{ "cells": [ { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from pandas.io.json import json_normalize #package for flattening json in pandas df\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
bad_behaviourball_receiptball_recoveryblockdribbledueldurationfoul_committedfoul_wongoalkeeper...possession_teamrelated_eventssecondshotsubstitutiontacticsteamtimestamptypeunder_pressure
0NaNNaNNaNNaNNaNNaN0.00NaNNaNNaN...{'id': 746, 'name': 'Manchester City WFC'}NaN0NaNNaN{'formation': 433, 'lineup': [{'player': {'id'...{'id': 746, 'name': 'Manchester City WFC'}2018-11-30 00:00:00.000{'id': 35, 'name': 'Starting XI'}NaN
1NaNNaNNaNNaNNaNNaN0.00NaNNaNNaN...{'id': 746, 'name': 'Manchester City WFC'}NaN0NaNNaN{'formation': 352, 'lineup': [{'player': {'id'...{'id': 971, 'name': 'Chelsea LFC'}2018-11-30 00:00:00.000{'id': 35, 'name': 'Starting XI'}NaN
2NaNNaNNaNNaNNaNNaN8.16NaNNaNNaN...{'id': 746, 'name': 'Manchester City WFC'}[c771a4d4-51cb-41de-83aa-7103cd199c92]0NaNNaNNaN{'id': 971, 'name': 'Chelsea LFC'}2018-11-30 00:00:00.000{'id': 18, 'name': 'Half Start'}NaN
3NaNNaNNaNNaNNaNNaN7.96NaNNaNNaN...{'id': 746, 'name': 'Manchester City WFC'}[48b94b06-ebbd-47e9-958c-44bf63622f5e]0NaNNaNNaN{'id': 746, 'name': 'Manchester City WFC'}2018-11-30 00:00:00.000{'id': 18, 'name': 'Half Start'}NaN
4NaNNaNNaNNaNNaNNaN0.00NaNNaNNaN...{'id': 971, 'name': 'Chelsea LFC'}[237cac8c-5cb0-4015-9d08-c7df9699a136]0NaNNaNNaN{'id': 971, 'name': 'Chelsea LFC'}2018-11-30 00:00:00.100{'id': 30, 'name': 'Pass'}NaN
\n", "

5 rows × 33 columns

\n", "
" ], "text/plain": [ " bad_behaviour ball_receipt ball_recovery block dribble duel duration \\\n", "0 NaN NaN NaN NaN NaN NaN 0.00 \n", "1 NaN NaN NaN NaN NaN NaN 0.00 \n", "2 NaN NaN NaN NaN NaN NaN 8.16 \n", "3 NaN NaN NaN NaN NaN NaN 7.96 \n", "4 NaN NaN NaN NaN NaN NaN 0.00 \n", "\n", " foul_committed foul_won goalkeeper ... \\\n", "0 NaN NaN NaN ... \n", "1 NaN NaN NaN ... \n", "2 NaN NaN NaN ... \n", "3 NaN NaN NaN ... \n", "4 NaN NaN NaN ... \n", "\n", " possession_team \\\n", "0 {'id': 746, 'name': 'Manchester City WFC'} \n", "1 {'id': 746, 'name': 'Manchester City WFC'} \n", "2 {'id': 746, 'name': 'Manchester City WFC'} \n", "3 {'id': 746, 'name': 'Manchester City WFC'} \n", "4 {'id': 971, 'name': 'Chelsea LFC'} \n", "\n", " related_events second shot substitution \\\n", "0 NaN 0 NaN NaN \n", "1 NaN 0 NaN NaN \n", "2 [c771a4d4-51cb-41de-83aa-7103cd199c92] 0 NaN NaN \n", "3 [48b94b06-ebbd-47e9-958c-44bf63622f5e] 0 NaN NaN \n", "4 [237cac8c-5cb0-4015-9d08-c7df9699a136] 0 NaN NaN \n", "\n", " tactics \\\n", "0 {'formation': 433, 'lineup': [{'player': {'id'... \n", "1 {'formation': 352, 'lineup': [{'player': {'id'... \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "\n", " team timestamp \\\n", "0 {'id': 746, 'name': 'Manchester City WFC'} 2018-11-30 00:00:00.000 \n", "1 {'id': 971, 'name': 'Chelsea LFC'} 2018-11-30 00:00:00.000 \n", "2 {'id': 971, 'name': 'Chelsea LFC'} 2018-11-30 00:00:00.000 \n", "3 {'id': 746, 'name': 'Manchester City WFC'} 2018-11-30 00:00:00.000 \n", "4 {'id': 971, 'name': 'Chelsea LFC'} 2018-11-30 00:00:00.100 \n", "\n", " type under_pressure \n", "0 {'id': 35, 'name': 'Starting XI'} NaN \n", "1 {'id': 35, 'name': 'Starting XI'} NaN \n", "2 {'id': 18, 'name': 'Half Start'} NaN \n", "3 {'id': 18, 'name': 'Half Start'} NaN \n", "4 {'id': 30, 'name': 'Pass'} NaN \n", "\n", "[5 rows x 33 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_json(r\"C:\\Users\\Koushik\\Downloads\\open-data-master\\open-data-master\\data\\events\\7298.json\")\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "shot = df[(df.possession_team =={'id': 971, 'name': 'Chelsea LFC'}) & (df.type == {'id': 16, 'name': 'Shot'})] \n", "#change possession_team to get shots by a different team" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "df_2 = pd.DataFrame(shot.location)\n", "df_2[['X_axis','Y_axis']] = pd.DataFrame(shot.location.values.tolist(), index= shot.location.index) #df_2 has the x,y coordinate" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
locationX_axisY_axis
33[115.0, 25.0]115.025.0
186[109.0, 51.0]109.051.0
192[99.0, 52.0]99.052.0
196[107.0, 40.0]107.040.0
204[108.0, 32.0]108.032.0
583[108.0, 32.0]108.032.0
695[87.0, 41.0]87.041.0
749[108.0, 36.0]108.036.0
765[105.0, 43.0]105.043.0
1060[112.0, 39.0]112.039.0
1176[115.0, 54.0]115.054.0
1179[102.0, 34.0]102.034.0
1291[108.0, 47.0]108.047.0
1400[94.0, 54.0]94.054.0
1486[108.0, 27.0]108.027.0
1622[114.0, 34.0]114.034.0
1666[109.0, 39.0]109.039.0
1828[117.0, 31.0]117.031.0
2136[91.0, 52.0]91.052.0
2240[118.0, 39.0]118.039.0
2325[111.0, 32.0]111.032.0
2692[107.0, 47.0]107.047.0
2695[110.0, 36.0]110.036.0
2813[113.0, 42.0]113.042.0
2820[109.0, 52.0]109.052.0
\n", "
" ], "text/plain": [ " location X_axis Y_axis\n", "33 [115.0, 25.0] 115.0 25.0\n", "186 [109.0, 51.0] 109.0 51.0\n", "192 [99.0, 52.0] 99.0 52.0\n", "196 [107.0, 40.0] 107.0 40.0\n", "204 [108.0, 32.0] 108.0 32.0\n", "583 [108.0, 32.0] 108.0 32.0\n", "695 [87.0, 41.0] 87.0 41.0\n", "749 [108.0, 36.0] 108.0 36.0\n", "765 [105.0, 43.0] 105.0 43.0\n", "1060 [112.0, 39.0] 112.0 39.0\n", "1176 [115.0, 54.0] 115.0 54.0\n", "1179 [102.0, 34.0] 102.0 34.0\n", "1291 [108.0, 47.0] 108.0 47.0\n", "1400 [94.0, 54.0] 94.0 54.0\n", "1486 [108.0, 27.0] 108.0 27.0\n", "1622 [114.0, 34.0] 114.0 34.0\n", "1666 [109.0, 39.0] 109.0 39.0\n", "1828 [117.0, 31.0] 117.0 31.0\n", "2136 [91.0, 52.0] 91.0 52.0\n", "2240 [118.0, 39.0] 118.0 39.0\n", "2325 [111.0, 32.0] 111.0 32.0\n", "2692 [107.0, 47.0] 107.0 47.0\n", "2695 [110.0, 36.0] 110.0 36.0\n", "2813 [113.0, 42.0] 113.0 42.0\n", "2820 [109.0, 52.0] 109.0 52.0" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_2" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "x_axis = df_2.X_axis.values.tolist()\n", "y_axis = df_2.Y_axis.values.tolist()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "from scipy.misc import imread\n", "import matplotlib.cbook as cbook" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "img = plt.imread(r'C:\\Users\\Koushik\\Downloads\\football_field.png')\n", "fig, ax = plt.subplots()\n", "\n", "ax.imshow(img, extent=[0, 120, 0, 80])\n", "ax.plot(x_axis,y_axis,'ro')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }