{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tidy Data\n",
    "\n",
    "> Structuring datasets to facilitate analysis [(Wickham 2014)](http://www.jstatsoft.org/v59/i10/paper)\n",
    "\n",
    "If there's one maxim I can impart it's that your tools shouldn't get in the way of your analysis. Your problem is already difficult enough, don't let the data or your tools make it any harder.\n",
    "\n",
    "## The Rules\n",
    "\n",
    "In a tidy dataset...\n",
    "\n",
    "1. Each variable forms a column\n",
    "2. Each observation forms a row\n",
    "3. Each type of observational unit forms a table\n",
    "\n",
    "We'll cover a few methods that help you get there."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on [this](http://stackoverflow.com/questions/22695680/python-pandas-timedelta-specific-rows) StackOverflow question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "pd.options.display.max_rows = 10\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Earlier, I fetched some data\n",
    "\n",
    "```python\n",
    "tables = pd.read_html(\"http://www.basketball-reference.com/leagues/NBA_2015_games.html\")\n",
    "games = tables[0]\n",
    "games.to_csv('data/games.csv', index=False)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.read_html?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date,Unnamed: 1,Visitor/Neutral,PTS,Home/Neutral,PTS.1,Unnamed: 6,Notes\r\n",
      "\"Tue, Oct 28, 2014\",Box Score,Houston Rockets,108,Los Angeles Lakers,90,,\r\n"
     ]
    }
   ],
   "source": [
    "!head -n 2 data/games.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Question:\n",
    "> **How many days of rest did each team get between each game?**\n",
    "\n",
    "Whether or not your dataset is tidy depends on your question. Given our question, what is an observation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>away_team</th>\n",
       "      <th>away_points</th>\n",
       "      <th>home_team</th>\n",
       "      <th>home_points</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>game_id</th>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th>2014-10-28</th>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>108</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>2014-10-28</th>\n",
       "      <td>Orlando Magic</td>\n",
       "      <td>84</td>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>2014-10-28</th>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>100</td>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>2014-10-29</th>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>105</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>2014-10-29</th>\n",
       "      <td>Milwaukee Bucks</td>\n",
       "      <td>106</td>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           away_team  away_points             home_team  \\\n",
       "game_id date                                                              \n",
       "0       2014-10-28   Houston Rockets          108    Los Angeles Lakers   \n",
       "1       2014-10-28     Orlando Magic           84  New Orleans Pelicans   \n",
       "2       2014-10-28  Dallas Mavericks          100     San Antonio Spurs   \n",
       "3       2014-10-29     Brooklyn Nets          105        Boston Celtics   \n",
       "4       2014-10-29   Milwaukee Bucks          106     Charlotte Hornets   \n",
       "\n",
       "                    home_points  \n",
       "game_id date                     \n",
       "0       2014-10-28           90  \n",
       "1       2014-10-28          101  \n",
       "2       2014-10-28          101  \n",
       "3       2014-10-29          121  \n",
       "4       2014-10-29          108  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names = ['date', '_', 'away_team', 'away_points', 'home_team',\n",
    "                'home_points', 'n_ot', 'notes']\n",
    "games = (pd.read_csv('data/games.csv', names=column_names, parse_dates=['date'],\n",
    "                     skiprows=1)\n",
    "           .drop(['_', 'notes', 'n_ot'], axis='columns')\n",
    "           .set_index('date', append=True))\n",
    "games.index.names = ['game_id', 'date']\n",
    "games.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is `games` a tidy dataset, given our question? No, we have multiple observations (teams) per row. We'll use `pd.melt` to fix that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tidy = pd.melt(games.sort_index().reset_index(),\n",
    "               id_vars=['game_id', 'date'], value_vars=['away_team', 'home_team'],\n",
    "               value_name='team')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>game_id</th>\n",
       "      <th>date</th>\n",
       "      <th>variable</th>\n",
       "      <th>team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2014-10-28</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Houston Rockets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-10-28</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Orlando Magic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2014-10-28</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Dallas Mavericks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2014-10-29</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Brooklyn Nets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2014-10-29</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Milwaukee Bucks</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   game_id       date   variable              team\n",
       "0        0 2014-10-28  away_team   Houston Rockets\n",
       "1        1 2014-10-28  away_team     Orlando Magic\n",
       "2        2 2014-10-28  away_team  Dallas Mavericks\n",
       "3        3 2014-10-29  away_team     Brooklyn Nets\n",
       "4        4 2014-10-29  away_team   Milwaukee Bucks"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tidy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the translation from question to operation is direct:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      NaN\n",
       "1      NaN\n",
       "2      NaN\n",
       "3      NaN\n",
       "4      NaN\n",
       "        ..\n",
       "2455     1\n",
       "2456     4\n",
       "2457     4\n",
       "2458     1\n",
       "2459    10\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For each team... get number of dates between games\n",
    "tidy.groupby('team')['date'].diff().dt.days - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>game_id</th>\n",
       "      <th>date</th>\n",
       "      <th>variable</th>\n",
       "      <th>team</th>\n",
       "      <th>rest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>2014-10-29</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>2014-10-29</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>2014-10-30</td>\n",
       "      <td>away_team</td>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>2014-10-30</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>2014-10-30</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Oklahoma City Thunder</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    game_id       date   variable                   team  rest\n",
       "10       10 2014-10-29  away_team     Los Angeles Lakers     0\n",
       "14       14 2014-10-29  away_team        Houston Rockets     0\n",
       "15       15 2014-10-30  away_team        New York Knicks     0\n",
       "16       16 2014-10-30  away_team              Utah Jazz     0\n",
       "17       17 2014-10-30  away_team  Oklahoma City Thunder     0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tidy['rest'] = tidy.sort('date').groupby('team').date.diff().dt.days - 1\n",
    "tidy.dropna().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "un = pd.pivot_table(tidy, values='rest',\n",
    "                    index=['game_id', 'date'],\n",
    "                    columns='variable').rename(\n",
    "    columns={'away_team': 'away_rest', 'home_team': 'home_rest'}\n",
    ")\n",
    "un.columns.name = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>away_rest</th>\n",
       "      <th>home_rest</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>game_id</th>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <th>2014-10-30</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <th>2014-10-30</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <th>2014-10-30</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <th>2014-10-31</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <th>2014-10-31</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    away_rest  home_rest\n",
       "game_id date                            \n",
       "16      2014-10-30          0          1\n",
       "18      2014-10-30          0          0\n",
       "19      2014-10-30          0          1\n",
       "20      2014-10-31          0          1\n",
       "21      2014-10-31          1          1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "un.dropna().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>away_team</th>\n",
       "      <th>away_points</th>\n",
       "      <th>home_team</th>\n",
       "      <th>home_points</th>\n",
       "      <th>away_rest</th>\n",
       "      <th>home_rest</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>game_id</th>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th>2014-10-28</th>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>108</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>2014-10-28</th>\n",
       "      <td>Orlando Magic</td>\n",
       "      <td>84</td>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>2014-10-28</th>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>100</td>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>2014-10-29</th>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>105</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>121</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>2014-10-29</th>\n",
       "      <td>Milwaukee Bucks</td>\n",
       "      <td>106</td>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>108</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1225</th>\n",
       "      <th>2015-04-15</th>\n",
       "      <td>Oklahoma City Thunder</td>\n",
       "      <td>138</td>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>113</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1226</th>\n",
       "      <th>2015-04-15</th>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>103</td>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>108</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1227</th>\n",
       "      <th>2015-04-15</th>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>112</td>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1228</th>\n",
       "      <th>2015-04-15</th>\n",
       "      <td>Miami Heat</td>\n",
       "      <td>105</td>\n",
       "      <td>Philadelphia 76ers</td>\n",
       "      <td>101</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1229</th>\n",
       "      <th>2015-04-15</th>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>87</td>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1230 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                away_team  away_points  \\\n",
       "game_id date                                             \n",
       "0       2014-10-28        Houston Rockets          108   \n",
       "1       2014-10-28          Orlando Magic           84   \n",
       "2       2014-10-28       Dallas Mavericks          100   \n",
       "3       2014-10-29          Brooklyn Nets          105   \n",
       "4       2014-10-29        Milwaukee Bucks          106   \n",
       "...                                   ...          ...   \n",
       "1225    2015-04-15  Oklahoma City Thunder          138   \n",
       "1226    2015-04-15      San Antonio Spurs          103   \n",
       "1227    2015-04-15        Detroit Pistons          112   \n",
       "1228    2015-04-15             Miami Heat          105   \n",
       "1229    2015-04-15      Charlotte Hornets           87   \n",
       "\n",
       "                                 home_team  home_points  away_rest  home_rest  \n",
       "game_id date                                                                   \n",
       "0       2014-10-28      Los Angeles Lakers           90        NaN        NaN  \n",
       "1       2014-10-28    New Orleans Pelicans          101        NaN        NaN  \n",
       "2       2014-10-28       San Antonio Spurs          101        NaN        NaN  \n",
       "3       2014-10-29          Boston Celtics          121        NaN        NaN  \n",
       "4       2014-10-29       Charlotte Hornets          108        NaN        NaN  \n",
       "...                                    ...          ...        ...        ...  \n",
       "1225    2015-04-15  Minnesota Timberwolves          113          1          1  \n",
       "1226    2015-04-15    New Orleans Pelicans          108          2          1  \n",
       "1227    2015-04-15         New York Knicks           90          1          1  \n",
       "1228    2015-04-15      Philadelphia 76ers          101          1          1  \n",
       "1229    2015-04-15         Toronto Raptors           92          1          0  \n",
       "\n",
       "[1230 rows x 6 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([games, un], axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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u59cB/wHsUedxNbBvbZChXAP274wQEf/I2MtNa6afcqrpUuClETElynWVx9ZhfwUeGREb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108ef6278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(data=tidy.dropna(), col='team', col_wrap=5, hue='team')\n",
    "g.map(sns.barplot, \"variable\", \"rest\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10a769a90>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a15d048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "delta = (un.home_rest - un.away_rest).dropna().astype(int)\n",
    "(delta.value_counts()\n",
    "    .reindex(np.arange(delta.min(), delta.max() + 1), fill_value=0)\n",
    "    .sort_index().plot(kind='bar', color='k', width=.9, rot=0, figsize=(12, 6)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Stack / Unstack"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An \"observation\" depends on the question. Home team advantage?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Home win percent: 0.574796747967\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a724518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "home_adv = games.home_points - games.away_points\n",
    "ax = (home_adv).plot(kind='hist', bins=80, color='k', figsize=(10, 5))\n",
    "ax.set_xlim(-40, 40)\n",
    "mu = home_adv.mean()\n",
    "ax.vlines(mu, *ax.get_ylim(), color='steelblue', linewidth=3)\n",
    "print('Home win percent:', (home_adv > 0).mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Team Strength"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mini Project: Home Court Advantage?\n",
    "\n",
    "What's the effect (in terms of probability to win) of being\n",
    "the home team."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Calculate Win %"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to create an indicator for whether the home team won.\n",
    "Add it as a column called `home_win` in `games`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "games['home_win'] = ...  # fill this in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#%load -r 1:4 solutions_tidy.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Find the win percent for each team\n",
    "\n",
    "Teams are split across two columns. It's easiest to calculate the number of wins and\n",
    "number of games as away, and the number of wins and number of games as home. Then\n",
    "combine those two results to get the win percent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">away_points</th>\n",
       "      <th colspan=\"2\" halign=\"left\">home_points</th>\n",
       "      <th colspan=\"2\" halign=\"left\">home_win</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>&lt;lambda&gt;</th>\n",
       "      <th>a</th>\n",
       "      <th>&lt;lambda&gt;</th>\n",
       "      <th>a</th>\n",
       "      <th>&lt;lambda&gt;</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>away_team</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Atlanta Hawks</th>\n",
       "      <td>4164</td>\n",
       "      <td>-4165</td>\n",
       "      <td>4069</td>\n",
       "      <td>-4070</td>\n",
       "      <td>16</td>\n",
       "      <td>-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boston Celtics</th>\n",
       "      <td>4154</td>\n",
       "      <td>-4155</td>\n",
       "      <td>4205</td>\n",
       "      <td>-4206</td>\n",
       "      <td>22</td>\n",
       "      <td>-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brooklyn Nets</th>\n",
       "      <td>3978</td>\n",
       "      <td>-3979</td>\n",
       "      <td>4119</td>\n",
       "      <td>-4120</td>\n",
       "      <td>22</td>\n",
       "      <td>-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Charlotte Hornets</th>\n",
       "      <td>3825</td>\n",
       "      <td>-3826</td>\n",
       "      <td>4019</td>\n",
       "      <td>-4020</td>\n",
       "      <td>27</td>\n",
       "      <td>-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicago Bulls</th>\n",
       "      <td>4087</td>\n",
       "      <td>-4088</td>\n",
       "      <td>4018</td>\n",
       "      <td>-4019</td>\n",
       "      <td>18</td>\n",
       "      <td>-19</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sacramento Kings</th>\n",
       "      <td>4154</td>\n",
       "      <td>-4155</td>\n",
       "      <td>4419</td>\n",
       "      <td>-4420</td>\n",
       "      <td>30</td>\n",
       "      <td>-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Antonio Spurs</th>\n",
       "      <td>4057</td>\n",
       "      <td>-4058</td>\n",
       "      <td>3942</td>\n",
       "      <td>-3943</td>\n",
       "      <td>19</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toronto Raptors</th>\n",
       "      <td>4316</td>\n",
       "      <td>-4317</td>\n",
       "      <td>4242</td>\n",
       "      <td>-4243</td>\n",
       "      <td>19</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah Jazz</th>\n",
       "      <td>3853</td>\n",
       "      <td>-3854</td>\n",
       "      <td>3955</td>\n",
       "      <td>-3956</td>\n",
       "      <td>24</td>\n",
       "      <td>-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington Wizards</th>\n",
       "      <td>3885</td>\n",
       "      <td>-3886</td>\n",
       "      <td>4073</td>\n",
       "      <td>-4074</td>\n",
       "      <td>24</td>\n",
       "      <td>-25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   away_points          home_points          home_win         \n",
       "                             a <lambda>           a <lambda>        a <lambda>\n",
       "away_team                                                                     \n",
       "Atlanta Hawks             4164    -4165        4069    -4070       16      -17\n",
       "Boston Celtics            4154    -4155        4205    -4206       22      -23\n",
       "Brooklyn Nets             3978    -3979        4119    -4120       22      -23\n",
       "Charlotte Hornets         3825    -3826        4019    -4020       27      -28\n",
       "Chicago Bulls             4087    -4088        4018    -4019       18      -19\n",
       "...                        ...      ...         ...      ...      ...      ...\n",
       "Sacramento Kings          4154    -4155        4419    -4420       30      -31\n",
       "San Antonio Spurs         4057    -4058        3942    -3943       19      -20\n",
       "Toronto Raptors           4316    -4317        4242    -4243       19      -20\n",
       "Utah Jazz                 3853    -3854        3955    -3956       24      -25\n",
       "Washington Wizards        3885    -3886        4073    -4074       24      -25\n",
       "\n",
       "[30 rows x 6 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wins_as_home = games.groupby('').agg([])\n",
    "# hint: use `~` to flip an array of booleans\n",
    "wins_as_away = ...\n",
    "\n",
    "wins_as_home.columns = ['n_wins', 'n_games']\n",
    "wins_as_away.columns = ['n_wins', 'n_games']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load -r 5:13 solutions_tidy.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now add `wins_as_home` and `wins_as_away` to get a DataFrame with\n",
    "two columsn, `n_wins`, and `n_games` and one row per team.\n",
    "\n",
    "Finally, calculate the win percent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load -r 14:20 solutions_tidy.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10b6db438>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ae6QrGKtBgc7/ekEIIdScCLB22J5CurKxqtj+B+nKy9LtQ7qi/rzZM7uiTAgrtPj/qHNE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\nCKEq/T8/ddeqeWEl+QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b6cecf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "strength.order().plot(kind='barh', figsize=(5, 12))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bring the `strength` valuess in for each team, for each game."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>away_team</th>\n",
       "      <th>away_points</th>\n",
       "      <th>home_team</th>\n",
       "      <th>home_points</th>\n",
       "      <th>home_win</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>game_id</th>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th>2014-10-28</th>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>108</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>90</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>2014-10-28</th>\n",
       "      <td>Orlando Magic</td>\n",
       "      <td>84</td>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>101</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>2014-10-28</th>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>100</td>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>101</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>2014-10-29</th>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>105</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>121</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>2014-10-29</th>\n",
       "      <td>Milwaukee Bucks</td>\n",
       "      <td>106</td>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>108</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           away_team  away_points             home_team  \\\n",
       "game_id date                                                              \n",
       "0       2014-10-28   Houston Rockets          108    Los Angeles Lakers   \n",
       "1       2014-10-28     Orlando Magic           84  New Orleans Pelicans   \n",
       "2       2014-10-28  Dallas Mavericks          100     San Antonio Spurs   \n",
       "3       2014-10-29     Brooklyn Nets          105        Boston Celtics   \n",
       "4       2014-10-29   Milwaukee Bucks          106     Charlotte Hornets   \n",
       "\n",
       "                    home_points home_win  \n",
       "game_id date                              \n",
       "0       2014-10-28           90    False  \n",
       "1       2014-10-28          101     True  \n",
       "2       2014-10-28          101     True  \n",
       "3       2014-10-29          121     True  \n",
       "4       2014-10-29          108     True  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For SQL people\n",
    "\n",
    "```sql\n",
    "SELECT *\n",
    "FROM games NATURAL JOIN strength\n",
    "```\n",
    "\n",
    "We just need to get the names worked out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>away_team</th>\n",
       "      <th>away_strength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>0.731707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.487805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>0.463415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>0.402439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>0.609756</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           away_team  away_strength\n",
       "0      Atlanta Hawks       0.731707\n",
       "1     Boston Celtics       0.487805\n",
       "2      Brooklyn Nets       0.463415\n",
       "3  Charlotte Hornets       0.402439\n",
       "4      Chicago Bulls       0.609756"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strength.head().reset_index().rename(columns=lambda x: 'away_' + x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>away_team</th>\n",
       "      <th>away_points</th>\n",
       "      <th>home_team</th>\n",
       "      <th>home_points</th>\n",
       "      <th>home_win</th>\n",
       "      <th>away_strength</th>\n",
       "      <th>home_strength</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>game_id</th>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th>2014-10-28</th>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>108</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>90</td>\n",
       "      <td>False</td>\n",
       "      <td>0.682927</td>\n",
       "      <td>0.256098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <th>2015-01-25</th>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>99</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>87</td>\n",
       "      <td>False</td>\n",
       "      <td>0.682927</td>\n",
       "      <td>0.256098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <th>2015-01-09</th>\n",
       "      <td>Orlando Magic</td>\n",
       "      <td>84</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>101</td>\n",
       "      <td>True</td>\n",
       "      <td>0.304878</td>\n",
       "      <td>0.256098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>932</th>\n",
       "      <th>2015-03-08</th>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>100</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>93</td>\n",
       "      <td>False</td>\n",
       "      <td>0.609756</td>\n",
       "      <td>0.256098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1197</th>\n",
       "      <th>2015-04-12</th>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>120</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>106</td>\n",
       "      <td>False</td>\n",
       "      <td>0.609756</td>\n",
       "      <td>0.256098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <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>214</th>\n",
       "      <th>2014-11-26</th>\n",
       "      <td>Sacramento Kings</td>\n",
       "      <td>89</td>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>102</td>\n",
       "      <td>True</td>\n",
       "      <td>0.353659</td>\n",
       "      <td>0.682927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1112</th>\n",
       "      <th>2015-04-01</th>\n",
       "      <td>Sacramento Kings</td>\n",
       "      <td>111</td>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>115</td>\n",
       "      <td>True</td>\n",
       "      <td>0.353659</td>\n",
       "      <td>0.682927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>83</td>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>102</td>\n",
       "      <td>True</td>\n",
       "      <td>0.402439</td>\n",
       "      <td>0.682927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <th>2014-12-18</th>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>99</td>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>90</td>\n",
       "      <td>False</td>\n",
       "      <td>0.548780</td>\n",
       "      <td>0.682927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <th>2015-04-12</th>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>114</td>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>121</td>\n",
       "      <td>True</td>\n",
       "      <td>0.548780</td>\n",
       "      <td>0.682927</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1230 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               away_team  away_points           home_team  \\\n",
       "game_id date                                                                \n",
       "0       2014-10-28       Houston Rockets          108  Los Angeles Lakers   \n",
       "661     2015-01-25       Houston Rockets           99  Los Angeles Lakers   \n",
       "541     2015-01-09         Orlando Magic           84  Los Angeles Lakers   \n",
       "932     2015-03-08      Dallas Mavericks          100  Los Angeles Lakers   \n",
       "1197    2015-04-12      Dallas Mavericks          120  Los Angeles Lakers   \n",
       "...                                  ...          ...                 ...   \n",
       "214     2014-11-26      Sacramento Kings           89     Houston Rockets   \n",
       "1112    2015-04-01      Sacramento Kings          111     Houston Rockets   \n",
       "476     2014-12-31     Charlotte Hornets           83     Houston Rockets   \n",
       "380     2014-12-18  New Orleans Pelicans           99     Houston Rockets   \n",
       "1195    2015-04-12  New Orleans Pelicans          114     Houston Rockets   \n",
       "\n",
       "                    home_points home_win  away_strength  home_strength  \n",
       "game_id date                                                            \n",
       "0       2014-10-28           90    False       0.682927       0.256098  \n",
       "661     2015-01-25           87    False       0.682927       0.256098  \n",
       "541     2015-01-09          101     True       0.304878       0.256098  \n",
       "932     2015-03-08           93    False       0.609756       0.256098  \n",
       "1197    2015-04-12          106    False       0.609756       0.256098  \n",
       "...                         ...      ...            ...            ...  \n",
       "214     2014-11-26          102     True       0.353659       0.682927  \n",
       "1112    2015-04-01          115     True       0.353659       0.682927  \n",
       "476     2014-12-31          102     True       0.402439       0.682927  \n",
       "380     2014-12-18           90    False       0.548780       0.682927  \n",
       "1195    2015-04-12          121     True       0.548780       0.682927  \n",
       "\n",
       "[1230 rows x 7 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(pd.merge(games.reset_index(), strength.reset_index().add_prefix('away_'))\n",
    "   .pipe(pd.merge, strength.reset_index().add_prefix('home_'))\n",
    "   .set_index(['game_id', 'date']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For python people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>away_team</th>\n",
       "      <th>away_points</th>\n",
       "      <th>home_team</th>\n",
       "      <th>home_points</th>\n",
       "      <th>home_win</th>\n",
       "      <th>away_strength</th>\n",
       "      <th>home_strength</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>game_id</th>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th>2014-10-28</th>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>108</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>90</td>\n",
       "      <td>False</td>\n",
       "      <td>0.682927</td>\n",
       "      <td>0.256098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>2014-10-28</th>\n",
       "      <td>Orlando Magic</td>\n",
       "      <td>84</td>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>101</td>\n",
       "      <td>True</td>\n",
       "      <td>0.304878</td>\n",
       "      <td>0.548780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>2014-10-28</th>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>100</td>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>101</td>\n",
       "      <td>True</td>\n",
       "      <td>0.609756</td>\n",
       "      <td>0.670732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>2014-10-29</th>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>105</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>121</td>\n",
       "      <td>True</td>\n",
       "      <td>0.463415</td>\n",
       "      <td>0.487805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>2014-10-29</th>\n",
       "      <td>Milwaukee Bucks</td>\n",
       "      <td>106</td>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>108</td>\n",
       "      <td>True</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.402439</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           away_team  away_points             home_team  \\\n",
       "game_id date                                                              \n",
       "0       2014-10-28   Houston Rockets          108    Los Angeles Lakers   \n",
       "1       2014-10-28     Orlando Magic           84  New Orleans Pelicans   \n",
       "2       2014-10-28  Dallas Mavericks          100     San Antonio Spurs   \n",
       "3       2014-10-29     Brooklyn Nets          105        Boston Celtics   \n",
       "4       2014-10-29   Milwaukee Bucks          106     Charlotte Hornets   \n",
       "\n",
       "                    home_points home_win  away_strength  home_strength  \n",
       "game_id date                                                            \n",
       "0       2014-10-28           90    False       0.682927       0.256098  \n",
       "1       2014-10-28          101     True       0.304878       0.548780  \n",
       "2       2014-10-28          101     True       0.609756       0.670732  \n",
       "3       2014-10-29          121     True       0.463415       0.487805  \n",
       "4       2014-10-29          108     True       0.500000       0.402439  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games = games.assign(away_strength=games.away_team.map(strength),\n",
    "                     home_strength=games.home_team.map(strength))\n",
    "games.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = pd.concat([games, un], axis=1).set_index(['away_team', 'home_team'], append=True).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>away_points</th>\n",
       "      <th>home_points</th>\n",
       "      <th>home_win</th>\n",
       "      <th>away_strength</th>\n",
       "      <th>home_strength</th>\n",
       "      <th>away_rest</th>\n",
       "      <th>home_rest</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>game_id</th>\n",
       "      <th>date</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_team</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <th>2014-10-30</th>\n",
       "      <th>Utah Jazz</th>\n",
       "      <th>Dallas Mavericks</th>\n",
       "      <td>102</td>\n",
       "      <td>120</td>\n",
       "      <td>True</td>\n",
       "      <td>0.463415</td>\n",
       "      <td>0.609756</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <th>2014-10-30</th>\n",
       "      <th>Detroit Pistons</th>\n",
       "      <th>Minnesota Timberwolves</th>\n",
       "      <td>91</td>\n",
       "      <td>97</td>\n",
       "      <td>True</td>\n",
       "      <td>0.390244</td>\n",
       "      <td>0.195122</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <th>2014-10-30</th>\n",
       "      <th>Washington Wizards</th>\n",
       "      <th>Orlando Magic</th>\n",
       "      <td>105</td>\n",
       "      <td>98</td>\n",
       "      <td>False</td>\n",
       "      <td>0.560976</td>\n",
       "      <td>0.304878</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <th>2014-10-31</th>\n",
       "      <th>Cleveland Cavaliers</th>\n",
       "      <th>Chicago Bulls</th>\n",
       "      <td>114</td>\n",
       "      <td>108</td>\n",
       "      <td>False</td>\n",
       "      <td>0.646341</td>\n",
       "      <td>0.609756</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <th>2014-10-31</th>\n",
       "      <th>Memphis Grizzlies</th>\n",
       "      <th>Indiana Pacers</th>\n",
       "      <td>97</td>\n",
       "      <td>89</td>\n",
       "      <td>False</td>\n",
       "      <td>0.670732</td>\n",
       "      <td>0.463415</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                               away_points  \\\n",
       "game_id date       away_team           home_team                             \n",
       "16      2014-10-30 Utah Jazz           Dallas Mavericks                102   \n",
       "18      2014-10-30 Detroit Pistons     Minnesota Timberwolves           91   \n",
       "19      2014-10-30 Washington Wizards  Orlando Magic                   105   \n",
       "20      2014-10-31 Cleveland Cavaliers Chicago Bulls                   114   \n",
       "21      2014-10-31 Memphis Grizzlies   Indiana Pacers                   97   \n",
       "\n",
       "                                                               home_points  \\\n",
       "game_id date       away_team           home_team                             \n",
       "16      2014-10-30 Utah Jazz           Dallas Mavericks                120   \n",
       "18      2014-10-30 Detroit Pistons     Minnesota Timberwolves           97   \n",
       "19      2014-10-30 Washington Wizards  Orlando Magic                    98   \n",
       "20      2014-10-31 Cleveland Cavaliers Chicago Bulls                   108   \n",
       "21      2014-10-31 Memphis Grizzlies   Indiana Pacers                   89   \n",
       "\n",
       "                                                              home_win  \\\n",
       "game_id date       away_team           home_team                         \n",
       "16      2014-10-30 Utah Jazz           Dallas Mavericks           True   \n",
       "18      2014-10-30 Detroit Pistons     Minnesota Timberwolves     True   \n",
       "19      2014-10-30 Washington Wizards  Orlando Magic             False   \n",
       "20      2014-10-31 Cleveland Cavaliers Chicago Bulls             False   \n",
       "21      2014-10-31 Memphis Grizzlies   Indiana Pacers            False   \n",
       "\n",
       "                                                               away_strength  \\\n",
       "game_id date       away_team           home_team                               \n",
       "16      2014-10-30 Utah Jazz           Dallas Mavericks             0.463415   \n",
       "18      2014-10-30 Detroit Pistons     Minnesota Timberwolves       0.390244   \n",
       "19      2014-10-30 Washington Wizards  Orlando Magic                0.560976   \n",
       "20      2014-10-31 Cleveland Cavaliers Chicago Bulls                0.646341   \n",
       "21      2014-10-31 Memphis Grizzlies   Indiana Pacers               0.670732   \n",
       "\n",
       "                                                               home_strength  \\\n",
       "game_id date       away_team           home_team                               \n",
       "16      2014-10-30 Utah Jazz           Dallas Mavericks             0.609756   \n",
       "18      2014-10-30 Detroit Pistons     Minnesota Timberwolves       0.195122   \n",
       "19      2014-10-30 Washington Wizards  Orlando Magic                0.304878   \n",
       "20      2014-10-31 Cleveland Cavaliers Chicago Bulls                0.609756   \n",
       "21      2014-10-31 Memphis Grizzlies   Indiana Pacers               0.463415   \n",
       "\n",
       "                                                               away_rest  \\\n",
       "game_id date       away_team           home_team                           \n",
       "16      2014-10-30 Utah Jazz           Dallas Mavericks                0   \n",
       "18      2014-10-30 Detroit Pistons     Minnesota Timberwolves          0   \n",
       "19      2014-10-30 Washington Wizards  Orlando Magic                   0   \n",
       "20      2014-10-31 Cleveland Cavaliers Chicago Bulls                   0   \n",
       "21      2014-10-31 Memphis Grizzlies   Indiana Pacers                  1   \n",
       "\n",
       "                                                               home_rest  \n",
       "game_id date       away_team           home_team                          \n",
       "16      2014-10-30 Utah Jazz           Dallas Mavericks                1  \n",
       "18      2014-10-30 Detroit Pistons     Minnesota Timberwolves          0  \n",
       "19      2014-10-30 Washington Wizards  Orlando Magic                   1  \n",
       "20      2014-10-31 Cleveland Cavaliers Chicago Bulls                   1  \n",
       "21      2014-10-31 Memphis Grizzlies   Indiana Pacers                  1  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X['home_win'] = X.home_win.astype(int)  # for statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.564564\n",
      "         Iterations 6\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>home_win</td>     <th>  No. Observations:  </th>  <td>  1213</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>               <td>Logit</td>      <th>  Df Residuals:      </th>  <td>  1208</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     4</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Thu, 23 Jul 2015</td> <th>  Pseudo R-squ.:     </th>  <td>0.1728</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>19:07:38</td>     <th>  Log-Likelihood:    </th> <td> -684.82</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -827.83</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th> <td>1.120e-60</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>     <td>   -0.3637</td> <td>    0.302</td> <td>   -1.205</td> <td> 0.228</td> <td>   -0.956     0.228</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>home_strength</th> <td>    5.5604</td> <td>    0.449</td> <td>   12.389</td> <td> 0.000</td> <td>    4.681     6.440</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>away_strength</th> <td>   -4.3474</td> <td>    0.442</td> <td>   -9.835</td> <td> 0.000</td> <td>   -5.214    -3.481</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>home_rest</th>     <td>    0.1327</td> <td>    0.069</td> <td>    1.913</td> <td> 0.056</td> <td>   -0.003     0.269</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>away_rest</th>     <td>   -0.0345</td> <td>    0.066</td> <td>   -0.526</td> <td> 0.599</td> <td>   -0.163     0.094</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:               home_win   No. Observations:                 1213\n",
       "Model:                          Logit   Df Residuals:                     1208\n",
       "Method:                           MLE   Df Model:                            4\n",
       "Date:                Thu, 23 Jul 2015   Pseudo R-squ.:                  0.1728\n",
       "Time:                        19:07:38   Log-Likelihood:                -684.82\n",
       "converged:                       True   LL-Null:                       -827.83\n",
       "                                        LLR p-value:                 1.120e-60\n",
       "=================================================================================\n",
       "                    coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
       "---------------------------------------------------------------------------------\n",
       "Intercept        -0.3637      0.302     -1.205      0.228        -0.956     0.228\n",
       "home_strength     5.5604      0.449     12.389      0.000         4.681     6.440\n",
       "away_strength    -4.3474      0.442     -9.835      0.000        -5.214    -3.481\n",
       "home_rest         0.1327      0.069      1.913      0.056        -0.003     0.269\n",
       "away_rest        -0.0345      0.066     -0.526      0.599        -0.163     0.094\n",
       "=================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod = sm.Logit.from_formula('home_win ~ home_strength + away_strength + home_rest + away_rest', X)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.682125\n",
      "         Iterations 4\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>home_win</td>     <th>  No. Observations:  </th>  <td>  1213</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>               <td>Logit</td>      <th>  Df Residuals:      </th>  <td>  1211</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     1</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Thu, 23 Jul 2015</td> <th>  Pseudo R-squ.:     </th> <td>0.0004950</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>19:08:09</td>     <th>  Log-Likelihood:    </th> <td> -827.42</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -827.83</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th>  <td>0.3653</td>  \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>            <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>       <td>    0.2815</td> <td>    0.060</td> <td>    4.721</td> <td> 0.000</td> <td>    0.165     0.398</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rest_difference</th> <td>    0.0473</td> <td>    0.052</td> <td>    0.903</td> <td> 0.366</td> <td>   -0.055     0.150</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:               home_win   No. Observations:                 1213\n",
       "Model:                          Logit   Df Residuals:                     1211\n",
       "Method:                           MLE   Df Model:                            1\n",
       "Date:                Thu, 23 Jul 2015   Pseudo R-squ.:               0.0004950\n",
       "Time:                        19:08:09   Log-Likelihood:                -827.42\n",
       "converged:                       True   LL-Null:                       -827.83\n",
       "                                        LLR p-value:                    0.3653\n",
       "===================================================================================\n",
       "                      coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
       "-----------------------------------------------------------------------------------\n",
       "Intercept           0.2815      0.060      4.721      0.000         0.165     0.398\n",
       "rest_difference     0.0473      0.052      0.903      0.366        -0.055     0.150\n",
       "===================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod = sm.Logit.from_formula('home_win ~ rest_difference',\n",
    "                            X.assign(rest_difference=lambda df: df.home_rest - df.away_rest))\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>spread</td>      <th>  R-squared:         </th> <td>   0.236</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.234</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   124.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 23 Jul 2015</td> <th>  Prob (F-statistic):</th> <td>3.22e-70</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:08:27</td>     <th>  Log-Likelihood:    </th> <td> -4712.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1213</td>      <th>  AIC:               </th> <td>   9433.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1209</td>      <th>  BIC:               </th> <td>   9453.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>       <td>   -0.3059</td> <td>    1.543</td> <td>   -0.198</td> <td> 0.843</td> <td>   -3.334     2.722</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>home_strength</th>   <td>   30.6895</td> <td>    2.096</td> <td>   14.639</td> <td> 0.000</td> <td>   26.576    34.802</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>away_strength</th>   <td>  -25.5859</td> <td>    2.106</td> <td>  -12.151</td> <td> 0.000</td> <td>  -29.717   -21.455</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rest_difference</th> <td>    0.5926</td> <td>    0.304</td> <td>    1.949</td> <td> 0.051</td> <td>   -0.004     1.189</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 9.779</td> <th>  Durbin-Watson:     </th> <td>   1.993</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.008</td> <th>  Jarque-Bera (JB):  </th> <td>  12.290</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.106</td> <th>  Prob(JB):          </th> <td> 0.00214</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.446</td> <th>  Cond. No.          </th> <td>    10.2</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 spread   R-squared:                       0.236\n",
       "Model:                            OLS   Adj. R-squared:                  0.234\n",
       "Method:                 Least Squares   F-statistic:                     124.4\n",
       "Date:                Thu, 23 Jul 2015   Prob (F-statistic):           3.22e-70\n",
       "Time:                        19:08:27   Log-Likelihood:                -4712.3\n",
       "No. Observations:                1213   AIC:                             9433.\n",
       "Df Residuals:                    1209   BIC:                             9453.\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===================================================================================\n",
       "                      coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "-----------------------------------------------------------------------------------\n",
       "Intercept          -0.3059      1.543     -0.198      0.843        -3.334     2.722\n",
       "home_strength      30.6895      2.096     14.639      0.000        26.576    34.802\n",
       "away_strength     -25.5859      2.106    -12.151      0.000       -29.717   -21.455\n",
       "rest_difference     0.5926      0.304      1.949      0.051        -0.004     1.189\n",
       "==============================================================================\n",
       "Omnibus:                        9.779   Durbin-Watson:                   1.993\n",
       "Prob(Omnibus):                  0.008   Jarque-Bera (JB):               12.290\n",
       "Skew:                           0.106   Prob(JB):                      0.00214\n",
       "Kurtosis:                       3.446   Cond. No.                         10.2\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod = sm.OLS.from_formula('spread ~ home_strength + away_strength + rest_difference',\n",
    "                            X.assign(rest_difference=lambda df: df.home_rest - df.away_rest,\n",
    "                                     spread=lambda df: df.home_points - df.away_points))\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Recap\n",
    "\n",
    "- Tidy data: one row per observation\n",
    "    - melt / stack: wide to long\n",
    "    - pivot_table / unstack: long to wide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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