113_arr_cenova_politika_a_obsazenost.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a3f2075a-08bc-4936-aaad-ae6ed13e0cb0",
   "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": 60,
   "id": "04100883-72b9-4052-b14a-ba76aca6d968",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_parquet(os.path.join('data','jizdenky.parquet'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ed10cc3a-721f-4508-a825-cfccb1c18e3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['prodejce'] == 'ARR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "d00b4020-cd48-4c1d-abd4-576fddee77fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['predstih_d'] >= 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e5993d8f-f9bd-4ccf-82dc-1528a730a12d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_28880\\2187393597.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\"]).size().nlargest(15)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "odkud                          kam                          \n",
       "Pardubice hl.n.                Liberec                          7725\n",
       "Liberec                        Pardubice hl.n.                  7565\n",
       "                               Ústí nad Labem hlavní nádraží    4620\n",
       "Praha hl.n.                    České Budějovice                 4499\n",
       "Ústí nad Labem hlavní nádraží  Liberec                          4190\n",
       "České Budějovice               Praha hl.n.                      4082\n",
       "Tanvald                        Praha hl.n.                      3870\n",
       "Praha hl.n.                    Tanvald                          2978\n",
       "Amsterdam Bijlmer ArenA        Amsterdam Bijlmer ArenA             0\n",
       "                               Amsterdam Centraal                  0\n",
       "                               Amsterdam Zuid                      0\n",
       "                               Berlin Betriebsbf Schöneweide       0\n",
       "                               Berlin Gesundbrunnen                0\n",
       "                               Berlin Hbf                          0\n",
       "                               Berlin Hbf (tief)                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"odkud\",\"kam\"]).size().nlargest(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "551becc5-01a6-4702-88f6-9ccd49c92caf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def filtr(dopravce, mesto1, mesto2):\n",
    "    dfc = df.copy()\n",
    "    dfc = dfc[dfc['prodejce'] == dopravce]\n",
    "    dfc = dfc[dfc['odkud'].str.contains(mesto1) | dfc['kam'].str.contains(mesto1)]\n",
    "    dfc = dfc[dfc['odkud'].str.contains(mesto2) | dfc['kam'].str.contains(mesto2)]\n",
    "    minimum_prestupu = dfc['prestupy'].min()\n",
    "    if minimum_prestupu == -1:\n",
    "        minimum_prestupu = 0\n",
    "    print(f\"Nejmenší počet přestupů: {minimum_prestupu}.\")\n",
    "    minimum_casu = dfc['jizdni_doba'].min()\n",
    "    print(f\"Nejrychlejší jízdní doba: {minimum_casu} min.\")\n",
    "    dfc = dfc[(dfc['prestupy'] == minimum_prestupu) & (df['jizdni_doba'] <= (minimum_casu * 1.25))]\n",
    "    dfc = dfc.drop_duplicates(subset=['odjezd','kam','predstih_d'], keep='last')\n",
    "    return dfc.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "db6767d6-f112-4d5d-9c57-1e01731b25a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nejmenší počet přestupů: 0.\n",
      "Nejrychlejší jízdní doba: 165.0 min.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_28880\\134202732.py:12: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  dfc = dfc[(dfc['prestupy'] == minimum_prestupu) & (df['jizdni_doba'] <= (minimum_casu * 1.25))]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='predstih_d', ylabel='cena'>"
      ]
     },
     "execution_count": 65,
     "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": [
    "filtr('ARR','Pardubi','Libere').groupby('predstih_d')['cena'].agg(list).reset_index().explode('cena').plot.scatter(x=\"predstih_d\",y=\"cena\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "b596010f-f03f-40bf-9e78-cd6537a017ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nejmenší počet přestupů: 0.\n",
      "Nejrychlejší jízdní doba: 192.0 min.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_28880\\134202732.py:12: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  dfc = dfc[(dfc['prestupy'] == minimum_prestupu) & (df['jizdni_doba'] <= (minimum_casu * 1.25))]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='predstih_d', ylabel='cena'>"
      ]
     },
     "execution_count": 66,
     "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": [
    "filtr('ARR','Praha','Buděj').groupby('predstih_d')['cena'].agg(list).reset_index().explode('cena').plot.scatter(x=\"predstih_d\",y=\"cena\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "e5a07d1d-35ab-421e-822f-b075f0f69499",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nejmenší počet přestupů: 0.\n",
      "Nejrychlejší jízdní doba: 116.0 min.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_28880\\134202732.py:12: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  dfc = dfc[(dfc['prestupy'] == minimum_prestupu) & (df['jizdni_doba'] <= (minimum_casu * 1.25))]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='predstih_d', ylabel='cena'>"
      ]
     },
     "execution_count": 67,
     "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": [
    "filtr('ARR','Libe','Ústí').groupby('predstih_d')['cena'].agg(list).reset_index().explode('cena').plot.scatter(x=\"predstih_d\",y=\"cena\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "36310ff0-beba-4f09-a8b2-930daacc7d7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nejmenší počet přestupů: 0.\n",
      "Nejrychlejší jízdní doba: 154.0 min.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_28880\\134202732.py:12: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  dfc = dfc[(dfc['prestupy'] == minimum_prestupu) & (df['jizdni_doba'] <= (minimum_casu * 1.25))]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='predstih_d', ylabel='cena'>"
      ]
     },
     "execution_count": 68,
     "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": [
    "filtr('ARR','Praha','Tanvald').groupby('predstih_d')['cena'].agg(list).reset_index().explode('cena').plot.scatter(x=\"predstih_d\",y=\"cena\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "00df8cc3-8bff-4446-8e55-c30688951a16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='predstih_d', ylabel='volnych_mist'>"
      ]
     },
     "execution_count": 69,
     "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[['predstih_d','volnych_mist']].plot.scatter(x=\"predstih_d\",y=\"volnych_mist\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c80b731d-c75e-4966-99ac-2cbbaf941795",
   "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>133112</th>\n",
       "      <td>2024-11-07 19:11:12</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>Pardubice hl.n.</td>\n",
       "      <td>2024-11-08 10:55:00</td>\n",
       "      <td>0 days 15:43:48</td>\n",
       "      <td>0</td>\n",
       "      <td>15.730000</td>\n",
       "      <td>170.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>2.0</td>\n",
       "      <td>None</td>\n",
       "      <td>170.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1065]</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>505053</th>\n",
       "      <td>2024-11-17 00:35:16</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>Pardubice hl.n.</td>\n",
       "      <td>2024-11-17 12:55:00</td>\n",
       "      <td>0 days 12:19:44</td>\n",
       "      <td>0</td>\n",
       "      <td>12.328889</td>\n",
       "      <td>170.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>2.0</td>\n",
       "      <td>None</td>\n",
       "      <td>170.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>ne</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1067]</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>409073</th>\n",
       "      <td>2024-11-14 16:24:59</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>Ústí nad Labem hlavní nádraží</td>\n",
       "      <td>2024-11-14 18:28:00</td>\n",
       "      <td>0 days 02:03:01</td>\n",
       "      <td>0</td>\n",
       "      <td>2.050278</td>\n",
       "      <td>145.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>2.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>čt</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1332]</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>132357</th>\n",
       "      <td>2024-11-07 18:34:55</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>Pardubice hl.n.</td>\n",
       "      <td>2024-11-08 10:55:00</td>\n",
       "      <td>0 days 16:20:05</td>\n",
       "      <td>0</td>\n",
       "      <td>16.334722</td>\n",
       "      <td>170.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>2.0</td>\n",
       "      <td>None</td>\n",
       "      <td>170.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1065]</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>356507</th>\n",
       "      <td>2024-11-13 10:27:59</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Pardubice hl.n.</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-13 15:53:00</td>\n",
       "      <td>0 days 05:25:01</td>\n",
       "      <td>0</td>\n",
       "      <td>5.416944</td>\n",
       "      <td>170.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>3.0</td>\n",
       "      <td>None</td>\n",
       "      <td>168.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>st</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1068]</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>169285</th>\n",
       "      <td>2024-11-08 21:46:20</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Pardubice hl.n.</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-09 13:55:00</td>\n",
       "      <td>0 days 16:08:40</td>\n",
       "      <td>0</td>\n",
       "      <td>16.144444</td>\n",
       "      <td>170.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>4.0</td>\n",
       "      <td>None</td>\n",
       "      <td>170.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>so</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1066]</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>314903</th>\n",
       "      <td>2024-11-12 10:25:30</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Pardubice hl.n.</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-15 15:53:00</td>\n",
       "      <td>3 days 05:27:30</td>\n",
       "      <td>3</td>\n",
       "      <td>77.458333</td>\n",
       "      <td>170.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>4.0</td>\n",
       "      <td>None</td>\n",
       "      <td>168.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1068]</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>290209</th>\n",
       "      <td>2024-11-11 18:25:28</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Pardubice hl.n.</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-15 15:53:00</td>\n",
       "      <td>3 days 21:27:32</td>\n",
       "      <td>3</td>\n",
       "      <td>93.458889</td>\n",
       "      <td>170.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>4.0</td>\n",
       "      <td>None</td>\n",
       "      <td>168.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1068]</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>302339</th>\n",
       "      <td>2024-11-12 00:28:32</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Pardubice hl.n.</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-15 15:53:00</td>\n",
       "      <td>3 days 15:24:28</td>\n",
       "      <td>3</td>\n",
       "      <td>87.407778</td>\n",
       "      <td>170.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>4.0</td>\n",
       "      <td>None</td>\n",
       "      <td>168.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1068]</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>318854</th>\n",
       "      <td>2024-11-12 12:27:19</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Pardubice hl.n.</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-15 15:53:00</td>\n",
       "      <td>3 days 03:25:41</td>\n",
       "      <td>3</td>\n",
       "      <td>75.428056</td>\n",
       "      <td>170.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>4.0</td>\n",
       "      <td>None</td>\n",
       "      <td>168.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1068]</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>326310</th>\n",
       "      <td>2024-11-12 16:25:11</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Pardubice hl.n.</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-15 15:53:00</td>\n",
       "      <td>2 days 23:27:49</td>\n",
       "      <td>2</td>\n",
       "      <td>71.463611</td>\n",
       "      <td>170.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>4.0</td>\n",
       "      <td>None</td>\n",
       "      <td>168.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1068]</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>330311</th>\n",
       "      <td>2024-11-12 18:25:24</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Pardubice hl.n.</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-15 15:53:00</td>\n",
       "      <td>2 days 21:27:36</td>\n",
       "      <td>2</td>\n",
       "      <td>69.460000</td>\n",
       "      <td>170.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>4.0</td>\n",
       "      <td>None</td>\n",
       "      <td>168.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1068]</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>297679</th>\n",
       "      <td>2024-11-11 22:30:43</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Pardubice hl.n.</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-15 15:53:00</td>\n",
       "      <td>3 days 17:22:17</td>\n",
       "      <td>3</td>\n",
       "      <td>89.371389</td>\n",
       "      <td>170.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>4.0</td>\n",
       "      <td>None</td>\n",
       "      <td>168.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1068]</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>334805</th>\n",
       "      <td>2024-11-12 19:28:01</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Pardubice hl.n.</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-15 15:53:00</td>\n",
       "      <td>2 days 20:24:59</td>\n",
       "      <td>2</td>\n",
       "      <td>68.416389</td>\n",
       "      <td>170.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>4.0</td>\n",
       "      <td>None</td>\n",
       "      <td>168.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1068]</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>293225</th>\n",
       "      <td>2024-11-11 19:23:13</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Pardubice hl.n.</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-15 15:53:00</td>\n",
       "      <td>3 days 20:29:47</td>\n",
       "      <td>3</td>\n",
       "      <td>92.496389</td>\n",
       "      <td>170.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>4.0</td>\n",
       "      <td>None</td>\n",
       "      <td>168.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1068]</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>311559</th>\n",
       "      <td>2024-11-12 08:29:18</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Pardubice hl.n.</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-15 15:53:00</td>\n",
       "      <td>3 days 07:23:42</td>\n",
       "      <td>3</td>\n",
       "      <td>79.395000</td>\n",
       "      <td>170.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>4.0</td>\n",
       "      <td>None</td>\n",
       "      <td>168.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1068]</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>339527</th>\n",
       "      <td>2024-11-12 22:32:30</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Pardubice hl.n.</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-15 15:53:00</td>\n",
       "      <td>2 days 17:20:30</td>\n",
       "      <td>2</td>\n",
       "      <td>65.341667</td>\n",
       "      <td>170.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>4.0</td>\n",
       "      <td>None</td>\n",
       "      <td>168.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1068]</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>306855</th>\n",
       "      <td>2024-11-12 06:29:21</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Pardubice hl.n.</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-15 15:53:00</td>\n",
       "      <td>3 days 09:23:39</td>\n",
       "      <td>3</td>\n",
       "      <td>81.394167</td>\n",
       "      <td>170.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>4.0</td>\n",
       "      <td>None</td>\n",
       "      <td>168.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pá</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1068]</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>532286</th>\n",
       "      <td>2024-11-17 18:27:55</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Pardubice hl.n.</td>\n",
       "      <td>Liberec</td>\n",
       "      <td>2024-11-17 21:51:00</td>\n",
       "      <td>0 days 03:23:05</td>\n",
       "      <td>0</td>\n",
       "      <td>3.384722</td>\n",
       "      <td>170.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>5.0</td>\n",
       "      <td>None</td>\n",
       "      <td>166.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>ne</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1074]</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>516683</th>\n",
       "      <td>2024-11-17 10:27:05</td>\n",
       "      <td>ARR</td>\n",
       "      <td>Tanvald</td>\n",
       "      <td>Praha hl.n.</td>\n",
       "      <td>2024-11-18 08:32:00</td>\n",
       "      <td>0 days 22:04:55</td>\n",
       "      <td>0</td>\n",
       "      <td>22.081944</td>\n",
       "      <td>160.0</td>\n",
       "      <td>vlak</td>\n",
       "      <td>5.0</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>po</td>\n",
       "      <td>0</td>\n",
       "      <td>[R 1141]</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",
       "133112 2024-11-07 19:11:12      ARR          Liberec   \n",
       "505053 2024-11-17 00:35:16      ARR          Liberec   \n",
       "409073 2024-11-14 16:24:59      ARR          Liberec   \n",
       "132357 2024-11-07 18:34:55      ARR          Liberec   \n",
       "356507 2024-11-13 10:27:59      ARR  Pardubice hl.n.   \n",
       "169285 2024-11-08 21:46:20      ARR  Pardubice hl.n.   \n",
       "314903 2024-11-12 10:25:30      ARR  Pardubice hl.n.   \n",
       "290209 2024-11-11 18:25:28      ARR  Pardubice hl.n.   \n",
       "302339 2024-11-12 00:28:32      ARR  Pardubice hl.n.   \n",
       "318854 2024-11-12 12:27:19      ARR  Pardubice hl.n.   \n",
       "326310 2024-11-12 16:25:11      ARR  Pardubice hl.n.   \n",
       "330311 2024-11-12 18:25:24      ARR  Pardubice hl.n.   \n",
       "297679 2024-11-11 22:30:43      ARR  Pardubice hl.n.   \n",
       "334805 2024-11-12 19:28:01      ARR  Pardubice hl.n.   \n",
       "293225 2024-11-11 19:23:13      ARR  Pardubice hl.n.   \n",
       "311559 2024-11-12 08:29:18      ARR  Pardubice hl.n.   \n",
       "339527 2024-11-12 22:32:30      ARR  Pardubice hl.n.   \n",
       "306855 2024-11-12 06:29:21      ARR  Pardubice hl.n.   \n",
       "532286 2024-11-17 18:27:55      ARR  Pardubice hl.n.   \n",
       "516683 2024-11-17 10:27:05      ARR          Tanvald   \n",
       "\n",
       "                                  kam              odjezd        predstih  \\\n",
       "133112                Pardubice hl.n. 2024-11-08 10:55:00 0 days 15:43:48   \n",
       "505053                Pardubice hl.n. 2024-11-17 12:55:00 0 days 12:19:44   \n",
       "409073  Ústí nad Labem hlavní nádraží 2024-11-14 18:28:00 0 days 02:03:01   \n",
       "132357                Pardubice hl.n. 2024-11-08 10:55:00 0 days 16:20:05   \n",
       "356507                        Liberec 2024-11-13 15:53:00 0 days 05:25:01   \n",
       "169285                        Liberec 2024-11-09 13:55:00 0 days 16:08:40   \n",
       "314903                        Liberec 2024-11-15 15:53:00 3 days 05:27:30   \n",
       "290209                        Liberec 2024-11-15 15:53:00 3 days 21:27:32   \n",
       "302339                        Liberec 2024-11-15 15:53:00 3 days 15:24:28   \n",
       "318854                        Liberec 2024-11-15 15:53:00 3 days 03:25:41   \n",
       "326310                        Liberec 2024-11-15 15:53:00 2 days 23:27:49   \n",
       "330311                        Liberec 2024-11-15 15:53:00 2 days 21:27:36   \n",
       "297679                        Liberec 2024-11-15 15:53:00 3 days 17:22:17   \n",
       "334805                        Liberec 2024-11-15 15:53:00 2 days 20:24:59   \n",
       "293225                        Liberec 2024-11-15 15:53:00 3 days 20:29:47   \n",
       "311559                        Liberec 2024-11-15 15:53:00 3 days 07:23:42   \n",
       "339527                        Liberec 2024-11-15 15:53:00 2 days 17:20:30   \n",
       "306855                        Liberec 2024-11-15 15:53:00 3 days 09:23:39   \n",
       "532286                        Liberec 2024-11-17 21:51:00 0 days 03:23:05   \n",
       "516683                    Praha hl.n. 2024-11-18 08:32:00 0 days 22:04:55   \n",
       "\n",
       "        predstih_d  predstih_h   cena prostredek  volnych_mist obsazenost  \\\n",
       "133112           0   15.730000  170.0       vlak           2.0       None   \n",
       "505053           0   12.328889  170.0       vlak           2.0       None   \n",
       "409073           0    2.050278  145.0       vlak           2.0       None   \n",
       "132357           0   16.334722  170.0       vlak           2.0       None   \n",
       "356507           0    5.416944  170.0       vlak           3.0       None   \n",
       "169285           0   16.144444  170.0       vlak           4.0       None   \n",
       "314903           3   77.458333  170.0       vlak           4.0       None   \n",
       "290209           3   93.458889  170.0       vlak           4.0       None   \n",
       "302339           3   87.407778  170.0       vlak           4.0       None   \n",
       "318854           3   75.428056  170.0       vlak           4.0       None   \n",
       "326310           2   71.463611  170.0       vlak           4.0       None   \n",
       "330311           2   69.460000  170.0       vlak           4.0       None   \n",
       "297679           3   89.371389  170.0       vlak           4.0       None   \n",
       "334805           2   68.416389  170.0       vlak           4.0       None   \n",
       "293225           3   92.496389  170.0       vlak           4.0       None   \n",
       "311559           3   79.395000  170.0       vlak           4.0       None   \n",
       "339527           2   65.341667  170.0       vlak           4.0       None   \n",
       "306855           3   81.394167  170.0       vlak           4.0       None   \n",
       "532286           0    3.384722  170.0       vlak           5.0       None   \n",
       "516683           0   22.081944  160.0       vlak           5.0       None   \n",
       "\n",
       "        jizdni_doba  vzdalenost zpozdeni cena_poznamka den  prestupy  \\\n",
       "133112        170.0       161.0     None          None  pá         0   \n",
       "505053        170.0       161.0     None          None  ne         0   \n",
       "409073          NaN       113.0     None          None  čt         0   \n",
       "132357        170.0       161.0     None          None  pá         0   \n",
       "356507        168.0       161.0     None          None  st         0   \n",
       "169285        170.0       161.0     None          None  so         0   \n",
       "314903        168.0       161.0     None          None  pá         0   \n",
       "290209        168.0       161.0     None          None  pá         0   \n",
       "302339        168.0       161.0     None          None  pá         0   \n",
       "318854        168.0       161.0     None          None  pá         0   \n",
       "326310        168.0       161.0     None          None  pá         0   \n",
       "330311        168.0       161.0     None          None  pá         0   \n",
       "297679        168.0       161.0     None          None  pá         0   \n",
       "334805        168.0       161.0     None          None  pá         0   \n",
       "293225        168.0       161.0     None          None  pá         0   \n",
       "311559        168.0       161.0     None          None  pá         0   \n",
       "339527        168.0       161.0     None          None  pá         0   \n",
       "306855        168.0       161.0     None          None  pá         0   \n",
       "532286        166.0       161.0     None          None  ne         0   \n",
       "516683        154.0       133.0     None          None  po         0   \n",
       "\n",
       "           vlaky mistenka_zdarma nahradni_bus  volna_mista_economy  \\\n",
       "133112  [R 1065]            None         None                  NaN   \n",
       "505053  [R 1067]            None         None                  NaN   \n",
       "409073  [R 1332]            None         None                  NaN   \n",
       "132357  [R 1065]            None         None                  NaN   \n",
       "356507  [R 1068]            None         None                  NaN   \n",
       "169285  [R 1066]            None         None                  NaN   \n",
       "314903  [R 1068]            None         None                  NaN   \n",
       "290209  [R 1068]            None         None                  NaN   \n",
       "302339  [R 1068]            None         None                  NaN   \n",
       "318854  [R 1068]            None         None                  NaN   \n",
       "326310  [R 1068]            None         None                  NaN   \n",
       "330311  [R 1068]            None         None                  NaN   \n",
       "297679  [R 1068]            None         None                  NaN   \n",
       "334805  [R 1068]            None         None                  NaN   \n",
       "293225  [R 1068]            None         None                  NaN   \n",
       "311559  [R 1068]            None         None                  NaN   \n",
       "339527  [R 1068]            None         None                  NaN   \n",
       "306855  [R 1068]            None         None                  NaN   \n",
       "532286  [R 1074]            None         None                  NaN   \n",
       "516683  [R 1141]            None         None                  NaN   \n",
       "\n",
       "        volna_mista_economy_plus  volna_mista_economy_business  \\\n",
       "133112                       NaN                           NaN   \n",
       "505053                       NaN                           NaN   \n",
       "409073                       NaN                           NaN   \n",
       "132357                       NaN                           NaN   \n",
       "356507                       NaN                           NaN   \n",
       "169285                       NaN                           NaN   \n",
       "314903                       NaN                           NaN   \n",
       "290209                       NaN                           NaN   \n",
       "302339                       NaN                           NaN   \n",
       "318854                       NaN                           NaN   \n",
       "326310                       NaN                           NaN   \n",
       "330311                       NaN                           NaN   \n",
       "297679                       NaN                           NaN   \n",
       "334805                       NaN                           NaN   \n",
       "293225                       NaN                           NaN   \n",
       "311559                       NaN                           NaN   \n",
       "339527                       NaN                           NaN   \n",
       "306855                       NaN                           NaN   \n",
       "532286                       NaN                           NaN   \n",
       "516683                       NaN                           NaN   \n",
       "\n",
       "        volna_mista_premium  \n",
       "133112                  NaN  \n",
       "505053                  NaN  \n",
       "409073                  NaN  \n",
       "132357                  NaN  \n",
       "356507                  NaN  \n",
       "169285                  NaN  \n",
       "314903                  NaN  \n",
       "290209                  NaN  \n",
       "302339                  NaN  \n",
       "318854                  NaN  \n",
       "326310                  NaN  \n",
       "330311                  NaN  \n",
       "297679                  NaN  \n",
       "334805                  NaN  \n",
       "293225                  NaN  \n",
       "311559                  NaN  \n",
       "339527                  NaN  \n",
       "306855                  NaN  \n",
       "532286                  NaN  \n",
       "516683                  NaN  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='volnych_mist').head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "16532c7c-53ec-452b-a85d-cdb7e1a50fea",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['spoj'] = df.apply(lambda row: f\"\"\"{row['prodejce']} {row['den']} {row['odjezd'].hour:02}:{row['odjezd'].minute:02}\"\"\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b1a94ef6-8a03-4d8e-9030-3b5a7a96dd8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_28880\\865817959.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_d'] < 1].groupby(['odkud','kam','spoj'])['volnych_mist'].median().nsmallest(10)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "odkud            kam              spoj        \n",
       "Praha hl.n.      Tanvald          ARR čt 17:57    10.0\n",
       "Tanvald          Praha hl.n.      ARR čt 14:32    10.0\n",
       "Pardubice hl.n.  Liberec          ARR st 15:53    11.5\n",
       "Praha hl.n.      Tanvald          ARR čt 09:58    13.0\n",
       "Liberec          Pardubice hl.n.  ARR st 08:55    13.5\n",
       "Pardubice hl.n.  Liberec          ARR pá 13:55    15.0\n",
       "                                  ARR út 11:53    15.0\n",
       "Praha hl.n.      Tanvald          ARR ne 19:57    15.0\n",
       "Pardubice hl.n.  Liberec          ARR pá 11:53    17.0\n",
       "                                  ARR so 09:53    18.0\n",
       "Name: volnych_mist, dtype: float64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['predstih_d'] < 1].groupby(['odkud','kam','spoj'])['volnych_mist'].median().nsmallest(10)"
   ]
  }
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