20:56:00</td>\n",
       "      <td>4 days 23:14:48</td>\n",
       "      <td>4</td>\n",
       "      <td>119.246667</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>294.0</td>\n",
       "      <td>None</td>\n",
       "      <td>157.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>po</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>119.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354840</th>\n",
       "      <td>2024-11-13 10:09:11</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Brno</td>\n",
       "      <td>2024-11-14 20:56:00</td>\n",
       "      <td>1 days 10:46:49</td>\n",
       "      <td>1</td>\n",
       "      <td>34.780278</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>268.0</td>\n",
       "      <td>None</td>\n",
       "      <td>157.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>čt</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252667</th>\n",
       "      <td>2024-11-10 16:06:38</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Brno</td>\n",
       "      <td>2024-11-14 20:56:00</td>\n",
       "      <td>4 days 04:49:22</td>\n",
       "      <td>4</td>\n",
       "      <td>100.822778</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>270.0</td>\n",
       "      <td>None</td>\n",
       "      <td>157.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>čt</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263124</th>\n",
       "      <td>2024-11-10 22:13:05</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Brno</td>\n",
       "      <td>2024-11-11 20:56:00</td>\n",
       "      <td>0 days 22:42:55</td>\n",
       "      <td>0</td>\n",
       "      <td>22.715278</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>321.0</td>\n",
       "      <td>None</td>\n",
       "      <td>157.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>po</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219491</th>\n",
       "      <td>2024-11-09 19:12:14</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Brno</td>\n",
       "      <td>2024-11-14 20:56:00</td>\n",
       "      <td>5 days 01:43:46</td>\n",
       "      <td>5</td>\n",
       "      <td>121.729444</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>274.0</td>\n",
       "      <td>None</td>\n",
       "      <td>157.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>čt</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>122.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277059</th>\n",
       "      <td>2024-11-11 10:08:16</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Brno</td>\n",
       "      <td>2024-11-11 20:56:00</td>\n",
       "      <td>0 days 10:47:44</td>\n",
       "      <td>0</td>\n",
       "      <td>10.795556</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>313.0</td>\n",
       "      <td>None</td>\n",
       "      <td>157.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>po</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236301</th>\n",
       "      <td>2024-11-10 08:12:22</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Brno</td>\n",
       "      <td>2024-11-11 20:56:00</td>\n",
       "      <td>1 days 12:43:38</td>\n",
       "      <td>1</td>\n",
       "      <td>36.727222</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>277.0</td>\n",
       "      <td>None</td>\n",
       "      <td>157.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>po</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231505</th>\n",
       "      <td>2024-11-10 06:09:59</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Brno</td>\n",
       "      <td>2024-11-12 20:56:00</td>\n",
       "      <td>2 days 14:46:01</td>\n",
       "      <td>2</td>\n",
       "      <td>62.766944</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>302.0</td>\n",
       "      <td>None</td>\n",
       "      <td>157.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>út</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210134</th>\n",
       "      <td>2024-11-09 16:06:27</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Brno</td>\n",
       "      <td>2024-11-12 20:56:00</td>\n",
       "      <td>3 days 04:49:33</td>\n",
       "      <td>3</td>\n",
       "      <td>76.825833</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>304.0</td>\n",
       "      <td>None</td>\n",
       "      <td>157.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>út</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346382</th>\n",
       "      <td>2024-11-13 06:07:37</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Brno</td>\n",
       "      <td>2024-11-13 20:56:00</td>\n",
       "      <td>0 days 14:48:23</td>\n",
       "      <td>0</td>\n",
       "      <td>14.806389</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>320.0</td>\n",
       "      <td>None</td>\n",
       "      <td>157.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354813</th>\n",
       "      <td>2024-11-13 10:09:08</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Praha</td>\n",
       "      <td>Brno</td>\n",
       "      <td>2024-11-13 20:56:00</td>\n",
       "      <td>0 days 10:46:52</td>\n",
       "      <td>0</td>\n",
       "      <td>10.781111</td>\n",
       "      <td>99.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>314.0</td>\n",
       "      <td>None</td>\n",
       "      <td>157.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               oscrapovano prodejce  odkud   kam              odjezd  \\\n",
       "375304 2024-11-13 19:07:06       RJ  Praha  Brno 2024-11-14 20:56:00   \n",
       "371762 2024-11-13 18:09:24       RJ  Praha  Brno 2024-11-13 20:56:00   \n",
       "288906 2024-11-11 18:09:17       RJ  Praha  Brno 2024-11-12 20:56:00   \n",
       "236349 2024-11-10 08:12:27       RJ  Praha  Brno 2024-11-12 20:56:00   \n",
       "205777 2024-11-09 14:12:00       RJ  Praha  Brno 2024-11-13 20:56:00   \n",
       "210158 2024-11-09 16:06:31       RJ  Praha  Brno 2024-11-13 20:56:00   \n",
       "292461 2024-11-11 19:05:45       RJ  Praha  Brno 2024-11-13 20:56:00   \n",
       "157105 2024-11-08 14:59:11       RJ  Praha  Brno 2024-11-11 20:56:00   \n",
       "240140 2024-11-10 10:04:57       RJ  Praha  Brno 2024-11-14 20:56:00   \n",
       "111056 2024-11-06 21:41:12       RJ  Praha  Brno 2024-11-11 20:56:00   \n",
       "354840 2024-11-13 10:09:11       RJ  Praha  Brno 2024-11-14 20:56:00   \n",
       "252667 2024-11-10 16:06:38       RJ  Praha  Brno 2024-11-14 20:56:00   \n",
       "263124 2024-11-10 22:13:05       RJ  Praha  Brno 2024-11-11 20:56:00   \n",
       "219491 2024-11-09 19:12:14       RJ  Praha  Brno 2024-11-14 20:56:00   \n",
       "277059 2024-11-11 10:08:16       RJ  Praha  Brno 2024-11-11 20:56:00   \n",
       "236301 2024-11-10 08:12:22       RJ  Praha  Brno 2024-11-11 20:56:00   \n",
       "231505 2024-11-10 06:09:59       RJ  Praha  Brno 2024-11-12 20:56:00   \n",
       "210134 2024-11-09 16:06:27       RJ  Praha  Brno 2024-11-12 20:56:00   \n",
       "346382 2024-11-13 06:07:37       RJ  Praha  Brno 2024-11-13 20:56:00   \n",
       "354813 2024-11-13 10:09:08       RJ  Praha  Brno 2024-11-13 20:56:00   \n",
       "\n",
       "              predstih  predstih_d  predstih_h  cena prostredek  volnych_mist  \\\n",
       "375304 1 days 01:48:54           1   25.815000  99.0       vlak         251.0   \n",
       "371762 0 days 02:46:36           0    2.776667  99.0       vlak         307.0   \n",
       "288906 1 days 02:46:43           1   26.778611  99.0       vlak         283.0   \n",
       "236349 2 days 12:43:33           2   60.725833  99.0       vlak         302.0   \n",
       "205777 4 days 06:44:00           4  102.733333  99.0       vlak         299.0   \n",
       "210158 4 days 04:49:29           4  100.824722  99.0       vlak         299.0   \n",
       "292461 2 days 01:50:15           2   49.837500  99.0       vlak         288.0   \n",
       "157105 3 days 05:56:49           3   77.946944  99.0       vlak         286.0   \n",
       "240140 4 days 10:51:03           4  106.850833  99.0       vlak         270.0   \n",
       "111056 4 days 23:14:48           4  119.246667  99.0       vlak         294.0   \n",
       "354840 1 days 10:46:49           1   34.780278  99.0       vlak         268.0   \n",
       "252667 4 days 04:49:22           4  100.822778  99.0       vlak         270.0   \n",
       "263124 0 days 22:42:55           0   22.715278  99.0       vlak         321.0   \n",
       "219491 5 days 01:43:46           5  121.729444  99.0       vlak         274.0   \n",
       "277059 0 days 10:47:44           0   10.795556  99.0       vlak         313.0   \n",
       "236301 1 days 12:43:38           1   36.727222  99.0       vlak         277.0   \n",
       "231505 2 days 14:46:01           2   62.766944  99.0       vlak         302.0   \n",
       "210134 3 days 04:49:33           3   76.825833  99.0       vlak         304.0   \n",
       "346382 0 days 14:48:23           0   14.806389  99.0       vlak         320.0   \n",
       "354813 0 days 10:46:52           0   10.781111  99.0       vlak         314.0   \n",
       "\n",
       "       obsazenost  jizdni_doba  vzdalenost zpozdeni cena_poznamka den  \\\n",
       "375304       None        157.0         NaN     None          None  čt   \n",
       "371762       None        157.0         NaN     None          None  st   \n",
       "288906       None        157.0         NaN     None          None  út   \n",
       "236349       None        157.0         NaN     None          None  út   \n",
       "205777       None        157.0         NaN     None          None  st   \n",
       "210158       None        157.0         NaN     None          None  st   \n",
       "292461       None        157.0         NaN     None          None  st   \n",
       "157105       None        157.0         NaN     None          None  po   \n",
       "240140       None        157.0         NaN     None          None  čt   \n",
       "111056       None        157.0         NaN     None          None  po   \n",
       "354840       None        157.0         NaN     None          None  čt   \n",
       "252667       None        157.0         NaN     None          None  čt   \n",
       "263124       None        157.0         NaN     None          None  po   \n",
       "219491       None        157.0         NaN     None          None  čt   \n",
       "277059       None        157.0         NaN     None          None  po   \n",
       "236301       None        157.0         NaN     None          None  po   \n",
       "231505       None        157.0         NaN     None          None  út   \n",
       "210134       None        157.0         NaN     None          None  út   \n",
       "346382       None        157.0         NaN     None          None  st   \n",
       "354813       None        157.0         NaN     None          None  st   \n",
       "\n",
       "        prestupy vlaky mistenka_zdarma nahradni_bus  volna_mista_economy  \\\n",
       "375304         0  None            None         None                  NaN   \n",
       "371762         0  None            None         None                  NaN   \n",
       "288906         0  None            None         None                  NaN   \n",
       "236349         0  None            None         None                  NaN   \n",
       "205777         0  None            None         None                  NaN   \n",
       "210158         0  None            None         None                  NaN   \n",
       "292461         0  None            None         None                  NaN   \n",
       "157105         0  None            None         None                  NaN   \n",
       "240140         0  None            None         None                  NaN   \n",
       "111056         0  None            None         None                  NaN   \n",
       "354840         0  None            None         None                  NaN   \n",
       "252667         0  None            None         None                  NaN   \n",
       "263124         0  None            None         None                  NaN   \n",
       "219491         0  None            None         None                  NaN   \n",
       "277059         0  None            None         None                  NaN   \n",
       "236301         0  None            None         None                  NaN   \n",
       "231505         0  None            None         None                  NaN   \n",
       "210134         0  None            None         None                  NaN   \n",
       "346382         0  None            None         None                  NaN   \n",
       "354813         0  None            None         None                  NaN   \n",
       "\n",
       "        volna_mista_economy_plus  volna_mista_economy_business  \\\n",
       "375304                       NaN                           NaN   \n",
       "371762                       NaN                           NaN   \n",
       "288906                       NaN                           NaN   \n",
       "236349                       NaN                           NaN   \n",
       "205777                       NaN                           NaN   \n",
       "210158                       NaN                           NaN   \n",
       "292461                       NaN                           NaN   \n",
       "157105                       NaN                           NaN   \n",
       "240140                       NaN                           NaN   \n",
       "111056                       NaN                           NaN   \n",
       "354840                       NaN                           NaN   \n",
       "252667                       NaN                           NaN   \n",
       "263124                       NaN                           NaN   \n",
       "219491                       NaN                           NaN   \n",
       "277059                       NaN                           NaN   \n",
       "236301                       NaN                           NaN   \n",
       "231505                       NaN                           NaN   \n",
       "210134                       NaN                           NaN   \n",
       "346382                       NaN                           NaN   \n",
       "354813                       NaN                           NaN   \n",
       "\n",
       "        volna_mista_premium  predstih_i  \n",
       "375304                  NaN        26.0  \n",
       "371762                  NaN         3.0  \n",
       "288906                  NaN        27.0  \n",
       "236349                  NaN        61.0  \n",
       "205777                  NaN       103.0  \n",
       "210158                  NaN       101.0  \n",
       "292461                  NaN        50.0  \n",
       "157105                  NaN        78.0  \n",
       "240140                  NaN       107.0  \n",
       "111056                  NaN       119.0  \n",
       "354840                  NaN        35.0  \n",
       "252667                  NaN       101.0  \n",
       "263124                  NaN        23.0  \n",
       "219491                  NaN       122.0  \n",
       "277059                  NaN        11.0  \n",
       "236301                  NaN        37.0  \n",
       "231505                  NaN        63.0  \n",
       "210134                  NaN        77.0  \n",
       "346382                  NaN        15.0  \n",
       "354813                  NaN        11.0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['odkud'].str.contains('Prah') & df['kam'].str.contains('Brn')].sort_values(by='cena').head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "542052db-3b58-4bd0-a9ee-dbcb102c169b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='predstih_d'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['odkud'].str.contains('Prah') & df['kam'].str.contains('Brn')].groupby('predstih_d')['cena'].min().plot.bar(figsize=(25,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d8c27541-6184-4c2d-a21f-7eaef34191c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='predstih_d'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['odkud'].str.contains('Prah') & df['kam'].str.contains('Berl')].groupby('predstih_d')['cena'].min().plot.bar(figsize=(25,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4eb7d022-9f46-47ed-aa34-507ddb6cac2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='predstih_d'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAB9IAAAHECAYAAABlWmHzAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAABRhUlEQVR4nO3dfZyVdZ0//vdhZoARYbhRZpwYYNxYxYVWf2gElWAiSCKZW+RioK13rbekdoNuRW4h6SZs0LLpkhh417cVtnIlUfOGB2qKUWqmmKCYjGjRcCMyCJ/fH34532u4UYY5A8Ph+Xw8zuPBua7P+bw/H66Z6+a8zjUnl1JKAQAAAAAAAABERESbfT0AAAAAAAAAAGhNBOkAAAAAAAAAkCFIBwAAAAAAAIAMQToAAAAAAAAAZAjSAQAAAAAAACBDkA4AAAAAAAAAGYJ0AAAAAAAAAMgQpAMAAAAAAABARum+HkBL2bp1a7z22mvRsWPHyOVy+3o4AAAAAAAAAOxjKaVYt25dVFdXR5s2u77vvGiD9Ndeey1qamr29TAAAAAAAAAAaGVWrlwZPXr02OX6og3SO3bsGBHv/gd06tRpH48GAAAAAAAAgH1t7dq1UVNTk8+Td6Vog/Rtf869U6dOgnQAAAAAAAAA8t7v68F3/UffAQAAAAAAAOAA1OQg/eGHH45TTz01qqurI5fLxfz583fZ9oILLohcLhfTpk1rtHzTpk1xySWXxCGHHBIdOnSI0aNHx6uvvtqozZo1a2LcuHFRUVERFRUVMW7cuPjrX//a1OECAAAAAAAAQJM0OUjfsGFD/P3f/33MmDHjPdvNnz8/Hn/88aiurt5h3YQJE2LevHlxxx13xKJFi2L9+vUxatSo2LJlS77N2LFjY+nSpbFgwYJYsGBBLF26NMaNG9fU4QIAAAAAAABAkzT5O9JHjhwZI0eOfM82f/rTn+Liiy+OX/7yl3HKKac0WldfXx+zZs2KOXPmxLBhwyIiYu7cuVFTUxP33XdfjBgxIp577rlYsGBBPPbYYzFw4MCIiLjpppti0KBB8fzzz8cRRxzR1GEDAAAAAAAAwG4p+Hekb926NcaNGxdf/vKX4+/+7u92WL9kyZLYvHlzDB8+PL+suro6+vXrF4sXL46IiEcffTQqKiryIXpExEc+8pGoqKjIt9nepk2bYu3atY0eAAAAAAAAANBUBQ/Sv/vd70ZpaWlceumlO11fV1cXbdu2jS5dujRaXllZGXV1dfk23bt33+G13bt3z7fZ3rXXXpv/PvWKioqoqalp5kwAAAAAAAAAOBAVNEhfsmRJ/Pu//3vMnj07crlck16bUmr0mp29fvs2WRMnToz6+vr8Y+XKlU0bPAAAAAAAAABEgYP0Rx55JFavXh09e/aM0tLSKC0tjZdffjmuuOKK6N27d0REVFVVRUNDQ6xZs6bRa1evXh2VlZX5Nq+//voO/b/xxhv5Nttr165ddOrUqdEDAAAAAAAAAJqqoEH6uHHj4ne/+10sXbo0/6iuro4vf/nL8ctf/jIiIgYMGBBlZWWxcOHC/OtWrVoVzzzzTAwePDgiIgYNGhT19fXx61//Ot/m8ccfj/r6+nwbAAAAAAAAAGgJpU19wfr16+PFF1/MP1++fHksXbo0unbtGj179oxu3bo1al9WVhZVVVVxxBFHRERERUVFnHPOOXHFFVdEt27domvXrnHllVdG//79Y9iwYRER0bdv3zj55JPjvPPOix/+8IcREXH++efHqFGj8v0AAAAAAAAAQEtocpD+5JNPxgknnJB/fvnll0dExFlnnRWzZ8/erT6mTp0apaWlMWbMmNi4cWOceOKJMXv27CgpKcm3ufXWW+PSSy+N4cOHR0TE6NGjY8aMGU0dLgAAAAAAAAA0SS6llPb1IFrC2rVro6KiIurr631fOgAAAAAAAAC7nSMX9DvSAQAAAAAAAGB/J0gHAAAAAAAAgAxBOgAAAAAAAABklO7rARSr3l+7u8mvWTHllFZXAwAAAAAAAOBA4450AAAAAAAAAMgQpAMAAAAAAABAhiAdAAAAAAAAADIE6QAAAAAAAACQIUgHAAAAAAAAgAxBOgAAAAAAAABkCNIBAAAAAAAAIEOQDgAAAAAAAAAZgnQAAAAAAAAAyBCkAwAAAAAAAECGIB0AAAAAAAAAMgTpAAAAAAAAAJAhSAcAAAAAAACADEE6AAAAAAAAAGQI0gEAAAAAAAAgQ5AOAAAAAAAAABmCdAAAAAAAAADIEKQDAAAAAAAAQIYgHQAAAAAAAAAyBOkAAAAAAAAAkCFIBwAAAAAAAIAMQToAAAAAAAAAZAjSAQAAAAAAACBDkA4AAAAAAAAAGYJ0AAAAAAAAAMgQpAMAAAAAAABARum+HgD0/trdTWq/YsopLTQS2H1+bg8se2N7+5naPU39f4o4cP+vgH2jWPZTjn1Aa2cfQqEVyzEc2JHfbwD2lDvSAQAAAAAAACBDkA4AAAAAAAAAGYJ0AAAAAAAAAMgQpAMAAAAAAABAhiAdAAAAAAAAADIE6QAAAAAAAACQIUgHAAAAAAAAgIzSfT0A2Bt6f+3uJrVfMeWUFhoJAAAAAAAA0Nq5Ix0AAAAAAAAAMgTpAAAAAAAAAJAhSAcAAAAAAACADEE6AAAAAAAAAGQ0OUh/+OGH49RTT43q6urI5XIxf/78/LrNmzfHV7/61ejfv3906NAhqqurY/z48fHaa6816mPTpk1xySWXxCGHHBIdOnSI0aNHx6uvvtqozZo1a2LcuHFRUVERFRUVMW7cuPjrX/+6R5MEAAAAAAAAgN3V5CB9w4YN8fd///cxY8aMHda99dZb8dRTT8XXv/71eOqpp+Kuu+6KF154IUaPHt2o3YQJE2LevHlxxx13xKJFi2L9+vUxatSo2LJlS77N2LFjY+nSpbFgwYJYsGBBLF26NMaNG7cHUwQAAAAAAACA3Vfa1BeMHDkyRo4cudN1FRUVsXDhwkbLpk+fHh/+8IfjlVdeiZ49e0Z9fX3MmjUr5syZE8OGDYuIiLlz50ZNTU3cd999MWLEiHjuuediwYIF8dhjj8XAgQMjIuKmm26KQYMGxfPPPx9HHHFEU4cNAAAAAAAAQAvo/bW7m/yaFVNOaYGRFE6Lf0d6fX195HK56Ny5c0RELFmyJDZv3hzDhw/Pt6muro5+/frF4sWLIyLi0UcfjYqKinyIHhHxkY98JCoqKvJttrdp06ZYu3ZtowcAAAAAAAAANFWLBulvv/12fO1rX4uxY8dGp06dIiKirq4u2rZtG126dGnUtrKyMurq6vJtunfvvkN/3bt3z7fZ3rXXXpv/PvWKioqoqakp8GwAAAAAAAAAOBC0WJC+efPmOOOMM2Lr1q3xH//xH+/bPqUUuVwu/zz77121yZo4cWLU19fnHytXrtzzwQMAAAAAAABwwGqRIH3z5s0xZsyYWL58eSxcuDB/N3pERFVVVTQ0NMSaNWsavWb16tVRWVmZb/P666/v0O8bb7yRb7O9du3aRadOnRo9AAAAAAAAAKCpCh6kbwvRly1bFvfdd19069at0foBAwZEWVlZLFy4ML9s1apV8cwzz8TgwYMjImLQoEFRX18fv/71r/NtHn/88aivr8+3AQAAAAAAAICWUNrUF6xfvz5efPHF/PPly5fH0qVLo2vXrlFdXR2f+cxn4qmnnopf/OIXsWXLlvx3mnft2jXatm0bFRUVcc4558QVV1wR3bp1i65du8aVV14Z/fv3j2HDhkVERN++fePkk0+O8847L374wx9GRMT5558fo0aNiiOOOKIQ8wYAAAAAAACAnWpykP7kk0/GCSeckH9++eWXR0TEWWedFZMmTYqf/exnERFx9NFHN3rdr371qxg6dGhEREydOjVKS0tjzJgxsXHjxjjxxBNj9uzZUVJSkm9/6623xqWXXhrDhw+PiIjRo0fHjBkzmjpcAAAAAAAAAGiSJgfpQ4cOjZTSLte/17pt2rdvH9OnT4/p06fvsk3Xrl1j7ty5TR0eAAAAAAAAADRLwb8jHQAAAAAAAAD2Z4J0AAAAAAAAAMgQpAMAAAAAAABAhiAdAAAAAAAAADIE6QAAAAAAAACQUbqvBwAAAMDu6/21u5v8mhVTTmmBkQAAAAAUL3ekAwAAAAAAAECGIB0AAAAAAAAAMgTpAAAAAAAAAJAhSAcAAAAAAACADEE6AAAAAAAAAGQI0gEAAAAAAAAgQ5AOAAAAAAAAABmCdAAAAAAAAADIEKQDAAAAAAAAQIYgHQAAAAAAAAAyBOkAAAAAAAAAkCFIBwAAAAAAAIAMQToAAAAAAAAAZAjSAQAAAAAAACBDkA4AAAAAAAAAGYJ0AAAAAAAAAMgQpAMAAAAAAABAhiAdAAAAAAAAADIE6QAAAAAAAACQUbqvBwAAFIfeX7u7ya9ZMeWUFhgJwM7ZTwEAAACwu9yRDgAAAAAAAAAZgnQAAAAAAAAAyBCkAwAAAAAAAECGIB0AAAAAAAAAMgTpAAAAAAAAAJAhSAcAAAAAAACADEE6AAAAAAAAAGQI0gEAAAAAAAAgQ5AOAAAAAAAAABmCdAAAAAAAAADIEKQDAAAAAAAAQIYgHQAAAAAAAAAyBOkAAAAAAAAAkCFIBwAAAAAAAIAMQToAAAAAAAAAZAjSAQAAAAAAACBDkA4AAAAAAAAAGU0O0h9++OE49dRTo7q6OnK5XMyfP7/R+pRSTJo0Kaqrq6O8vDyGDh0azz77bKM2mzZtiksuuSQOOeSQ6NChQ4wePTpeffXVRm3WrFkT48aNi4qKiqioqIhx48bFX//61yZPEAAAAAAAAACaoslB+oYNG+Lv//7vY8aMGTtdf91118UNN9wQM2bMiCeeeCKqqqripJNOinXr1uXbTJgwIebNmxd33HFHLFq0KNavXx+jRo2KLVu25NuMHTs2li5dGgsWLIgFCxbE0qVLY9y4cXswRQAAAAAAAADYfaVNfcHIkSNj5MiRO12XUopp06bF1VdfHaeffnpERNxyyy1RWVkZt912W1xwwQVRX18fs2bNijlz5sSwYcMiImLu3LlRU1MT9913X4wYMSKee+65WLBgQTz22GMxcODAiIi46aabYtCgQfH888/HEUccsafzBQAAAAAAAID3VNDvSF++fHnU1dXF8OHD88vatWsXQ4YMicWLF0dExJIlS2Lz5s2N2lRXV0e/fv3ybR599NGoqKjIh+gRER/5yEeioqIi32Z7mzZtirVr1zZ6AAAAAAAAAEBTFTRIr6uri4iIysrKRssrKyvz6+rq6qJt27bRpUuX92zTvXv3Hfrv3r17vs32rr322vz3qVdUVERNTU2z5wMAAAAAAADAgaegQfo2uVyu0fOU0g7Ltrd9m521f69+Jk6cGPX19fnHypUr92DkAAAAAAAAABzoChqkV1VVRUTscNf46tWr83epV1VVRUNDQ6xZs+Y927z++us79P/GG2/scLf7Nu3atYtOnTo1egAAAAAAAABAUxU0SK+trY2qqqpYuHBhfllDQ0M89NBDMXjw4IiIGDBgQJSVlTVqs2rVqnjmmWfybQYNGhT19fXx61//Ot/m8ccfj/r6+nwbAAAAAAAAAGgJpU19wfr16+PFF1/MP1++fHksXbo0unbtGj179owJEybE5MmTo0+fPtGnT5+YPHlyHHTQQTF27NiIiKioqIhzzjknrrjiiujWrVt07do1rrzyyujfv38MGzYsIiL69u0bJ598cpx33nnxwx/+MCIizj///Bg1alQcccQRhZg3AAAAAAAAAOxUk4P0J598Mk444YT888svvzwiIs4666yYPXt2fOUrX4mNGzfGhRdeGGvWrImBAwfGvffeGx07dsy/ZurUqVFaWhpjxoyJjRs3xoknnhizZ8+OkpKSfJtbb701Lr300hg+fHhERIwePTpmzJixxxMFAAAAAAAAgN3R5CB96NChkVLa5fpcLheTJk2KSZMm7bJN+/btY/r06TF9+vRdtunatWvMnTu3qcMDAAAAAAAAgGYp6HekAwAAAAAAAMD+TpAOAAAAAAAAABmCdAAAAAAAAADIEKQDAAAAAAAAQIYgHQAAAAAAAAAyBOkAAAAAAAAAkCFIBwAAAAAAAIAMQToAAAAAAAAAZAjSAQAAAAAAACBDkA4AAAAAAAAAGYJ0AAAAAAAAAMgQpAMAAAAAAABAhiAdAAAAAAAAADIE6QAAAAAAAACQIUgHAAAAAAAAgAxBOgAAAAAAAABkCNIBAAAAAAAAIEOQDgAAAAAAAAAZgnQAAAAAAAAAyBCkAwAAAAAAAECGIB0AAAAAAAAAMgTpAAAAAAAAAJAhSAcAAAAAAACADEE6AAAAAAAAAGQI0gEAAAAAAAAgQ5AOAAAAAAAAABmCdAAAAAAAAADIEKQDAAAAAAAAQIYgHQAAAAAAAAAyBOkAAAAAAAAAkCFIBwAAAAAAAIAMQToAAAAAAAAAZAjSAQAAAAAAACBDkA4AAAAAAAAAGYJ0AAAAAAAAAMgQpAMAAAAAAABAhiAdAAAAAAAAADIE6QAAAAAAAACQIUgHAAAAAAAAgAxBOgAAAAAAAABkCNIBAAAAAAAAIEOQDgAAAAAAAAAZBQ/S33nnnfiXf/mXqK2tjfLy8jj88MPjmmuuia1bt+bbpJRi0qRJUV1dHeXl5TF06NB49tlnG/WzadOmuOSSS+KQQw6JDh06xOjRo+PVV18t9HABAAAAAAAAoJGCB+nf/e534z//8z9jxowZ8dxzz8V1110X119/fUyfPj3f5rrrrosbbrghZsyYEU888URUVVXFSSedFOvWrcu3mTBhQsybNy/uuOOOWLRoUaxfvz5GjRoVW7ZsKfSQAQAAAAAAACCvtNAdPvroo/GpT30qTjnllIiI6N27d9x+++3x5JNPRsS7d6NPmzYtrr766jj99NMjIuKWW26JysrKuO222+KCCy6I+vr6mDVrVsyZMyeGDRsWERFz586NmpqauO+++2LEiBGFHjYAAAAAAAAAREQL3JH+sY99LO6///544YUXIiLit7/9bSxatCg++clPRkTE8uXLo66uLoYPH55/Tbt27WLIkCGxePHiiIhYsmRJbN68uVGb6urq6NevX74NAAAAAAAAALSEgt+R/tWvfjXq6+vjyCOPjJKSktiyZUt85zvfiX/8x3+MiIi6urqIiKisrGz0usrKynj55Zfzbdq2bRtdunTZoc22129v06ZNsWnTpvzztWvXFmxOAAAAAAAAABw4Cn5H+p133hlz586N2267LZ566qm45ZZb4t/+7d/illtuadQul8s1ep5S2mHZ9t6rzbXXXhsVFRX5R01NTfMmAgAAAAAAAMABqeBB+pe//OX42te+FmeccUb0798/xo0bF1/60pfi2muvjYiIqqqqiIgd7ixfvXp1/i71qqqqaGhoiDVr1uyyzfYmTpwY9fX1+cfKlSsLPTUAAAAAAAAADgAFD9LfeuutaNOmcbclJSWxdevWiIiora2NqqqqWLhwYX59Q0NDPPTQQzF48OCIiBgwYECUlZU1arNq1ap45pln8m22165du+jUqVOjBwAAAAAAAAA0VcG/I/3UU0+N73znO9GzZ8/4u7/7u/jNb34TN9xwQ/zTP/1TRLz7J90nTJgQkydPjj59+kSfPn1i8uTJcdBBB8XYsWMjIqKioiLOOeecuOKKK6Jbt27RtWvXuPLKK6N///4xbNiwQg8ZAAAAAAAAAPIKHqRPnz49vv71r8eFF14Yq1evjurq6rjgggviG9/4Rr7NV77yldi4cWNceOGFsWbNmhg4cGDce++90bFjx3ybqVOnRmlpaYwZMyY2btwYJ554YsyePTtKSkoKPWQAAAAAAAAAyCt4kN6xY8eYNm1aTJs2bZdtcrlcTJo0KSZNmrTLNu3bt4/p06fH9OnTCz1EAAAAAAAAANilgn9HOgAAAAAAAADszwTpAAAAAAAAAJAhSAcAAAAAAACADEE6AAAAAAAAAGQI0gEAAAAAAAAgQ5AOAAAAAAAAABmCdAAAAAAAAADIEKQDAAAAAAAAQIYgHQAAAAAAAAAyBOkAAAAAAAAAkCFIBwAAAAAAAIAMQToAAAAAAAAAZAjSAQAAAAAAACBDkA4AAAAAAAAAGYJ0AAAAAAAAAMgQpAMAAAAAAABAhiAdAAAAAAAAADIE6QAAAAAAAACQIUgHAAAAAAAAgAxBOgAAAAAAAABkCNIBAAAAAAAAIEOQDgAAAAAAAAAZgnQAAAAAAAAAyBCkAwAAAAAAAECGIB0AAAAAAAAAMgTpAAAAAAAAAJAhSAcAAAAAAACADEE6AAAAAAAAAGQI0gEAAAAAAAAgQ5AOAAAAAAAAABmCdAAAAAAAAADIEKQDAAAAAAAAQIYgHQAAAAAAAAAyBOkAAAAAAAAAkCFIBwAAAAAAAICM0n09AFq33l+7u0ntV0w5pYVG0ro19f8poun/VwdqjT35mSqWn9uWnkdr3N57o0Zr3d57g/+r3VMs+6liqNEa9yHFUqM1bu+9VYMDSzH83LbGfcjeqNEat0Wx1GiN23tPauwNxbC990YNP1O7rxi2996oUSw/U62xRmvc3nurRktrjdt7b9Rordu7GGq0xu29N2q0xm1RLDWK5Xxqb3NHOgAAAAAAAABkCNIBAAAAAAAAIEOQDgAAAAAAAAAZgnQAAAAAAAAAyBCkAwAAAAAAAECGIB0AAAAAAAAAMgTpAAAAAAAAAJDRIkH6n/70p/j85z8f3bp1i4MOOiiOPvroWLJkSX59SikmTZoU1dXVUV5eHkOHDo1nn322UR+bNm2KSy65JA455JDo0KFDjB49Ol599dWWGC4AAAAAAAAA5BU8SF+zZk189KMfjbKysrjnnnvi97//fXzve9+Lzp0759tcd911ccMNN8SMGTPiiSeeiKqqqjjppJNi3bp1+TYTJkyIefPmxR133BGLFi2K9evXx6hRo2LLli2FHjIAAAAAAAAA5JUWusPvfve7UVNTEzfffHN+We/evfP/TinFtGnT4uqrr47TTz89IiJuueWWqKysjNtuuy0uuOCCqK+vj1mzZsWcOXNi2LBhERExd+7cqKmpifvuuy9GjBhR6GEDAAAAAAAAQES0wB3pP/vZz+LYY4+Nz372s9G9e/c45phj4qabbsqvX758edTV1cXw4cPzy9q1axdDhgyJxYsXR0TEkiVLYvPmzY3aVFdXR79+/fJtAAAAAAAAAKAlFDxIf+mll2LmzJnRp0+f+OUvfxlf/OIX49JLL40f//jHERFRV1cXERGVlZWNXldZWZlfV1dXF23bto0uXbrsss32Nm3aFGvXrm30AAAAAAAAAICmKvifdt+6dWsce+yxMXny5IiIOOaYY+LZZ5+NmTNnxvjx4/Ptcrlco9ellHZYtr33anPttdfGt771rWaOHgAAAAAAAIADXcHvSD/ssMPiqKOOarSsb9++8corr0RERFVVVUTEDneWr169On+XelVVVTQ0NMSaNWt22WZ7EydOjPr6+vxj5cqVBZkPAAAAAAAAAAeWggfpH/3oR+P5559vtOyFF16IXr16RUREbW1tVFVVxcKFC/PrGxoa4qGHHorBgwdHRMSAAQOirKysUZtVq1bFM888k2+zvXbt2kWnTp0aPQAAAAAAAACgqQr+p92/9KUvxeDBg2Py5MkxZsyY+PWvfx033nhj3HjjjRHx7p90nzBhQkyePDn69OkTffr0icmTJ8dBBx0UY8eOjYiIioqKOOecc+KKK66Ibt26RdeuXePKK6+M/v37x7Bhwwo9ZAAAAAAAAADIK3iQftxxx8W8efNi4sSJcc0110RtbW1MmzYtzjzzzHybr3zlK7Fx48a48MILY82aNTFw4MC49957o2PHjvk2U6dOjdLS0hgzZkxs3LgxTjzxxJg9e3aUlJQUesgAAAAAAAAAkFfwID0iYtSoUTFq1Khdrs/lcjFp0qSYNGnSLtu0b98+pk+fHtOnT2+BEQIAAAAAAADAzhX8O9IBAAAAAAAAYH8mSAcAAAAAAACADEE6AAAAAAAAAGQI0gEAAAAAAAAgQ5AOAAAAAAAAABmCdAAAAAAAAADIEKQDAAAAAAAAQIYgHQAAAAAAAAAyBOkAAAAAAAAAkCFIBwAAAAAAAIAMQToAAAAAAAAAZAjSAQAAAAAAACBDkA4AAAAAAAAAGYJ0AAAAAAAAAMgQpAMAAAAAAABAhiAdAAAAAAAAADIE6QAAAAAAAACQIUgHAAAAAAAAgAxBOgAAAAAAAABkCNIBAAAAAAAAIEOQDgAAAAAAAAAZgnQAAAAAAAAAyBCkAwAAAAAAAECGIB0AAAAAAAAAMgTpAAAAAAAAAJAhSAcAAAAAAACADEE6AAAAAAAAAGQI0gEAAAAAAAAgQ5AOAAAAAAAAABmCdAAAAAAAAADIEKQDAAAAAAAAQIYgHQAAAAAAAAAyBOkAAAAAAAAAkCFIBwAAAAAAAIAMQToAAAAAAAAAZAjSAQAAAAAAACBDkA4AAAAAAAAAGYJ0AAAAAAAAAMgQpAMAAAAAAABAhiAdAAAAAAAAADIE6QAAAAAAAACQIUgHAAAAAAAAgAxBOgAAAAAAAABktHiQfu2110Yul4sJEybkl6WUYtKkSVFdXR3l5eUxdOjQePbZZxu9btOmTXHJJZfEIYccEh06dIjRo0fHq6++2tLDBQAAAAAAAOAA16JB+hNPPBE33nhjfOhDH2q0/LrrrosbbrghZsyYEU888URUVVXFSSedFOvWrcu3mTBhQsybNy/uuOOOWLRoUaxfvz5GjRoVW7ZsackhAwAAAAAAAHCAa7Egff369XHmmWfGTTfdFF26dMkvTynFtGnT4uqrr47TTz89+vXrF7fccku89dZbcdttt0VERH19fcyaNSu+973vxbBhw+KYY46JuXPnxtNPPx333XdfSw0ZAAAAAAAAAFouSL/ooovilFNOiWHDhjVavnz58qirq4vhw4fnl7Vr1y6GDBkSixcvjoiIJUuWxObNmxu1qa6ujn79+uXbbG/Tpk2xdu3aRg8AAAAAAAAAaKrSluj0jjvuiKeeeiqeeOKJHdbV1dVFRERlZWWj5ZWVlfHyyy/n27Rt27bRnezb2mx7/fauvfba+Na3vlWI4QMAAAAAAABwACv4HekrV66Myy67LObOnRvt27ffZbtcLtfoeUpph2Xbe682EydOjPr6+vxj5cqVTR88AAAAAAAAAAe8ggfpS5YsidWrV8eAAQOitLQ0SktL46GHHorvf//7UVpamr8Tffs7y1evXp1fV1VVFQ0NDbFmzZpdttleu3btolOnTo0eAAAAAAAAANBUBQ/STzzxxHj66adj6dKl+cexxx4bZ555ZixdujQOP/zwqKqqioULF+Zf09DQEA899FAMHjw4IiIGDBgQZWVljdqsWrUqnnnmmXwbAAAAAAAAAGgJBf+O9I4dO0a/fv0aLevQoUN069Ytv3zChAkxefLk6NOnT/Tp0ycmT54cBx10UIwdOzYiIioqKuKcc86JK664Irp16xZdu3aNK6+8Mvr37x/Dhg0r9JABAAAAAAAAIK/gQfru+MpXvhIbN26MCy+8MNasWRMDBw6Me++9Nzp27JhvM3Xq1CgtLY0xY8bExo0b48QTT4zZs2dHSUnJvhgyAAAAAAAAAAeIvRKkP/jgg42e53K5mDRpUkyaNGmXr2nfvn1Mnz49pk+f3rKDAwAAAAAAAICMgn9HOgAAAAAAAADszwTpAAAAAAAAAJAhSAcAAAAAAACADEE6AAAAAAAAAGQI0gEAAAAAAAAgQ5AOAAAAAAAAABmCdAAAAAAAAADIEKQDAAAAAAAAQIYgHQAAAAAAAAAyBOkAAAAAAAAAkCFIBwAAAAAAAIAMQToAAAAAAAAAZAjSAQAAAAAAACBDkA4AAAAAAAAAGYJ0AAAAAAAAAMgQpAMAAAAAAABAhiAdAAAAAAAAADIE6QAAAAAAAACQIUgHAAAAAAAAgAxBOgAAAAAAAABkCNIBAAAAAAAAIEOQDgAAAAAAAAAZgnQAAAAAAAAAyBCkAwAAAAAAAECGIB0AAAAAAAAAMgTpAAAAAAAAAJAhSAcAAAAAAACADEE6AAAAAAAAAGQI0gEAAAAAAAAgQ5AOAAAAAAAAABmCdAAAAAAAAADIEKQDAAAAAAAAQIYgHQAAAAAAAAAyBOkAAAAAAAAAkCFIBwAAAAAAAIAMQToAAAAAAAAAZAjSAQAAAAAAACBDkA4AAAAAAAAAGYJ0AAAAAAAAAMgQpAMAAAAAAABAhiAdAAAAAAAAADIE6QAAAAAAAACQIUgHAAAAAAAAgIyCB+nXXnttHHfccdGxY8fo3r17nHbaafH88883apNSikmTJkV1dXWUl5fH0KFD49lnn23UZtOmTXHJJZfEIYccEh06dIjRo0fHq6++WujhAgAAAAAAAEAjBQ/SH3roobjooovisccei4ULF8Y777wTw4cPjw0bNuTbXHfddXHDDTfEjBkz4oknnoiqqqo46aSTYt26dfk2EyZMiHnz5sUdd9wRixYtivXr18eoUaNiy5YthR4yAAAAAAAAAOSVFrrDBQsWNHp+8803R/fu3WPJkiVx/PHHR0oppk2bFldffXWcfvrpERFxyy23RGVlZdx2221xwQUXRH19fcyaNSvmzJkTw4YNi4iIuXPnRk1NTdx3330xYsSIQg8bAAAAAAAAACJiL3xHen19fUREdO3aNSIili9fHnV1dTF8+PB8m3bt2sWQIUNi8eLFERGxZMmS2Lx5c6M21dXV0a9fv3yb7W3atCnWrl3b6AEAAAAAAAAATdWiQXpKKS6//PL42Mc+Fv369YuIiLq6uoiIqKysbNS2srIyv66uri7atm0bXbp02WWb7V177bVRUVGRf9TU1BR6OgAAAAAAAAAcAFo0SL/44ovjd7/7Xdx+++07rMvlco2ep5R2WLa992ozceLEqK+vzz9Wrly55wMHAAAAAAAA4IDVYkH6JZdcEj/72c/iV7/6VfTo0SO/vKqqKiJihzvLV69enb9LvaqqKhoaGmLNmjW7bLO9du3aRadOnRo9AAAAAAAAAKCpCh6kp5Ti4osvjrvuuiseeOCBqK2tbbS+trY2qqqqYuHChfllDQ0N8dBDD8XgwYMjImLAgAFRVlbWqM2qVavimWeeybcBAAAAAAAAgJZQWugOL7roorjtttvif/7nf6Jjx475O88rKiqivLw8crlcTJgwISZPnhx9+vSJPn36xOTJk+Oggw6KsWPH5tuec845ccUVV0S3bt2ia9euceWVV0b//v1j2LBhhR4yAAAAAAAAAOQVPEifOXNmREQMHTq00fKbb745zj777IiI+MpXvhIbN26MCy+8MNasWRMDBw6Me++9Nzp27JhvP3Xq1CgtLY0xY8bExo0b48QTT4zZs2dHSUlJoYcMAAAAAAAAAHkFD9JTSu/bJpfLxaRJk2LSpEm7bNO+ffuYPn16TJ8+vYCjAwAAAAAAAID3VvDvSAcAAAAAAACA/ZkgHQAAAAAAAAAyBOkAAAAAAAAAkCFIBwAAAAAAAIAMQToAAAAAAAAAZAjSAQAAAAAAACBDkA4AAAAAAAAAGYJ0AAAAAAAAAMgQpAMAAAAAAABAhiAdAAAAAAAAADIE6QAAAAAAAACQIUgHAAAAAAAAgAxBOgAAAAAAAABkCNIBAAAAAAAAIEOQDgAAAAAAAAAZgnQAAAAAAAAAyBCkAwAAAAAAAECGIB0AAAAAAAAAMgTpAAAAAAAAAJAhSAcAAAAAAACADEE6AAAAAAAAAGQI0gEAAAAAAAAgQ5AOAAAAAAAAABmCdAAAAAAAAADIEKQDAAAAAAAAQIYgHQAAAAAAAAAyBOkAAAAAAAAAkCFIBwAAAAAAAIAMQToAAAAAAAAAZAjSAQAAAAAAACBDkA4AAAAAAAAAGYJ0AAAAAAAAAMgQpAMAAAAAAABAhiAdAAAAAAAAADIE6QAAAAAAAACQIUgHAAAAAAAAgAxBOgAAAAAAAABkCNIBAAAAAAAAIEOQDgAAAAAAAAAZgnQAAAAAAAAAyBCkAwAAAAAAAECGIB0AAAAAAAAAMlp9kP4f//EfUVtbG+3bt48BAwbEI488sq+HBAAAAAAAAEARa9VB+p133hkTJkyIq6++On7zm9/Exz/+8Rg5cmS88sor+3poAAAAAAAAABSpVh2k33DDDXHOOefEueeeG3379o1p06ZFTU1NzJw5c18PDQAAAAAAAIAi1WqD9IaGhliyZEkMHz680fLhw4fH4sWL99GoAAAAAAAAACh2pft6ALvy5ptvxpYtW6KysrLR8srKyqirq9uh/aZNm2LTpk355/X19RERsXbt2pYd6C5s3fRWk1/T1LG2xhp78v9dDDVa47YolhqtcXsXS43WuL33Ro3WuC2KpUZr3N57o0Zr3BbFUqM1bu9iqdEat/feqNEat0Wx1GiN27tYarTG7b03arTGbVEsNVrj9t4bNVrjtiiWGq1xexdLjda4vfdGjda4LYqlRmvc3nujRmvcFsVSozVu72Kp0Rq3996o0Rq3RbHUaI3be09qFMq2uiml92yXS+/XYh957bXX4gMf+EAsXrw4Bg0alF/+ne98J+bMmRN/+MMfGrWfNGlSfOtb39rbwwQAAAAAAABgP7Ny5cro0aPHLte32jvSDznkkCgpKdnh7vPVq1fvcJd6RMTEiRPj8ssvzz/funVr/OUvf4lu3bpFLpfbrZpr166NmpqaWLlyZXTq1Kl5E9gH/auhhp9bNfaHGsUwBzVaV41imIMaratGMcxBjQOvRjHMQY3WVaMY5qBG66pRDHNQo3XVKIY5qHHg1SiGOajRumoUwxzUaF01imEOarSuGq11DimlWLduXVRXV79nu1YbpLdt2zYGDBgQCxcujE9/+tP55QsXLoxPfepTO7Rv165dtGvXrtGyzp0771HtTp06tdjG3Bv9q6HG/ti/GgdejWKYgxqtq0YxzEGN1lWjGOagxoFXoxjmoEbrqlEMc1CjddUohjmo0bpqFMMc1DjwahTDHNRoXTWKYQ5qtK4axTAHNVpXjdY4h4qKivdt02qD9IiIyy+/PMaNGxfHHntsDBo0KG688cZ45ZVX4otf/OK+HhoAAAAAAAAARapVB+mf+9zn4s9//nNcc801sWrVqujXr1/87//+b/Tq1WtfDw0AAAAAAACAItWqg/SIiAsvvDAuvPDCvVKrXbt28c1vfnOHPxG/v/Svhhr7Y/9qHHg1imEOarSuGsUwBzVaV41imIMaB16NYpiDGq2rRjHMQY3WVaMY5qBG66pRDHNQ48CrUQxzUKN11SiGOajRumoUwxzUaF019vc55FJKqeC9AgAAAAAAAMB+qs2+HgAAAAAAAAAAtCaCdAAAAAAAAADIEKQDAAAAAAAAQIYgHQAAAAAAAAAyBOkc0FJK+3oIAAAAAAAAQCsjSN+J119/Pa655ppm9/Pqq6/G1VdfHSeccEL07ds3jjrqqDjhhBPi6quvjpUrVxZgpDRXu3bt4rnnntvXw6AFrFq1Kr7xjW/EJz7xiejbt2/069cvTj311Jg1a1Zs2bJlXw8PaOU2bNgQN910U3zhC1+IkSNHxic/+cn4whe+EP/1X/8VGzZsaPH6hToXiXj3fGT9+vU7LN+8eXM8/PDDze7/z3/+c/zqV7+Kv/zlLxER8eabb8Z3v/vduOaaa1r0GHv44YfHsmXLCt7v5s2bY/78+XH99dfH3LlzC7K9X3311XjzzTfzzx955JE488wz4+Mf/3h8/vOfj0cffbTZNb73ve/Fyy+/3Ox+3s/Pf/7z+OY3v5kf8wMPPBCf/OQn4+STT44bb7yxIDU2btwYP/rRj+Kf/umfYuTIkTFq1Ki45JJL4v777y9I/7A32Z+/v5ban0fsn/t0+3M4sDluvL/96bjhOqBpHDdg5/ble1SFPC5tsz+eo0fY3zZFMb2vGhERiR0sXbo0tWnTpll9PPLII+nggw9Offv2TZdddlmaPHly+s53vpMuu+yydNRRR6WOHTumRYsWFWjEu/bKK6+kL3zhC83q46233kqPPPJIevbZZ3dYt3HjxnTLLbc0q/+UUvr973+ffvSjH6XnnnsupZTSc889l774xS+mL3zhC+n+++9vdv9f+tKXdvpo06ZNGj9+fP55If3lL39JU6dOTRdeeGH613/91/TKK680u8+nnnoqvfTSS/nnc+bMSYMHD049evRIH/3oR9Ptt9/erP4vvvji9PDDDzd3mO/r+9//fho/fny68847U0op/fjHP059+/ZNRxxxRJo4cWLavHlzs/p/4oknUkVFRTr66KPToEGDUps2bdK4cePS5z73udS5c+c0aNCgtHbt2mbPY/369enGG29MZ599djr55JPTyJEj09lnn51uuummtH79+mb3/37q6urSt771rYL0tXLlyrRu3bodljc0NKSHHnqo2f2/+eab6YEHHkh//vOfU0opvfHGG2nKlCnpW9/6Vvr973/f7P53pba2Nr3wwgst0ndDQ0OaN29euu6669KcOXOavc1XrlyZ3njjjfzzhx9+OI0dOzZ97GMfS2eeeWZavHhxc4ec/u3f/i2tWLGi2f28n5/97GfpG9/4Rn7M999/fxo5cmQaMWJE+uEPf1iQGm+99VaaNWtW+sIXvpBOPvnkdMopp6SLL7443Xfffc3u+9lnn03V1dWpc+fO6VOf+lQ6//zz03nnnZc+9alPpc6dO6cPfOADOz0mFlIhzkVee+21dNxxx6U2bdqkkpKSNH78+Ea/53V1dc2u8fjjj6eKioqUy+VSly5d0pNPPplqa2tTnz590gc/+MFUXl6elixZ0qwa//7v/77TR0lJSZo4cWL++Z4aNGhQWrNmTUoppdWrV6f+/funtm3bpj59+qT27dunnj17pldffbVZcxg0aFD63//935RSSvPnz09t2rRJo0ePTl/96lfTpz/96VRWVpZ+/vOfN6tGLpdLJSUladiwYemOO+5ImzZtalZ/OzNz5sxUWlqaBgwYkDp16pTmzp2bOnbsmM4999x0wQUXpPLy8jRt2rRm1Vi2bFnq1atX6tatWzrssMNSLpdLp5xySho4cGAqKSlJn/3sZ5t9npDS/n8Md8xompY8Zrwf+/P/p6X35ykVxz7d/rxp9vf9eZZrsqbbH6/J3o/jxv9TDMcN1wG7r9iPG/aFjbnW2H37+j2qQhyXiuEcPSX72921r39mUyrMz23WARmk//a3v33Px5133tns/+Rjjz02TZgwYZfrJ0yYkI499thm1dgdzf2Bef7551OvXr1SLpdLbdq0SUOGDEmvvfZafn0hTr7vueee1LZt29S1a9fUvn37dM8996RDDz00DRs2LJ144omptLS02WF6LpdLRx99dBo6dGijRy6XS8cdd1waOnRoOuGEE5pV47DDDktvvvlmSimll156KVVVVaWqqqp00kknpR49eqSKior8BwX21DHHHJMeeOCBlFJKN910UyovL0+XXnppmjlzZpowYUI6+OCD06xZs/a4/23buU+fPmnKlClp1apVzRrvzlxzzTWpY8eO6R/+4R9SVVVVmjJlSurWrVv69re/nSZPnpwOPfTQ9I1vfKNZNT760Y+mSZMm5Z/PmTMnDRw4MKX07gccjj766HTppZc2q0axHBBcYO8+F9i7pxhO+IYOHZrOOOOMnf7/bNq0Kf3jP/5jGjp0aHOmsFfORcaPH58+8pGPpCeeeCItXLgwHXvssWnAgAHpL3/5S0rp3d/vXC7XrBrDhg1L5557blq7dm26/vrrU48ePdK5556bX3/OOeek0047rVk1crlc6tGjR+rdu3ejRy6XSx/4wAdS7969U21tbbP6f/3111NKKZ133nnp6KOPzh//3nzzzTR48OD0T//0T82aQ8eOHdPy5ctTSikNHDgwTZkypdH66dOnp2OOOaZZNXK5XLr55pvTpz71qVRWVpa6deuWLrvssvT00083q9+svn37phtvvDGllNIDDzyQ2rdvn37wgx/k1998882pb9++zaoxcuTIdMEFF6QtW7aklFK69tpr08iRI1NKKb3wwgupd+/e6Zvf/GazahTDMdwxY/e19DHD/nz3tfT+fFuN/X2fbn+++4phf56Sa7KmKIZrMseN3VcMxw3XAbuvWI4b9oW7x7XG7mvp96j2xnGpGM7RU7K/3V3F8r5q1gEZpG8LC3O53A6Pbcub+5/cvn379Ic//GGX65977rnUvn37ZtVIKaX/+Z//ec/H1KlTmzWX0047LY0aNSq98cYbadmyZenUU09NtbW16eWXX04pFeaCbtCgQenqq69OKaV0++23py5duqSrrroqv/6qq65KJ510UrNqTJ48OdXW1u4QyJeWlhbswjp7QDjjjDPS0KFD04YNG1JKKb399ttp1KhR6TOf+Uyzahx00EH5//tjjjlmh0/O3Xrrremoo47a4/5zuVy677770mWXXZYOOeSQVFZWlkaPHp1+/vOf53eszXX44Yen//7v/04pvfumQ0lJSZo7d25+/V133ZU++MEPNqtGeXl5+uMf/5h/vmXLllRWVpbq6upSSinde++9qbq6ulk1iuWA4AK7aTVcYL+/YjjhKy8vf89jw9NPP53Ky8v3uP+U9s65SHV1dXr88cfzz99+++30qU99Kh199NHpz3/+c0GO4V26dMnfwdTQ0JDatGnTqOZTTz2VPvCBDzSrxvnnn5+OPvroHe6UKtQxPPu7/bd/+7fpF7/4RaP1v/rVr1Lv3r2bVaOioiL99re/TSml1L179/y/t3nxxRfTQQcd1Kwa2Xm8/vrr6bvf/W468sgjU5s2bdJxxx2Xbrzxxmb/NZby8vL8eUhKKZWVlTXafyxfvrzZ8zjooIMa3amxadOmVFZWlv+w4vz585u9PYrhGO6Ysfta+phhf777Wnp/nlJx7NPtz3dfMezPU3JN1tQaxXBN5rixe4rhuOE6YPcVy3HDvnD3uNbYfS39HtXeOC4Vwzl6Sva3u6tY3lfNOiCD9EMOOSTNmjUrrVixYqePu+++u9n/ybW1telHP/rRLtf/6Ec/avZBM6X3/oHJ/uDsqe7du6ff/e53jZZdeOGFqWfPnumPf/xjQU6+O3XqlJYtW5ZSejfwLC0tbfRJ6KeffjpVVlY2q0ZKKf36179Of/u3f5uuuOKK1NDQkFJquZPvnYX2jz32WOrRo0ezanTr1i09+eSTKaV3t83SpUsbrX/xxRebfeDcNoeGhoZ05513phEjRqSSkpJUXV2drrrqqvy22lM7Oxg888wz+ecrVqxo9sGgV69ejb464bXXXku5XC699dZbKaV3DzjN/SBLsRwQXGDvPhfYu6cYTviqq6vT/Pnzd7l+3rx5zf4wzt44F+nQocMOfzpu8+bN6bTTTksf+tCH0u9+97uC1Nh2gZ1SSgcffHCjDzK9/PLLBfng4Lx581JNTU2aPn16flkhg/TVq1enlN793du+zxUrVqR27do1q8bo0aPT1772tZRSSiNGjNjhDoSbbrop9enTp1k1sr/fWQ8//HA666yzUocOHVKHDh2aVaNHjx75r4D505/+lHK5XLr77rvz6x988MFmn+tUV1c3Og9cs2ZNyuVy+X3TSy+91OztUQzHcMeM3dfSxwz786Zpyf15SsWxT7c/333FsD9PyTVZUxTDNZnjRtPs78cN1wG7r1iOG/aFu8e1xu5r6feo9sZxqRjO0VOyv21K/8XwvmrWARmkjxgxIv3rv/7rLtcvXbq02Z/2/cEPfpDatm2bLrroojR//vz06KOPpsceeyzNnz8/XXTRRaldu3Zp5syZzaqR0rs/lPPmzdvl+t/85jfNvttlZ9+VdfHFF+d/qQsZpKe048n3ihUrCnLynVJK69atS+PHj89fOJSVlbXIyXd1dXWjcDildw/OzT0gfP7zn0/nnHNOSimlz372s+lf/uVfGq2fPHly6t+//x73v6uDwcsvv5y++c1vpl69ehXkQyb33HNPSundT+a1adMm/eQnP8mvv/vuu5t9gnHZZZelfv36pXvuuSc98MAD6YQTTmj0idIFCxakv/mbv2lWjWI5ILjA3n0usHdPMZzwffOb30wVFRXp+uuvT0uXLk2rVq1KdXV1aenSpen6669PXbp0afZ3Ye6Nc5H+/funn/70pzss3/Y73rNnz2b/fh955JGNPjj2i1/8Iv+hpZQK8yGybV599dX0iU98Ip188slp1apVBQ3SP/nJT6ZPf/rTqUuXLvk/YbfNo48+2uwP9P3+979P3bp1S+PHj0//+q//mg4++OD0+c9/Pn3nO99J48ePT+3atUs333xzs2q0adNmp7/f29TX1+c/jb+nLrrootSnT5/07W9/O334wx9OZ511VjryyCPTPffckxYsWJD69+/f7D/HdtZZZ6UhQ4ak5557Lr300kvpc5/7XKO7HR588MFUU1PTrBrFcAx3zNh9LX3MsD9vupban6dUHPt0+/PdVwz785RckzVFMVyTOW403f583HAdsPuK5bixrR/7wvfmWmP3tfR7VHvjuFQM5+gp2d/urmJ5XzXrgAzS77rrrjRnzpxdrv/LX/6SZs+e3ew6d9xxRxo4cGAqLS3Nf2K5tLQ0DRw4MN15553N7j+llE499dT09a9/fZfrm/sDc9xxx6Uf//jHO1130UUXpc6dOzf75PtDH/pQPlhN6d1P/2W/O+SRRx4pyN37WbfffnuqrKxMbdq0KehJTP/+/dMxxxyTDj744HTXXXc1Wv/QQw81+xPef/rTn1Lv3r3T8ccfny6//PJUXl6ePvaxj6XzzjsvHX/88alt27aNTgj2ZA7vdTDYunVruvfee/e4/5RSuvrqq9Ohhx6azj333FRbW5smTpyYevbsmWbOnJn+8z//M9XU1KQvfelLzaqxbt26NGbMmPzv3uDBg9NLL72UX//LX/6yUXi/J4rlgOACe/e5wN49xXDCl1JKU6ZMyX+/VZs2bfJ3HB122GHpu9/9brP6TmnvnIt85StfScOHD9/pus2bN6fRo0c3+/d70qRJ6fbbb9/l+quuuiqdfvrpzaqRtXXr1jR58uRUVVWVSkpKCvL7ffbZZzd6bH98uPLKK9OIESOaXefFF19MZ5xxRurYsWP+vLCsrCwNHjz4PT8Uubve7xheCOvXr0/nnntu6tevX/riF7+YGhoa0vXXX5/atm2bcrlcGjp0aLPH8Prrr6ePfOQj+d+93r17p6eeeiq//v/8n/+Tvv/97zerRjEcwx0zdl9LHzPsz/dMS+zPUyqOfbr9+e4rhv15Sq7JmqIYrskcN/bM/nzccB2we4rluLGNfeF7c63RNC35HtXeOC4Vwzl6Sva3TVEM76tmHZBB+s4sWrQovf322y3Sd0NDQ3rttdfSa6+9lv+T4oXy8MMPNwqht7d+/fr04IMP7nH/kydPzn+vx8788z//c7MvGmfOnLnDn6DJuuqqq/J3YRfSypUr0/z589P69esL0t+kSZMaPRYsWNBo/ZVXXpnOOOOMZtdZs2ZN+upXv5qOOuqo1L59+9S2bdvUq1evNHbs2PTEE080q+/evXvn//RMS3nnnXfSt7/97TRq1Kj8d+zcfvvtqaamJnXr1i2dffbZBdsmGzduTOvWrStIXztTDAeE3bnAbu7vuAvs3eeEb/fsjRO+bV566aW0ePHitHjx4kYfyNkfbN68OdXX1+9y/TvvvJNWrFjRomPYsGFDi5xfPfnkk2natGn57w5tSevXr08bN24sWH9bt25NdXV1LXJeuK9s3Lix2X92b3svvPDCDh+uLKRiOIa/+OKL6XOf+5xjxvvYm8eMrK1btxasr13tz7fVaMn9+bYaLbk/v+GGG1p8f75tHi25T9/Z960WUiF/pnZlw4YNLb4/L/Q8imF/LvTcfcVwTbY3uA4onEIeN1wH7J79/Togq6X2hWedddZ+vy90rbFn9uf3qN6L913e3/64v02peH5mcymlFESnTp1i6dKlcfjhh+/roQD7meXLl0ddXV1ERFRVVUVtbe0+HtHue+edd+Ktt96KTp067XT9li1b4tVXX41evXq12BjeeuutKCkpiXbt2hW03yVLlsSiRYti/Pjx0aVLl4L2vTMbNmyIkpKSaN++fbP7SinF6tWrY+vWrXHIIYdEWVlZAUa4b7399tuxefPm6NixY8H6XLZsWWzatCmOPPLIKC0tLVi/wIFjfz6Gb+OYsXv29jGjbdu28dvf/jb69u2rhhr7Rf/7e439eX9ezNdkTz31VDzyyCOuyYBWZ28eN7w/1bq0xLXGCy+8EA0NDd6fgv9r1apVMXPmzFi0aFGsWrUqSkpKora2Nk477bQ4++yzo6SkZL+osY3f6v/L5wmAPVVbW7vDCffKlSvjm9/8ZvzoRz9qsbqFqFFaWrrLN2wiIl577bX41re+1aLz+POf/9wi/1cDBgyIAQMGRMTe2R5/+ctfClYjl8tFZWVlo2X7y8/UrrRv3z7at29f0Bp9+vTZ6fJC1Ni4cWMsWbIkunbtGkcddVSjdW+//Xb85Cc/ifHjx+9x/2q0rhrFMAc1mua5556Lxx57LAYPHhyDBg2KP/zhD3HdddfFpk2b4vOf/3x84hOfaFb/2RqDBg2KI488Mv7whz/Ev//7vxesRnYORxxxRPzhD3+Ia665Zr+aw67m0ZI1SktLC1rj8ssv3+nyLVu2xJQpU6Jbt24REXHDDTeooUar6L+YamTV1tZG586d45Zbbolly5ZFdXV1jB8/PmpqagrS/zZr1qzJ1zjssMPirLPOanaN7a/Jtq9x9tlnFzxEb4l57KzGww8/HMuWLYsf/OAHLVajpeaRy+Wibdu2Bf+Z+s1vfhOdO3fOv4cwd+7cmDlzZrzyyivRq1evuPjii+OMM85Qo0hqFMMciqnGJZdcEmPGjImPf/zjO30vrxCyNbbJvj/VUjWyOnToULD+d/b+VCG83xxassa296daskahTZ8+PZ588sk45ZRTYsyYMTFnzpy49tprY+vWrXH66afHNddc06wAv6X7L5YaKaWYMWPGfj+Plq7x5JNPxrBhw6K2tjbKy8vjhRdeiDPPPDMaGhriyiuvjFmzZsUvf/nLZn2YZW/UaGTf3Qzfuhx88MHpj3/8474eBlAkli5d2uw/v6eGGnuzfzX+n+effz716tUr/6e5hgwZkl577bX8+rq6umbPQY3WU6MY5qBG09xzzz2pbdu2qWvXrql9+/bpnnvuSYceemgaNmxYOvHEE1NpaWmj75FtjTWKYQ7FUiOXy6Wjjz46DR06tNEjl8ul4447Lg0dOjSdcMIJzZqDGgdWjWKYw96qcdhhh+W/muyll15Khx12WKqqqkonnXRS6tGjR6qoqEjPPfdcQWtUVVWpUcQ19sYcjjnmmPTAAw+klFK66aabUnl5ebr00kvTzJkz04QJE9LBBx+cZs2apUaR1CiGORRTjW3XGH369ElTpkxJq1atalZ/xVqjGOZQTDWuueaa1LFjx/QP//APqaqqKk2ZMiV169Ytffvb306TJ09Ohx56aPrGN77RavtX48Cr8dGPfjRNmjQp/3zOnDlp4MCBKaV3vxbp6KOPTpdeemmz5rA3amQJ0v+vW2+9tWDfywwUv//5n/95z8fUqVOb/Ua/GgdWjWKYQ7HUOO2009KoUaPSG2+8kZYtW5ZOPfXUVFtbm15++eWUUmGCPDVaT41imIMaTTNo0KB09dVXp5RSuv3221OXLl3SVVddlV9/1VVXpZNOOqlV1yiGORRLjcmTJ6fa2todwvjS0tKCfQ+mGgdWjWKYw96qkf1+1TPOOCMNHTo0bdiwIaWU0ttvv51GjRqVPvOZz6ihRqvpP6WUDjrooPx5zTHHHJN++MMfNlp/6623pqOOOkqNIqlRDHMophq5XC7dd9996bLLLkuHHHJIKisrS6NHj04///nP05YtW5rVdzHVKIY5FFONww8/PP33f/93SundG0dKSkrS3Llz8+vvuuuu9MEPfrDV9q/GgVejvLy80U3LW7ZsSWVlZamuri6llNK9996bqqur97j/vVUjS5AOsAe2feIwl8vt8tHcN/rVOLBqFMMciqVG9+7d0+9+97tGyy688MLUs2fP9Mc//rEgQZ4aradGMcxBjabp1KlTWrZsWUrp3Yut0tLStGTJkvz6p59+OlVWVrbqGsUwh2Kq8etf/zr97d/+bbriiitSQ0NDSqmwYaEaB16NYpjD3qiRDT13Fto/9thjqUePHmqo0Wr6Tymlbt26pSeffDKl9O55z9KlSxutf/HFF1N5ebkaRVKjGOZQTDWyv+MNDQ3pzjvvTCNGjEglJSWpuro6XXXVVfnzxgO5RjHMoZhqlJeX5z9kklJKZWVl6Zlnnsk/X7FiRTrooINabf9qHHg1evXqlRYtWpR//tprr6VcLpfeeuutlFJKy5cvT+3bt9/j/vdWjaw2hfkD8QAHlsMOOyz++7//O7Zu3brTx1NPPaWGGq2qfzV238aNG3f4HqAf/OAHMXr06BgyZEi88MILzepfjdZVoxjmoMaea9OmTbRv3z46d+6cX9axY8eor6/fb2oUwxz29xrHHXdcLFmyJN5444049thj4+mnn45cLtfM0apxINcohjnsrRrb+tu0adMO3+FaWVkZb7zxhhpqtKr+R44cGTNnzoyIiCFDhsRPf/rTRut/8pOfxAc/+EE1iqRGMcyhmGpklZWVxZgxY2LBggXx0ksvxXnnnRe33nprHHHEEWrsxf7VeH9VVVXx+9//PiIili1bFlu2bMk/j4h49tlno3v37q22fzUOvBqnnXZafPGLX4wFCxbEr371qzjzzDNjyJAhUV5eHhERzz//fHzgAx9o1hz2Ro1GChbJAxxATj311PT1r399l+uXLl2acrmcGmq0mv7V2H3HHXdc+vGPf7zTdRdddFHq3Llzs++IVaP11CiGOajRNB/60IfSPffck3/+9NNPp82bN+efP/LII6m2trZV1yiGORRTjazbb789VVZWpjZt2hT0zl41DtwaxTCHlqqRy+VS//790zHHHJMOPvjgdNdddzVa/9BDD6UPfOADaqjRavpPKaU//elPqXfv3un4449Pl19+eSovL08f+9jH0nnnnZeOP/741LZt23T33XerUSQ1imEOxVQje/fwzmzdujXde++9B3yNYphDMdW4+uqr06GHHprOPffcVFtbmyZOnJh69uyZZs6cmf7zP/8z1dTUpC996Uuttn81Drwa69atS2PGjEmlpaUpl8ulwYMHp5deeim//pe//GX6yU9+0qw57I0aWYJ0gD3w8MMPN3pTdnvr169PDz74oBpqtJr+1dh9kydPTiNHjtzl+n/+539u9ocB1Gg9NYphDmo0zcyZM9MvfvGLXa6/6qqr0jnnnNOqaxTDHIqpxvZWrlyZ5s+fn9avX1/QftU4cGsUwxxaosakSZMaPRYsWNBo/ZVXXpnOOOMMNdRoNf1vs2bNmvTVr341HXXUUal9+/apbdu2qVevXmns2LHpiSeeaHb/arSuGsUwh2Kp0bt37/Tmm28WYKTFXaMY5lBMNd5555307W9/O40aNSpNmTIlpfTuBxRrampSt27d0tlnn92sc6uW7l+NA7NGSilt3LgxrVu3rtn97OsaKaWUSymlwt3fDgAAAAAAAAD7N9+RDgAAAAAAAAAZgnQAAAAAAAAAyBCkAwAAAAAAAECGIB0AAAAAAAAAMgTpAAAAUAR69+4d06ZN2+PXz549Ozp37px/PmnSpDj66KObPa6soUOHxoQJEwraJwAAALQEQToAAAAcYHYWun/uc5+LF154Yd8MCAAAAFqZ0n09AAAAAOBdDQ0N0bZt231Su7y8PMrLy/dJbQAAAGht3JEOAAAALWTo0KFx8cUXx8UXXxydO3eObt26xb/8y79ESiki3r0z/Nvf/nacffbZUVFREeedd15ERCxevDiOP/74KC8vj5qamrj00ktjw4YN+X5Xr14dp556apSXl0dtbW3ceuutO9SeNGlS9OzZM9q1axfV1dVx6aWX5sf08ssvx5e+9KXI5XKRy+UiYsc/7b7NnDlzonfv3lFRURFnnHFGrFu3brfmvmHDhhg/fnwcfPDBcdhhh8X3vve9Jv3fAQAAwL4kSAcAAIAWdMstt0RpaWk8/vjj8f3vfz+mTp0a//Vf/5Vff/3110e/fv1iyZIl8fWvfz2efvrpGDFiRJx++unxu9/9Lu68885YtGhRXHzxxfnXnH322bFixYp44IEH4qc//Wn8x3/8R6xevTq//qc//WlMnTo1fvjDH8ayZcti/vz50b9//4iIuOuuu6JHjx5xzTXXxKpVq2LVqlW7HPsf//jHmD9/fvziF7+IX/ziF/HQQw/FlClTdmveX/7yl+NXv/pVzJs3L+6999548MEHY8mSJU397wMAAIB9wp92BwAAgBZUU1MTU6dOjVwuF0cccUQ8/fTTMXXq1Pzd55/4xCfiyiuvzLcfP358jB07NiZMmBAREX369Invf//7MWTIkJg5c2a88sorcc8998Rjjz0WAwcOjIiIWbNmRd++ffN9vPLKK1FVVRXDhg2LsrKy6NmzZ3z4wx+OiIiuXbtGSUlJdOzYMaqqqt5z7Fu3bo3Zs2dHx44dIyJi3Lhxcf/998d3vvOd93zd+vXrY9asWfHjH/84TjrppIh49wMFPXr0aML/HAAAAOw77kgHAACAFvSRj3wk/+fTIyIGDRoUy5Ytiy1btkRExLHHHtuo/ZIlS2L27Nlx8MEH5x8jRoyIrVu3xvLly+O5556L0tLSRq878sgjG/1Z9s9+9rOxcePGOPzww+O8886LefPmxTvvvNPksffu3TsfokdEHHbYYY3ufN+VP/7xj9HQ0BCDBg3KL+vatWscccQRTR4DAAAA7AuCdAAAANiHOnTo0Oj51q1b44ILLoilS5fmH7/97W9j2bJl8Td/8zf571fPhvPbq6mpieeffz5+8IMfRHl5eVx44YVx/PHHx+bNm5s0trKyskbPc7lcbN269X1ft22MAAAAsL8SpAMAAEALeuyxx3Z43qdPnygpKdlp+//v//v/4tlnn40PfvCDOzzatm0bffv2jXfeeSeefPLJ/Guef/75+Otf/9qon/Ly8hg9enR8//vfjwcffDAeffTRePrppyMiom3btvk74lvCBz/4wSgrK2s09zVr1sQLL7zQYjUBAACgkATpAAAA0IJWrlwZl19+eTz//PNx++23x/Tp0+Oyyy7bZfuvfvWr8eijj8ZFF10US5cujWXLlsXPfvazuOSSSyIi4ogjjoiTTz45zjvvvHj88cdjyZIlce6550Z5eXm+j9mzZ8esWbPimWeeiZdeeinmzJkT5eXl0atXr4h490+2P/zww/GnP/0p3nzzzYLP+eCDD45zzjknvvzlL8f9998fzzzzTJx99tnRpo23IQAAANg/uIIFAACAFjR+/PjYuHFjfPjDH46LLrooLrnkkjj//PN32f5DH/pQPPTQQ7Fs2bL4+Mc/Hsccc0x8/etfj8MOOyzf5uabb46ampoYMmRInH766XH++edH9+7d8+s7d+4cN910U3z0ox+ND33oQ3H//ffHz3/+8+jWrVtERFxzzTWxYsWK+Ju/+Zs49NBDW2Te119/fRx//PExevToGDZsWHzsYx+LAQMGtEgtAAAAKLRc8sVlAAAA0CKGDh0aRx99dEybNm1fDwUAAABoAnekAwAAAAAAAECGIB0AAABokldeeSUOPvjgXT5eeeWVfT1EAAAAaBZ/2h0AAABoknfeeSdWrFixy/W9e/eO0tLSvTcgAAAAKDBBOgAAAAAAAABk+NPuAAAAAAAAAJAhSAcAAAAAAACADEE6AAAAAAAAAGQI0gEAAAAAAAAgQ5AOAAAAAAAAABmCdAAAAAAAAADIEKQDAAAAAAAAQIYgHQAAAAAAAAAy/n/CwbKerX7rLQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 2500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['odkud'].str.contains('Brno') & df['kam'].str.contains('Hamb')].groupby('predstih_d')['cena'].min().plot.bar(figsize=(25,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "59dbd640-597e-4f60-800c-99ec5a80b6dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>oscrapovano</th>\n",
       "      <th>prodejce</th>\n",
       "      <th>odkud</th>\n",
       "      <th>kam</th>\n",
       "      <th>odjezd</th>\n",
       "      <th>predstih</th>\n",
       "      <th>predstih_d</th>\n",
       "      <th>predstih_h</th>\n",
       "      <th>cena</th>\n",
       "      <th>prostredek</th>\n",
       "      <th>volnych_mist</th>\n",
       "      <th>obsazenost</th>\n",
       "      <th>jizdni_doba</th>\n",
       "      <th>vzdalenost</th>\n",
       "      <th>zpozdeni</th>\n",
       "      <th>cena_poznamka</th>\n",
       "      <th>den</th>\n",
       "      <th>prestupy</th>\n",
       "      <th>vlaky</th>\n",
       "      <th>mistenka_zdarma</th>\n",
       "      <th>nahradni_bus</th>\n",
       "      <th>volna_mista_economy</th>\n",
       "      <th>volna_mista_economy_plus</th>\n",
       "      <th>volna_mista_economy_business</th>\n",
       "      <th>volna_mista_premium</th>\n",
       "      <th>predstih_i</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>405246</th>\n",
       "      <td>2024-11-14 14:28:58</td>\n",
       "      <td>LE</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Kraków Główny</td>\n",
       "      <td>2025-02-14 20:58:00</td>\n",
       "      <td>92 days 06:29:02</td>\n",
       "      <td>92</td>\n",
       "      <td>2214.483889</td>\n",
       "      <td>279.0</td>\n",
       "      <td>None</td>\n",
       "      <td>67.0</td>\n",
       "      <td>None</td>\n",
       "      <td>408.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE413]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2214.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405247</th>\n",
       "      <td>2024-11-14 14:29:14</td>\n",
       "      <td>LE</td>\n",
       "      <td>Kraków Główny</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-02-15 04:09:00</td>\n",
       "      <td>92 days 13:39:46</td>\n",
       "      <td>92</td>\n",
       "      <td>2221.662778</td>\n",
       "      <td>359.0</td>\n",
       "      <td>None</td>\n",
       "      <td>67.0</td>\n",
       "      <td>None</td>\n",
       "      <td>378.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>so</td>\n",
       "      <td>0</td>\n",
       "      <td>[LE412]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2222.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476224</th>\n",
       "      <td>2024-11-16 11:59:28</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Hamburg Hbf</td>\n",
       "      <td>Brno hl.n.</td>\n",
       "      <td>2025-02-14 14:51:00</td>\n",
       "      <td>90 days 02:51:32</td>\n",
       "      <td>90</td>\n",
       "      <td>2162.858889</td>\n",
       "      <td>645.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané nízké obsazení, Téměr vyprodaná...</td>\n",
       "      <td>594.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0]</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>2</td>\n",
       "      <td>[ICE 935, EC 179 Berliner, IC 577 Metropol]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2163.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40801</th>\n",
       "      <td>2024-11-03 09:49:27</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Ústí n.L.hl.n.</td>\n",
       "      <td>Dresden Hbf</td>\n",
       "      <td>2025-02-01 17:45:00</td>\n",
       "      <td>90 days 07:55:33</td>\n",
       "      <td>90</td>\n",
       "      <td>2167.925833</td>\n",
       "      <td>310.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Téměr vyprodaná místa k sezení]</td>\n",
       "      <td>65.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>[0]</td>\n",
       "      <td>None</td>\n",
       "      <td>so</td>\n",
       "      <td>0</td>\n",
       "      <td>[EC 170 Berliner]</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2168.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40777</th>\n",
       "      <td>2024-11-03 09:48:36</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Bern</td>\n",
       "      <td>2025-02-01 10:35:00</td>\n",
       "      <td>90 days 00:46:24</td>\n",
       "      <td>90</td>\n",
       "      <td>2160.773333</td>\n",
       "      <td>5418.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Téměr vyprodaná místa k sezení, Téměr vyproda...</td>\n",
       "      <td>681.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 0, 0]</td>\n",
       "      <td>None</td>\n",
       "      <td>so</td>\n",
       "      <td>4</td>\n",
       "      <td>[IC 512 Ostravan, Sp 5286, IC 2068, ICE 201, I...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2161.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40784</th>\n",
       "      <td>2024-11-03 09:49:27</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Ústí n.L.hl.n.</td>\n",
       "      <td>Dresden Hbf</td>\n",
       "      <td>2025-02-01 11:45:00</td>\n",
       "      <td>90 days 01:55:33</td>\n",
       "      <td>90</td>\n",
       "      <td>2161.925833</td>\n",
       "      <td>310.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Téměr vyprodaná místa k sezení]</td>\n",
       "      <td>65.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>[0]</td>\n",
       "      <td>None</td>\n",
       "      <td>so</td>\n",
       "      <td>0</td>\n",
       "      <td>[EC 174 Berliner]</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2162.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476250</th>\n",
       "      <td>2024-11-16 12:00:13</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Berlin Hbf (tief)</td>\n",
       "      <td>2025-02-14 14:33:00</td>\n",
       "      <td>90 days 02:32:47</td>\n",
       "      <td>90</td>\n",
       "      <td>2162.546389</td>\n",
       "      <td>748.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané střední obsazení - doporučujeme...</td>\n",
       "      <td>250.0</td>\n",
       "      <td>397.0</td>\n",
       "      <td>[0]</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[EC 172 Hungaria]</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2163.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476256</th>\n",
       "      <td>2024-11-16 12:00:13</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Berlin Hbf (tief)</td>\n",
       "      <td>2025-02-14 18:28:00</td>\n",
       "      <td>90 days 06:27:47</td>\n",
       "      <td>90</td>\n",
       "      <td>2166.463056</td>\n",
       "      <td>3277.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané střední obsazení - doporučujeme...</td>\n",
       "      <td>349.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>1</td>\n",
       "      <td>[EC 458 Canopus, ICE 1018]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2166.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40800</th>\n",
       "      <td>2024-11-03 09:49:27</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Ústí n.L.hl.n.</td>\n",
       "      <td>Dresden Hbf</td>\n",
       "      <td>2025-02-01 18:42:00</td>\n",
       "      <td>90 days 08:52:33</td>\n",
       "      <td>90</td>\n",
       "      <td>2168.875833</td>\n",
       "      <td>310.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Téměr vyprodaná místa k sezení]</td>\n",
       "      <td>68.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>[0]</td>\n",
       "      <td>None</td>\n",
       "      <td>so</td>\n",
       "      <td>0</td>\n",
       "      <td>[rj 256 Vindobona]</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2169.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40802</th>\n",
       "      <td>2024-11-03 09:49:27</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Ústí n.L.hl.n.</td>\n",
       "      <td>Dresden Hbf</td>\n",
       "      <td>2025-02-01 15:45:00</td>\n",
       "      <td>90 days 05:55:33</td>\n",
       "      <td>90</td>\n",
       "      <td>2165.925833</td>\n",
       "      <td>310.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Téměr vyprodaná místa k sezení]</td>\n",
       "      <td>65.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>[0]</td>\n",
       "      <td>None</td>\n",
       "      <td>so</td>\n",
       "      <td>0</td>\n",
       "      <td>[EC 172 Hungaria]</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2166.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130042</th>\n",
       "      <td>2024-11-07 15:30:28</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Budapest-Nyugati pu</td>\n",
       "      <td>2025-02-05 15:42:00</td>\n",
       "      <td>90 days 00:11:32</td>\n",
       "      <td>90</td>\n",
       "      <td>2160.192222</td>\n",
       "      <td>606.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané nízké obsazení]</td>\n",
       "      <td>408.0</td>\n",
       "      <td>610.0</td>\n",
       "      <td>[0]</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>0</td>\n",
       "      <td>[EC 281 Metropolitan]</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476264</th>\n",
       "      <td>2024-11-16 12:00:57</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Berlin Hbf (tief)</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-02-14 13:16:00</td>\n",
       "      <td>90 days 01:15:03</td>\n",
       "      <td>90</td>\n",
       "      <td>2161.250833</td>\n",
       "      <td>748.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané střední obsazení - doporučujeme...</td>\n",
       "      <td>247.0</td>\n",
       "      <td>397.0</td>\n",
       "      <td>[0]</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[EC 175 Berliner]</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2161.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40804</th>\n",
       "      <td>2024-11-03 09:49:27</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Ústí n.L.hl.n.</td>\n",
       "      <td>Dresden Hbf</td>\n",
       "      <td>2025-02-01 13:45:00</td>\n",
       "      <td>90 days 03:55:33</td>\n",
       "      <td>90</td>\n",
       "      <td>2163.925833</td>\n",
       "      <td>310.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Téměr vyprodaná místa k sezení]</td>\n",
       "      <td>65.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>[0]</td>\n",
       "      <td>None</td>\n",
       "      <td>so</td>\n",
       "      <td>0</td>\n",
       "      <td>[EC 378 Berliner]</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2164.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476270</th>\n",
       "      <td>2024-11-16 12:00:57</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Berlin Hbf (tief)</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-02-14 15:16:00</td>\n",
       "      <td>90 days 03:15:03</td>\n",
       "      <td>90</td>\n",
       "      <td>2163.250833</td>\n",
       "      <td>748.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané nízké obsazení]</td>\n",
       "      <td>247.0</td>\n",
       "      <td>397.0</td>\n",
       "      <td>[0]</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[EC 177 Berliner]</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2163.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130029</th>\n",
       "      <td>2024-11-07 15:30:28</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Budapest-Nyugati pu</td>\n",
       "      <td>2025-02-05 22:01:00</td>\n",
       "      <td>90 days 06:30:32</td>\n",
       "      <td>90</td>\n",
       "      <td>2166.508889</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané nízké obsazení]</td>\n",
       "      <td>628.0</td>\n",
       "      <td>610.0</td>\n",
       "      <td>[0]</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>0</td>\n",
       "      <td>[IC 577 Metropol /EN 477]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2167.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40952</th>\n",
       "      <td>2024-11-03 09:54:26</td>\n",
       "      <td>ČD</td>\n",
       "      <td>České Budějovice</td>\n",
       "      <td>Zürich HB</td>\n",
       "      <td>2025-02-01 14:05:00</td>\n",
       "      <td>90 days 04:10:34</td>\n",
       "      <td>90</td>\n",
       "      <td>2164.176111</td>\n",
       "      <td>3687.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Téměr vyprodaná místa k sezení, Téměr vyproda...</td>\n",
       "      <td>555.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>None</td>\n",
       "      <td>so</td>\n",
       "      <td>1</td>\n",
       "      <td>[Sp 3805 Vltava Dunaj, rjx 168]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2164.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476283</th>\n",
       "      <td>2024-11-16 12:00:57</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Berlin Hbf (tief)</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2025-02-14 17:16:00</td>\n",
       "      <td>90 days 05:15:03</td>\n",
       "      <td>90</td>\n",
       "      <td>2165.250833</td>\n",
       "      <td>387.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané nízké obsazení]</td>\n",
       "      <td>247.0</td>\n",
       "      <td>397.0</td>\n",
       "      <td>[0]</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[EC 179 Berliner]</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118736</th>\n",
       "      <td>2024-11-07 05:56:32</td>\n",
       "      <td>ČD</td>\n",
       "      <td>České Budějovice</td>\n",
       "      <td>Zürich HB</td>\n",
       "      <td>2025-02-05 10:05:00</td>\n",
       "      <td>90 days 04:08:28</td>\n",
       "      <td>90</td>\n",
       "      <td>2164.141111</td>\n",
       "      <td>3687.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Téměr vyprodaná místa k sezení, Téměr vyproda...</td>\n",
       "      <td>617.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 0, 0]</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>4</td>\n",
       "      <td>[Sp 3803 Vltava Dunaj, rjx 862, R 5728, IR 327...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2164.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40772</th>\n",
       "      <td>2024-11-03 09:48:36</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Bern</td>\n",
       "      <td>2025-02-01 12:20:00</td>\n",
       "      <td>90 days 02:31:24</td>\n",
       "      <td>90</td>\n",
       "      <td>2162.523333</td>\n",
       "      <td>5132.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Téměr vyprodaná místa k sezení, Téměr vyproda...</td>\n",
       "      <td>731.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 0]</td>\n",
       "      <td>Zjistit cenu</td>\n",
       "      <td>so</td>\n",
       "      <td>3</td>\n",
       "      <td>[IC 535 Jižní expres, Sp 3805 Vltava Dunaj, rj...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2163.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130043</th>\n",
       "      <td>2024-11-07 15:30:28</td>\n",
       "      <td>ČD</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>Budapest-Kelenföld</td>\n",
       "      <td>2025-02-05 16:42:00</td>\n",
       "      <td>90 days 01:11:32</td>\n",
       "      <td>90</td>\n",
       "      <td>2161.192222</td>\n",
       "      <td>2495.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Předpokládané vysoké obsazení - doporučujeme ...</td>\n",
       "      <td>426.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>1</td>\n",
       "      <td>[rj 373 Vindobona, rjx 261 railjet express]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2161.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               oscrapovano prodejce              odkud                  kam  \\\n",
       "405246 2024-11-14 14:28:58       LE        Praha hl.n.        Kraków Główny   \n",
       "405247 2024-11-14 14:29:14       LE      Kraków Główny          Praha hl.n.   \n",
       "476224 2024-11-16 11:59:28       ČD        Hamburg Hbf           Brno hl.n.   \n",
       "40801  2024-11-03 09:49:27       ČD     Ústí n.L.hl.n.          Dresden Hbf   \n",
       "40777  2024-11-03 09:48:36       ČD        Praha hl.n.                 Bern   \n",
       "40784  2024-11-03 09:49:27       ČD     Ústí n.L.hl.n.          Dresden Hbf   \n",
       "476250 2024-11-16 12:00:13       ČD        Praha hl.n.    Berlin Hbf (tief)   \n",
       "476256 2024-11-16 12:00:13       ČD        Praha hl.n.    Berlin Hbf (tief)   \n",
       "40800  2024-11-03 09:49:27       ČD     Ústí n.L.hl.n.          Dresden Hbf   \n",
       "40802  2024-11-03 09:49:27       ČD     Ústí n.L.hl.n.          Dresden Hbf   \n",
       "130042 2024-11-07 15:30:28       ČD        Praha hl.n.  Budapest-Nyugati pu   \n",
       "476264 2024-11-16 12:00:57       ČD  Berlin Hbf (tief)          Praha hl.n.   \n",
       "40804  2024-11-03 09:49:27       ČD     Ústí n.L.hl.n.          Dresden Hbf   \n",
       "476270 2024-11-16 12:00:57       ČD  Berlin Hbf (tief)          Praha hl.n.   \n",
       "130029 2024-11-07 15:30:28       ČD        Praha hl.n.  Budapest-Nyugati pu   \n",
       "40952  2024-11-03 09:54:26       ČD   České Budějovice            Zürich HB   \n",
       "476283 2024-11-16 12:00:57       ČD  Berlin Hbf (tief)          Praha hl.n.   \n",
       "118736 2024-11-07 05:56:32       ČD   České Budějovice            Zürich HB   \n",
       "40772  2024-11-03 09:48:36       ČD        Praha hl.n.                 Bern   \n",
       "130043 2024-11-07 15:30:28       ČD        Praha hl.n.   Budapest-Kelenföld   \n",
       "\n",
       "                    odjezd         predstih  predstih_d   predstih_h    cena  \\\n",
       "405246 2025-02-14 20:58:00 92 days 06:29:02          92  2214.483889   279.0   \n",
       "405247 2025-02-15 04:09:00 92 days 13:39:46          92  2221.662778   359.0   \n",
       "476224 2025-02-14 14:51:00 90 days 02:51:32          90  2162.858889   645.0   \n",
       "40801  2025-02-01 17:45:00 90 days 07:55:33          90  2167.925833   310.0   \n",
       "40777  2025-02-01 10:35:00 90 days 00:46:24          90  2160.773333  5418.0   \n",
       "40784  2025-02-01 11:45:00 90 days 01:55:33          90  2161.925833   310.0   \n",
       "476250 2025-02-14 14:33:00 90 days 02:32:47          90  2162.546389   748.0   \n",
       "476256 2025-02-14 18:28:00 90 days 06:27:47          90  2166.463056  3277.0   \n",
       "40800  2025-02-01 18:42:00 90 days 08:52:33          90  2168.875833   310.0   \n",
       "40802  2025-02-01 15:45:00 90 days 05:55:33          90  2165.925833   310.0   \n",
       "130042 2025-02-05 15:42:00 90 days 00:11:32          90  2160.192222   606.0   \n",
       "476264 2025-02-14 13:16:00 90 days 01:15:03          90  2161.250833   748.0   \n",
       "40804  2025-02-01 13:45:00 90 days 03:55:33          90  2163.925833   310.0   \n",
       "476270 2025-02-14 15:16:00 90 days 03:15:03          90  2163.250833   748.0   \n",
       "130029 2025-02-05 22:01:00 90 days 06:30:32          90  2166.508889  1367.0   \n",
       "40952  2025-02-01 14:05:00 90 days 04:10:34          90  2164.176111  3687.0   \n",
       "476283 2025-02-14 17:16:00 90 days 05:15:03          90  2165.250833   387.0   \n",
       "118736 2025-02-05 10:05:00 90 days 04:08:28          90  2164.141111  3687.0   \n",
       "40772  2025-02-01 12:20:00 90 days 02:31:24          90  2162.523333  5132.0   \n",
       "130043 2025-02-05 16:42:00 90 days 01:11:32          90  2161.192222  2495.0   \n",
       "\n",
       "       prostredek  volnych_mist  \\\n",
       "405246       None          67.0   \n",
       "405247       None          67.0   \n",
       "476224       vlak           NaN   \n",
       "40801        vlak           NaN   \n",
       "40777        vlak           NaN   \n",
       "40784        vlak           NaN   \n",
       "476250       vlak           NaN   \n",
       "476256       vlak           NaN   \n",
       "40800        vlak           NaN   \n",
       "40802        vlak           NaN   \n",
       "130042       vlak           NaN   \n",
       "476264       vlak           NaN   \n",
       "40804        vlak           NaN   \n",
       "476270       vlak           NaN   \n",
       "130029       vlak           NaN   \n",
       "40952        vlak           NaN   \n",
       "476283       vlak           NaN   \n",
       "118736       vlak           NaN   \n",
       "40772        vlak           NaN   \n",
       "130043       vlak           NaN   \n",
       "\n",
       "                                               obsazenost  jizdni_doba  \\\n",
       "405246                                               None        408.0   \n",
       "405247                                               None        378.0   \n",
       "476224  [Předpokládané nízké obsazení, Téměr vyprodaná...        594.0   \n",
       "40801                    [Téměr vyprodaná místa k sezení]         65.0   \n",
       "40777   [Téměr vyprodaná místa k sezení, Téměr vyproda...        681.0   \n",
       "40784                    [Téměr vyprodaná místa k sezení]         65.0   \n",
       "476250  [Předpokládané střední obsazení - doporučujeme...        250.0   \n",
       "476256  [Předpokládané střední obsazení - doporučujeme...        349.0   \n",
       "40800                    [Téměr vyprodaná místa k sezení]         68.0   \n",
       "40802                    [Téměr vyprodaná místa k sezení]         65.0   \n",
       "130042                     [Předpokládané nízké obsazení]        408.0   \n",
       "476264  [Předpokládané střední obsazení - doporučujeme...        247.0   \n",
       "40804                    [Téměr vyprodaná místa k sezení]         65.0   \n",
       "476270                     [Předpokládané nízké obsazení]        247.0   \n",
       "130029                     [Předpokládané nízké obsazení]        628.0   \n",
       "40952   [Téměr vyprodaná místa k sezení, Téměr vyproda...        555.0   \n",
       "476283                     [Předpokládané nízké obsazení]        247.0   \n",
       "118736  [Téměr vyprodaná místa k sezení, Téměr vyproda...        617.0   \n",
       "40772   [Téměr vyprodaná místa k sezení, Téměr vyproda...        731.0   \n",
       "130043  [Předpokládané vysoké obsazení - doporučujeme ...        426.0   \n",
       "\n",
       "        vzdalenost         zpozdeni cena_poznamka den  prestupy  \\\n",
       "405246       501.0             None          None  pá         0   \n",
       "405247       501.0             None          None  so         0   \n",
       "476224         NaN        [0, 0, 0]          None  pá         2   \n",
       "40801         86.0              [0]          None  so         0   \n",
       "40777          NaN  [0, 0, 0, 0, 0]          None  so         4   \n",
       "40784         86.0              [0]          None  so         0   \n",
       "476250       397.0              [0]          None  pá         0   \n",
       "476256         NaN           [0, 0]          None  pá         1   \n",
       "40800         86.0              [0]          None  so         0   \n",
       "40802         86.0              [0]          None  so         0   \n",
       "130042       610.0              [0]          None  st         0   \n",
       "476264       397.0              [0]          None  pá         0   \n",
       "40804         86.0              [0]          None  so         0   \n",
       "476270       397.0              [0]          None  pá         0   \n",
       "130029       610.0              [0]          None  st         0   \n",
       "40952          NaN           [0, 0]          None  so         1   \n",
       "476283       397.0              [0]          None  pá         0   \n",
       "118736         NaN  [0, 0, 0, 0, 0]          None  st         4   \n",
       "40772          NaN     [0, 0, 0, 0]  Zjistit cenu  so         3   \n",
       "130043         NaN           [0, 0]          None  st         1   \n",
       "\n",
       "                                                    vlaky mistenka_zdarma  \\\n",
       "405246                                            [LE413]            None   \n",
       "405247                                            [LE412]            None   \n",
       "476224        [ICE 935, EC 179 Berliner, IC 577 Metropol]            None   \n",
       "40801                                   [EC 170 Berliner]            True   \n",
       "40777   [IC 512 Ostravan, Sp 5286, IC 2068, ICE 201, I...            None   \n",
       "40784                                   [EC 174 Berliner]            True   \n",
       "476250                                  [EC 172 Hungaria]            True   \n",
       "476256                         [EC 458 Canopus, ICE 1018]            None   \n",
       "40800                                  [rj 256 Vindobona]            True   \n",
       "40802                                   [EC 172 Hungaria]            True   \n",
       "130042                              [EC 281 Metropolitan]            True   \n",
       "476264                                  [EC 175 Berliner]            True   \n",
       "40804                                   [EC 378 Berliner]            True   \n",
       "476270                                  [EC 177 Berliner]            True   \n",
       "130029                          [IC 577 Metropol /EN 477]            None   \n",
       "40952                     [Sp 3805 Vltava Dunaj, rjx 168]            None   \n",
       "476283                                  [EC 179 Berliner]            True   \n",
       "118736  [Sp 3803 Vltava Dunaj, rjx 862, R 5728, IR 327...            None   \n",
       "40772   [IC 535 Jižní expres, Sp 3805 Vltava Dunaj, rj...            None   \n",
       "130043        [rj 373 Vindobona, rjx 261 railjet express]            None   \n",
       "\n",
       "       nahradni_bus  volna_mista_economy  volna_mista_economy_plus  \\\n",
       "405246         None                 21.0                      21.0   \n",
       "405247         None                 21.0                      21.0   \n",
       "476224         None                  NaN                       NaN   \n",
       "40801          None                  NaN                       NaN   \n",
       "40777          None                  NaN                       NaN   \n",
       "40784          None                  NaN                       NaN   \n",
       "476250         None                  NaN                       NaN   \n",
       "476256         None                  NaN                       NaN   \n",
       "40800          None                  NaN                       NaN   \n",
       "40802          None                  NaN                       NaN   \n",
       "130042         None                  NaN                       NaN   \n",
       "476264         None                  NaN                       NaN   \n",
       "40804          None                  NaN                       NaN   \n",
       "476270         None                  NaN                       NaN   \n",
       "130029         None                  NaN                       NaN   \n",
       "40952          None                  NaN                       NaN   \n",
       "476283         None                  NaN                       NaN   \n",
       "118736         None                  NaN                       NaN   \n",
       "40772          None                  NaN                       NaN   \n",
       "130043         None                  NaN                       NaN   \n",
       "\n",
       "        volna_mista_economy_business  volna_mista_premium  predstih_i  \n",
       "405246                          19.0                  6.0      2214.0  \n",
       "405247                          19.0                  6.0      2222.0  \n",
       "476224                           NaN                  NaN      2163.0  \n",
       "40801                            NaN                  NaN      2168.0  \n",
       "40777                            NaN                  NaN      2161.0  \n",
       "40784                            NaN                  NaN      2162.0  \n",
       "476250                           NaN                  NaN      2163.0  \n",
       "476256                           NaN                  NaN      2166.0  \n",
       "40800                            NaN                  NaN      2169.0  \n",
       "40802                            NaN                  NaN      2166.0  \n",
       "130042                           NaN                  NaN      2160.0  \n",
       "476264                           NaN                  NaN      2161.0  \n",
       "40804                            NaN                  NaN      2164.0  \n",
       "476270                           NaN                  NaN      2163.0  \n",
       "130029                           NaN                  NaN      2167.0  \n",
       "40952                            NaN                  NaN      2164.0  \n",
       "476283                           NaN                  NaN      2165.0  \n",
       "118736                           NaN                  NaN      2164.0  \n",
       "40772                            NaN                  NaN      2163.0  \n",
       "130043                           NaN                  NaN      2161.0  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(subset=['cena']).sort_values(by='predstih_d',ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "5a790ca7-09f8-4a8e-a8fe-881a4ef661cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "predstih_d\n",
       "-1        3\n",
       " 0     2550\n",
       " 1     2924\n",
       " 2     2946\n",
       " 3     2917\n",
       " 4     2810\n",
       " 5     2645\n",
       " 6     2485\n",
       " 7     2331\n",
       " 8     2134\n",
       " 9     1961\n",
       " 10    1745\n",
       " 11    1547\n",
       " 12    1370\n",
       " 13    1168\n",
       " 14     929\n",
       " 15     736\n",
       " 16     497\n",
       " 17     323\n",
       " 18     277\n",
       " 19     240\n",
       " 20     226\n",
       " 21     209\n",
       " 22     182\n",
       " 23     172\n",
       " 24     163\n",
       " 25     157\n",
       " 26     145\n",
       " 27     127\n",
       " 28     112\n",
       " 29      85\n",
       " 30      66\n",
       " 31      41\n",
       " 32      24\n",
       " 33      12\n",
       " 34      12\n",
       " 35       5\n",
       " 36       2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['prodejce'] == 'ARR'].groupby('predstih_d').size()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}