Completed and attempted extra points from footballdb.com

extra_points.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from __future__ import unicode_literals\n",
    "from collections import defaultdict\n",
    "import os\n",
    "\n",
    "import matplotlib\n",
    "matplotlib.style.use('ggplot')\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "workspace = '/Users/avitale/Documents/extra_points'\n",
    "\n",
    "with open(os.path.join(workspace, 'footballdb_2014.txt'), 'r') as f:\n",
    "    lines_2014 = f.read()\n",
    "with open(os.path.join(workspace, 'footballdb_2015.txt'), 'r') as f:\n",
    "    lines_2015 = f.read()\n",
    "    \n",
    "lines = defaultdict(list)\n",
    "for line in lines_2014.split('\\n'):\n",
    "    lines['2014'].append(line.split('\\t'))\n",
    "for line in lines_2015.split('\\n'):\n",
    "    lines['2015'].append(line.split('\\t'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Find the list index of the item 'PAT', as we want the data from that column\n",
    "index = [i for i, x in enumerate(lines['2014'][0]) if x == u'PAT'][0]\n",
    "\n",
    "data = defaultdict(list)\n",
    "for year in lines.keys():\n",
    "    for line in lines[year]:\n",
    "        # If length of the line is less than 2, break the loop to avoid errors\n",
    "        if len(line) < 2:\n",
    "            continue\n",
    "        # Skip the player column\n",
    "        if line[0] == u'Player':\n",
    "            continue\n",
    "        # Collect the PAT attempts/good as nexted lists in a defaultdict\n",
    "        data[year].append(map(int, line.pop(index).split('/')))\n",
    "\n",
    "# Print each kickers attempts and number good\n",
    "# for year in data.keys():\n",
    "#     for line in data[year]:\n",
    "#         print('Good: {}, Attempt: {}'.format(*line))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      good  attempts    percent\n",
      "year                           \n",
      "2014  1222      1230  99.349593\n",
      "2015   825       879  93.856655\n"
     ]
    }
   ],
   "source": [
    "# Barely use pandas to summarize the data by year\n",
    "df_2014 = pd.DataFrame(data['2014'])\n",
    "df_2015 = pd.DataFrame(data['2015'])\n",
    "df_2014.columns = ['good', 'attempts']\n",
    "df_2015.columns = ['good', 'attempts']\n",
    "df_2014['year'] = 2014\n",
    "df_2015['year'] = 2015\n",
    "\n",
    "df = pd.concat([df_2014, df_2015])\n",
    "summary_df = df.groupby(['year']).sum()\n",
    "summary_df['percent'] = (summary_df['good'] / summary_df['attempts']) * 100.\n",
    "print(summary_df)\n",
    "\n",
    "# print(summary_df['good'].tolist())\n",
    "# summary_df.boxplot(by='year')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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Oo8o4Iy+fWskOQkQkxXz//ffJDiGhKrNm5iXgb8CEiLK5wD3OuUJr7aPAcGC4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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109e35fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ceate barcharts using matplotlib\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches((8, 6))\n",
    "rects1 = ax.bar([0.30, 0.60], summary_df['good'].tolist(), width=0.1)\n",
    "rects2 = ax.bar([0.40, 0.70], summary_df['attempts'].tolist(), width=0.1, color='r')\n",
    "# rects1 = ax.bar([0.25, 0.5], summary_df['good'].tolist(), width=0.1)\n",
    "# rects2 = ax.bar([0.35, 0.6], summary_df['attempts'].tolist(), width=0.1, color='r')\n",
    "\n",
    "ax.set_title('NFL Extra Point Attempts and Completions')\n",
    "ax.set_ylabel('Number of PATs')\n",
    "ax.set_xbound(0, 1)\n",
    "label_xy = [[0.375, 1250], [0.675, 900]]\n",
    "label = summary_df['percent'].tolist()\n",
    "\n",
    "ax.text(label_xy[0][0], label_xy[0][1], '{:.02f}%'.format(label[0]))\n",
    "ax.text(label_xy[1][0], label_xy[1][1], '{:.02f}%'.format(label[1]))\n",
    "\n",
    "ax.set_xticks([0.4, 0.7])\n",
    "ax.set_xticklabels(('2014', '2015',))\n",
    "ax.set_xlim([0.25, 0.85])\n",
    "plt.legend([rects1, rects2], ['Completions', 'Attempts'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
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