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sportowe_wizualizacja/Through_Ball.ipynb

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adding 5 ipynb files
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"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",
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"execution_count": 13,
adding 5 ipynb files
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"metadata": {},
added assist and through_ball
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"outputs": [
{
"data": {
"text/plain": [
"0 NaN\n",
"1 NaN\n",
"2 NaN\n",
"3 NaN\n",
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"5 NaN\n",
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"8 NaN\n",
"9 NaN\n",
"10 NaN\n",
"11 NaN\n",
"12 NaN\n",
"13 NaN\n",
"14 NaN\n",
"15 NaN\n",
"16 NaN\n",
"17 NaN\n",
"18 NaN\n",
"19 NaN\n",
"20 NaN\n",
"21 NaN\n",
"22 NaN\n",
"23 NaN\n",
"24 NaN\n",
"25 NaN\n",
"26 NaN\n",
"27 NaN\n",
"28 NaN\n",
"29 NaN\n",
" ... \n",
"179346 NaN\n",
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"179348 NaN\n",
"179349 NaN\n",
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"179351 NaN\n",
"179352 NaN\n",
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"179354 NaN\n",
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"179360 NaN\n",
"179361 NaN\n",
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"179364 NaN\n",
"179365 NaN\n",
"179366 NaN\n",
"179367 NaN\n",
"179368 NaN\n",
"179369 NaN\n",
"179370 NaN\n",
"179371 NaN\n",
"179372 Free Kick\n",
"179373 NaN\n",
"179374 NaN\n",
"179375 NaN\n",
"Name: shot_type_name, Length: 179376, dtype: object"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
adding 5 ipynb files
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"source": [
"through_ball = df.query('pass_through_ball == True')"
]
},
{
"cell_type": "code",
added assist and through_ball
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"execution_count": 14,
adding 5 ipynb files
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"30\n",
"0\n"
adding 5 ipynb files
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]
}
],
"source": [
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"assist = df.query('pass_goal_assist == True')\n",
adding 5 ipynb files
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"print(len(assist.index))\n",
"goal = assist.query('shot_outcome_id == 97')\n",
"#assist = df.query('pass_goal_assist == True')\n",
"print(len(goal.index))"
]
},
{
"cell_type": "code",
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"execution_count": 4,
adding 5 ipynb files
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"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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>27</th>\n",
" <td>95ffe924-77f3-40e2-afc6-23e5fed8865f</td>\n",
" <td>1</td>\n",
" <td>00:00:38.660</td>\n",
" <td>[44.0, 17.0]</td>\n",
" <td>[95.0, 23.0]</td>\n",
" <td>Francesca Kirby</td>\n",
" </tr>\n",
" <tr>\n",
" <th>257</th>\n",
" <td>ce817472-51c1-4b9a-89d5-2b929f422b2e</td>\n",
" <td>1</td>\n",
" <td>00:08:03.753</td>\n",
" <td>[89.0, 65.0]</td>\n",
" <td>[116.0, 55.0]</td>\n",
" <td>Nikita Parris</td>\n",
" </tr>\n",
" <tr>\n",
" <th>623</th>\n",
" <td>cf2ed5ec-1d2f-4746-a482-4ff7a1823adf</td>\n",
" <td>1</td>\n",
" <td>00:19:30.020</td>\n",
" <td>[39.0, 38.0]</td>\n",
" <td>[96.0, 42.0]</td>\n",
" <td>Ramona Bachmann</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1574</th>\n",
" <td>898b1dba-d2e6-431c-b830-4d897c946ad2</td>\n",
" <td>2</td>\n",
" <td>00:01:56.558</td>\n",
" <td>[41.0, 79.0]</td>\n",
" <td>[101.0, 47.0]</td>\n",
" <td>Ramona Bachmann</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3292</th>\n",
" <td>7799fb29-209f-4b3a-a73e-2c35f9d83c51</td>\n",
" <td>1</td>\n",
" <td>00:09:35.360</td>\n",
" <td>[74.0, 48.0]</td>\n",
" <td>[98.0, 32.0]</td>\n",
" <td>Mallory Weber</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id period timestamp \\\n",
"27 95ffe924-77f3-40e2-afc6-23e5fed8865f 1 00:00:38.660 \n",
"257 ce817472-51c1-4b9a-89d5-2b929f422b2e 1 00:08:03.753 \n",
"623 cf2ed5ec-1d2f-4746-a482-4ff7a1823adf 1 00:19:30.020 \n",
"1574 898b1dba-d2e6-431c-b830-4d897c946ad2 2 00:01:56.558 \n",
"3292 7799fb29-209f-4b3a-a73e-2c35f9d83c51 1 00:09:35.360 \n",
"\n",
" location pass_end_location pass_recipient_name \n",
"27 [44.0, 17.0] [95.0, 23.0] Francesca Kirby \n",
"257 [89.0, 65.0] [116.0, 55.0] Nikita Parris \n",
"623 [39.0, 38.0] [96.0, 42.0] Ramona Bachmann \n",
"1574 [41.0, 79.0] [101.0, 47.0] Ramona Bachmann \n",
"3292 [74.0, 48.0] [98.0, 32.0] Mallory Weber "
]
},
added assist and through_ball
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"execution_count": 4,
adding 5 ipynb files
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"through_ball= through_ball[[\"id\", \"period\", \"timestamp\", \"location\", \"pass_end_location\", \"pass_recipient_name\"]]\n",
"through_ball.head()"
]
},
{
"cell_type": "code",
added assist and through_ball
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"execution_count": 15,
adding 5 ipynb files
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"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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>199</th>\n",
" <td>c547c736-4bf5-484f-9360-463abb19e465</td>\n",
" <td>1</td>\n",
" <td>00:05:45.020</td>\n",
" <td>[104.0, 36.0]</td>\n",
" <td>[109.0, 29.0]</td>\n",
" <td>Millie Bright</td>\n",
" </tr>\n",
" <tr>\n",
" <th>746</th>\n",
" <td>e3eca08d-7c54-4c65-a19a-faa9708225b5</td>\n",
" <td>1</td>\n",
" <td>00:23:20.340</td>\n",
" <td>[111.0, 24.0]</td>\n",
" <td>[111.0, 35.0]</td>\n",
" <td>So-yun Ji</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1649</th>\n",
" <td>d48d70cf-3464-40c9-80c0-f81d7ce46b25</td>\n",
" <td>2</td>\n",
" <td>00:03:39.570</td>\n",
" <td>[108.0, 5.0]</td>\n",
" <td>[115.0, 43.0]</td>\n",
" <td>Nikita Parris</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2739</th>\n",
" <td>7f1fe749-1914-4f5d-8f0b-62c7c9f79196</td>\n",
" <td>2</td>\n",
" <td>00:40:33.251</td>\n",
" <td>[109.0, 48.0]</td>\n",
" <td>[97.0, 46.0]</td>\n",
" <td>Georgia Stanway</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63663</th>\n",
" <td>59c55828-1423-4cf5-aef4-58f0cab1d9fe</td>\n",
" <td>2</td>\n",
" <td>00:06:57.202</td>\n",
" <td>[81.0, 28.0]</td>\n",
" <td>[87.0, 36.0]</td>\n",
" <td>Hayley Raso</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id period timestamp \\\n",
"199 c547c736-4bf5-484f-9360-463abb19e465 1 00:05:45.020 \n",
"746 e3eca08d-7c54-4c65-a19a-faa9708225b5 1 00:23:20.340 \n",
"1649 d48d70cf-3464-40c9-80c0-f81d7ce46b25 2 00:03:39.570 \n",
"2739 7f1fe749-1914-4f5d-8f0b-62c7c9f79196 2 00:40:33.251 \n",
"63663 59c55828-1423-4cf5-aef4-58f0cab1d9fe 2 00:06:57.202 \n",
"\n",
" location pass_end_location pass_recipient_name \n",
"199 [104.0, 36.0] [109.0, 29.0] Millie Bright \n",
"746 [111.0, 24.0] [111.0, 35.0] So-yun Ji \n",
"1649 [108.0, 5.0] [115.0, 43.0] Nikita Parris \n",
"2739 [109.0, 48.0] [97.0, 46.0] Georgia Stanway \n",
"63663 [81.0, 28.0] [87.0, 36.0] Hayley Raso "
]
},
added assist and through_ball
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"execution_count": 15,
adding 5 ipynb files
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"assist= assist[[\"id\", \"period\", \"timestamp\", \"location\", \"pass_end_location\", \"pass_recipient_name\"]]\n",
"assist.head()"
]
},
{
"cell_type": "code",
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"execution_count": 6,
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"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",
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"execution_count": 8,
adding 5 ipynb files
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"metadata": {},
"outputs": [
{
"data": {
added assist and through_ball
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"image/png": "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
adding 5 ipynb files
2019-06-03 13:31:10 +02:00
"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",
added assist and through_ball
2019-06-04 07:03:23 +02:00
"for i in range(len(through_ball)):\n",
adding 5 ipynb files
2019-06-03 13:31:10 +02:00
" # annotate draw an arrow from a current position to pass_end_location\n",
" ax.annotate(\"\", xy = (through_ball.iloc[i]['pass_end_location'][0], through_ball.iloc[i]['pass_end_location'][1]), xycoords = 'data',\n",
" xytext = (through_ball.iloc[i]['location'][0], through_ball.iloc[i]['location'][1]), textcoords = 'data',\n",
" arrowprops=dict(arrowstyle=\"->\",connectionstyle=\"arc3\", color = \"blue\"),)\n",
"\"\"\"\n",
"for i in range(len(assist)):\n",
" # annotate draw an arrow from a current position to pass_end_location\n",
" ax.annotate(\"\", xy = (assist.iloc[i]['pass_end_location'][0], assist.iloc[i]['pass_end_location'][1]), xycoords = 'data',\n",
" xytext = (assist.iloc[i]['location'][0], assist.iloc[i]['location'][1]), textcoords = 'data',\n",
" arrowprops=dict(arrowstyle=\"->\",connectionstyle=\"arc3\", color = \"red\"),)\n",
added assist and through_ball
2019-06-04 07:03:23 +02:00
"\"\"\"\n",
adding 5 ipynb files
2019-06-03 13:31:10 +02:00
"\n",
"plt.show()"
]
added assist and through_ball
2019-06-04 07:03:23 +02:00
},
{
"cell_type": "code",
"execution_count": 16,
"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(assist)):\n",
" # annotate draw an arrow from a current position to pass_end_location\n",
" ax.annotate(\"\", xy = (assist.iloc[i]['pass_end_location'][0], assist.iloc[i]['pass_end_location'][1]), xycoords = 'data',\n",
" xytext = (assist.iloc[i]['location'][0], assist.iloc[i]['location'][1]), textcoords = 'data',\n",
" arrowprops=dict(arrowstyle=\"->\",connectionstyle=\"arc3\", color = \"red\"),)\n",
"\n",
"plt.show() "
]
adding 5 ipynb files
2019-06-03 13:31:10 +02:00
}
],
"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
}
Reference in New Issue Copy Permalink