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