2020-10-20 10:05:57 +02:00
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import matplotlib as mpl\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import seaborn as sns"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>team</th>\n",
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" <th>Win League</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>Alavés</td>\n",
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" <td>[1, 1]</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>Athletic Bilbao</td>\n",
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" <td>[1, 1]</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>Atlético Madrid</td>\n",
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" <td>[12, 17]</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>Barcelona</td>\n",
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" <td>[44, 43]</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>Celta Vigo</td>\n",
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" <td>[1, 1]</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" team Win League\n",
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"0 Alavés [1, 1]\n",
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"1 Athletic Bilbao [1, 1]\n",
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"2 Atlético Madrid [12, 17]\n",
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"3 Barcelona [44, 43]\n",
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"4 Celta Vigo [1, 1]"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df = pd.read_csv('la liga.csv',dtype={'team':'str','Win League':'str'})\n",
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"df.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"from ast import literal_eval\n",
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"df['Win League'] = df['Win League'].apply(literal_eval)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"# get the latest prob value\n",
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"array_length = len(list(df.iloc[0]['Win League'])) #len of all lists should be same\n",
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"def get_latest_prob(row):\n",
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" row['latest_prob'] = int(row['Win League'][array_length -1])\n",
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" return row\n",
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"\n",
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"df = df.apply(get_latest_prob,axis=1)\n",
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"df =df.sort_values(by=['latest_prob'],ascending = False)[:5].copy()\n",
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"#display(df)"
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]
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},
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{
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"cell_type": "code",
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2020-10-22 07:30:45 +02:00
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"execution_count": 23,
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2020-10-20 10:05:57 +02:00
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"metadata": {},
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"outputs": [
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{
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"data": {
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2020-10-22 07:30:45 +02:00
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"image/png": "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2020-10-20 10:05:57 +02:00
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"text/plain": [
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"<Figure size 576x576 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"\"\"\" Most of the code is taken from \n",
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"https://github.com/petermckeeverPerform/friends-of-tracking-viz-lecture/blob/master/notebooks/lecture-notebook.ipynb\"\"\"\n",
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"title_font = \"DejaVu Sans\"\n",
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"body_font = \"Open Sans\"\n",
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"text_color = \"w\"\n",
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"background = \"#313332\"\n",
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"filler = \"grey\"\n",
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"primary = \"red\"\n",
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"\n",
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"mpl.rcParams['xtick.color'] = text_color\n",
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"mpl.rcParams['ytick.color'] = text_color\n",
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"mpl.rcParams['xtick.labelsize'] = 10\n",
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"mpl.rcParams['ytick.labelsize'] = 10\n",
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"array_length = len(list(df.iloc[0]['Win League'])) #len of all lists should be same\n",
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"matchday = np.asarray(list(range(1, array_length +1)))\n",
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"\n",
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"fig, ax = plt.subplots(figsize=(8,8))\n",
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"fig.set_facecolor(background)\n",
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"ax.patch.set_alpha(0)\n",
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"spines = [\"top\",\"right\",\"bottom\",\"left\"]\n",
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"for s in spines:\n",
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" if s in [\"top\",\"right\"]:\n",
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" ax.spines[s].set_visible(False)\n",
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" else:\n",
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" ax.spines[s].set_color(text_color)\n",
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"\n",
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2020-10-22 07:30:45 +02:00
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"#this will hide the x-axis values\n",
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"frame1 = plt.gca()\n",
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"frame1.axes.get_xaxis().set_ticks([])\n",
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"\n",
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2020-10-20 10:05:57 +02:00
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"ax.set_xlabel(\"Matchday\", fontfamily=title_font, fontweight=\"regular\", fontsize=16, color=text_color)\n",
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"ax.set_ylabel(\"Percentage\", fontfamily=title_font, fontweight=\"regular\", fontsize= 16, color=text_color)\n",
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"\n",
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"ax.tick_params(axis=\"both\",length=0)\n",
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"for i in range(len(df)):\n",
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" x = np.asarray(list(df.iloc[i]['Win League']))\n",
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" plt.annotate(df.iloc[i]['team'],(matchday[-1],np.asarray(list(df.iloc[i]['Win League']))[array_length -1]+0.5),color=text_color)\n",
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" plt.plot(matchday,x)\n",
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2020-10-22 07:30:45 +02:00
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" ax.scatter(matchday,x,s=120,color='#841F27',edgecolors='#841F27', alpha=1, lw=0.25, zorder=4)\n",
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"fig.text(0,1,\"538 La Liga Predictions for 2020/2021\",fontweight=\"bold\", fontsize=18,fontfamily=title_font, color=text_color)\n",
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2020-10-20 10:05:57 +02:00
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"plt.tight_layout()\n",
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"plt.show() \n",
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"ax.grid(ls=\"dotted\",lw=\"0.5\",color=\"lightgrey\", zorder=1)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
|
|
|
|
|
"version": 3
|
|
|
|
|
},
|
|
|
|
|
"file_extension": ".py",
|
|
|
|
|
"mimetype": "text/x-python",
|
|
|
|
|
"name": "python",
|
|
|
|
|
"nbconvert_exporter": "python",
|
|
|
|
|
"pygments_lexer": "ipython3",
|
|
|
|
|
"version": "3.7.6"
|
|
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"nbformat": 4,
|
|
|
|
|
"nbformat_minor": 4
|
|
|
|
|
}
|
