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sportowe_wizualizacja/PassMap.ipynb

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adding 5 ipynb files
2019-06-03 13:31:10 +02:00
{
"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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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</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>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",
" <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>20823</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>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>20917</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>21975</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>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>152707</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",
" </tr>\n",
" <tr>\n",
" <th>155048</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>158183</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>161937</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>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",
" </tr>\n",
" <tr>\n",
" <th>162329</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>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",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>163634</th>\n",
" <td>NaN</td>\n",
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" <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",
" <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",
" </tr>\n",
" <tr>\n",
" <th>163781</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",
" </tr>\n",
" <tr>\n",
" <th>164961</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",
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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>165854</th>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>166107</th>\n",
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" <td>NaN</td>\n",
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" <td>True</td>\n",
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" <tr>\n",
" <th>166301</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",
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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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" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166464</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",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166557</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>167304</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",
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" <tr>\n",
" <th>168706</th>\n",
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" <td>NaN</td>\n",
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" <tr>\n",
" <th>171222</th>\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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" <tr>\n",
" <th>171224</th>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>171226</th>\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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" <tr>\n",
" <th>171228</th>\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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" </tr>\n",
" <tr>\n",
" <th>171232</th>\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",
" <td>NaN</td>\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>171234</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",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>171236</th>\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",
" <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",
" <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>NaN</td>\n",
" <td>NaN</td>\n",
" <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",
" <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",
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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>176100</th>\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",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <th>176213</th>\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",
" <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",
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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>178726</th>\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",
" <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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"text/plain": [
" 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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"... ... ... ... \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",
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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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"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",
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"162329 NaN NaN NaN NaN \n",
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"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",
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"171226 NaN NaN NaN NaN \n",
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"171236 NaN NaN NaN NaN \n",
"172725 True NaN NaN NaN \n",
"173646 True NaN NaN NaN \n",
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"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",
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"4784 NaN NaN NaN \n",
"4918 NaN NaN NaN \n",
"6271 NaN NaN NaN \n",
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"7924 NaN NaN NaN \n",
"9525 NaN NaN NaN \n",
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"10877 NaN NaN True \n",
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"13839 NaN NaN NaN \n",
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"14561 NaN NaN NaN \n",
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"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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"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",
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"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",
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"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",
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"165854 NaN \n",
"166107 True \n",
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"171222 NaN \n",
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"171226 NaN \n",
"171228 NaN \n",
"171232 NaN \n",
"171234 NaN \n",
"171236 NaN \n",
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"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>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": {
"image/png": "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
"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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
"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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