05-Tidy-Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tidy Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "> Structuring datasets to facilitate analysis [(Wickham 2014)](http://www.jstatsoft.org/v59/i10/paper)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tidy Data (and the closely related topic of reshaping data) is a way of structuring your data and APIs in a consistent (sensible) way to make the data analysis process flow more smoothly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Rules\n",
    "\n",
    "In a tidy dataset...\n",
    "\n",
    "1. Each variable forms a column\n",
    "2. Each observation forms a row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.set(style=\"ticks\", context=\"talk\")\n",
    "plt.rcParams['figure.figsize'] = 12, 8\n",
    "pd.options.display.max_rows = 10\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NBA Games\n",
    "\n",
    "Earlier, I fetched some data\n",
    "\n",
    "```python\n",
    "tables = pd.read_html(\n",
    "  \"http://www.basketball-reference.com/leagues/\"\n",
    "  \"NBA_2016_games.html\"\n",
    ")\n",
    "games = tables[0]\n",
    "games.to_csv('data/games.csv', index=False)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date,Start (ET),Unnamed: 2,Visitor/Neutral,PTS,Home/Neutral,PTS.1,Unnamed: 7,Notes\r\n",
      "October,,,,,,,,\r\n",
      "\"Tue, Oct 27, 2015\",8:00 pm,Box Score,Detroit Pistons,106.0,Atlanta Hawks,94.0,,\r\n",
      "\"Tue, Oct 27, 2015\",8:00 pm,Box Score,Cleveland Cavaliers,95.0,Chicago Bulls,97.0,,\r\n",
      "\"Tue, Oct 27, 2015\",10:30 pm,Box Score,New Orleans Pelicans,95.0,Golden State Warriors,111.0,,\r\n"
     ]
    }
   ],
   "source": [
    "!head -n  5 data/games.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the data is roughly like\n",
    "\n",
    "| Date        | Visitor Team | Visitor Points | Home Team | Home Points |\n",
    "| ----------- | ------------ | -------------- | --------- | ----------- |\n",
    "| 2015-10-07  | Detroit      | 106            | Atlanta   | 94          |\n",
    "| ...         | ...          | ...            | ...       | ...         |\n",
    "\n",
    "Plus some extra junk we don't really care about."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [The Question](http://stackoverflow.com/questions/22695680/python-pandas-timedelta-specific-rows):\n",
    "\n",
    "> **How many days of rest did each team get between each game?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Whether or not your dataset is tidy depends on your question. Given our question, Is our data tidy?\n",
    "\n",
    "What should the columns (variables) be?\n",
    "\n",
    "What's an observation?\n",
    "\n",
    "<a href=\"#answer\" class=\"btn btn-default\" data-toggle=\"collapse\">Show Answer</a>\n",
    "<div id=\"answer\" class=\"collapse\">\n",
    "An observation is a (team, game) pair. So no, we don't have a tidy dataset.\n",
    "A tidy dataset would be like\n",
    "\n",
    "<table>\n",
    "<thead>\n",
    "<tr class=\"header\">\n",
    "<th>Date</th>\n",
    "<th>Team</th>\n",
    "<th>Home / Away</th>\n",
    "<th>Points</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody>\n",
    "<tr class=\"odd\">\n",
    "<td>2015-10-07</td>\n",
    "<td>Detroit</td>\n",
    "<td>Away</td>\n",
    "<td>106</td>\n",
    "</tr>\n",
    "<tr class=\"even\">\n",
    "<td>2015-10-07</td>\n",
    "<td>Atlanta</td>\n",
    "<td>Home</td>\n",
    "<td>94</td>\n",
    "</tr>\n",
    "<tr class=\"odd\">\n",
    "<td>...</td>\n",
    "<td>...</td>\n",
    "<td>...</td>\n",
    "<td>...</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table>\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do a bit of general cleaning to remove that extra junk before reshaping the data to be tidy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<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>1</th>\n",
       "      <th>2015-10-27</th>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>106.0</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>2015-10-27</th>\n",
       "      <td>Cleveland Cavaliers</td>\n",
       "      <td>95.0</td>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>2015-10-27</th>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>95.0</td>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>2015-10-28</th>\n",
       "      <td>Philadelphia 76ers</td>\n",
       "      <td>95.0</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>2015-10-28</th>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>115.0</td>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               away_team  away_points              home_team  \\\n",
       "game_id date                                                                   \n",
       "1       2015-10-27       Detroit Pistons        106.0          Atlanta Hawks   \n",
       "2       2015-10-27   Cleveland Cavaliers         95.0          Chicago Bulls   \n",
       "3       2015-10-27  New Orleans Pelicans         95.0  Golden State Warriors   \n",
       "4       2015-10-28    Philadelphia 76ers         95.0         Boston Celtics   \n",
       "5       2015-10-28         Chicago Bulls        115.0          Brooklyn Nets   \n",
       "\n",
       "                    home_points  \n",
       "game_id date                     \n",
       "1       2015-10-27         94.0  \n",
       "2       2015-10-27         97.0  \n",
       "3       2015-10-27        111.0  \n",
       "4       2015-10-28        112.0  \n",
       "5       2015-10-28        100.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names = {'Date': 'date', 'Start (ET)': 'start',\n",
    "                'Unamed: 2': 'box', 'Visitor/Neutral': 'away_team', \n",
    "                'PTS': 'away_points', 'Home/Neutral': 'home_team',\n",
    "                'PTS.1': 'home_points', 'Unamed: 7': 'n_ot'}\n",
    "\n",
    "keep = ['date', 'away_team', 'away_points', 'home_team', 'home_points']\n",
    "\n",
    "games = (\n",
    "    pd.read_csv(\"data/games.csv\")\n",
    "      .rename(columns=column_names)\n",
    "      .dropna(thresh=4)                  # Drop rows with >= 4 missing values\n",
    "      [keep]\n",
    "      .assign(date=lambda x: pd.to_datetime(x['date'], format='%a, %b %d, %Y'))\n",
    "      .set_index('date', append=True)    # Move the 'date' col to the index\n",
    "      .rename_axis([\"game_id\", \"date\"])  # Change the `.index.names` list\n",
    "      .sort_index()\n",
    ")\n",
    "games.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above, we saw that we need to collapse the away / home teams down to two columns: one identifier and one for the value.\n",
    "Likewise with the points.\n",
    "We'll also need to repeat the metadata fields, like the date and `game_id`, so that each observation is matched with the correct date.\n",
    "`pd.melt` does all this for us."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Melt\n",
    "\n",
    "![melt](figures/melt.png)\n",
    "\n",
    "- Collect a variable spread across multiple columns into one, but\n",
    "- Repeat the metadata to stay with each observation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<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>home_away</th>\n",
       "      <th>team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-10-27</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Detroit Pistons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1230</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-10-27</td>\n",
       "      <td>home_team</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-10-27</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Cleveland Cavaliers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1231</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-10-27</td>\n",
       "      <td>home_team</td>\n",
       "      <td>Chicago Bulls</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2015-10-27</td>\n",
       "      <td>away_team</td>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      game_id       date  home_away                  team\n",
       "0           1 2015-10-27  away_team       Detroit Pistons\n",
       "1230        1 2015-10-27  home_team         Atlanta Hawks\n",
       "1           2 2015-10-27  away_team   Cleveland Cavaliers\n",
       "1231        2 2015-10-27  home_team         Chicago Bulls\n",
       "2           3 2015-10-27  away_team  New Orleans Pelicans"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tidy = pd.melt(games.reset_index(),\n",
    "               id_vars=['game_id', 'date'], value_vars=['away_team', 'home_team'],\n",
    "               value_name='team', var_name='home_away').sort_values(['game_id', 'date'])\n",
    "\n",
    "tidy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the translation from question to operation is direct:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       NaN\n",
       "1230    NaN\n",
       "1       NaN\n",
       "1231    NaN\n",
       "2       NaN\n",
       "       ... \n",
       "2457    1.0\n",
       "1228    2.0\n",
       "2458    3.0\n",
       "1229    1.0\n",
       "2459    1.0\n",
       "Name: date, Length: 2460, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many days of rest for each team?\n",
    "# For each team...  get number of days between games\n",
    "tidy.groupby('team').date.diff().dt.days - 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We haven't seen `.diff` yet. It takes the value in the current row minus the value in the previous. Since `.date` is a `datetime`, the result is a `timedelta`. Timedeltas have that special `.dt` namespace for accessing properties of each timedelta, like the number of days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "<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>home_away</th>\n",
       "      <th>team</th>\n",
       "      <th>rest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2015-10-28</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1235</th>\n",
       "      <td>6</td>\n",
       "      <td>2015-10-28</td>\n",
       "      <td>home_team</td>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>2015-10-28</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Cleveland Cavaliers</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>2015-10-28</td>\n",
       "      <td>away_team</td>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>2015-10-29</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Memphis Grizzlies</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      game_id       date  home_away                  team  rest\n",
       "4           5 2015-10-28  away_team         Chicago Bulls   0.0\n",
       "1235        6 2015-10-28  home_team       Detroit Pistons   0.0\n",
       "8           9 2015-10-28  away_team   Cleveland Cavaliers   0.0\n",
       "14         15 2015-10-28  away_team  New Orleans Pelicans   0.0\n",
       "17         18 2015-10-29  away_team     Memphis Grizzlies   0.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assign tidy['rest']\n",
    "tidy['rest'] = tidy.groupby('team').date.diff().dt.days - 1\n",
    "tidy.dropna().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `pivot_table`\n",
    "\n",
    "You can \"invert\" a `melt` with `pd.pivot_table`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>18</th>\n",
       "      <th>2015-10-29</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <th>2015-10-29</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <th>2015-10-29</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <th>2015-10-30</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <th>2015-10-30</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    away_rest  home_rest\n",
       "game_id date                            \n",
       "18      2015-10-29        0.0        0.0\n",
       "19      2015-10-29        0.0        0.0\n",
       "20      2015-10-29        1.0        0.0\n",
       "21      2015-10-30        1.0        0.0\n",
       "22      2015-10-30        1.0        1.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_game = (pd.pivot_table(tidy, values='rest',\n",
    "                          index=['game_id', 'date'],\n",
    "                          columns='home_away')\n",
    "             .rename(columns={'away_team': 'away_rest',\n",
    "                              'home_team': 'home_rest'})\n",
    "             .rename_axis(None, axis='columns'))\n",
    "by_game.dropna().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`concat` will merge the two dataframes (`games, by_game`), expanding an `axis`, while aligning on the other axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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></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>18</th>\n",
       "      <th>2015-10-29</th>\n",
       "      <td>Memphis Grizzlies</td>\n",
       "      <td>112.0</td>\n",
       "      <td>Indiana Pacers</td>\n",
       "      <td>103.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <th>2015-10-29</th>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>88.0</td>\n",
       "      <td>Los Angeles Clippers</td>\n",
       "      <td>104.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <th>2015-10-29</th>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>112.0</td>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>101.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <th>2015-10-30</th>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>94.0</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <th>2015-10-30</th>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>113.0</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            away_team  away_points             home_team  \\\n",
       "game_id date                                                               \n",
       "18      2015-10-29  Memphis Grizzlies        112.0        Indiana Pacers   \n",
       "19      2015-10-29   Dallas Mavericks         88.0  Los Angeles Clippers   \n",
       "20      2015-10-29      Atlanta Hawks        112.0       New York Knicks   \n",
       "21      2015-10-30  Charlotte Hornets         94.0         Atlanta Hawks   \n",
       "22      2015-10-30    Toronto Raptors        113.0        Boston Celtics   \n",
       "\n",
       "                    home_points  away_rest  home_rest  \n",
       "game_id date                                           \n",
       "18      2015-10-29        103.0        0.0        0.0  \n",
       "19      2015-10-29        104.0        0.0        0.0  \n",
       "20      2015-10-29        101.0        1.0        0.0  \n",
       "21      2015-10-30         97.0        1.0        0.0  \n",
       "22      2015-10-30        103.0        1.0        1.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([games, by_game], axis='columns')\n",
    "df.dropna().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Two datasets\n",
    "\n",
    "- `tidy`: For team-level questions\n",
    "- `df`: For game-level questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even though I called one of the variables `tidy`, both datasets are tidy in their own way, it just depends on the question your trying to answer.\n",
    "\n",
    "For example, let's say we wanted to look at the rest durations, split by home/away, faceted by team. We'd use `tidy` for that since it's a team-level question. We'll facet by `team` (wrapping the columns so it doesn't get too wide. And `map` a barplot with `x=variable` and `y=rest`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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Y5XfbrUx92rKZr71PcNen3G1uZvCGckhZqf07NY3VragMo0dS1xSop00nRsQ2VCP22TQ3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\nkiRJtff/Aa0OD7aXr7IAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ce86b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(tidy, col=\"team\", col_wrap=5, hue=\"team\")\n",
    "g.map(sns.barplot, \"home_away\", \"rest\", order=['away_team', 'home_team']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we wanted to look at the distribution of the *differences* in rest, we'd use `df` since the difference is a game-level statistic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SJElSBQZrSZIkqQKDtSRJklSBwVqSJEmqwGAtSZIkVWCwliRJkiowWEuSJEkVGKwlSZKk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\nrCVJkqQKDNaSJElSBf8fxvfwt1zSyeQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1789a4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "delta = (df['home_rest'] - df['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)))\n",
    "\n",
    "sns.despine()\n",
    "plt.xlabel(\"Difference in Rest (home - away)\")\n",
    "plt.grid(axis='y');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" data-title=\"Win Percent\">\n",
    "  <h1><i class=\"fa fa-tasks\" aria-hidden=\"true\"></i> Exercise: Win Percent</h1>\n",
    "</div>\n",
    "\n",
    "<p>Find the win-percent for each team, by whether they're home or away.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hints:\n",
    "\n",
    "- You'll need a new variable in `df` called `winning_team` (use `np.where`?). That should have the name of the winning team\n",
    "- You'll need to `melt` that along with the home / away columns (which column is `id_vars`? Which columns are `value_vars`?)\n",
    "- A tidy dataset looks like\n",
    "\n",
    "<table border=\"1\" class=\"dataframe\">\n",
    "  <thead>\n",
    "    <tr style=\"text-align: right;\">\n",
    "      <th></th>\n",
    "      <th>winning_team</th>\n",
    "      <th>home_or_away</th>\n",
    "      <th>team</th>\n",
    "      <th>won</th>\n",
    "    </tr>\n",
    "  </thead>\n",
    "  <tbody>\n",
    "    <tr>\n",
    "      <th>0</th>\n",
    "      <td>Detroit Pistons</td>\n",
    "      <td>away_team</td>\n",
    "      <td>Detroit Pistons</td>\n",
    "      <td>True</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>1</th>\n",
    "      <td>Chicago Bulls</td>\n",
    "      <td>away_team</td>\n",
    "      <td>Cleveland Cavaliers</td>\n",
    "      <td>False</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>2</th>\n",
    "      <td>Golden State Warriors</td>\n",
    "      <td>away_team</td>\n",
    "      <td>New Orleans Pelicans</td>\n",
    "      <td>False</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>3</th>\n",
    "      <td>Boston Celtics</td>\n",
    "      <td>away_team</td>\n",
    "      <td>Philadelphia 76ers</td>\n",
    "      <td>False</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>4</th>\n",
    "      <td>Chicago Bulls</td>\n",
    "      <td>away_team</td>\n",
    "      <td>Chicago Bulls</td>\n",
    "      <td>True</td>\n",
    "    </tr>\n",
    "  </tbody>\n",
    "</table>\n",
    "\n",
    "- With the tidy dataset we can `groupby` and take the mean. Teams play the same number of home and away games, so a simple (un-weighted) average will work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['winning_team'] = np.where(\n",
    "    ...  # the condition\n",
    "    ...  # pick from this array if condition is true\n",
    "    ...  # pick from this array if condition is false\n",
    ")\n",
    "\n",
    "win = pd.melt(\n",
    "    df,\n",
    "    id_vars=...,\n",
    "    value_vars=...\n",
    "    var_names='home_or_away',\n",
    "    value_name='team'\n",
    ")\n",
    "\n",
    "win['won'] = ... # True when the `winning_team` equals `team`\n",
    "win_pct = ...    # win percent for each team, by home or away. Sounds like a groupby."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "team            home_or_away\n",
       "Atlanta Hawks   away_team       0.512195\n",
       "                home_team       0.658537\n",
       "Boston Celtics  away_team       0.487805\n",
       "                home_team       0.682927\n",
       "Brooklyn Nets   away_team       0.170732\n",
       "Name: won, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%load solutions/tidy_00.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Stack / Unstack\n",
    "\n",
    "- stack: `DataFrame` -> `Series` with `MultiIndex`\n",
    "- unstack: `Series` with `MultiIndex` -> `DataFrame`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The exact shape of a tidy dataset depends on the question being asked.\n",
    "Additionally, not all APIs expect tidy data, so you need to convert between \"wide\" and \"long\" form data.\n",
    "\n",
    "Let's create a `rest` varible that's the average `rest` by `home_away` and `date` across teams:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "home_away  date      \n",
       "away_team  2015-10-27         NaN\n",
       "           2015-10-28    0.000000\n",
       "           2015-10-29    0.333333\n",
       "           2015-10-30    1.083333\n",
       "           2015-10-31    0.166667\n",
       "Name: rest, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest = tidy.groupby(['home_away', 'date']).rest.mean()\n",
    "rest.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`rest` is in \"long\" form. `DataFrame.plot` for example, expects wide form data, so use `.unstack`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>home_away</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_team</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-10-27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-28</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-29</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-30</th>\n",
       "      <td>1.083333</td>\n",
       "      <td>0.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-31</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "home_away   away_team  home_team\n",
       "date                            \n",
       "2015-10-27        NaN        NaN\n",
       "2015-10-28   0.000000   0.000000\n",
       "2015-10-29   0.333333   0.000000\n",
       "2015-10-30   1.083333   0.916667\n",
       "2015-10-31   0.166667   0.833333"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest.unstack('home_away').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can plot the rolling mean:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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niAHsRg1QvsckAu2895+SzWYrAAqOO69c3yyEEG1Q7TBWe5Pv5tCeR+/G6fJg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f3OjAE7NpGeStlk9PTmtcspT3osAJ5yinT4rHt9ZlCfGbTHWtdnBt02UDPW3Bx4MPABsF9EvA9M\noazi7tYK48S612cG9ZlBe6x1fWbQbQM1Y40jgDOA2cCdmblkCJ+p/2bd6zOD+sygPda6PjPoqGE0\nY98ANwFLMvP3IXyedo91r88M6jOD9ljr+sygo4axHdIEr1O3z7rXZwb1mUF7rHV9ZtBdAzdjkiRJ\n6l/fe1NKkiRpcDZjkiRJFdmMSZIkVWQzJkmSVJHNmKROiogLIuKH3Tz35Ig4arzHJEm7YjMmSbAS\nOLD2ICSNJpsxSZKkioaxAr8kVRcRhwH3AXOAz4BXxhybDSwGZlL+E/oxcEVmromIr5rTXoqIRZl5\nc0QcBywFjgK+Bu4F7nbBTUnjwZkxSXu8iJhEab42AsdSGqlrmmOTm2PvUfb2OwGYCCxr3j6z+Xk+\ncEdETAFeBV5ozr8OuAG4vI3vImn0ODMmqQtOBQ4Ajs3MjcDnETEDuAjYB7iNsrHyNoCIeAS4BSAz\nv48IgJ8yc1NEXA+8n5mLm8/+MiKmAtdTZsgkaahsxiR1weHAuqYR6/kQuCgzN0TEw8BVEXEkEMDR\nwKZ/+axTImLs8YnApIiYlJmbx2H8kkaYzZikrpjwl9+3AETE/pR7xD6lXH58jNJwLfqHz9kLeA64\ncRfHtg5lpJI0hs2YpC74BJgeEVMy87vmtWOan2cCm4HTejfgR8R8/t689awF5mfml70XIuIcYG5m\nXjwuo5c00mzGJHXBG0ACj0bEtcBhwBXANuBHYAowLyLWAvOAK4E/xrx/E3B4RLwN3EO5pHkX5R6x\nQ5rXHmjpu0gaMT5NKWmPl5lbgfmU5msV5eb8pc3hZ4CHgCeA1cB5wGXAPhFxaHPOMsply1szcz2l\nYZsFrKE0Yfex68uWkjSwCdu3u2yOJElSLc6MSZIkVWQzJkmSVJHNmCRJUkU2Y5IkSRXZjEmSJFVk\nMyZJklSRzZgkSVJFNmOSJEkV2YxJkiRV9CeNIWPxp7+jUQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cf069b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rest.unstack('home_away').rolling(7).mean().plot(figsize=(10, 5));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stack and unstack are inverses:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date        home_away\n",
       "2015-10-28  away_team    0.000000\n",
       "            home_team    0.000000\n",
       "2015-10-29  away_team    0.333333\n",
       "            home_team    0.000000\n",
       "2015-10-30  away_team    1.083333\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest.unstack(\"home_away\").stack().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" data-title=\"Home Court Advantage?\">\n",
    "  <h1><i class=\"fa fa-tasks\" aria-hidden=\"true\"></i> Exercise: Home Court Advantage?</h1>\n",
    "</div>\n",
    "<p>How much of home court advantage can be explained by rest?</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reproducing [this](http://www.nessis.org/nessis07/Dylan_Small.pdf).\n",
    "Basically, we want to run a regression of\n",
    "\n",
    "    point_spread ~ rest_spread + other_stuff\n",
    "\n",
    "We'll talk about the `other_stuff` later when we get to it.\n",
    "For now, we need to augment `df` with a couple additional varibles.\n",
    "\n",
    "If your DataFrame is out of sync, you might want to load in a fresh one, by copying this code into a new cell and running it.\n",
    "\n",
    "```python\n",
    "df = pd.read_csv('data/tidy_checkpoint.csv', parse_dates=['date'],\n",
    "                 index_col=['game_id', 'date'])\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 0: Outcome variables\n",
    "\n",
    "Modify `df` to include a couple potential targets\n",
    "\n",
    "- `point_spread`: the home score minus the away score\n",
    "- `home_win`: binary indicator for whether the home team won"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['point_spread'] = ...\n",
    "df['home_win'] = df['point_spread'] ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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></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",
       "      <th>winning_team</th>\n",
       "      <th>point_spread</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>2015-10-27</th>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>106.0</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>94.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>2015-10-27</th>\n",
       "      <td>Cleveland Cavaliers</td>\n",
       "      <td>95.0</td>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>97.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>2.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>2015-10-27</th>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>95.0</td>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>111.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>16.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>2015-10-28</th>\n",
       "      <td>Philadelphia 76ers</td>\n",
       "      <td>95.0</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>112.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>17.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>2015-10-28</th>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>115.0</td>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               away_team  away_points              home_team  \\\n",
       "game_id date                                                                   \n",
       "1       2015-10-27       Detroit Pistons        106.0          Atlanta Hawks   \n",
       "2       2015-10-27   Cleveland Cavaliers         95.0          Chicago Bulls   \n",
       "3       2015-10-27  New Orleans Pelicans         95.0  Golden State Warriors   \n",
       "4       2015-10-28    Philadelphia 76ers         95.0         Boston Celtics   \n",
       "5       2015-10-28         Chicago Bulls        115.0          Brooklyn Nets   \n",
       "\n",
       "                    home_points  away_rest  home_rest           winning_team  \\\n",
       "game_id date                                                                   \n",
       "1       2015-10-27         94.0        NaN        NaN        Detroit Pistons   \n",
       "2       2015-10-27         97.0        NaN        NaN          Chicago Bulls   \n",
       "3       2015-10-27        111.0        NaN        NaN  Golden State Warriors   \n",
       "4       2015-10-28        112.0        NaN        NaN         Boston Celtics   \n",
       "5       2015-10-28        100.0        0.0        NaN          Chicago Bulls   \n",
       "\n",
       "                    point_spread  home_win  \n",
       "game_id date                                \n",
       "1       2015-10-27         -12.0     False  \n",
       "2       2015-10-27           2.0      True  \n",
       "3       2015-10-27          16.0      True  \n",
       "4       2015-10-28          17.0      True  \n",
       "5       2015-10-28         -15.0     False  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%load solutions/tidy_01.py\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Team Strength\n",
    "\n",
    "Most examples I've seen use a \"team strength\" variable in their regression estimating the home court advantage. We'll grab one from ESPN."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use RPI from prior year: http://www.espn.com/nba/stats/rpi/_/year/2015 as a measure of team strength\n",
    "\n",
    "```python\n",
    "rpi = pd.read_html(\"http://www.espn.com/nba/stats/rpi/_/year/2015\")[0]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-15 NBA RPI Rankings</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RK</td>\n",
       "      <td>TEAM</td>\n",
       "      <td>RPI</td>\n",
       "      <td>W</td>\n",
       "      <td>L</td>\n",
       "      <td>PCT</td>\n",
       "      <td>SOS</td>\n",
       "      <td>PWR</td>\n",
       "      <td>PF</td>\n",
       "      <td>PA</td>\n",
       "      <td>EWL</td>\n",
       "      <td>EWP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Golden State</td>\n",
       "      <td>.582</td>\n",
       "      <td>67</td>\n",
       "      <td>15</td>\n",
       "      <td>.817</td>\n",
       "      <td>.504</td>\n",
       "      <td>1</td>\n",
       "      <td>9016</td>\n",
       "      <td>8188</td>\n",
       "      <td>68-14</td>\n",
       "      <td>.831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>Houston</td>\n",
       "      <td>.552</td>\n",
       "      <td>56</td>\n",
       "      <td>26</td>\n",
       "      <td>.683</td>\n",
       "      <td>.509</td>\n",
       "      <td>7</td>\n",
       "      <td>8522</td>\n",
       "      <td>8240</td>\n",
       "      <td>52-30</td>\n",
       "      <td>.635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>LA</td>\n",
       "      <td>.552</td>\n",
       "      <td>56</td>\n",
       "      <td>26</td>\n",
       "      <td>.683</td>\n",
       "      <td>.509</td>\n",
       "      <td>4</td>\n",
       "      <td>8751</td>\n",
       "      <td>8211</td>\n",
       "      <td>61-21</td>\n",
       "      <td>.741</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          0             1     2    3    4     5     6    7  \\\n",
       "0  2014-15 NBA RPI Rankings           NaN   NaN  NaN  NaN   NaN   NaN  NaN   \n",
       "1                        RK          TEAM   RPI    W    L   PCT   SOS  PWR   \n",
       "2                         1  Golden State  .582   67   15  .817  .504    1   \n",
       "3                         2       Houston  .552   56   26  .683  .509    7   \n",
       "4                       NaN            LA  .552   56   26  .683  .509    4   \n",
       "\n",
       "      8     9     10    11  \n",
       "0   NaN   NaN    NaN   NaN  \n",
       "1    PF    PA    EWL   EWP  \n",
       "2  9016  8188  68-14  .831  \n",
       "3  8522  8240  52-30  .635  \n",
       "4  8751  8211  61-21  .741  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_rpi = pd.read_csv(\"data/rpi.csv\")\n",
    "raw_rpi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>RK</th>\n",
       "      <th>RPI</th>\n",
       "      <th>W</th>\n",
       "      <th>L</th>\n",
       "      <th>PCT</th>\n",
       "      <th>SOS</th>\n",
       "      <th>PWR</th>\n",
       "      <th>PF</th>\n",
       "      <th>PA</th>\n",
       "      <th>EWL</th>\n",
       "      <th>EWP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEAM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>Golden State</th>\n",
       "      <td>1</td>\n",
       "      <td>0.582</td>\n",
       "      <td>67</td>\n",
       "      <td>15</td>\n",
       "      <td>.817</td>\n",
       "      <td>.504</td>\n",
       "      <td>1</td>\n",
       "      <td>9016</td>\n",
       "      <td>8188</td>\n",
       "      <td>68-14</td>\n",
       "      <td>.831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Houston</th>\n",
       "      <td>2</td>\n",
       "      <td>0.552</td>\n",
       "      <td>56</td>\n",
       "      <td>26</td>\n",
       "      <td>.683</td>\n",
       "      <td>.509</td>\n",
       "      <td>7</td>\n",
       "      <td>8522</td>\n",
       "      <td>8240</td>\n",
       "      <td>52-30</td>\n",
       "      <td>.635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LA</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.552</td>\n",
       "      <td>56</td>\n",
       "      <td>26</td>\n",
       "      <td>.683</td>\n",
       "      <td>.509</td>\n",
       "      <td>4</td>\n",
       "      <td>8751</td>\n",
       "      <td>8211</td>\n",
       "      <td>61-21</td>\n",
       "      <td>.741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atlanta</th>\n",
       "      <td>4</td>\n",
       "      <td>0.551</td>\n",
       "      <td>60</td>\n",
       "      <td>22</td>\n",
       "      <td>.732</td>\n",
       "      <td>.491</td>\n",
       "      <td>5</td>\n",
       "      <td>8409</td>\n",
       "      <td>7964</td>\n",
       "      <td>58-24</td>\n",
       "      <td>.710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Memphis</th>\n",
       "      <td>5</td>\n",
       "      <td>0.549</td>\n",
       "      <td>55</td>\n",
       "      <td>27</td>\n",
       "      <td>.671</td>\n",
       "      <td>.509</td>\n",
       "      <td>8</td>\n",
       "      <td>8062</td>\n",
       "      <td>7796</td>\n",
       "      <td>52-30</td>\n",
       "      <td>.635</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               RK    RPI   W   L   PCT   SOS PWR    PF    PA    EWL   EWP\n",
       "TEAM                                                                     \n",
       "Golden State    1  0.582  67  15  .817  .504   1  9016  8188  68-14  .831\n",
       "Houston         2  0.552  56  26  .683  .509   7  8522  8240  52-30  .635\n",
       "LA            NaN  0.552  56  26  .683  .509   4  8751  8211  61-21  .741\n",
       "Atlanta         4  0.551  60  22  .732  .491   5  8409  7964  58-24  .710\n",
       "Memphis         5  0.549  55  27  .671  .509   8  8062  7796  52-30  .635"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rpi_cols = raw_rpi.loc[1]\n",
    "rpi = (\n",
    "    raw_rpi.drop([0, 1])\n",
    "       .rename(columns={str(i): v for i, v in enumerate(rpi_cols)})\n",
    "       .set_index(\"TEAM\")\n",
    "       .assign(RPI=lambda x: pd.to_numeric(x.RPI))\n",
    ")\n",
    "rpi.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merge `df` and `rpi` on team. One uses just the city, while the other uses the city and team name.\n",
    "Fortunately, both versions have the same sort order, so we're able to unambigusly map `<city>` to `<city> <team>`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Atlanta': 'Atlanta Hawks',\n",
       " 'Boston': 'Boston Celtics',\n",
       " 'Brooklyn': 'Brooklyn Nets',\n",
       " 'Charlotte': 'Charlotte Hornets',\n",
       " 'Chicago': 'Chicago Bulls',\n",
       " 'Cleveland': 'Cleveland Cavaliers',\n",
       " 'Dallas': 'Dallas Mavericks',\n",
       " 'Denver': 'Denver Nuggets',\n",
       " 'Detroit': 'Detroit Pistons',\n",
       " 'Golden State': 'Golden State Warriors',\n",
       " 'Houston': 'Houston Rockets',\n",
       " 'Indiana': 'Indiana Pacers',\n",
       " 'LA': 'Los Angeles Clippers',\n",
       " 'LA Lakers': 'Los Angeles Lakers',\n",
       " 'Memphis': 'Memphis Grizzlies',\n",
       " 'Miami': 'Miami Heat',\n",
       " 'Milwaukee': 'Milwaukee Bucks',\n",
       " 'Minnesota': 'Minnesota Timberwolves',\n",
       " 'New Orleans': 'New Orleans Pelicans',\n",
       " 'New York': 'New York Knicks',\n",
       " 'Oklahoma City': 'Oklahoma City Thunder',\n",
       " 'Orlando': 'Orlando Magic',\n",
       " 'Philadelphia': 'Philadelphia 76ers',\n",
       " 'Phoenix': 'Phoenix Suns',\n",
       " 'Portland': 'Portland Trail Blazers',\n",
       " 'Sacramento': 'Sacramento Kings',\n",
       " 'San Antonio': 'San Antonio Spurs',\n",
       " 'Toronto': 'Toronto Raptors',\n",
       " 'Utah': 'Utah Jazz',\n",
       " 'Washington': 'Washington Wizards'}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapping = dict(zip(\n",
    "    sorted(rpi.index),\n",
    "    sorted(set(df.away_team.unique()))\n",
    "))\n",
    "mapping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create two new columns in `df`\n",
    "\n",
    "- `home_strength`\n",
    "- `away_strength`\n",
    "\n",
    "Using either `.map` or `pd.merge` and the `mapping` varaible we just created.\n",
    "The strength columns should contain the `RPI` for the home / away team from `rpi`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to go from\n",
    "\n",
    "<table border=\"1\" class=\"dataframe\">\n",
    "  <thead>\n",
    "    <tr style=\"text-align: right;\">\n",
    "      <th></th>\n",
    "      <th>RPI</th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>TEAM</th>\n",
    "      <th></th>\n",
    "    </tr>\n",
    "  </thead>\n",
    "  <tbody>\n",
    "    <tr>\n",
    "      <th>Golden State</th>\n",
    "      <td>0.582</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>Houston</th>\n",
    "      <td>0.552</td>\n",
    "    </tr>\n",
    "  </tbody>\n",
    "</table>\n",
    "\n",
    "to\n",
    "\n",
    "<table border=\"1\" class=\"dataframe\">\n",
    "  <thead>\n",
    "    <tr style=\"text-align: right;\">\n",
    "      <th></th>\n",
    "      <th>RPI</th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>TEAM</th>\n",
    "      <th></th>\n",
    "    </tr>\n",
    "  </thead>\n",
    "  <tbody>\n",
    "    <tr>\n",
    "      <th>Golden State Warriors</th>\n",
    "      <td>0.582</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>Houston Rockets</th>\n",
    "      <td>0.552</td>\n",
    "    </tr>\n",
    "  </tbody>\n",
    "</table>\n",
    "\n",
    "to\n",
    "\n",
    "<table border=\"1\" class=\"dataframe\">\n",
    "  <thead>\n",
    "    <tr style=\"text-align: right;\">\n",
    "      <th></th>\n",
    "      <th></th>\n",
    "      <th>home_team</th>\n",
    "      <th>away_team</th>\n",
    "      <th>home_strength</th>\n",
    "      <th>away_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",
    "    </tr>\n",
    "  </thead>\n",
    "  <tbody>\n",
    "    <tr>\n",
    "      <th>1</th>\n",
    "      <th>2015-10-27</th>\n",
    "      <td>Atlanta Hawks</td>\n",
    "      <td>Detroit Pistons</td>\n",
    "      <td>0.551</td>\n",
    "      <td>0.467</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>2</th>\n",
    "      <th>2015-10-27</th>\n",
    "      <td>Chicago Bulls</td>\n",
    "      <td>Cleveland Cavaliers</td>\n",
    "      <td>0.523</td>\n",
    "      <td>0.535</td>\n",
    "    </tr>\n",
    "  </tbody>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TEAM\n",
       "Golden State Warriors     0.582\n",
       "Houston Rockets           0.552\n",
       "Los Angeles Clippers      0.552\n",
       "Atlanta Hawks             0.551\n",
       "Memphis Grizzlies         0.549\n",
       "                          ...  \n",
       "Orlando Magic             0.447\n",
       "Los Angeles Lakers        0.444\n",
       "Minnesota Timberwolves    0.430\n",
       "Philadelphia 76ers        0.425\n",
       "New York Knicks           0.419\n",
       "Name: RPI, Length: 30, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_rpi = rpi.RPI.rename(mapping)\n",
    "city_rpi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['home_strength'] = df.home_team.map(...)\n",
    "df['away_strength'] = ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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",
       "      <th>winning_team</th>\n",
       "      <th>point_spread</th>\n",
       "      <th>home_win</th>\n",
       "      <th>home_strength</th>\n",
       "      <th>away_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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>2015-10-27</th>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>106.0</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>94.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.551</td>\n",
       "      <td>0.467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>2015-10-27</th>\n",
       "      <td>Cleveland Cavaliers</td>\n",
       "      <td>95.0</td>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>97.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>2.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0.523</td>\n",
       "      <td>0.535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>2015-10-27</th>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>95.0</td>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>111.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>16.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0.582</td>\n",
       "      <td>0.517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>2015-10-28</th>\n",
       "      <td>Philadelphia 76ers</td>\n",
       "      <td>95.0</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>112.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>17.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>2015-10-28</th>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>115.0</td>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.487</td>\n",
       "      <td>0.523</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               away_team  away_points              home_team  \\\n",
       "game_id date                                                                   \n",
       "1       2015-10-27       Detroit Pistons        106.0          Atlanta Hawks   \n",
       "2       2015-10-27   Cleveland Cavaliers         95.0          Chicago Bulls   \n",
       "3       2015-10-27  New Orleans Pelicans         95.0  Golden State Warriors   \n",
       "4       2015-10-28    Philadelphia 76ers         95.0         Boston Celtics   \n",
       "5       2015-10-28         Chicago Bulls        115.0          Brooklyn Nets   \n",
       "\n",
       "                    home_points  away_rest  home_rest           winning_team  \\\n",
       "game_id date                                                                   \n",
       "1       2015-10-27         94.0        NaN        NaN        Detroit Pistons   \n",
       "2       2015-10-27         97.0        NaN        NaN          Chicago Bulls   \n",
       "3       2015-10-27        111.0        NaN        NaN  Golden State Warriors   \n",
       "4       2015-10-28        112.0        NaN        NaN         Boston Celtics   \n",
       "5       2015-10-28        100.0        0.0        NaN          Chicago Bulls   \n",
       "\n",
       "                    point_spread  home_win  home_strength  away_strength  \n",
       "game_id date                                                              \n",
       "1       2015-10-27         -12.0     False          0.551          0.467  \n",
       "2       2015-10-27           2.0      True          0.523          0.535  \n",
       "3       2015-10-27          16.0      True          0.582          0.517  \n",
       "4       2015-10-28          17.0      True          0.490          0.425  \n",
       "5       2015-10-28         -15.0     False          0.487          0.523  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%load solutions/tidy_02.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Rest Difference\n",
    "\n",
    "Create a new column `rest_spread` that contains the difference in rest (home - away)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
       "      <th>winning_team</th>\n",
       "      <th>point_spread</th>\n",
       "      <th>home_win</th>\n",
       "      <th>home_strength</th>\n",
       "      <th>away_strength</th>\n",
       "      <th>rest_spread</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>2015-10-27</th>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>106.0</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>94.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.551</td>\n",
       "      <td>0.467</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>2015-10-27</th>\n",
       "      <td>Cleveland Cavaliers</td>\n",
       "      <td>95.0</td>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>97.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>2.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0.523</td>\n",
       "      <td>0.535</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>2015-10-27</th>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>95.0</td>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>111.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>16.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0.582</td>\n",
       "      <td>0.517</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>2015-10-28</th>\n",
       "      <td>Philadelphia 76ers</td>\n",
       "      <td>95.0</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>112.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>17.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.425</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>2015-10-28</th>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>115.0</td>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.487</td>\n",
       "      <td>0.523</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               away_team  away_points              home_team  \\\n",
       "game_id date                                                                   \n",
       "1       2015-10-27       Detroit Pistons        106.0          Atlanta Hawks   \n",
       "2       2015-10-27   Cleveland Cavaliers         95.0          Chicago Bulls   \n",
       "3       2015-10-27  New Orleans Pelicans         95.0  Golden State Warriors   \n",
       "4       2015-10-28    Philadelphia 76ers         95.0         Boston Celtics   \n",
       "5       2015-10-28         Chicago Bulls        115.0          Brooklyn Nets   \n",
       "\n",
       "                    home_points  away_rest  home_rest           winning_team  \\\n",
       "game_id date                                                                   \n",
       "1       2015-10-27         94.0        NaN        NaN        Detroit Pistons   \n",
       "2       2015-10-27         97.0        NaN        NaN          Chicago Bulls   \n",
       "3       2015-10-27        111.0        NaN        NaN  Golden State Warriors   \n",
       "4       2015-10-28        112.0        NaN        NaN         Boston Celtics   \n",
       "5       2015-10-28        100.0        0.0        NaN          Chicago Bulls   \n",
       "\n",
       "                    point_spread  home_win  home_strength  away_strength  \\\n",
       "game_id date                                                               \n",
       "1       2015-10-27         -12.0     False          0.551          0.467   \n",
       "2       2015-10-27           2.0      True          0.523          0.535   \n",
       "3       2015-10-27          16.0      True          0.582          0.517   \n",
       "4       2015-10-28          17.0      True          0.490          0.425   \n",
       "5       2015-10-28         -15.0     False          0.487          0.523   \n",
       "\n",
       "                    rest_spread  \n",
       "game_id date                     \n",
       "1       2015-10-27          NaN  \n",
       "2       2015-10-27          NaN  \n",
       "3       2015-10-27          NaN  \n",
       "4       2015-10-28          NaN  \n",
       "5       2015-10-28          NaN  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%load solutions/tidy_03.py\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove rows with missing values (rest), fixup a dtype for statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "</style>\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",
       "      <th>winning_team</th>\n",
       "      <th>point_spread</th>\n",
       "      <th>home_win</th>\n",
       "      <th>home_strength</th>\n",
       "      <th>away_strength</th>\n",
       "      <th>rest_spread</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <th>2015-10-29</th>\n",
       "      <td>Memphis Grizzlies</td>\n",
       "      <td>112.0</td>\n",
       "      <td>Indiana Pacers</td>\n",
       "      <td>103.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Memphis Grizzlies</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.484</td>\n",
       "      <td>0.549</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <th>2015-10-29</th>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>88.0</td>\n",
       "      <td>Los Angeles Clippers</td>\n",
       "      <td>104.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Los Angeles Clippers</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.552</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <th>2015-10-29</th>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>112.0</td>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>101.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.419</td>\n",
       "      <td>0.551</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <th>2015-10-30</th>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>94.0</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.551</td>\n",
       "      <td>0.471</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <th>2015-10-30</th>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>113.0</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            away_team  away_points             home_team  \\\n",
       "game_id date                                                               \n",
       "18      2015-10-29  Memphis Grizzlies        112.0        Indiana Pacers   \n",
       "19      2015-10-29   Dallas Mavericks         88.0  Los Angeles Clippers   \n",
       "20      2015-10-29      Atlanta Hawks        112.0       New York Knicks   \n",
       "21      2015-10-30  Charlotte Hornets         94.0         Atlanta Hawks   \n",
       "22      2015-10-30    Toronto Raptors        113.0        Boston Celtics   \n",
       "\n",
       "                    home_points  away_rest  home_rest          winning_team  \\\n",
       "game_id date                                                                  \n",
       "18      2015-10-29        103.0        0.0        0.0     Memphis Grizzlies   \n",
       "19      2015-10-29        104.0        0.0        0.0  Los Angeles Clippers   \n",
       "20      2015-10-29        101.0        1.0        0.0         Atlanta Hawks   \n",
       "21      2015-10-30         97.0        1.0        0.0         Atlanta Hawks   \n",
       "22      2015-10-30        103.0        1.0        1.0       Toronto Raptors   \n",
       "\n",
       "                    point_spread  home_win  home_strength  away_strength  \\\n",
       "game_id date                                                               \n",
       "18      2015-10-29          -9.0         0          0.484          0.549   \n",
       "19      2015-10-29          16.0         1          0.552          0.536   \n",
       "20      2015-10-29         -11.0         0          0.419          0.551   \n",
       "21      2015-10-30           3.0         1          0.551          0.471   \n",
       "22      2015-10-30         -10.0         0          0.490          0.517   \n",
       "\n",
       "                    rest_spread  \n",
       "game_id date                     \n",
       "18      2015-10-29          0.0  \n",
       "19      2015-10-29          0.0  \n",
       "20      2015-10-29         -1.0  \n",
       "21      2015-10-30         -1.0  \n",
       "22      2015-10-30          0.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.dropna().copy()\n",
    "df.loc[:, 'home_win'] = df['home_win'].astype(int)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Sanity Check\n",
    "\n",
    "Let's do some checks to see if we're on the right track.\n",
    "Does the home team typically have more rest? What's the average difference in rest?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19455894476504534"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%load solutions/tidy_drest.py\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What's the relationship between the difference in rest and home win percent? I did a barplot, but you can explore it however you want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18033160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%load solutions/tidy_04.py\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot each team's win percent by home or away:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/taugspurger/miniconda3/envs/ph2t2/lib/python3.6/site-packages/seaborn/axisgrid.py:703: UserWarning: Using the pointplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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eeHp6siK6mRpJSUnYoEEDVqdu3Lhx7uPJmzdvkBCiVgRWrFghqt1mzZoJEpcc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\n9hWrG8V0Ql8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18034828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(win_pct.reset_index(), hue='team', size=8, aspect=.5, palette=['k'])\n",
    "g.map(sns.pointplot, 'home_or_away', 'won').set(ylim=(0, 1));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Regression\n",
    "\n",
    "Now we can fit the model using statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/taugspurger/miniconda3/envs/ph2t2/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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></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",
       "      <th>winning_team</th>\n",
       "      <th>point_spread</th>\n",
       "      <th>home_win</th>\n",
       "      <th>home_strength</th>\n",
       "      <th>away_strength</th>\n",
       "      <th>rest_spread</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <th>2015-10-29</th>\n",
       "      <td>Memphis Grizzlies</td>\n",
       "      <td>112.0</td>\n",
       "      <td>Indiana Pacers</td>\n",
       "      <td>103.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Memphis Grizzlies</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.484</td>\n",
       "      <td>0.549</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <th>2015-10-29</th>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>88.0</td>\n",
       "      <td>Los Angeles Clippers</td>\n",
       "      <td>104.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Los Angeles Clippers</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.552</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <th>2015-10-29</th>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>112.0</td>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>101.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.419</td>\n",
       "      <td>0.551</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <th>2015-10-30</th>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>94.0</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.551</td>\n",
       "      <td>0.471</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <th>2015-10-30</th>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>113.0</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            away_team  away_points             home_team  \\\n",
       "game_id date                                                               \n",
       "18      2015-10-29  Memphis Grizzlies        112.0        Indiana Pacers   \n",
       "19      2015-10-29   Dallas Mavericks         88.0  Los Angeles Clippers   \n",
       "20      2015-10-29      Atlanta Hawks        112.0       New York Knicks   \n",
       "21      2015-10-30  Charlotte Hornets         94.0         Atlanta Hawks   \n",
       "22      2015-10-30    Toronto Raptors        113.0        Boston Celtics   \n",
       "\n",
       "                    home_points  away_rest  home_rest          winning_team  \\\n",
       "game_id date                                                                  \n",
       "18      2015-10-29        103.0        0.0        0.0     Memphis Grizzlies   \n",
       "19      2015-10-29        104.0        0.0        0.0  Los Angeles Clippers   \n",
       "20      2015-10-29        101.0        1.0        0.0         Atlanta Hawks   \n",
       "21      2015-10-30         97.0        1.0        0.0         Atlanta Hawks   \n",
       "22      2015-10-30        103.0        1.0        1.0       Toronto Raptors   \n",
       "\n",
       "                    point_spread  home_win  home_strength  away_strength  \\\n",
       "game_id date                                                               \n",
       "18      2015-10-29          -9.0         0          0.484          0.549   \n",
       "19      2015-10-29          16.0         1          0.552          0.536   \n",
       "20      2015-10-29         -11.0         0          0.419          0.551   \n",
       "21      2015-10-30           3.0         1          0.551          0.471   \n",
       "22      2015-10-30         -10.0         0          0.490          0.517   \n",
       "\n",
       "                    rest_spread  \n",
       "game_id date                     \n",
       "18      2015-10-29          0.0  \n",
       "19      2015-10-29          0.0  \n",
       "20      2015-10-29         -1.0  \n",
       "21      2015-10-30         -1.0  \n",
       "22      2015-10-30          0.0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>point_spread</td>   <th>  R-squared:         </th> <td>   0.119</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.117</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   54.28</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 29 Nov 2017</td> <th>  Prob (F-statistic):</th> <td>6.48e-33</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:18:58</td>     <th>  Log-Likelihood:    </th> <td> -4783.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1213</td>      <th>  AIC:               </th> <td>   9575.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1209</td>      <th>  BIC:               </th> <td>   9596.</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>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>     <td>   -6.5106</td> <td>    6.147</td> <td>   -1.059</td> <td> 0.290</td> <td>  -18.571</td> <td>    5.550</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rest_spread</th>   <td>    0.3939</td> <td>    0.341</td> <td>    1.155</td> <td> 0.248</td> <td>   -0.275</td> <td>    1.063</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>home_strength</th> <td>   85.5022</td> <td>    8.604</td> <td>    9.938</td> <td> 0.000</td> <td>   68.622</td> <td>  102.382</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>away_strength</th> <td>  -67.1119</td> <td>    8.617</td> <td>   -7.789</td> <td> 0.000</td> <td>  -84.017</td> <td>  -50.207</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 6.326</td> <th>  Durbin-Watson:     </th> <td>   1.983</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.042</td> <th>  Jarque-Bera (JB):  </th> <td>   7.183</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.093</td> <th>  Prob(JB):          </th> <td>  0.0276</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.328</td> <th>  Cond. No.          </th> <td>    37.7</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:           point_spread   R-squared:                       0.119\n",
       "Model:                            OLS   Adj. R-squared:                  0.117\n",
       "Method:                 Least Squares   F-statistic:                     54.28\n",
       "Date:                Wed, 29 Nov 2017   Prob (F-statistic):           6.48e-33\n",
       "Time:                        11:18:58   Log-Likelihood:                -4783.6\n",
       "No. Observations:                1213   AIC:                             9575.\n",
       "Df Residuals:                    1209   BIC:                             9596.\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=================================================================================\n",
       "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------\n",
       "Intercept        -6.5106      6.147     -1.059      0.290     -18.571       5.550\n",
       "rest_spread       0.3939      0.341      1.155      0.248      -0.275       1.063\n",
       "home_strength    85.5022      8.604      9.938      0.000      68.622     102.382\n",
       "away_strength   -67.1119      8.617     -7.789      0.000     -84.017     -50.207\n",
       "==============================================================================\n",
       "Omnibus:                        6.326   Durbin-Watson:                   1.983\n",
       "Prob(Omnibus):                  0.042   Jarque-Bera (JB):                7.183\n",
       "Skew:                           0.093   Prob(JB):                       0.0276\n",
       "Kurtosis:                       3.328   Cond. No.                         37.7\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod = smf.ols('point_spread ~ rest_spread + home_strength + away_strength', data=df)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So an extra day of rest is worth between -.28 and 1.06 points. Not that impresssive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Road Trips"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do teams on longer road trips perform less well?\n",
    "Basically, what is the probability of winning as a function of number of *consecutive* games on the road?\n",
    "\n",
    "We'll need to compute an `away_streak` variable that contains the number of consecutive away games.\n",
    "This will reset to 0 each time the team returns home.\n",
    "For example:\n",
    "\n",
    "home_away  | away_streak\n",
    "---------- | -----------\n",
    "H          | 0\n",
    "A          | 1\n",
    "A          | 2\n",
    "H          | 0\n",
    "A          | 1\n",
    "A          | 2\n",
    "A          | 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This will need to be done in the context of a `groupby`.\n",
    "When developing complicated groupby functions, I like to grab and individual group and prototype on that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1231    home_team\n",
       "4       away_team\n",
       "24      away_team\n",
       "1269    home_team\n",
       "52      away_team\n",
       "Name: home_away, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gr = tidy.groupby('team').home_away\n",
    "v = gr.get_group(\"Chicago Bulls\")  # Use `.get_group` to extract a single group\n",
    "v.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is an example of a computation that's difficult (though not impossible) to vectorize.\n",
    "The relative simplicity of the iterative solution (like the skeleton below) outweighs the performance cost.\n",
    "\n",
    "Fill in the ellipses (`...`) below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E\n",
      "======================================================================\n",
      "ERROR: test_compute_streaks (__main__.TestAwayStreak)\n",
      "----------------------------------------------------------------------\n",
      "Traceback (most recent call last):\n",
      "  File \"<ipython-input-37-46ec53f2abbe>\", line 25, in test_compute_streaks\n",
      "    result = compute_away_streaks(v).head(10)  # easier to check\n",
      "  File \"<ipython-input-37-46ec53f2abbe>\", line 15, in compute_away_streaks\n",
      "    streaks.append(streak)  # add to the container\n",
      "NameError: name 'streak' is not defined\n",
      "\n",
      "----------------------------------------------------------------------\n",
      "Ran 1 test in 0.002s\n",
      "\n",
      "FAILED (errors=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<unittest.runner.TextTestResult run=1 errors=1 failures=0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import unittest\n",
    "import pandas.util.testing as tm\n",
    "\n",
    "\n",
    "def compute_away_streaks(v):\n",
    "    # streaks will contain one entry per game.\n",
    "    streaks = []\n",
    "    current_streak = 0\n",
    "\n",
    "    for row in v:\n",
    "        if row == 'away_team':\n",
    "            ...  # what happens here?\n",
    "        else:\n",
    "            # The team is playing at home. What should the\n",
    "            # away streak be here?\n",
    "            current_streak = ...\n",
    "        streaks.append(streak)  # add to the container\n",
    "\n",
    "    streaks = pd.Series(streaks, index=v.index)\n",
    "    return streaks\n",
    "\n",
    "\n",
    "class TestAwayStreak(unittest.TestCase):\n",
    "    maxDiff = None\n",
    "\n",
    "    def test_compute_streaks(self):\n",
    "        result = compute_away_streaks(v).head(10)  # easier to check\n",
    "        expected = pd.Series([0, 1, 2, 0, 1, 0, 0, 1, 0, 0],\n",
    "                             index=[1231, 4, 24, 1269, 52, 1299, 1315, 103, 1357, 1381])\n",
    "\n",
    "        tm.assert_series_equal(result, expected)\n",
    "\n",
    "suite = unittest.TestLoader().loadTestsFromModule(TestAwayStreak())\n",
    "unittest.TextTestRunner().run(suite)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load solutions/tidy_05.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's put `compute_away_streaks` to use. Before looking at the next cell, can you tell which `groupby` method we want to use? Recall that it can be\n",
    "\n",
    "- `.agg`: one output row per group\n",
    "- `.transform`: one output row per input row (same shape)\n",
    "- `.apply`: arbitrary output shape\n",
    "\n",
    "In this case it will be a `.transform`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>game_id</th>\n",
       "      <th>date</th>\n",
       "      <th>home_away</th>\n",
       "      <th>team</th>\n",
       "      <th>rest</th>\n",
       "      <th>away_streak</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-10-27</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1230</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-10-27</td>\n",
       "      <td>home_team</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-10-27</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Cleveland Cavaliers</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1231</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-10-27</td>\n",
       "      <td>home_team</td>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2015-10-27</td>\n",
       "      <td>away_team</td>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      game_id       date  home_away                  team  rest  away_streak\n",
       "0           1 2015-10-27  away_team       Detroit Pistons   NaN            1\n",
       "1230        1 2015-10-27  home_team         Atlanta Hawks   NaN            0\n",
       "1           2 2015-10-27  away_team   Cleveland Cavaliers   NaN            1\n",
       "1231        2 2015-10-27  home_team         Chicago Bulls   NaN            0\n",
       "2           3 2015-10-27  away_team  New Orleans Pelicans   NaN            1"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "streaks = tidy.groupby(\"team\").home_away.transform(compute_away_streaks).astype(int)\n",
    "tidy['away_streak'] = streaks\n",
    "tidy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need that `.astype` since currently `.transform` always returns the same dtype as the input (`object` in this case). There's an open pull request to fix that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAEICAYAAACnL3iHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAFbZJREFUeJzt3X+0XWV95/H3kUBMYlWIrsyQa0jW\nGvhiqdSyWqgUUKMuKw4tpEEtglZIGCukdNmGoRYKOA2xWcVqQlMsk0zVkLbDwsEfWH/0xtWldQpt\nKZUx8IUZQiEtkWUiwZDEXOT2j73vmsPhJpwn+959ws37tdZdyX6eve/zPTe553OeZ+9zdmd0dBRJ\nkkq8ZNAFSJJefAwPSVIxw0OSVMzwkCQVMzwkScUMD0lSMcNDklTM8JAkFTM8JEnFDA9JUrFpgy5g\nokTENGAI2JqZzwy6HkmayqZMeFAFx5bh4eFB1yFJLzad0gNctpIkFTM8JEnFDA9JUjHDQ5JUzPCQ\nJBUzPCRJxQwPSVIxw0OSVMzwkCQVMzwkScWm0seTvKBOp/gd+I2Njo62PqYkTbai8IiIU4E7MvPY\nensIuAk4ExgBbgN+OzN/FBEd4AZgST3OZ4APZ+aP62N/E1gO/ATwBeC/ZObTE/KoJEmTqq9lq4jo\nRMTFwNeAo7q6NgBbgbnA64GfA66p+y4D3gmcDLwW+AXgQ/X3+89UwfFm4DXAMcBHGz4WSVJL+j3n\n8RHgCmDFWENEHAU8Dfx+Zu7NzG3ArcDp9S4XAZ/IzMfrvpXApV196zLzwczcSRU4l0TEEY0fkSRp\n0vW7bLWeagnqjWMNmbmPambR7Rzgn+u/nwhs7upL4Cfr5awTgf/V0/cKqhnMoy9UTETMBmb3NM99\nwUchSZoQfYVHZj4OEBHj9teB8EmqULiwbp4F7O7abTfVTGf6fvoAZvZZ9zLg2j73lSRNsMaX6kbE\nDKoT5W8H3piZT9Rdu4EZXbvOBJ7JzL376QPY1eewa4Do+Vp4UA9AklSs0aW6EXEM8BWqJ/03ZOaO\nru77qZ7U7xrbvW7r7qOrbyfwb/2Mm5nbge09tewrrV+SdHAOOjzqparPAduAX8nMkZ5dNgDLI2IT\n1WW8vwN8tqvv5oi4HXiM6kqrWzPz2YOtR5LUniYzjzdQnUDfC/yg63zIPZl5FrAWmAPcTXWeYwPw\ncYDM/GJELADuBF5Z/7m8QS2SpBZ1pso7oCNiPrBleHiYoaGhcffxHeaSNK7iJ0c/20qSVMzwkCQV\nMzwkScUMD0lSMcNDklTM8JAkFTM8JEnFDA9JUjHDQ5JUzPCQJBUzPCRJxQwPSVIxw0OSVMzwkCQV\nMzwkScUMD0lSMcNDklTM8JAkFTM8JEnFDA9JUjHDQ5JUzPCQJBUzPCRJxQwPSVKxaSU7R8SpwB2Z\neWy9fTSwHlgI7ASuz8x1dd90YC1wHjACrM7MFXVfB7gBWFLX8Bngw5n544l4UJKkydXXzCMiOhFx\nMfA14KiurluAXcAcYDGwKiJOrvtWAMcBC4AzgCURcU7ddxnwTuBk4LXALwAfavZQJElt6Xfm8RHg\nXVSB8F8BIuJlwLnACZm5F7g7IjYCS4FlwIXABZm5E9gZETcBlwJfBC4CPpGZj9ffayVwHbCmn2Ii\nYjYwu6d5bp+PRZLUUL/nPNYDrwf+vqvteGAkMx/uakvgpHo5aw6wubev/vuJ4/T9ZL2c1Y9l9THd\nX5v6PFaS1FBfM4+uGUJ38yxgT8+uu4GZdd/Ydm/f2LG9fS8BpgN7+yhpDbCxp20uBogktaLohHmP\n3cCMnraZVOdAxoJhBvBUT994x84EnqmXv15QZm4Htne3RcS+viuXJDXS5FLdh4AjI2JeV1sAmzNz\nB/BEvf2cvvrv94/Td3+DWiRJLTromUdm/jAiPg+sjIilVOczLgDOrnfZAFwXEYupTm5fDlzZ1bc8\nIjZRXcb7O8BnD7YWSVK7mixbQXVl1c3AVqolqeWZeVfddzXwR8ADwCjwycy8re5bS3VC/W6q8xwb\ngI83rEWS1JLO6OjooGuYEBExH9gyPDzM0NDQuPt0Ov1ezDVxpsrPV9KUVvzk6MeTSJKKGR6SpGKG\nhySpmOEhSSpmeEiSihkekqRihockqZjhIUkqZnhIkooZHpKkYoaHJKmY4SFJKmZ4SJKKGR6SpGKG\nhySpmOEhSSpmeEiSihkekqRihockqZjhIUkqZnhIkooZHpKkYoaHJKnYtKbfICJOB1YDJwCPA9dn\n5saIOBpYDywEdtbt6+pjpgNrgfOAEWB1Zq5oWoskqR2NZh4RcQRwB/CxzHw5sAT4dETMB24BdgFz\ngMXAqog4uT50BXAcsAA4A1gSEec0qUWS1J6my1avBF4NTIuIDvAssA/4MXAucG1m7s3Mu4GNwNL6\nuAuBGzJzZ2Y+BNwEXNqwFklSSxotW2Xm9ohYC/w5sIEqjC4BXgWMZObD3bsDi+rlrDnA5p6+y/od\nNyJmA7N7mueWPwJJ0sFoumz1EmA3cD4wEzgH+ATwcmBPz+67631mdW339vVrGVXgdH9tKixfknSQ\nmp4wXwSclpnL6+07I+JO4DpgRs++M6nOgYyFxgzgqZ6+fq2hWgbrNhcDRJJa0TQ85gHTe9pGgHuA\nMyNiXmY+WrcHsDkzd0TEE/X297r7+h00M7cD27vbImLfQdQvSToITcPj68DKiPgA8GfAWVSX3y4E\n5td9S4GTgAuAs+vjNgDXRcRiqnMXlwNXNqxFktSSRuc8MvM+qstwr6B6L8cfA+/PzH+gurLqSGAr\ncDuwPDPvqg+9GngQeAD4FnBLZt7WpBZJUns6o6Ojg65hQtTvLdkyPDzM0NDQuPt0Op1WawKYKj9f\nSVNa8ZOjH08iSSpmeEiSihkekqRihockqZjhIUkqZnhIkooZHpKkYoaHJKmY4SFJKmZ4SJKKGR6S\npGKGhySpmOEhSSpmeEiSihkekqRihockqZjhIUkqZnhIkooZHpKkYoaHJKmY4SFJKmZ4SJKKGR6S\npGKGhySp2LSm3yAihoCbgbOAp4BVmbk6Io4G1gMLgZ3A9Zm5rj5mOrAWOA8YAVZn5oqmtUiS2tFo\n5hERHeAO4H5gNvB24LqIOB24BdgFzAEWA6si4uT60BXAccAC4AxgSUSc06QWSVJ7mi5bnQYcC1yV\nmSOZ+V3gDcC/AucC12bm3sy8G9gILK2PuxC4ITN3ZuZDwE3ApQ1rkSS1pOmy1SnAd6lmFe+lWrZa\nAXwHGMnMh7v2TWBRvZw1B9jc03dZv4NGxGyqmU63ueXlt6vT6bQ+5ujoaOtjSpr6mobHMcCbgU3A\nPOBnga8A7wT29Oy7G5gJzOra7u3r1zLg2oOoV5I0AZqGx4+AHZm5st7+dkTcDlwPzOjZdybVOZCx\n0JhBNVPp7uvXGqplsG5zqUJMkjTJmoZHArMiYlpmPlO3HQH8E3BWRMzLzEfr9gA2Z+aOiHii3v5e\nd1/fg2ZuB7Z3t0XEvgaPQ5JUoGl4fB34AfCxiLgKOJXq8tu3AfOBlRGxFDgJuAA4uz5uA9VVWYup\nzl1cDlzZsBZJUksaXW2VmXuANwE/BTxBtZT0G5n5d1RXVh0JbAVuB5Zn5l31oVcDDwIPAN8CbsnM\n25rUIklqT2eqXI0TEfOBLcPDwwwNDY27z6Cvdhr0+JK0H8VPTn48iSSpmOEhSSpmeEiSihkekqRi\nhockqZjhIUkqZnhIkooZHpKkYoaHJKmY4SFJKmZ4SJKKGR6SpGKGhySpmOEhSSpmeEiSihkekqRi\nhockqZjhIUkqZnhIkooZHpKkYoaHJKmY4SFJKmZ4SJKKGR6SpGLTJuobRcQc4D7g4sz8UkTMB9YB\npwKPAx/OzC/V+x4NrAcWAjuB6zNz3UTVIkmaXBM581gHzO7avg24GzgGuALYGBGvrvtuAXYBc4DF\nwKqIOHkCa5EkTaIJmXlExAeBp4HH6u3XAq8DzsrMEeCvIuJvgPdFxKeAc4ETMnMvcHdEbASWAsv6\nHG82zw0qgLkT8VgkSS+scXhExPHAbwGnAffUzScCj2Tmnq5dEzgJOB4YycyHe/oWFQy7DLj2oIuW\nJDXSaNkqIqYBG4ArMnNHV9csYHfP7ruBmXXfnv309WsNED1fCwuOlyQ10HTmcQ1wb2Z+uad9NzCj\np20m1XmOA/X1JTO3A9u72yJiX7/HS5KaaXrC/N3AeyLiyYh4EpgH/AXVTGB+REzv2jeAzcBDwJER\nMW+cPknSi0CjmUdmnti9HRGPAJfXl+ouBv5bRFxDtaT0JuBDmfnDiPg8sDIillKdB7kAOLtJLZKk\n9kzY+zzGsQj4U+AJYBvwq5n5WN23FLgZ2Eq1XLU8M++axFokSROoMzo6OugaJkT9psQtw8PDDA0N\njbtPp9NptSaA7p/voMeXpP0ofnLy40kkScUMD0lSMcNDklTM8JAkFTM8JEnFDA9JUjHDQ5JUzPCQ\nJBWbzHeY6xDjmxQlTRRnHpKkYoaHJKmY4SFJKmZ4SJKKGR6SpGKGhySpmOEhSSpmeEiSihkekqRi\nhockqZjhIUkqZnhIkooZHpKkYoaHJKlY449kj4gzgBuBE4HvA6sy81MRcTSwHlgI7ASuz8x19THT\ngbXAecAIsDozVzStRZLUjkYzjzogvgCsBo4GzgdWRsRbgVuAXcAcYDGwKiJOrg9dARwHLADOAJZE\nxDlNapEktafpzOM44M7MvLXevicivgGcDpwLnJCZe4G7I2IjsBRYBlwIXJCZO4GdEXETcCnwxYb1\nSJJa0Cg8MvNe4KKx7XomcibwHWAkMx/u3h1YVO8zB9jc03dZv+NGxGxgdk/z3LLqJUkHa8JOmEfE\nK6hmDv8IfAPY07PLbmAmMKtru7evX8uoAqf7a1N51ZKkgzEh4RERC4BvAzuARVTnOmb07Dazbh8L\njRnj9PVrDRA9XwuLC5ckHZSJuNrqFOArwAbgtzPz2Yh4CDgyIuZl5qNjuwKbM3NHRDxRb3+vu6/f\nMTNzO7C9p459DR+KJKlPjcIjIuZQBceNmfkHY+2Z+cOI+DzVlVdLgZOAC4Cz6102ANdFxGKqcxeX\nA1c2qUWS1J6mM49LgFcD10TENV3tn6S6supmYCvVktTyzLyr7r8a+CPgAWAU+GRm3tawFklSSzqj\no6ODrmFCRMR8YMvw8DBDQ0Pj7tPpdFqtCaD753u4jy/pkFX85ODHk0iSijU+YS6VcPYjTQ3OPCRJ\nxQwPSVIxw0OSVMzwkCQVMzwkScUMD0lSMcNDklTM8JAkFTM8JEnFDA9JUjHDQ5JUzM+20mHFz9aS\nJoYzD0lSMcNDklTM8JAkFTM8JEnFDA9JUjHDQ5JUzPCQJBXzfR5Si3yfiaYKZx6SpGKGhySp2MCW\nrSLiZ4BPAScBDwEfzMy/G1Q9kqT+DWTmEREvBb4I/A/glcBq4HMRcdQg6pEOF51Op/UvTU2DWrZ6\nM/BsZv5JZo5k5npgO3DOgOqRJBUY1LLVicDmnrakWsK6/YUOjojZwOye5tcAbNu2bb/HTZvW/sPd\nunWr43cZdA2OP9jxFyxY0Pr4W7Zsec72oVDDoeYtb3nLfGBrZj7T7zGdQVzGFxFXA6dk5qKuts8A\n/5aZV/Vx/HXAtZNXoSQddhZk5iP97jyomcduYEZP20xgV5/HrwE29rQdBcyjmsH8uFF1z7cA2AQs\nBAbxEsLxD+/xD4UaHH/qj7/1hXf5/wYVHvcDl/e0Bc8PhHFl5naqcyS9/k/DusbVdSL/X0uS2fEd\nf6rU4PiH9/jjGVR4bAKmR8Qy4GbgImAO8NUB1SNJKjCQq60y80fAO4BfBXYAy4BfysynB1GPJKnM\nwN4kmJnfAU4f1PiSpIPnx5P0ZztwPeOfZ3F8xz8canD8w3v85xnIpbqSpBc3Zx6SpGKGhySpmOEh\nSSpmeEiSihkekqRihockqZjhIUkqZnhIkooN7ONJXiwOlXutR8SpwB2ZeWzL454B3Eh1A6/vA6sy\n81Mtjv8uqnfWvgb4F+B3M/OOtsbvqmMOcB9wcWZ+qcVxlwMrgH1dze/IzG+2WMMQ1QeYngU8RfV/\nYHVLY7+X6vev20zgv2fmpS3VcDrVrbJPAB4Hrs/Mvj4BfILGfwvwh8B/ovrk8N/MzLvaGn9/nHkc\nwKFwr/WI6ETExcDXqO5Z0pqIOBr4AtXjPho4H1gZEW9tafwTqH72l2Tmy4ArgL+MiFe1MX6PdTz/\n7pVteD3wkcx8WddXm8HRAe6guo3CbODtwHX1E+qky8xbux878MvANuCjbYwfEUdQPf6PZebLgSXA\npyNifkvjz6f6HVxL9Tv4+8CXI+I/tDH+gRgeB3Yo3Gv9I1RPmitaHHPMccCd9S/ws5l5D/ANWvpA\ny8x8EJiTmd+OiJcB/xH4Ic99FT7pIuKDwNPAY22OW/sZ4N4BjDvmNOBY4Kr6d+C7wBuobrrWqvr/\nwKeBD2Vm0Y2LGngl8GpgWh2kz1L9/5voG87tzzuA+zLzlsx8JjPvBO6ieiE3UIbHgR3oXuttWU/1\n6vPvWxwTgMy8NzMvGtuuZyJnAv/cYg27ImIBsJPqieN3M/OptsaPiOOB3wJ+va0xu8aeSbVUckVE\nbIuI++tZaJtOAb4LrKpreBD4+fqGbG27kuqJtLVly/pxrgX+HBgBvglcnpltvZA4gurOq92eBY5v\nafz9MjwObBbP/4fbTbXm2orMfDwzB/7plRHxCqolvH+s/2zTY8BLgbcCN0bEwjYGjYhpwAbgiszc\n0caYPeYAfwv8CdUtli8FPh4R72ixhmOoZuDfr2v4NWBNRJzZYg1js45lVOe/2hz3JVS/8+dT/d6f\nA3wiIn66pRK+CpwWEedHxJER8YtUt6J9aUvj75cnzA+s6b3Wp4T6lf+XgP8HvDszn21z/Mx8pv7r\npoi4HTiX6m6Uk+0a4N7M/HILYz1PZm4B3tjV9M2I+CzV4/+rlsr4EbAjM1fW29+u/w1+mepVeFvO\nBf5lABerLAJOy8zl9fadEXEn8D6qGemkysyH6otGbqB6EfFl4Fbgycke+4U48ziw+6nurd4teP5S\n1pQVEadQrbF+FTg3M/e0OPbZEfHXPc1H0d4vzruB90TEkxHxJNUr77+IiKvaGDwiThlnrJcCe9sY\nv5bArHoWNuYIoNNiDVC94v+fLY8J1b/59J62kfpr0kXETwCPZuZPZ+arMvN9wOuAf2pj/ANx5nFg\nh/W91uvLU78C3JiZfzCAEu4BfjYiLqJ6tfWLwNlUJ3EnXWae2L0dEY9QrXe3danuLuDaiPi/wOeo\nlo/ew3NnI5Pt68APgI/VQXYqcB7wthZrAPh5qt/Btn2d6grDDwB/RnW58nlUS0dtmA3874g4i+oy\n3SVUgfaFlsbfL2ceB+C91rmE6kqTayJiV9dXK1d+ZeY2qlecV1DNNj5KNft5oI3xB62+2uxdwO9R\nXWW2FvhAfdVbWzXsAd4E/BTwBLAR+I02l4/qy2WHqN5j0arMvA9YTPV/cCfwx8D7M/MfWhr/EeCD\nwO1U553eBbztUHgO8k6CkqRizjwkScUMD0lSMcNDklTM8JAkFTM8JEnFDA9JUjHDQ5JUzPCQJBX7\nd0WTK8d2vPcPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c195d0a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "streaks.value_counts().plot.bar(color='k', width=.9, rot=0)\n",
    "sns.despine();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And let's go from `tidy` to our game-level data with another `pivot_table`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "game_away_streak = (\n",
    "    pd.pivot_table(\n",
    "        tidy, values='away_streak', index=['game_id', 'date'],\n",
    "        columns='home_away'\n",
    "    ).rename(columns={'away_team': 'away_streak'}))['away_streak']\n",
    "df['game_away_streak'] = game_away_streak"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A quick sanity check, and it doesn't seem like there's much to the hypothesis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a17887710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot('game_away_streak', 'home_win', data=df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If teams actually performed worse the longer they were on the road, we'd expect gradually declining bars.\n",
    "Still, we can run the regression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>point_spread</td>   <th>  R-squared:         </th> <td>   0.120</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.117</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   40.99</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 29 Nov 2017</td> <th>  Prob (F-statistic):</th> <td>3.01e-32</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:19:12</td>     <th>  Log-Likelihood:    </th> <td> -4783.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1213</td>      <th>  AIC:               </th> <td>   9576.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1208</td>      <th>  BIC:               </th> <td>   9602.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</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>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>        <td>   -7.0447</td> <td>    6.168</td> <td>   -1.142</td> <td> 0.254</td> <td>  -19.146</td> <td>    5.056</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rest_spread</th>      <td>    0.3825</td> <td>    0.341</td> <td>    1.121</td> <td> 0.262</td> <td>   -0.287</td> <td>    1.052</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>game_away_streak</th> <td>    0.2947</td> <td>    0.280</td> <td>    1.054</td> <td> 0.292</td> <td>   -0.254</td> <td>    0.843</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>home_strength</th>    <td>   85.5030</td> <td>    8.604</td> <td>    9.938</td> <td> 0.000</td> <td>   68.623</td> <td>  102.382</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>away_strength</th>    <td>  -67.2074</td> <td>    8.617</td> <td>   -7.800</td> <td> 0.000</td> <td>  -84.113</td> <td>  -50.302</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 6.437</td> <th>  Durbin-Watson:     </th> <td>   1.982</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.040</td> <th>  Jarque-Bera (JB):  </th> <td>   7.219</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.100</td> <th>  Prob(JB):          </th> <td>  0.0271</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.320</td> <th>  Cond. No.          </th> <td>    76.9</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:           point_spread   R-squared:                       0.120\n",
       "Model:                            OLS   Adj. R-squared:                  0.117\n",
       "Method:                 Least Squares   F-statistic:                     40.99\n",
       "Date:                Wed, 29 Nov 2017   Prob (F-statistic):           3.01e-32\n",
       "Time:                        11:19:12   Log-Likelihood:                -4783.1\n",
       "No. Observations:                1213   AIC:                             9576.\n",
       "Df Residuals:                    1208   BIC:                             9602.\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "====================================================================================\n",
       "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------\n",
       "Intercept           -7.0447      6.168     -1.142      0.254     -19.146       5.056\n",
       "rest_spread          0.3825      0.341      1.121      0.262      -0.287       1.052\n",
       "game_away_streak     0.2947      0.280      1.054      0.292      -0.254       0.843\n",
       "home_strength       85.5030      8.604      9.938      0.000      68.623     102.382\n",
       "away_strength      -67.2074      8.617     -7.800      0.000     -84.113     -50.302\n",
       "==============================================================================\n",
       "Omnibus:                        6.437   Durbin-Watson:                   1.982\n",
       "Prob(Omnibus):                  0.040   Jarque-Bera (JB):                7.219\n",
       "Skew:                           0.100   Prob(JB):                       0.0271\n",
       "Kurtosis:                       3.320   Cond. No.                         76.9\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = smf.ols(\"point_spread ~ rest_spread + game_away_streak + home_strength + away_strength\", df)\n",
    "res = m.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, there doesn't seem to be much to the \"away streak\" hypothesis. The upper bound of the 95% CI is less than 1 point for each additional consecutive game on the road."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Recap\n",
    "\n",
    "- Tidy data:\n",
    "    - one variable per column\n",
    "    - one row per observation\n",
    "- Methods:\n",
    "    - melt / stack: wide to long\n",
    "    - pivot_table / unstack: long to wide"
   ]
  }
 ],
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