diff --git a/901_graf_jizdneho_do_zahranici.ipynb b/901_graf_jizdneho_do_zahranici.ipynb index 2b250b7..ee5a8eb 100644 --- a/901_graf_jizdneho_do_zahranici.ipynb +++ b/901_graf_jizdneho_do_zahranici.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, + "execution_count": 60, "id": "5dc8b73c-4aa3-47ee-8d7d-6f2afbbca48c", "metadata": {}, "outputs": [], @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 61, "id": "5663adf0-aabe-4849-aadb-032b5b5a0b76", "metadata": {}, "outputs": [], @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 62, "id": "03465b58-c782-44ab-955f-1dea0804d65c", "metadata": {}, "outputs": [], @@ -37,7 +37,17 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 63, + "id": "5a407312-e4de-4392-b099-a4fcbdce1ec7", + "metadata": {}, + "outputs": [], + "source": [ + "df = df[df['odjezd'] >= '2024-11-08']" + ] + }, + { + "cell_type": "code", + "execution_count": 64, "id": "7763f1da-f649-4a38-9d33-1942c3905a72", "metadata": {}, "outputs": [], @@ -56,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 66, "id": "4ae26759-e1c6-413f-8bd1-1a1e7839c684", "metadata": {}, "outputs": [], @@ -82,7 +92,129 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 82, + "id": "28726b7e-d00d-4670-be2c-7854b3b7f16b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "prodejce odkud kam \n", + "ČD Praha hl.n. Brno hl.n. 49653\n", + " Brno hl.n. Praha hl.n. 47890\n", + " Praha hl.n. Ostrava hl.n. 31473\n", + " Ostrava hl.n. Praha hl.n. 27939\n", + "RJ Brno Praha 20232\n", + " Praha Brno 18349\n", + " Ostrava Praha 11751\n", + "ČD Praha hl.n. Bratislava hl.st. 10998\n", + " Wien Hbf 9964\n", + "ARR Liberec Pardubice hl.n. 9568\n", + " Pardubice hl.n. Liberec 9547\n", + "RJ Praha Ostrava 9130\n", + "ČD Praha hl.n. Warszawa Centralna 8672\n", + " Bratislava hl.st. Praha hl.n. 8639\n", + " Berlin Hbf (tief) Praha hl.n. 8012\n", + " Praha hl.n. Bruxelles-Midi 7811\n", + " Budapest-Nyugati pu 7167\n", + " Berlin Hbf (tief) 7074\n", + " Wien Hbf Praha hl.n. 6767\n", + " Brno hl.n. Hamburg Hbf 6289\n", + " Ústí n.L.hl.n. Dresden Hbf 6246\n", + " Praha hl.n. Budapest-Kelenföld 6174\n", + "ARR Liberec Ústí nad Labem hlavní nádraží 6109\n", + "ČD Warszawa Centralna Praha hl.n. 5763\n", + "LE Praha hl.n. Ostrava hl.n. 5682\n", + " Ostrava-Svinov 5682\n", + "ČD Karlovy Vary Ostrava hl.n. 5527\n", + "ARR Praha hl.n. České Budějovice 5521\n", + "LE Ostrava hl.n. Praha hl.n. 5511\n", + " Ostrava-Svinov Praha hl.n. 5511\n", + "ARR Ústí nad Labem hlavní nádraží Liberec 5483\n", + "ČD Olomouc hl.n. Przemysl Gl. 5448\n", + "ARR České Budějovice Praha hl.n. 5214\n", + "ČD Praha hl.n. Ljubljana 4966\n", + " Slavonice Plzeň hl.n. 4929\n", + " Budapest-Nyugati pu Praha hl.n. 4849\n", + "RJ Brno Vídeň 4847\n", + "ČD Brno hl.n. Ljubljana 4638\n", + "ARR Tanvald Praha hl.n. 4530\n", + "ČD Pardubice hl.n. Košice 4525\n", + " Bruxelles-Midi Praha hl.n. 4423\n", + "RJ Vídeň Brno 3829\n", + "ČD Budapest-Kelenföld Praha hl.n. 3770\n", + " Ostrava hl.n. Gdansk Glowny 3754\n", + "RJ Ostrava Brno 3717\n", + " Brno Ostrava 3693\n", + "ARR Praha hl.n. Tanvald 3632\n", + "ČD Praha hl.n. Bern 3366\n", + " Plzeň hl.n. Ostrava hl.n. 3163\n", + " Dresden Hbf Ústí n.L.hl.n. 2874\n", + "RJ Praha Budapešť 2819\n", + " Bratislava 2280\n", + "ČD Ljubljana Praha hl.n. 2256\n", + " Plzeň hl.n. Slavonice 2073\n", + " Praha hl.n. Zagreb Glavni kolodvor 2060\n", + " České Budějovice Zürich HB 2034\n", + " Berlin Hbf Warszawa Centralna 1934\n", + " Bruxelles-Midi 1869\n", + " Ljubljana Brno hl.n. 1810\n", + " Praha hl.n. Krakow Glowny 1777\n", + " Köln Messe/Deutz Bruxelles-Midi 1705\n", + "RJ Praha Vídeň 1620\n", + "ČD Liberec Salzburg Hbf 1596\n", + " Krakow Glowny Praha hl.n. 1594\n", + " Praha hl.n. Bratislava-Petržalka 1592\n", + " Znojmo Jihlava 1552\n", + " Praha hl.n. Wien Franz-Josefs-Bahnhof 1492\n", + " České Budějovice Plzeň hl.n. 1475\n", + " Hamburg Hbf Brno hl.n. 1404\n", + " Praha-Holešovice Ostrava hl.n. 1380\n", + "RJ Vídeň Praha 1362\n", + "ČD Bern Praha hl.n. 1353\n", + " Ostrava hl.n. Karlovy Vary 1351\n", + " München Hbf Gl.27-36 Bern 1339\n", + " Praha-Holešovice Brno hl.n. 1334\n", + " Brno hl.n. Praha-Holešovice 1256\n", + "RJ Bratislava Praha 1209\n", + "ČD Berlin Hbf Amsterdam Centraal 1194\n", + "RJ Praha Košice 1167\n", + "ČD Praha hl.n. Berlin Hbf 1158\n", + " Harmica Zagreb Glavni kolodvor 1137\n", + " Praha hl.n. Ostrava-Svinov 1040\n", + " Wien Franz-Josefs-Bahnhof Praha hl.n. 1037\n", + " Praha hl.n. Amsterdam Centraal 1034\n", + " Ostrava hl.n. Praha-Holešovice 1015\n", + " Gdansk Glowny Ostrava hl.n. 1010\n", + " Ostrava-Svinov Praha hl.n. 998\n", + " Praha-Holešovice Bratislava hl.st. 988\n", + " Köln Messe/Deutz Gl.11-12 Praha hl.n. 874\n", + "LE Pardubice hl.n. Košice 868\n", + " Košice Pardubice hl.n. 843\n", + "ČD Košice Pardubice hl.n. 827\n", + " Budapest-Deli pu Praha hl.n. 798\n", + " Praha-Holešovice Budapest-Nyugati pu 763\n", + " Bruxelles-Midi 732\n", + "RJ Budapešť Praha 731\n", + "ČD Berlin Hbf Praha hl.n. 710\n", + " Praha-Holešovice Berlin Hbf (tief) 679\n", + " Bratislava-Petržalka Praha hl.n. 677\n", + " Bratislava hl.st. Praha-Holešovice 661\n", + "dtype: int64" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby([\"prodejce\",\"odkud\",\"kam\"]).size().nlargest(100)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, "id": "86606670-d9cc-4fd3-aa6b-228d6b166425", "metadata": {}, "outputs": [ @@ -93,52 +225,64 @@ "ČD: Praha hl.n.-Berlin Hbf\n", "Nejmenší počet přestupů: 0.\n", "Nejrychlejší jízdní doba: 247.0 min.\n", - "Celkem řádků: 5992\n", + "Celkem řádků: 6010\n", + "\n", "ČD: Praha hl.n.-Warszawa Centralna\n", "Nejmenší počet přestupů: 0.\n", "Nejrychlejší jízdní doba: 479.0 min.\n", - "Celkem řádků: 2050\n", + "Celkem řádků: 2168\n", + "\n", "ČD: Praha hl.n.-Wien Hbf\n", "Nejmenší počet přestupů: 0.\n", "Nejrychlejší jízdní doba: 241.0 min.\n", - "Celkem řádků: 5233\n", + "Celkem řádků: 5532\n", + "\n", "RJ: Praha-Víde\n", "Nejmenší počet přestupů: 0.\n", "Nejrychlejší jízdní doba: 238.0 min.\n", - "Celkem řádků: 1527\n", + "Celkem řádků: 1591\n", + "\n", "ČD: Praha hl.n.-Bratislava hl.st.\n", "Nejmenší počet přestupů: 0.\n", "Nejrychlejší jízdní doba: 253.0 min.\n", - "Celkem řádků: 7009\n", + "Celkem řádků: 7560\n", + "\n", "RJ: Praha-Bratislava\n", "Nejmenší počet přestupů: 0.\n", "Nejrychlejší jízdní doba: 250.0 min.\n", - "Celkem řádků: 1755\n", + "Celkem řádků: 1821\n", + "\n", "RJ: Praha-Čop\n", "Nejmenší počet přestupů: 0.\n", "Nejrychlejší jízdní doba: 645.0 min.\n", - "Celkem řádků: 270\n", + "Celkem řádků: 272\n", + "\n", "ČD: Praha hl.n.-Bern\n", "Nejmenší počet přestupů: 1.\n", "Nejrychlejší jízdní doba: 678.0 min.\n", - "Celkem řádků: 340\n", + "Celkem řádků: 325\n", + "\n", "RJ: Praha-Budap\n", "Nejmenší počet přestupů: 0.\n", "Nejrychlejší jízdní doba: 399.0 min.\n", - "Celkem řádků: 1949\n", + "Celkem řádků: 2072\n", + "\n", "ČD: Praha-Budap\n", "Nejmenší počet přestupů: 0.\n", "Nejrychlejší jízdní doba: 401.0 min.\n", - "Celkem řádků: 5377\n", + "Celkem řádků: 5636\n", + "\n", "LE: Praha-Krak\n", "Nejmenší počet přestupů: 0.\n", "Nejrychlejší jízdní doba: 344.0 min.\n", - "Celkem řádků: 214\n", + "Celkem řádků: 233\n", + "\n", "LE: Pardub-Košic\n", "Pozor, vylezlo nám tu -1 přestupů. Opravuji.\n", "Nejmenší počet přestupů: 0.\n", "Nejrychlejší jízdní doba: 428.0 min.\n", - "Celkem řádků: 854\n" + "Celkem řádků: 934\n", + "\n" ] } ], @@ -184,7 +328,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 69, "id": "f84084bd-a54a-4de8-99d5-7baf1bcf4ffc", "metadata": {}, "outputs": [], @@ -469,14 +613,14 @@ " f.write(code)\n", "\n", " with open(os.path.join(\"grafy\", target + \".txt\"), \"w+\") as f:\n", - " f.write(f\"{pred}{as_js_literal}\")\n", + " f.write(f\"{pred}{as_js_literal2}\")\n", "\n", " print(\"Graf uložen.\")" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 80, "id": "6cac8198-ad17-4420-9410-401eb9fd3126", "metadata": {}, "outputs": [ @@ -525,7 +669,7 @@ } ], "source": [ - "irozhlas_graf(rozpeti=ukazat, skryte=ukazat[1:], target='mezistatni_jizdne', titulek='Mezistátní jízdné podle předstihu nákupu', osaymin=0, podtitulek='Srovnávají se pouze spoje s nejnižším možným počtem přestupů.', kredity=['Zdroj dat: e-shopy dopravců v listopadu 2024. Vizualizace: iROZHLAS.cz','https://www.irozhlas.cz/zpravy-tag/datova-zurnalistika'])" + "irozhlas_graf(rozpeti=ukazat, skryte=ukazat[1:], target='mezistatni_jizdne', titulek='Mezistátní jízdné podle předstihu nákupu', osaymin=0, podtitulek='Graf zobrazuje nejnižší, střední a nejvyšší cenu. Zahrnuje pouze přímé vlaky anebo spoje s minimálním počtem přestupů na dané trase.', kredity=['Zdroj dat: e-shopy dopravců od 7. 11. do 25. 11. 2024. Vizualizace: iROZHLAS.cz','https://www.irozhlas.cz/zpravy-tag/datova-zurnalistika'])" ] } ], diff --git a/902_graf_jizdneho_prg_ova.ipynb b/902_graf_jizdneho_prg_ova.ipynb index da2d14e..6cd8afb 100644 --- a/902_graf_jizdneho_prg_ova.ipynb +++ b/902_graf_jizdneho_prg_ova.ipynb @@ -220,7 +220,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 144, "id": "db0fe4a1-f6f3-40e5-8204-e61a52d24f01", "metadata": {}, "outputs": [ @@ -248,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 146, "id": "2b9244be-b904-426c-bdf9-4aca43a442ee", "metadata": {}, "outputs": [], @@ -258,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 148, "id": "5fee0b04-9e07-4cf8-a2f2-bef88983e0aa", "metadata": {}, "outputs": [], @@ -266,6 +266,752 @@ "df['predstih_d2'] = df['predstih_h'] / 24" ] }, + { + "cell_type": "code", + "execution_count": 150, + "id": "008a6b63-3dfe-4a4c-82da-068b99b38a42", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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4419152024-11-15 13:57:53ČDOstrava hl.n.Praha hl.n.2024-12-09 09:10:0023 days 19:12:0723571.201944504.0vlakNaN[Předpokládané nízké obsazení]189.0356.0[0]Nonepo0[IC 510 Ostravan]NoneNoneNaNNaNNaNNaNČD po 09:101-223.800081
6297502024-11-20 08:19:34LEOstrava hl.n.Praha hl.n.2024-12-17 08:58:0027 days 00:38:2627648.640556199.0None65.0None209.0356.0NoneNoneút0[LE1252]NoneNone21.019.019.06.0LE út 08:582-227.026690
1880092024-11-09 06:09:11RJPrahaOstrava2024-11-09 15:50:000 days 09:40:4909.680278369.0vlak130.0None191.0NaNNoneNoneso0NoneNoneNoneNaNNaNNaNNaNRJ so 15:506-30.403345
4701832024-11-16 08:16:45LEPraha hl.n.Ostrava hl.n.2024-11-20 16:00:004 days 07:43:154103.720833479.0None33.0None211.0356.0NoneNonest0[LE413]NoneNone21.05.05.02.0LE st 16:003-44.321701
4225752024-11-15 00:05:19RJPrahaOstrava2024-11-17 16:50:002 days 16:44:41264.744722579.0vlak9.0None191.0NaNNoneNonene0NoneNoneNoneNaNNaNNaNNaNRJ ne 16:507-42.697697
5039272024-11-17 00:17:17RJOstravaPraha2024-12-04 08:53:0017 days 08:35:4317416.595278369.0vlak180.0None194.0NaNNoneNonest0NoneNoneNoneNaNNaNNaNNaNRJ st 08:533-217.358137
2604212024-11-10 19:22:03LEOstrava hl.n.Praha hl.n.2024-12-04 03:56:0023 days 08:33:5723560.565833199.0None52.0None212.0356.0NoneNonest0[LE1250]NoneNone21.015.016.00.0LE st 03:563-023.356910
1960152024-11-09 10:05:18ČDPraha hl.n.Ostrava hl.n.2025-01-31 20:22:0083 days 10:16:42832002.278333NaNvlakNaN[Předpokládané nízké obsazení]206.0356.0[0]Nonepá0[IC 549 Ostravan]NoneNoneNaNNaNNaNNaNČD pá 20:225-583.428264
5731172024-11-18 21:46:53ČDPraha hl.n.Ostrava hl.n.2024-11-26 12:19:007 days 14:32:077182.535278369.0vlakNaN[Předpokládané nízké obsazení]209.0356.0[0]Noneút0[EC 143 Ostravan]TrueNoneNaNNaNNaNNaNČD út 12:192-37.605637
3913052024-11-14 07:57:12ČDOstrava hl.n.Praha hl.n.2024-12-13 04:03:0028 days 20:05:4828692.096667289.0vlakNaN[Předpokládané nízké obsazení]195.0356.0[0]Nonepá0[IC 518 Ostravan]TrueNoneNaNNaNNaNNaNČD pá 04:035-128.837361
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" + ], + "text/plain": [ + " oscrapovano prodejce odkud kam \\\n", + "441915 2024-11-15 13:57:53 ČD Ostrava hl.n. Praha hl.n. \n", + "629750 2024-11-20 08:19:34 LE Ostrava hl.n. Praha hl.n. \n", + "188009 2024-11-09 06:09:11 RJ Praha Ostrava \n", + "470183 2024-11-16 08:16:45 LE Praha hl.n. Ostrava hl.n. \n", + "422575 2024-11-15 00:05:19 RJ Praha Ostrava \n", + "503927 2024-11-17 00:17:17 RJ Ostrava Praha \n", + "260421 2024-11-10 19:22:03 LE Ostrava hl.n. Praha hl.n. \n", + "196015 2024-11-09 10:05:18 ČD Praha hl.n. Ostrava hl.n. \n", + "573117 2024-11-18 21:46:53 ČD Praha hl.n. Ostrava hl.n. \n", + "391305 2024-11-14 07:57:12 ČD Ostrava hl.n. Praha hl.n. \n", + "\n", + " odjezd predstih predstih_d predstih_h cena \\\n", + "441915 2024-12-09 09:10:00 23 days 19:12:07 23 571.201944 504.0 \n", + "629750 2024-12-17 08:58:00 27 days 00:38:26 27 648.640556 199.0 \n", + "188009 2024-11-09 15:50:00 0 days 09:40:49 0 9.680278 369.0 \n", + "470183 2024-11-20 16:00:00 4 days 07:43:15 4 103.720833 479.0 \n", + "422575 2024-11-17 16:50:00 2 days 16:44:41 2 64.744722 579.0 \n", + "503927 2024-12-04 08:53:00 17 days 08:35:43 17 416.595278 369.0 \n", + "260421 2024-12-04 03:56:00 23 days 08:33:57 23 560.565833 199.0 \n", + "196015 2025-01-31 20:22:00 83 days 10:16:42 83 2002.278333 NaN \n", + "573117 2024-11-26 12:19:00 7 days 14:32:07 7 182.535278 369.0 \n", + "391305 2024-12-13 04:03:00 28 days 20:05:48 28 692.096667 289.0 \n", + "\n", + " prostredek volnych_mist obsazenost jizdni_doba \\\n", + "441915 vlak NaN [Předpokládané nízké obsazení] 189.0 \n", + "629750 None 65.0 None 209.0 \n", + "188009 vlak 130.0 None 191.0 \n", + "470183 None 33.0 None 211.0 \n", + "422575 vlak 9.0 None 191.0 \n", + "503927 vlak 180.0 None 194.0 \n", + "260421 None 52.0 None 212.0 \n", + "196015 vlak NaN [Předpokládané nízké obsazení] 206.0 \n", + "573117 vlak NaN [Předpokládané nízké obsazení] 209.0 \n", + "391305 vlak NaN [Předpokládané nízké obsazení] 195.0 \n", + "\n", + " vzdalenost zpozdeni cena_poznamka den prestupy vlaky \\\n", + "441915 356.0 [0] None po 0 [IC 510 Ostravan] \n", + "629750 356.0 None None út 0 [LE1252] \n", + "188009 NaN None None so 0 None \n", + "470183 356.0 None None st 0 [LE413] \n", + "422575 NaN None None ne 0 None \n", + "503927 NaN None None st 0 None \n", + "260421 356.0 None None st 0 [LE1250] \n", + "196015 356.0 [0] None pá 0 [IC 549 Ostravan] \n", + "573117 356.0 [0] None út 0 [EC 143 Ostravan] \n", + "391305 356.0 [0] None pá 0 [IC 518 Ostravan] \n", + "\n", + " mistenka_zdarma nahradni_bus volna_mista_economy \\\n", + "441915 None None NaN \n", + "629750 None None 21.0 \n", + "188009 None None NaN \n", + "470183 None None 21.0 \n", + "422575 None None NaN \n", + "503927 None None NaN \n", + "260421 None None 21.0 \n", + "196015 None None NaN \n", + "573117 True None NaN \n", + "391305 True None NaN \n", + "\n", + " volna_mista_economy_plus volna_mista_economy_business \\\n", + "441915 NaN NaN \n", + "629750 19.0 19.0 \n", + "188009 NaN NaN \n", + "470183 5.0 5.0 \n", + "422575 NaN NaN \n", + "503927 NaN NaN \n", + "260421 15.0 16.0 \n", + "196015 NaN NaN \n", + "573117 NaN NaN \n", + "391305 NaN NaN \n", + "\n", + " volna_mista_premium spoj obdobi predstih_d2 \n", + "441915 NaN ČD po 09:10 1-2 23.800081 \n", + "629750 6.0 LE út 08:58 2-2 27.026690 \n", + "188009 NaN RJ so 15:50 6-3 0.403345 \n", + "470183 2.0 LE st 16:00 3-4 4.321701 \n", + "422575 NaN RJ ne 16:50 7-4 2.697697 \n", + "503927 NaN RJ st 08:53 3-2 17.358137 \n", + "260421 0.0 LE st 03:56 3-0 23.356910 \n", + "196015 NaN ČD pá 20:22 5-5 83.428264 \n", + "573117 NaN ČD út 12:19 2-3 7.605637 \n", + "391305 NaN ČD pá 04:03 5-1 28.837361 " + ] + }, + "execution_count": 150, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.sample(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 244, + "id": "b90b5f86-ab55-4d48-86a0-ee0cbd05d97b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_3368\\716896998.py:2: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " cd2 = cd2.groupby('predstih_d').apply(lambda x: list(zip(x['cena'], x[0])))\n" + ] + }, + { + "data": { + "text/plain": [ + "predstih_d\n", + "0 [(535.0, 5), (569.0, 4), (609.0, 90), (619.0, ...\n", + "1 [(535.0, 3), (569.0, 27), (604.0, 8), (609.0, ...\n", + "2 [(535.0, 15), (569.0, 26), (604.0, 20), (609.0...\n", + "3 [(535.0, 11), (569.0, 35), (604.0, 20), (609.0...\n", + "4 [(535.0, 14), (569.0, 72), (604.0, 24), (609.0...\n", + "5 [(535.0, 20), (569.0, 130), (604.0, 41), (609....\n", + "6 [(535.0, 16), (569.0, 135), (604.0, 69), (609....\n", + "7 [(535.0, 10), (569.0, 107), (604.0, 95), (609....\n", + "8 [(535.0, 10), (569.0, 53), (604.0, 65), (609.0...\n", + "9 [(469.0, 5), (535.0, 5), (569.0, 37), (604.0, ...\n", + "10 [(469.0, 2), (535.0, 13), (569.0, 27), (604.0,...\n", + "11 [(469.0, 3), (535.0, 8), (569.0, 30), (604.0, ...\n", + "12 [(469.0, 2), (535.0, 14), (569.0, 32), (604.0,...\n", + "13 [(469.0, 3), (535.0, 13), (569.0, 79), (604.0,...\n", + "14 [(469.0, 2), (535.0, 3), (569.0, 36), (604.0, ...\n", + "15 [(469.0, 2), (535.0, 6), (569.0, 47), (604.0, ...\n", + "16 [(469.0, 5), (535.0, 6), (569.0, 45), (604.0, ...\n", + "17 [(469.0, 1), (569.0, 22), (604.0, 17), (609.0,...\n", + "18 [(469.0, 5), (535.0, 4), (569.0, 24), (604.0, ...\n", + "19 [(469.0, 6), (535.0, 3), (569.0, 29), (604.0, ...\n", + "20 [(469.0, 5), (535.0, 3), (569.0, 21), (604.0, ...\n", + "21 [(469.0, 1), (569.0, 21), (604.0, 18), (609.0,...\n", + "22 [(469.0, 1), (535.0, 1), (569.0, 16), (604.0, ...\n", + "23 [(469.0, 9), (535.0, 1), (569.0, 25), (604.0, ...\n", + "24 [(469.0, 6), (549.0, 2), (569.0, 14), (589.0, ...\n", + "25 [(469.0, 5), (549.0, 10), (569.0, 12), (584.0,...\n", + "26 [(469.0, 10), (549.0, 13), (569.0, 18), (584.0...\n", + "27 [(469.0, 6), (549.0, 17), (569.0, 4), (584.0, ...\n", + "28 [(469.0, 1), (549.0, 5), (569.0, 11), (584.0, ...\n", + "29 [(469.0, 2), (549.0, 9), (569.0, 12), (584.0, ...\n", + "30 [(469.0, 3), (549.0, 7), (569.0, 3), (584.0, 2...\n", + "31 [(469.0, 6), (535.0, 1), (549.0, 8), (569.0, 3...\n", + "32 [(549.0, 12), (584.0, 5), (589.0, 1), (619.0, ...\n", + "34 [(549.0, 8), (569.0, 2), (584.0, 3), (589.0, 1...\n", + "35 [(549.0, 16), (584.0, 8), (589.0, 2), (619.0, ...\n", + "36 [(549.0, 3), (584.0, 1)]\n", + "37 [(549.0, 6), (584.0, 4), (589.0, 1), (649.0, 1)]\n", + "38 [(549.0, 15), (584.0, 7), (589.0, 1), (619.0, ...\n", + "42 [(460.0, 2), (549.0, 13), (584.0, 4), (619.0, ...\n", + "43 [(460.0, 2), (549.0, 16), (584.0, 7), (619.0, ...\n", + "44 [(469.0, 3), (549.0, 8), (584.0, 3), (619.0, 3...\n", + "45 [(404.0, 2), (549.0, 12), (584.0, 4), (619.0, ...\n", + "46 [(404.0, 1), (549.0, 4), (584.0, 1), (619.0, 1...\n", + "47 [(469.0, 2), (549.0, 7), (584.0, 4), (619.0, 1...\n", + "48 [(469.0, 3), (549.0, 5), (584.0, 3), (619.0, 3...\n", + "49 [(404.0, 1), (549.0, 5), (584.0, 1), (619.0, 1...\n", + "50 [(404.0, 1), (549.0, 13), (584.0, 4), (619.0, ...\n", + "51 [(404.0, 2), (469.0, 3), (549.0, 23), (584.0, ...\n", + "52 [(469.0, 2), (549.0, 2), (619.0, 3), (649.0, 1)]\n", + "53 [(469.0, 3), (549.0, 5), (584.0, 3), (619.0, 3...\n", + "54 [(404.0, 1), (469.0, 3), (549.0, 14), (584.0, ...\n", + "55 [(404.0, 1), (549.0, 9), (584.0, 3), (619.0, 1...\n", + "57 [(469.0, 3), (549.0, 7), (584.0, 5), (619.0, 3...\n", + "58 [(469.0, 2), (549.0, 2), (619.0, 3), (649.0, 1)]\n", + "59 [(469.0, 3), (549.0, 6), (584.0, 4), (619.0, 3...\n", + "60 [(469.0, 2), (549.0, 2), (619.0, 3), (649.0, 1)]\n", + "dtype: object" + ] + }, + "execution_count": 244, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cd2 = df[df['obdobi'].isin(spicka) & (df['predstih_d'] <= 60) & (df['prodejce'] == \"ČD\")].groupby(['predstih_d','cena']).size().reset_index()\n", + "cd2 = cd2.groupby('predstih_d').apply(lambda x: list(zip(x['cena'], x[0])))\n", + "cd2" + ] + }, + { + "cell_type": "code", + "execution_count": 258, + "id": "ea1dc7a0-d7e3-4975-bdf2-42893cb9b1cf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " predstih_d cena 0\n", + "0 0 535.0 5\n", + "1 0 569.0 4\n", + "2 0 609.0 90\n", + "3 0 619.0 153\n", + "4 0 644.0 45\n", + ".. ... ... ...\n", + "518 59 649.0 1\n", + "519 60 469.0 2\n", + "520 60 549.0 2\n", + "521 60 619.0 3\n", + "522 60 649.0 1\n", + "\n", + "[523 rows x 3 columns]" + ] + }, + "execution_count": 258, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cd3" + ] + }, + { + "cell_type": "code", + "execution_count": 310, + "id": "092946b7-4004-48be-81c6-5240ceeddedd", + "metadata": {}, + "outputs": [], + "source": [ + "def dopravce(dopr):\n", + " x = df[df['obdobi'].isin(spicka) & (df['predstih_d'] <= 60) & (df['prodejce'] == dopr)].groupby(['predstih_d','cena']).size().reset_index()\n", + " x = x[['predstih_d','cena', 0]].values.tolist()\n", + " x = pd.Series(x, name=dopr)\n", + " x.index = x.index.map(lambda x: f\"{x} d\")\n", + " return x" + ] + }, + { + "cell_type": "code", + "execution_count": 314, + "id": "222b3d1c-ef9c-4e93-aac0-5535127c6232", + "metadata": {}, + "outputs": [], + "source": [ + "cd = dopravce(\"ČD\")\n", + "le = dopravce(\"LE\")\n", + "rj = dopravce(\"RJ\")" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "id": "57012422-38dd-43bc-9496-85f18f3ed9ea", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "predstih_d2\n", + "0.000185 [nan]\n", + "0.000278 [654.0]\n", + "0.000336 [nan]\n", + "0.003056 [744.0]\n", + "0.003345 [769.0]\n", + " ... \n", + "60.062454 [619.0]\n", + "60.091169 [469.0]\n", + "60.097870 [549.0]\n", + "60.130752 [619.0]\n", + "60.145787 [649.0]\n", + "Name: cena, Length: 11892, dtype: object" + ] + }, + "execution_count": 164, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pokus = df[df['obdobi'].isin(spicka) & (df['predstih_d'] <= 60)].groupby('predstih_d2')['cena'].agg(list)\n", + "pokus" + ] + }, { "cell_type": "code", "execution_count": 104, @@ -304,82 +1050,37 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 142, "id": "2f280102-159d-4b24-ad1e-eeb48072d21b", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "predstih_d\n", - "0 2279.0\n", - "1 1649.0\n", - "2 1649.0\n", - "3 1499.0\n", - "4 1689.0\n", - "5 1599.0\n", - "6 1349.0\n", - "7 1279.0\n", - "8 1099.0\n", - "9 1399.0\n", - "10 1099.0\n", - "12 1699.0\n", - "13 1459.0\n", - "14 1099.0\n", - "15 1099.0\n", - "16 779.0\n", - "17 694.0\n", - "18 779.0\n", - "19 1939.0\n", - "20 999.0\n", - "21 999.0\n", - "22 999.0\n", - "23 669.0\n", - "24 999.0\n", - "25 999.0\n", - "26 999.0\n", - "27 999.0\n", - "28 999.0\n", - "29 674.0\n", - "30 659.0\n", - "31 979.0\n", - "34 979.0\n", - "35 649.0\n", - "36 979.0\n", - "37 999.0\n", - "38 649.0\n", - "40 609.0\n", - "41 869.0\n", - "42 649.0\n", - "43 649.0\n", - "44 649.0\n", - "46 649.0\n", - "47 649.0\n", - "48 649.0\n", - "49 649.0\n", - "50 649.0\n", - "51 649.0\n", - "52 649.0\n", - "53 879.0\n", - "54 649.0\n", - "55 649.0\n", - "56 479.0\n", - "57 859.0\n", - "58 489.0\n", - "59 649.0\n", - "60 649.0\n", - "Name: pokus, dtype: float64" - ] - }, - "execution_count": 128, - "metadata": {}, - "output_type": "execute_result" + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_3368\\1592083683.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", + " pokus = df[df['obdobi'].isin(spicka) & (df['predstih_d'] <= 60)].groupby('prodejce')[['predstih_d','cena']].agg(list)\n" + ] + }, + { + "ename": "ValueError", + "evalue": "2", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[142], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m pokus \u001b[38;5;241m=\u001b[39m df[df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mobdobi\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39misin(spicka) \u001b[38;5;241m&\u001b[39m (df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpredstih_d\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m60\u001b[39m)]\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprodejce\u001b[39m\u001b[38;5;124m'\u001b[39m)[[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpredstih_d\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcena\u001b[39m\u001b[38;5;124m'\u001b[39m]]\u001b[38;5;241m.\u001b[39magg(\u001b[38;5;28mlist\u001b[39m)\n\u001b[1;32m----> 2\u001b[0m pokus \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mSeries(cd_pokus\u001b[38;5;241m.\u001b[39mdropna(), name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mČD\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 3\u001b[0m pokus\n", + "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\series.py:584\u001b[0m, in \u001b[0;36mSeries.__init__\u001b[1;34m(self, data, index, dtype, name, copy, fastpath)\u001b[0m\n\u001b[0;32m 582\u001b[0m data \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[0;32m 583\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 584\u001b[0m data \u001b[38;5;241m=\u001b[39m sanitize_array(data, index, dtype, copy)\n\u001b[0;32m 586\u001b[0m manager \u001b[38;5;241m=\u001b[39m _get_option(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode.data_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m, silent\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 587\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m manager \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mblock\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", + "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\construction.py:633\u001b[0m, in \u001b[0;36msanitize_array\u001b[1;34m(data, index, dtype, copy, allow_2d)\u001b[0m\n\u001b[0;32m 631\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 632\u001b[0m data \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(data, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[1;32m--> 633\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m sanitize_array(\n\u001b[0;32m 634\u001b[0m data,\n\u001b[0;32m 635\u001b[0m index\u001b[38;5;241m=\u001b[39mindex,\n\u001b[0;32m 636\u001b[0m dtype\u001b[38;5;241m=\u001b[39mdtype,\n\u001b[0;32m 637\u001b[0m copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 638\u001b[0m allow_2d\u001b[38;5;241m=\u001b[39mallow_2d,\n\u001b[0;32m 639\u001b[0m )\n\u001b[0;32m 641\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 642\u001b[0m _sanitize_non_ordered(data)\n", + "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\construction.py:606\u001b[0m, in \u001b[0;36msanitize_array\u001b[1;34m(data, index, dtype, copy, allow_2d)\u001b[0m\n\u001b[0;32m 604\u001b[0m subarr \u001b[38;5;241m=\u001b[39m data\n\u001b[0;32m 605\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mobject\u001b[39m:\n\u001b[1;32m--> 606\u001b[0m subarr \u001b[38;5;241m=\u001b[39m maybe_infer_to_datetimelike(data)\n\u001b[0;32m 607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m 608\u001b[0m object_index\n\u001b[0;32m 609\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m using_pyarrow_string_dtype()\n\u001b[0;32m 610\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m is_string_dtype(subarr)\n\u001b[0;32m 611\u001b[0m ):\n\u001b[0;32m 612\u001b[0m \u001b[38;5;66;03m# Avoid inference when string option is set\u001b[39;00m\n\u001b[0;32m 613\u001b[0m subarr \u001b[38;5;241m=\u001b[39m data\n", + "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\dtypes\\cast.py:1182\u001b[0m, in \u001b[0;36mmaybe_infer_to_datetimelike\u001b[1;34m(value)\u001b[0m\n\u001b[0;32m 1179\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;28mtype\u001b[39m(value)) \u001b[38;5;66;03m# pragma: no cover\u001b[39;00m\n\u001b[0;32m 1180\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m value\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m 1181\u001b[0m \u001b[38;5;66;03m# Caller is responsible\u001b[39;00m\n\u001b[1;32m-> 1182\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(value\u001b[38;5;241m.\u001b[39mndim) \u001b[38;5;66;03m# pragma: no cover\u001b[39;00m\n\u001b[0;32m 1184\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(value):\n\u001b[0;32m 1185\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m value\n", + "\u001b[1;31mValueError\u001b[0m: 2" + ] } ], "source": [ - "pokus = df[df['obdobi'].isin(spicka) & (df['predstih_d'] <= 60)].groupby('predstih_d')['cena'].agg(list)\n", - "pokus = pokus.apply(lambda x: max(x))\n", - "pokus = pd.Series(pokus.dropna(), name='pokus')\n", + "pokus = df[df['obdobi'].isin(spicka) & (df['predstih_d'] <= 60)].groupby('prodejce')[['predstih_d','cena']].agg(list)\n", + "pokus = pd.Series(cd_pokus.dropna(), name='ČD')\n", "pokus" ] }, @@ -432,7 +1133,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 272, "id": "2e0d0fe8-5906-4aa8-9836-d27630e45f00", "metadata": {}, "outputs": [], @@ -485,7 +1186,9 @@ " from highcharts_core.options.series.bar import ColumnSeries\n", " from highcharts_core.options.series.bar import BarSeries\n", " from highcharts_core.options.series.histogram import HistogramSeries\n", + " from highcharts_core.options.series.bubble import BubbleSeries\n", " from highcharts_core.options.plot_options.scatter import ScatterOptions\n", + " from highcharts_core.options.plot_options.bubble import BubbleOptions\n", " from highcharts_core.options.series.scatter import ScatterSeries\n", " from highcharts_core.options.legend import Legend\n", " from highcharts_core.options.title import Title\n", @@ -495,7 +1198,10 @@ " nastaveni = {}\n", "\n", " if prvni:\n", - " zdrojaky = f\"\"\"\"\"\"\n", + " zdrojaky = f\"\"\"\n", + "\n", + "\n", + "\"\"\"\n", " else:\n", " zdrojaky = \"\"\n", "\n", @@ -616,7 +1322,7 @@ " if len(vodorovny) > 0:\n", " vykresleni(vodorovny, BarSeries)\n", " if len(scatter) > 0:\n", - " vykresleni(scatter, ScatterSeries)\n", + " vykresleni(scatter, BubbleSeries)\n", "\n", " if len(barvy) > 0:\n", " my_chart.options.colors = barvy\n", @@ -664,40 +1370,104 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "6c7b005c-02e3-4a69-95cf-d1cd23bc68f9", + "execution_count": 284, + "id": "c096a292-d6f8-49b8-b6a0-10324c986dff", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "['7-3', '5-2', '5-3', '7-4']" + ] + }, + "execution_count": 284, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spicka" + ] + }, + { + "cell_type": "code", + "execution_count": 288, + "id": "92bfd06f-56d3-4f95-adaf-3a50142bdffe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "148297 1-0\n", + "14859 1-1\n", + "5536 1-2\n", + "5526 1-3\n", + "5512 1-4\n", + "5534 1-5\n", + "160388 2-0\n", + "5538 2-1\n", + "3957 2-2\n", + "3973 2-3\n", + "3977 2-4\n", + "5514 2-5\n", + "160426 3-0\n", + "3983 3-1\n", + "3951 3-2\n", + "3952 3-3\n", + "3950 3-4\n", + "7091 3-5\n", + "160447 4-0\n", + "3961 4-1\n", + "3959 4-2\n", + "8573 4-3\n", + "8578 4-4\n", + "8607 4-5\n", + "148574 5-0\n", + "1149 5-1\n", + "1121 5-2\n", + "1138 5-3\n", + "1119 5-4\n", + "1125 5-5\n", + "148010 6-0\n", + "1130 6-1\n", + "1120 6-2\n", + "1126 6-3\n", + "1137 6-4\n", + "147953 6-5\n", + "148244 7-0\n", + "8315 7-1\n", + "8300 7-2\n", + "13009 7-3\n", + "16194 7-4\n", + "19661 7-5\n", + "Name: obdobi, dtype: object" + ] + }, + "execution_count": 288, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['obdobi'].drop_duplicates().sort_values()" + ] }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 324, "id": "94454e4f-2c40-4fa9-9f2b-35444323d1a4", "metadata": {}, "outputs": [ { - "ename": "HighchartsValueError", - "evalue": "data expects either a 1D or 2D collection. Collection received had 1162 dimensions.", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mHighchartsValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[112], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m irozhlas_graf(scatter\u001b[38;5;241m=\u001b[39m[pokus], target\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprgova\u001b[39m\u001b[38;5;124m'\u001b[39m, titulek\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMezistátní jízdné podle předstihu nákupu\u001b[39m\u001b[38;5;124m'\u001b[39m, osaymin\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, podtitulek\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSrovnávají se pouze spoje s nejnižším možným počtem přestupů.\u001b[39m\u001b[38;5;124m'\u001b[39m, kredity\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mZdroj dat: e-shopy dopravců v listopadu 2024. Vizualizace: iROZHLAS.cz\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhttps://www.irozhlas.cz/zpravy-tag/datova-zurnalistika\u001b[39m\u001b[38;5;124m'\u001b[39m])\n", - "Cell \u001b[1;32mIn[76], line 180\u001b[0m, in \u001b[0;36mirozhlas_graf\u001b[1;34m(carovy, sloupcovy, vodorovny, scatter, procenta, skryte, barvy, histogram, max_procenta, target, titulek, podtitulek, naproti, osay, osay2, osaymin, osaymax, kredity, zaokrouhleni, prvni, skladany, naopak, vzhurunohama, skrytnuly)\u001b[0m\n\u001b[0;32m 178\u001b[0m vykresleni(vodorovny, BarSeries)\n\u001b[0;32m 179\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(scatter) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m--> 180\u001b[0m vykresleni(scatter, ScatterSeries)\n\u001b[0;32m 182\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(barvy) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m 183\u001b[0m my_chart\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mcolors \u001b[38;5;241m=\u001b[39m barvy\n", - "Cell \u001b[1;32mIn[76], line 165\u001b[0m, in \u001b[0;36mirozhlas_graf..vykresleni\u001b[1;34m(serie, typ)\u001b[0m\n\u001b[0;32m 153\u001b[0m my_chart\u001b[38;5;241m.\u001b[39madd_series(\n\u001b[0;32m 154\u001b[0m typ(\n\u001b[0;32m 155\u001b[0m data\u001b[38;5;241m=\u001b[39ms,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 160\u001b[0m )\n\u001b[0;32m 161\u001b[0m )\n\u001b[0;32m 163\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 164\u001b[0m my_chart\u001b[38;5;241m.\u001b[39madd_series(\n\u001b[1;32m--> 165\u001b[0m typ(\n\u001b[0;32m 166\u001b[0m data\u001b[38;5;241m=\u001b[39ms\u001b[38;5;241m.\u001b[39mfillna(\u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39mto_list(),\n\u001b[0;32m 167\u001b[0m visible\u001b[38;5;241m=\u001b[39mviditelnost,\n\u001b[0;32m 168\u001b[0m name\u001b[38;5;241m=\u001b[39mpopisek,\n\u001b[0;32m 169\u001b[0m y_axis\u001b[38;5;241m=\u001b[39mktera_osa,\n\u001b[0;32m 170\u001b[0m )\n\u001b[0;32m 171\u001b[0m )\n", - "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\highcharts_core\\options\\series\\scatter.py:25\u001b[0m, in \u001b[0;36mScatterSeries.__init__\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m 24\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m---> 25\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\highcharts_core\\options\\series\\base.py:43\u001b[0m, in \u001b[0;36mSeriesBase.__init__\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m 40\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_y_axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 41\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_z_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m---> 43\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 44\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mid \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mid\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 45\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mindex\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n", - "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\highcharts_core\\options\\series\\base.py:133\u001b[0m, in \u001b[0;36mSeriesBase.__setattr__\u001b[1;34m(self, name, value)\u001b[0m\n\u001b[0;32m 129\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Updates the series attribute, or descendent attributes on the ``.data`` \u001b[39;00m\n\u001b[0;32m 130\u001b[0m \u001b[38;5;124;03mproperties.\u001b[39;00m\n\u001b[0;32m 131\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 132\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 133\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__setattr__\u001b[39m(name, value)\n\u001b[0;32m 134\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m 135\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m:\n", - "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\highcharts_core\\options\\series\\scatter.py:119\u001b[0m, in \u001b[0;36mScatterSeries.data\u001b[1;34m(self, value)\u001b[0m\n\u001b[0;32m 117\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 118\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 119\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data \u001b[38;5;241m=\u001b[39m CartesianData\u001b[38;5;241m.\u001b[39mfrom_array(value)\n", - "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\highcharts_core\\options\\series\\data\\base.py:710\u001b[0m, in \u001b[0;36mDataBase.from_array\u001b[1;34m(cls, value)\u001b[0m\n\u001b[0;32m 707\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(value, \u001b[38;5;28mdict\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdataPoints\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m value:\n\u001b[0;32m 708\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mfrom_list(value[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdataPoints\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m--> 710\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mfrom_list(value)\n", - "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\highcharts_core\\options\\series\\data\\cartesian.py:184\u001b[0m, in \u001b[0;36mCartesianData.from_list\u001b[1;34m(cls, value)\u001b[0m\n\u001b[0;32m 182\u001b[0m as_obj \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m(y \u001b[38;5;241m=\u001b[39m item[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m 183\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 184\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m errors\u001b[38;5;241m.\u001b[39mHighchartsValueError(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdata expects either a 1D or 2D \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m 185\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcollection. Collection received \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m 186\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhad \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(item)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m dimensions.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 187\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 188\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m errors\u001b[38;5;241m.\u001b[39mHighchartsValueError(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124meach data point supplied must either \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m 189\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbe a Cartesian Data Point or be \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m 190\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcoercable to one. Could not coerce: \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m 191\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mitem\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n", - "\u001b[1;31mHighchartsValueError\u001b[0m: data expects either a 1D or 2D collection. Collection received had 1162 dimensions." + "name": "stdout", + "output_type": "stream", + "text": [ + "Graf uložen.\n" ] } ], "source": [ - "irozhlas_graf(scatter=[pokus], target='prgova', titulek='Mezistátní jízdné podle předstihu nákupu', osaymin=0, podtitulek='Srovnávají se pouze spoje s nejnižším možným počtem přestupů.', kredity=['Zdroj dat: e-shopy dopravců v listopadu 2024. Vizualizace: iROZHLAS.cz','https://www.irozhlas.cz/zpravy-tag/datova-zurnalistika'])" + "irozhlas_graf(scatter=[cd, rj, le], target='prgova_bubble', titulek='Jízdné Praha ↔ Ostrava na začátku a konci víkendu', osaymin=0, podtitulek='Graf sleduje ceny jízdenek v páteční (8.00-16.00) a nedělní (12.00-18.00) špičce v závislosti na předstihu nákupu.', kredity=['Zdroj dat: e-shopy dopravců od 7. 11. do 25. 11. 2024. Vizualizace: iROZHLAS.cz','https://www.irozhlas.cz/zpravy-tag/datova-zurnalistika'])" ] } ], diff --git a/903_otevreni_predprodeje.ipynb b/903_otevreni_predprodeje.ipynb new file mode 100644 index 0000000..29c1044 --- /dev/null +++ b/903_otevreni_predprodeje.ipynb @@ -0,0 +1,162 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "471ec2fa-15be-484b-9b36-a4c1dd32c3ae", + "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": 3, + "id": "0372a0b5-a4d2-4388-8e2f-0809ec3bb953", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_parquet(os.path.join('data','jizdenky.parquet'))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e50915db-88d8-4d0b-8819-a958e7599229", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_30532\\1561039113.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['cena'].notnull()].groupby(['prodejce','odkud','kam'])['predstih_d'].max().nlargest(50)\n" + ] + }, + { + "data": { + "text/plain": [ + "prodejce odkud kam \n", + "LE Kraków Główny Praha hl.n. 92.0\n", + " Praha hl.n. Kraków Główny 92.0\n", + " Košice Pardubice hl.n. 90.0\n", + " Pardubice hl.n. Košice 90.0\n", + " Praha hl.n. Ostrava hl.n. 90.0\n", + " Ostrava-Svinov 90.0\n", + "ČD Berlin Hbf Amsterdam Zuid 90.0\n", + " Bruxelles-Central 90.0\n", + " Berlin Hbf (tief) Praha hl.n. 90.0\n", + " Brno hl.n. Hamburg Hbf 90.0\n", + " Bruxelles-Midi Praha hl.n. 90.0\n", + " Hamburg Hbf Brno hl.n. 90.0\n", + " Praha hl.n. Berlin Hbf (tief) 90.0\n", + " Bern 90.0\n", + " Bruxelles-Midi 90.0\n", + " Budapest-Kelenföld 90.0\n", + " Budapest-Nyugati pu 90.0\n", + " Wien Franz-Josefs-Bahnhof 90.0\n", + " Wien Hbf 90.0\n", + " Zürich HB České Budějovice 90.0\n", + " Ústí n.L.hl.n. Dresden Hbf 90.0\n", + " České Budějovice Zürich HB 90.0\n", + "LE Ostrava hl.n. Praha hl.n. 89.0\n", + " Ostrava-Svinov Praha hl.n. 89.0\n", + "ČD Berlin Hbf Amsterdam Centraal 89.0\n", + " Praha hl.n. 89.0\n", + " Budapest-Kelenföld Praha hl.n. 89.0\n", + " Budapest-Nyugati pu Praha hl.n. 89.0\n", + " Praha hl.n. Amsterdam Centraal 89.0\n", + " Amsterdam Zuid 89.0\n", + " Berlin Hbf (tief) Brno hl.n. 88.0\n", + " Bratislava hl.st. Praha hl.n. 88.0\n", + " Bruxelles-Central Praha hl.n. 88.0\n", + " Wien Franz-Josefs-Bahnhof Praha hl.n. 88.0\n", + " Wien Hbf Praha hl.n. 88.0\n", + " Dresden Hbf Ústí n.L.hl.n. 85.0\n", + " Praha-Smíchov Bruxelles-Midi 85.0\n", + " München Hbf Praha hl.n. 84.0\n", + " Amsterdam Centraal Praha hl.n. 83.0\n", + " Amsterdam Zuid Praha hl.n. 83.0\n", + " Bern Praha hl.n. 83.0\n", + " Flughafen Wien Praha hl.n. 77.0\n", + " Praha hl.n. Berlin Ostbahnhof 75.0\n", + " Wien Hbf (Bahnsteige 1-2) Praha hl.n. 75.0\n", + " Berlin Hbf (tief) Hamburg Hbf 69.0\n", + " Praha-Dejvice Berlin Hbf (tief) 68.0\n", + "LE Praha-Libeň Ostrava hl.n. 67.0\n", + " Ostrava-Svinov 67.0\n", + " Ostrava hl.n. Praha-Libeň 64.0\n", + " Ostrava-Svinov Praha-Libeň 64.0\n", + "Name: predstih_d, dtype: float64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df['cena'].notnull()].groupby(['prodejce','odkud','kam'])['predstih_d'].max().nlargest(50)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "af9ea598-0e5e-4ba6-bc0d-37f19deada4c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_30532\\420619131.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['cena'].notnull()].groupby('prodejce')['predstih_d'].max().nlargest(50)\n" + ] + }, + { + "data": { + "text/plain": [ + "prodejce\n", + "LE 92\n", + "ČD 90\n", + "RJ 59\n", + "ARR 36\n", + "Name: predstih_d, dtype: int64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df['cena'].notnull()].groupby('prodejce')['predstih_d'].max().nlargest(50)" + ] + } + ], + "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 +} diff --git a/grafy/mezistatni_jizdne.html b/grafy/mezistatni_jizdne.html index 6d1f2bd..c8c8c7b 100644 --- a/grafy/mezistatni_jizdne.html +++ b/grafy/mezistatni_jizdne.html @@ -20,7 +20,7 @@ credits: { enabled: true, href: 'https://www.irozhlas.cz/zpravy-tag/datova-zurnalistika', - text: 'Zdroj dat: e-shopy dopravc v listopadu 2024. Vizualizace: iROZHLAS.cz' + text: 'Zdroj dat: e-shopy dopravc od 7. 11. do 25. 11. 2024. Vizualizace: iROZHLAS.cz' }, series: [{ data: [[1058.0], @@ -39,23 +39,23 @@ [774.0], [645.0], [645.0], -[645.0], +[774.0], [774.0], [774.0], [645.0], [645.0], [645.0], [645.0], -[490.0], +[645.0], [709.5], [645.0], -[774.0], [645.0], [645.0], [645.0], [645.0], -[774.0], -[774.0], +[645.0], +[787.0], +[800.0], [645.0], [645.0], [645.0], @@ -131,7 +131,7 @@ 490.0], [1471.0, 490.0], -[1187.0, +[1907.0, 490.0], [1907.0, 490.0], @@ -139,13 +139,13 @@ 490.0], [1058.0, 490.0], -[800.0, +[1187.0, 490.0], -[929.0, +[1058.0, 490.0], [1187.0, 490.0], -[1187.0, +[1471.0, 490.0], [1187.0, 490.0], @@ -159,7 +159,7 @@ 490.0], [1058.0, 490.0], -[800.0, +[1058.0, 490.0], [1058.0, 490.0], @@ -181,7 +181,7 @@ 490.0], [800.0, 490.0], -[800.0, +[1058.0, 490.0], [1058.0, 490.0], @@ -211,7 +211,7 @@ 490.0], [645.0, 490.0], -[645.0, +[800.0, 490.0], [645.0, 490.0]], @@ -447,17 +447,17 @@ [490.0], [490.0], [490.0], -[490.0], +[400.0], [400.0], [490.0], [490.0], [400.0], [400.0], -[445.0], [400.0], [400.0], [400.0], [400.0], +[490.0], [400.0], [400.0], [400.0], @@ -521,11 +521,11 @@ 400.0], [722.0, 400.0], -[722.0, +[1047.0, 400.0], -[722.0, +[1047.0, 400.0], -[722.0, +[1047.0, 400.0], [1047.0, 400.0], @@ -535,13 +535,13 @@ 400.0], [1047.0, 400.0], -[722.0, +[1047.0, 400.0], [1047.0, 400.0], [1047.0, 400.0], -[722.0, +[1047.0, 400.0], [1047.0, 400.0], @@ -571,7 +571,7 @@ 400.0], [722.0, 400.0], -[400.0, +[632.0, 400.0], [632.0, 400.0], @@ -593,7 +593,7 @@ 400.0], [490.0, 400.0], -[400.0, +[490.0, 400.0], [490.0, 400.0], @@ -828,7 +828,7 @@ data: [[903.0], [903.0], [903.0], -[645.0], +[903.0], [903.0], [645.0], [645.0], @@ -848,15 +848,16 @@ [645.0], [645.0], [516.0], -[516.0], +[774.0], +[903.0], [903.0], [645.0], [645.0], [645.0], [645.0], [645.0], -[645.0], -[774.0], +[903.0], +[903.0], [903.0], [645.0], [903.0], @@ -881,10 +882,9 @@ [516.0], [516.0], [451.5], -[387.0], +[451.5], [516.0], -[387.0], -[516.0]], +[387.0]], id: 'line_D Praha ↔ Vde', name: 'D Praha ↔ Vde', yAxis: 0, @@ -899,7 +899,7 @@ { data: [[1406.0, 645.0], -[1161.0, +[1406.0, 645.0], [1406.0, 387.0], @@ -929,7 +929,7 @@ 387.0], [1406.0, 387.0], -[1161.0, +[1406.0, 387.0], [1406.0, 387.0], @@ -943,11 +943,11 @@ 387.0], [903.0, 387.0], -[645.0, -387.0], [1161.0, 387.0], -[1161.0, +[1406.0, +387.0], +[1416.0, 387.0], [1161.0, 387.0], @@ -957,13 +957,15 @@ 387.0], [1161.0, 387.0], +[1545.0, +387.0], [1416.0, 387.0], -[1161.0, +[1545.0, +516.0], +[1032.0, 387.0], [903.0, -903.0], -[903.0, 387.0], [1161.0, 387.0], @@ -1007,15 +1009,13 @@ 387.0], [645.0, 387.0], -[645.0, +[771.0, 387.0], [645.0, 387.0], [645.0, 387.0], [516.0, -387.0], -[516.0, 387.0]], name: 'D Praha ↔ Vde', yAxis: 0, @@ -1036,7 +1036,7 @@ [599.0], [549.0], [599.0], -[549.0], +[504.0], [459.0], [459.0], [459.0], @@ -1122,7 +1122,7 @@ [799.0, 299.0], [799.0, -379.0], +299.0], [739.0, 299.0], [799.0, @@ -1214,66 +1214,66 @@ type: 'arearange',linkedTo: 'line_RJ Praha ↔ Vde', fillOpacity: 0.3, lineWidth: 0 }, { - 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text: 'Srovnvaj se pouze spoje s nejnim monm potem pestup.' + text: 'Graf zobrazuje nejni, stedn a nejvy cenu. Zahrnuje pouze spoje s nejnim monm potem pestup.' }, title: { align: 'left', diff --git a/grafy/mezistatni_jizdne.txt b/grafy/mezistatni_jizdne.txt index 195c958..a0fc2f1 100644 --- a/grafy/mezistatni_jizdne.txt +++ b/grafy/mezistatni_jizdne.txt @@ -2,4 +2,2469 @@
- \ No newline at end of file + \ No newline at end of file diff --git a/grafy/prgova.html b/grafy/prgova.html index 702dce3..9c796dd 100644 --- a/grafy/prgova.html +++ b/grafy/prgova.html @@ -20,10802 +20,12765 @@ text: 'Zdroj dat: e-shopy dopravc v listopadu 2024. 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