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sportowe_wizualizacja/PassMap.ipynb
Koushik R Kirugulige cfcaa3f321
adding 5 ipynb files
2019-06-03 17:01:10 +05:30

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{
"cells": [
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"done\n"
]
}
],
"source": [
"import json\n",
"import os\n",
"from pandas.io.json import json_normalize\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.patches import Arc, Rectangle, ConnectionPatch\n",
"from matplotlib.offsetbox import OffsetImage\n",
"#import squarify\n",
"from functools import reduce\n",
"path = \"\"\"C:\\\\Users\\\\Koushik\\\\Downloads\\\\open-data-master\\\\open-data-master\\\\data\\\\my_events\\\\\"\"\"\n",
"Xg_req = pd.DataFrame(data=None)\n",
"for filename in (os.listdir(path)):\n",
" #print(filename)\n",
" \n",
" with open(\"%s\" % path + filename,encoding=\"utf8\") as data_file: \n",
" data = json.load(data_file)\n",
" df = pd.DataFrame(data=None)\n",
" \n",
" df = json_normalize(data, sep = \"_\")\n",
" \n",
" #df = df[(df['type_name'] == \"Shot\")]\n",
" #df = df.loc[:,['location','shot_body_part_id','shot_end_location','shot_one_on_one','shot_technique_id','shot_type_id','under_pressure','shot_outcome_id']]\n",
" #print(df.shape)\n",
" Xg_req = Xg_req.append(df,ignore_index=True,sort=False)\n",
" #df.drop(df.index, inplace=True)\n",
" \n",
"print(\"done\")\n",
"df = Xg_req"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"scrolled": true
},
"outputs": [
{
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" <th></th>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19882</th>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20227</th>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20823</th>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20917</th>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21975</th>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
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" <tr>\n",
" <th>152707</th>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>155048</th>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>158183</th>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>161937</th>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>162329</th>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>163634</th>\n",
" <td>NaN</td>\n",
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" <th>163781</th>\n",
" <td>NaN</td>\n",
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" </tr>\n",
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" <th>164961</th>\n",
" <td>NaN</td>\n",
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" <th>165854</th>\n",
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" <th>166107</th>\n",
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" <th>166301</th>\n",
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" <th>166464</th>\n",
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" <th>166557</th>\n",
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" <th>167304</th>\n",
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" <th>168706</th>\n",
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" <th>171222</th>\n",
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" <tr>\n",
" <th>171224</th>\n",
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" <tr>\n",
" <th>171232</th>\n",
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" </tr>\n",
" <tr>\n",
" <th>171234</th>\n",
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" <tr>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>172725</th>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>173646</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>175473</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>175627</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>...</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>176100</th>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <th>176213</th>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>176750</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>178726</th>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
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" </tr>\n",
" </tbody>\n",
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"<p>200 rows × 118 columns</p>\n",
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" bad_behaviour_card_id bad_behaviour_card_name \\\n",
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"\n",
" ball_receipt_outcome_id ball_receipt_outcome_name \\\n",
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"\n",
" ball_recovery_offensive ball_recovery_recovery_failure \\\n",
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"\n",
" block_deflection dribble_nutmeg dribble_outcome_id \\\n",
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"749 NaN NaN NaN \n",
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"2741 NaN NaN NaN \n",
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"... ... ... ... \n",
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"\n",
" dribble_outcome_name ... pass_miscommunication shot_aerial_won \\\n",
"204 NaN ... NaN NaN \n",
"749 NaN ... NaN NaN \n",
"1651 NaN ... NaN NaN \n",
"2741 NaN ... NaN NaN \n",
"3890 NaN ... NaN NaN \n",
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"21975 NaN ... NaN NaN \n",
"... ... ... ... ... \n",
"152707 NaN ... NaN NaN \n",
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"165854 NaN ... NaN NaN \n",
"166107 NaN ... NaN NaN \n",
"166301 NaN ... NaN NaN \n",
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"166557 NaN ... NaN NaN \n",
"167304 NaN ... NaN NaN \n",
"168706 NaN ... NaN NaN \n",
"171222 NaN ... NaN NaN \n",
"171224 NaN ... NaN NaN \n",
"171226 NaN ... NaN NaN \n",
"171228 NaN ... NaN NaN \n",
"171232 NaN ... NaN NaN \n",
"171234 NaN ... NaN NaN \n",
"171236 NaN ... NaN NaN \n",
"172725 NaN ... NaN NaN \n",
"173646 NaN ... NaN NaN \n",
"175473 NaN ... NaN NaN \n",
"175627 NaN ... NaN NaN \n",
"176100 NaN ... NaN NaN \n",
"176213 NaN ... NaN True \n",
"176750 NaN ... NaN NaN \n",
"178726 NaN ... NaN NaN \n",
"\n",
" shot_open_goal 50_50_outcome_id 50_50_outcome_name block_offensive \\\n",
"204 NaN NaN NaN NaN \n",
"749 NaN NaN NaN NaN \n",
"1651 NaN NaN NaN NaN \n",
"2741 NaN NaN NaN NaN \n",
"3890 NaN NaN NaN NaN \n",
"4594 True NaN NaN NaN \n",
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"4784 NaN NaN NaN NaN \n",
"4918 NaN NaN NaN NaN \n",
"6271 NaN NaN NaN NaN \n",
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"9525 NaN NaN NaN NaN \n",
"10606 NaN NaN NaN NaN \n",
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"15928 NaN NaN NaN NaN \n",
"19882 NaN NaN NaN NaN \n",
"20227 NaN NaN NaN NaN \n",
"20823 NaN NaN NaN NaN \n",
"20917 NaN NaN NaN NaN \n",
"21975 NaN NaN NaN NaN \n",
"... ... ... ... ... \n",
"152707 True NaN NaN NaN \n",
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"158183 NaN NaN NaN NaN \n",
"161937 NaN NaN NaN NaN \n",
"162329 NaN NaN NaN NaN \n",
"163634 NaN NaN NaN NaN \n",
"163781 NaN NaN NaN NaN \n",
"164961 NaN NaN NaN NaN \n",
"165854 NaN NaN NaN NaN \n",
"166107 NaN NaN NaN NaN \n",
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"166464 NaN NaN NaN NaN \n",
"166557 NaN NaN NaN NaN \n",
"167304 NaN NaN NaN NaN \n",
"168706 NaN NaN NaN NaN \n",
"171222 NaN NaN NaN NaN \n",
"171224 NaN NaN NaN NaN \n",
"171226 NaN NaN NaN NaN \n",
"171228 NaN NaN NaN NaN \n",
"171232 NaN NaN NaN NaN \n",
"171234 NaN NaN NaN NaN \n",
"171236 NaN NaN NaN NaN \n",
"172725 True NaN NaN NaN \n",
"173646 True NaN NaN NaN \n",
"175473 NaN NaN NaN NaN \n",
"175627 NaN NaN NaN NaN \n",
"176100 NaN NaN NaN NaN \n",
"176213 NaN NaN NaN NaN \n",
"176750 NaN NaN NaN NaN \n",
"178726 NaN NaN NaN NaN \n",
"\n",
" miscontrol_aerial_won foul_committed_penalty shot_deflected \\\n",
"204 NaN NaN NaN \n",
"749 NaN NaN NaN \n",
"1651 NaN NaN NaN \n",
"2741 NaN NaN NaN \n",
"3890 NaN NaN NaN \n",
"4594 NaN NaN NaN \n",
"4647 NaN NaN NaN \n",
"4784 NaN NaN NaN \n",
"4918 NaN NaN NaN \n",
"6271 NaN NaN NaN \n",
"7406 NaN NaN NaN \n",
"7924 NaN NaN NaN \n",
"9525 NaN NaN NaN \n",
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"10877 NaN NaN True \n",
"12291 NaN NaN NaN \n",
"13231 NaN NaN NaN \n",
"13839 NaN NaN NaN \n",
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"14105 NaN NaN NaN \n",
"14173 NaN NaN NaN \n",
"14561 NaN NaN NaN \n",
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"15894 NaN NaN True \n",
"15928 NaN NaN NaN \n",
"19882 NaN NaN NaN \n",
"20227 NaN NaN NaN \n",
"20823 NaN NaN NaN \n",
"20917 NaN NaN NaN \n",
"21975 NaN NaN NaN \n",
"... ... ... ... \n",
"152707 NaN NaN NaN \n",
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"162329 NaN NaN NaN \n",
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"164961 NaN NaN NaN \n",
"165854 NaN NaN NaN \n",
"166107 NaN NaN NaN \n",
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"171232 NaN NaN NaN \n",
"171234 NaN NaN NaN \n",
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"172725 NaN NaN NaN \n",
"173646 NaN NaN NaN \n",
"175473 NaN NaN NaN \n",
"175627 NaN NaN NaN \n",
"176100 NaN NaN NaN \n",
"176213 NaN NaN NaN \n",
"176750 NaN NaN NaN \n",
"178726 NaN NaN True \n",
"\n",
" shot_redirect \n",
"204 NaN \n",
"749 NaN \n",
"1651 NaN \n",
"2741 NaN \n",
"3890 NaN \n",
"4594 NaN \n",
"4647 NaN \n",
"4784 NaN \n",
"4918 NaN \n",
"6271 NaN \n",
"7406 NaN \n",
"7924 NaN \n",
"9525 NaN \n",
"10606 NaN \n",
"10877 NaN \n",
"12291 NaN \n",
"13231 NaN \n",
"13839 NaN \n",
"13975 NaN \n",
"14105 NaN \n",
"14173 NaN \n",
"14561 NaN \n",
"15815 NaN \n",
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"15928 NaN \n",
"19882 NaN \n",
"20227 NaN \n",
"20823 NaN \n",
"20917 NaN \n",
"21975 NaN \n",
"... ... \n",
"152707 NaN \n",
"155048 NaN \n",
"158183 NaN \n",
"161937 NaN \n",
"162329 NaN \n",
"163634 NaN \n",
"163781 NaN \n",
"164961 NaN \n",
"165854 NaN \n",
"166107 True \n",
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"171222 NaN \n",
"171224 NaN \n",
"171226 NaN \n",
"171228 NaN \n",
"171232 NaN \n",
"171234 NaN \n",
"171236 NaN \n",
"172725 NaN \n",
"173646 NaN \n",
"175473 NaN \n",
"175627 NaN \n",
"176100 NaN \n",
"176213 NaN \n",
"176750 NaN \n",
"178726 NaN \n",
"\n",
"[200 rows x 118 columns]"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d = df.query('shot_outcome_id == 97')\n",
"d"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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" <th>id</th>\n",
" <th>period</th>\n",
" <th>timestamp</th>\n",
" <th>location</th>\n",
" <th>pass_end_location</th>\n",
" <th>pass_recipient_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>eea20658-0e9f-484a-90d3-dccdc589d81f</td>\n",
" <td>1</td>\n",
" <td>00:00:00.187</td>\n",
" <td>[60.0, 40.0]</td>\n",
" <td>[49.0, 35.0]</td>\n",
" <td>Toni Kroos</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>ad723dee-c477-4604-970a-48f6f3e54e45</td>\n",
" <td>1</td>\n",
" <td>00:00:04.200</td>\n",
" <td>[55.0, 43.0]</td>\n",
" <td>[37.0, 59.0]</td>\n",
" <td>Niklas Süle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td>89cd84d7-c140-4322-9d13-1cd4abd61829</td>\n",
" <td>1</td>\n",
" <td>00:02:53.600</td>\n",
" <td>[65.0, 23.0]</td>\n",
" <td>[71.0, 27.0]</td>\n",
" <td>Marco Reus</td>\n",
" </tr>\n",
" <tr>\n",
" <th>129</th>\n",
" <td>f189456f-9790-468f-937c-251068dfb181</td>\n",
" <td>1</td>\n",
" <td>00:03:03.517</td>\n",
" <td>[60.0, 25.0]</td>\n",
" <td>[56.0, 38.0]</td>\n",
" <td>Sami Khedira</td>\n",
" </tr>\n",
" <tr>\n",
" <th>143</th>\n",
" <td>408b675d-cbcb-4edd-baff-32bf2c93107d</td>\n",
" <td>1</td>\n",
" <td>00:03:15.080</td>\n",
" <td>[67.0, 45.0]</td>\n",
" <td>[56.0, 32.0]</td>\n",
" <td>Toni Kroos</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id period timestamp location \\\n",
"4 eea20658-0e9f-484a-90d3-dccdc589d81f 1 00:00:00.187 [60.0, 40.0] \n",
"10 ad723dee-c477-4604-970a-48f6f3e54e45 1 00:00:04.200 [55.0, 43.0] \n",
"112 89cd84d7-c140-4322-9d13-1cd4abd61829 1 00:02:53.600 [65.0, 23.0] \n",
"129 f189456f-9790-468f-937c-251068dfb181 1 00:03:03.517 [60.0, 25.0] \n",
"143 408b675d-cbcb-4edd-baff-32bf2c93107d 1 00:03:15.080 [67.0, 45.0] \n",
"\n",
" pass_end_location pass_recipient_name \n",
"4 [49.0, 35.0] Toni Kroos \n",
"10 [37.0, 59.0] Niklas Süle \n",
"112 [71.0, 27.0] Marco Reus \n",
"129 [56.0, 38.0] Sami Khedira \n",
"143 [56.0, 32.0] Toni Kroos "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ozil_pass = df[(df.type_name == 'Pass') & (df.team_name == 'Germany') & (df.player_name == 'Mesut Özil') ]\n",
"pass_column = [i for i in df.columns if i.startswith(\"pass\")]\n",
"ozil_pass = ozil_pass[[\"id\", \"period\", \"timestamp\", \"location\", \"pass_end_location\", \"pass_recipient_name\"]]\n",
"ozil_pass.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def draw_pitch(ax):\n",
" # focus on only half of the pitch\n",
" #Pitch Outline & Centre Line\n",
" Pitch = Rectangle([0,0], width = 120, height = 80, fill = False)\n",
" #Left, Right Penalty Area and midline\n",
" LeftPenalty = Rectangle([0,22.3], width = 14.6, height = 35.3, fill = False)\n",
" RightPenalty = Rectangle([105.4,22.3], width = 14.6, height = 35.3, fill = False)\n",
" midline = ConnectionPatch([60,0], [60,80], \"data\", \"data\")\n",
"\n",
" #Left, Right 6-yard Box\n",
" LeftSixYard = Rectangle([0,32], width = 4.9, height = 16, fill = False)\n",
" RightSixYard = Rectangle([115.1,32], width = 4.9, height = 16, fill = False)\n",
"\n",
"\n",
" #Prepare Circles\n",
" centreCircle = plt.Circle((60,40),8.1,color=\"black\", fill = False)\n",
" centreSpot = plt.Circle((60,40),0.71,color=\"black\")\n",
" #Penalty spots and Arcs around penalty boxes\n",
" leftPenSpot = plt.Circle((9.7,40),0.71,color=\"black\")\n",
" rightPenSpot = plt.Circle((110.3,40),0.71,color=\"black\")\n",
" leftArc = Arc((9.7,40),height=16.2,width=16.2,angle=0,theta1=310,theta2=50,color=\"black\")\n",
" rightArc = Arc((110.3,40),height=16.2,width=16.2,angle=0,theta1=130,theta2=230,color=\"black\")\n",
" \n",
" element = [Pitch, LeftPenalty, RightPenalty, midline, LeftSixYard, RightSixYard, centreCircle, \n",
" centreSpot, rightPenSpot, leftPenSpot, leftArc, rightArc]\n",
" for i in element:\n",
" ax.add_patch(i)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 504x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig=plt.figure() #set up the figures\n",
"fig.set_size_inches(7, 5)\n",
"ax=fig.add_subplot(1,1,1)\n",
"draw_pitch(ax) #overlay our different objects on the pitch\n",
"plt.ylim(-2, 82)\n",
"plt.xlim(-2, 122)\n",
"#plt.plot(x_axis,y_axis,'ro')\n",
"#plt.plot(x,y,'bo')\n",
"#plt.axis('off')\n",
"\n",
"for i in range(len(ozil_pass)):\n",
" # annotate draw an arrow from a current position to pass_end_location\n",
" ax.annotate(\"\", xy = (ozil_pass.iloc[i]['pass_end_location'][0], ozil_pass.iloc[i]['pass_end_location'][1]), xycoords = 'data',\n",
" xytext = (ozil_pass.iloc[i]['location'][0], ozil_pass.iloc[i]['location'][1]), textcoords = 'data',\n",
" arrowprops=dict(arrowstyle=\"->\",connectionstyle=\"arc3\", color = \"blue\"),)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 504x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots()\n",
"fig.set_size_inches(7, 5)\n",
"\n",
"x_coord = [i[0] for i in ozil_pass[\"location\"]]\n",
"y_coord = [i[1] for i in ozil_pass[\"location\"]]\n",
"\n",
"#shades: give us the heat map we desire\n",
"# n_levels: draw more lines, the larger n, the more blurry it looks\n",
"sns.kdeplot(x_coord, y_coord, shade = \"True\", color = \"black\", n_levels = 30)\n",
"plt.show()"
]
}
],
"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
}
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