modern_5_tidy.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "# Reshaping & Tidy Data\n",
    "\n",
    "> Structuring datasets to facilitate analysis [(Wickham 2014)](http://www.jstatsoft.org/v59/i10/paper)\n",
    "\n",
    "So, you've sat down to analyze a new dataset.\n",
    "What do you do first?\n",
    "\n",
    "In episode 11 of [Not So Standard Deviations](https://www.patreon.com/NSSDeviations?ty=h), Hilary and Roger discussed their typical approaches.\n",
    "I'm with Hilary on this one, you should make sure your data is tidy.\n",
    "Before you do any plots, filtering, transformations, summary statistics, regressions...\n",
    "Without a tidy dataset, you'll be fighting your tools to get the result you need.\n",
    "With a tidy dataset, it's relatively easy to do all of those.\n",
    "\n",
    "Hadley Wickham kindly summarized tidiness as a dataset where\n",
    "\n",
    "1. Each variable forms a column\n",
    "2. Each observation forms a row\n",
    "3. Each type of observational unit forms a table\n",
    "\n",
    "And today we'll only concern ourselves with the first two.\n",
    "As quoted at the top, this really is about facilitating analysis: going as quickly as possible from question to answer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "if int(os.environ.get(\"MODERN_PANDAS_EPUB\", 0)):\n",
    "    import prep # noqa\n",
    "\n",
    "pd.options.display.max_rows = 10\n",
    "sns.set(style='ticks', context='talk')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NBA Data\n",
    "\n",
    "[This](http://stackoverflow.com/questions/22695680/python-pandas-timedelta-specific-rows) StackOverflow question asked about calculating the number of days of rest NBA teams have between games.\n",
    "The answer would have been difficult to compute with the raw data.\n",
    "After transforming the dataset to be tidy, we're able to quickly get the answer.\n",
    "\n",
    "We'll grab some NBA game data from basketball-reference.com using pandas' `read_html` function, which returns a list of DataFrames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Start (ET)</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Visitor/Neutral</th>\n",
       "      <th>PTS</th>\n",
       "      <th>Home/Neutral</th>\n",
       "      <th>PTS.1</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>October</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>Tue, Oct 27, 2015</td>\n",
       "      <td>8:00 pm</td>\n",
       "      <td>Box Score</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tue, Oct 27, 2015</td>\n",
       "      <td>8:00 pm</td>\n",
       "      <td>Box Score</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tue, Oct 27, 2015</td>\n",
       "      <td>10:30 pm</td>\n",
       "      <td>Box Score</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wed, Oct 28, 2015</td>\n",
       "      <td>7:30 pm</td>\n",
       "      <td>Box Score</td>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Date Start (ET) Unnamed: 2       Visitor/Neutral    PTS  \\\n",
       "0            October        NaN        NaN                   NaN    NaN   \n",
       "1  Tue, Oct 27, 2015    8:00 pm  Box Score       Detroit Pistons  106.0   \n",
       "2  Tue, Oct 27, 2015    8:00 pm  Box Score   Cleveland Cavaliers   95.0   \n",
       "3  Tue, Oct 27, 2015   10:30 pm  Box Score  New Orleans Pelicans   95.0   \n",
       "4  Wed, Oct 28, 2015    7:30 pm  Box Score    Philadelphia 76ers   95.0   \n",
       "\n",
       "            Home/Neutral  PTS.1 Unnamed: 7 Notes  \n",
       "0                    NaN    NaN        NaN   NaN  \n",
       "1          Atlanta Hawks   94.0        NaN   NaN  \n",
       "2          Chicago Bulls   97.0        NaN   NaN  \n",
       "3  Golden State Warriors  111.0        NaN   NaN  \n",
       "4         Boston Celtics  112.0        NaN   NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp = 'data/nba.csv'\n",
    "\n",
    "if not os.path.exists(fp):\n",
    "    tables = pd.read_html(\"http://www.basketball-reference.com/leagues/NBA_2016_games.html\")\n",
    "    games = tables[0]\n",
    "    games.to_csv(fp)\n",
    "else:\n",
    "    games = pd.read_csv(fp)\n",
    "games.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Side note: pandas' `read_html` is pretty good. On simple websites it almost always works.\n",
    "It provides a couple parameters for controlling what gets selected from the webpage if the defaults fail.\n",
    "I'll always use it first, before moving on to [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/) or [lxml](http://lxml.de/) if the page is more complicated.\n",
    "\n",
    "As you can see, we have a bit of general munging to do before tidying.\n",
    "Each month slips in an extra row of mostly NaNs, the column names aren't too useful, and we have some dtypes to fix up."
   ]
  },
  {
   "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></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": 6,
     "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",
    "games = (games.rename(columns=column_names)\n",
    "    .dropna(thresh=4)\n",
    "    [['date', 'away_team', 'away_points', 'home_team', 'home_points']]\n",
    "    .assign(date=lambda x: pd.to_datetime(x['date'], format='%a, %b %d, %Y'))\n",
    "    .set_index('date', append=True)\n",
    "    .rename_axis([\"game_id\", \"date\"])\n",
    "    .sort_index())\n",
    "games.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A quick aside on that last block.\n",
    "\n",
    "- `dropna` has a `thresh` argument. If at least `thresh` items are missing, the row is dropped. We used it to remove the \"Month headers\" that slipped into the table.\n",
    "- `assign` can take a callable. This lets us refer to the DataFrame in the previous step of the chain. Otherwise we would have to assign `temp_df = games.dropna()...` And then do the `pd.to_datetime` on that.\n",
    "- `set_index` has an `append` keyword. We keep the original index around since it will be our unique identifier per game.\n",
    "- We use `.rename_axis` to set the index names (this behavior is new in pandas 0.18; before `.rename_axis` only took a mapping for changing labels)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Question:\n",
    "> **How many days of rest did each team get between each game?**\n",
    "\n",
    "Whether or not your dataset is tidy depends on your question. Given our question, what is an observation?\n",
    "\n",
    "In this case, an observation is a `(team, game)` pair, which we don't have yet. Rather, we have two observations per row, one for home and one for away. We'll fix that with `pd.melt`.\n",
    "\n",
    "`pd.melt` works by taking observations that are spread across columns (`away_team`, `home_team`), and melting them down into one column with multiple rows. However, we don't want to lose the metadata (like `game_id` and `date`) that is shared between the observations. By including those columns as `id_vars`, the values will be repeated as many times as needed to stay with their observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>variable</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>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>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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2015-10-28</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Philadelphia 76ers</td>\n",
       "    </tr>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   game_id       date   variable                  team\n",
       "0        1 2015-10-27  away_team       Detroit Pistons\n",
       "1        2 2015-10-27  away_team   Cleveland Cavaliers\n",
       "2        3 2015-10-27  away_team  New Orleans Pelicans\n",
       "3        4 2015-10-28  away_team    Philadelphia 76ers\n",
       "4        5 2015-10-28  away_team         Chicago Bulls"
      ]
     },
     "execution_count": 7,
     "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')\n",
    "tidy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The DataFrame `tidy` meets our rules for tidiness: each variable is in a column, and each observation (`team`, `date` pair) is on its own row.\n",
    "Now the translation from question (\"How many days of rest between games\") to operation (\"date of today's game - date of previous game - 1\") is direct:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       NaN\n",
       "1       NaN\n",
       "2       NaN\n",
       "3       NaN\n",
       "4       NaN\n",
       "       ... \n",
       "2455    7.0\n",
       "2456    1.0\n",
       "2457    1.0\n",
       "2458    3.0\n",
       "2459    2.0\n",
       "Name: date, Length: 2460, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For each team... get number of days between games\n",
    "tidy.groupby('team')['date'].diff().dt.days - 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's the essence of tidy data, the reason why it's worth considering what shape your data should be in.\n",
    "It's about setting yourself up for success so that the answers naturally flow from the data (just kidding, it's usually still difficult. But hopefully less so).\n",
    "\n",
    "Let's assign that back into our DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\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>game_id</th>\n",
       "      <th>date</th>\n",
       "      <th>variable</th>\n",
       "      <th>team</th>\n",
       "      <th>rest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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>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",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>2015-10-29</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    game_id       date   variable                  team  rest\n",
       "4         5 2015-10-28  away_team         Chicago Bulls   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\n",
       "18       19 2015-10-29  away_team      Dallas Mavericks   0.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tidy['rest'] = tidy.sort_values('date').groupby('team').date.diff().dt.days - 1\n",
    "tidy.dropna().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To show the inverse of `melt`, let's take `rest` values we just calculated and place them back in the original DataFrame with a `pivot_table`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_game = (pd.pivot_table(tidy, values='rest',\n",
    "                          index=['game_id', 'date'],\n",
    "                          columns='variable')\n",
    "             .rename(columns={'away_team': 'away_rest',\n",
    "                              'home_team': 'home_rest'}))\n",
    "df = pd.concat([games, by_game], axis=1)\n",
    "df.dropna().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "One somewhat subtle point: an \"observation\" depends on the question being asked.\n",
    "So really, we have two tidy datasets, `tidy` for answering team-level questions, and `df` for answering game-level questions.\n",
    "\n",
    "One potentially interesting question is \"what was each team's average days of rest, at home and on the road?\" With a tidy dataset (the DataFrame `tidy`, since it's team-level), `seaborn` makes this easy (more on seaborn in a future post):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.set(style='ticks', context='paper')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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f+qjjyZClOi+TiIiIqLZysDHC4OcN3YrKVSnJGjZsGEQEBw8eRO/evdG2bQ1P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\nmSNHjpi77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SVjjNq2bWvH1AAAeB2+sA4A1UuAHYNGRkZqwoQJGjx4sMv6uLg4tW7dWvn5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\nWIgmCwAAAAAs9P8Bbk7B5rnJisQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ae1c080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(tidy, col='team', col_wrap=6, hue='team', size=2)\n",
    "g.map(sns.barplot, 'variable', 'rest');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An example of a game-level statistic is the distribution of rest differences in games:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>home_win</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",
       "    </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>False</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>True</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>False</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>True</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>False</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  home_win  rest_spread  \n",
       "game_id date                                                                  \n",
       "18      2015-10-29        103.0        0.0        0.0     False          0.0  \n",
       "19      2015-10-29        104.0        0.0        0.0      True          0.0  \n",
       "20      2015-10-29        101.0        1.0        0.0     False         -1.0  \n",
       "21      2015-10-30         97.0        1.0        0.0      True         -1.0  \n",
       "22      2015-10-30        103.0        1.0        1.0     False          0.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['home_win'] = df['home_points'] > df['away_points']\n",
    "df['rest_spread'] = df['home_rest'] - df['away_rest']\n",
    "df.dropna().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WTJ8+PcuWLUtzc3O+9rWvdV4Jc+3atWlvb3/ZS5Z/4QtfyK9+9av07t07M2fO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\nAQCgoP8H5VBsGy6/2T8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e17ed68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "delta = (by_game.home_rest - by_game.away_rest).dropna().astype(int)\n",
    "ax = (delta.value_counts()\n",
    "    .reindex(np.arange(delta.min(), delta.max() + 1), fill_value=0)\n",
    "    .sort_index()\n",
    "    .plot(kind='bar', color='k', width=.9, rot=0, figsize=(12, 6))\n",
    ")\n",
    "sns.despine()\n",
    "ax.set(xlabel='Difference in Rest (Home - Away)', ylabel='Games');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or the win percent by rest difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ssVu2T5jw2vlDf+aU68knn8zee++dv/mbv8mf//mfZ3BwMNOnT9+pPvse/lKS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\ny9dxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e1294a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "sns.barplot(x='rest_spread', y='home_win', data=df.query('-3 <= rest_spread <= 3'),\n",
    "            color='#4c72b0', ax=ax)\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stack / Unstack\n",
    "\n",
    "Pandas has two useful methods for quickly converting from wide to long format (`stack`) and long to wide (`unstack`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date        variable \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",
       "Name: rest, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest = (tidy.groupby(['date', 'variable'])\n",
    "            .rest.mean()\n",
    "            .dropna())\n",
    "rest.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`rest` is in a \"long\" form since we have a single column of data, with multiple \"columns\" of metadata (in the MultiIndex). We use `.unstack` to move from long to wide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>variable</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>2015-10-28</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",
       "    <tr>\n",
       "      <th>2015-11-01</th>\n",
       "      <td>1.142857</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "variable    away_team  home_team\n",
       "date                            \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\n",
       "2015-11-01   1.142857   1.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest.unstack().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`unstack` moves a level of a MultiIndex (innermost by default) up to the columns.\n",
    "`stack` is the inverse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date        variable \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",
       "                           ...   \n",
       "2016-04-11  home_team    0.666667\n",
       "2016-04-12  away_team    1.000000\n",
       "            home_team    1.400000\n",
       "2016-04-13  away_team    0.500000\n",
       "            home_team    1.214286\n",
       "Length: 320, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest.unstack().stack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With `.unstack` you can move between those APIs that expect there data in long-format and those APIs that work with wide-format data. For example, `DataFrame.plot()`, works with wide-form data, one line per column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vumBNBi+3WV2xHHmcE00oGXqbw4bRiQYhMplM8rrTga9doAx9/DHKyW1y0rJD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8nDUiLNdtexdqQxMvQQzmR5mUoUEMdlqjndxKSg7+zsxE9+8hM89thjlCvO22+/jZ/+9Kd48skn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\nRERERERh7P8DyhNgATJx5bAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e759ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.color_palette() as pal:\n",
    "    b, g = pal.as_hex()[:2]\n",
    "\n",
    "ax=(rest.unstack()\n",
    "        .query('away_team < 7')\n",
    "        .rolling(7)\n",
    "        .mean()\n",
    "        .plot(figsize=(12, 6), linewidth=3, legend=False))\n",
    "ax.set(ylabel='Rest (7 day MA)')\n",
    "ax.annotate(\"Home\", (rest.index[-1][0], 1.02), color=g, size=14)\n",
    "ax.annotate(\"Away\", (rest.index[-1][0], 0.82), color=b, size=14)\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most conenient form will depend on exactly what you're doing.\n",
    "When interacting with databases you'll often deal with long form data.\n",
    "Pandas' `DataFrame.plot` often expects wide-form data, while `seaborn` often expect long-form data. Regressions will expect wide-form data. Either way, it's good to be comfortable with `stack` and `unstack` (and MultiIndexes) to quickly move between the two."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mini Project: Home Court Advantage?\n",
    "\n",
    "We've gone to all that work tidying our dataset, let's put it to use.\n",
    "What's the effect (in terms of probability to win) of being\n",
    "the home team?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Create an outcome variable\n",
    "\n",
    "We need to create an indicator for whether the home team won.\n",
    "Add it as a column called `home_win` in `games`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['home_win'] = df.home_points > df.away_points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Find the win percent for each team\n",
    "\n",
    "In the 10-minute literature review I did on the topic, it seems like people include a team-strength variable in their regressions.\n",
    "I suppose that makes sense; if stronger teams happened to play against weaker teams at home more often than away, it'd look like the home-effect is stronger than it actually is.\n",
    "We'll do a terrible job of controlling for team strength by calculating each team's win percent and using that as a predictor.\n",
    "It'd be better to use some kind of independent measure of team strength, but this will do for now.\n",
    "\n",
    "We'll use a similar `melt` operation as earlier, only now with the `home_win` variable we just created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>n_wins</th>\n",
       "      <th>n_games</th>\n",
       "      <th>win_pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>team</th>\n",
       "      <th>is_home</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Atlanta Hawks</th>\n",
       "      <th>away_team</th>\n",
       "      <td>21.0</td>\n",
       "      <td>41</td>\n",
       "      <td>0.512195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home_team</th>\n",
       "      <td>27.0</td>\n",
       "      <td>41</td>\n",
       "      <td>0.658537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Boston Celtics</th>\n",
       "      <th>away_team</th>\n",
       "      <td>20.0</td>\n",
       "      <td>41</td>\n",
       "      <td>0.487805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home_team</th>\n",
       "      <td>28.0</td>\n",
       "      <td>41</td>\n",
       "      <td>0.682927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brooklyn Nets</th>\n",
       "      <th>away_team</th>\n",
       "      <td>7.0</td>\n",
       "      <td>41</td>\n",
       "      <td>0.170732</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          n_wins  n_games   win_pct\n",
       "team           is_home                             \n",
       "Atlanta Hawks  away_team    21.0       41  0.512195\n",
       "               home_team    27.0       41  0.658537\n",
       "Boston Celtics away_team    20.0       41  0.487805\n",
       "               home_team    28.0       41  0.682927\n",
       "Brooklyn Nets  away_team     7.0       41  0.170732"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wins = (\n",
    "    pd.melt(df.reset_index(),\n",
    "            id_vars=['game_id', 'date', 'home_win'],\n",
    "            value_name='team', var_name='is_home',\n",
    "            value_vars=['home_team', 'away_team'])\n",
    "   .assign(win=lambda x: x.home_win == (x.is_home == 'home_team'))\n",
    "   .groupby(['team', 'is_home'])\n",
    "   .win\n",
    "   .agg(['sum', 'count', 'mean'])\n",
    "   .rename(columns=dict(sum='n_wins',\n",
    "                        count='n_games',\n",
    "                        mean='win_pct'))\n",
    ")\n",
    "wins.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pause for visualiztion, because why not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gP18ikeDw4cOp4BHE3CQNppwq2RAwUZsQx44dI1/SVq1aiT73u3XrFtUTb968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\nW3HGFX5hAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e076e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(wins.reset_index(), hue='team', size=7, aspect=.5, palette=['k'])\n",
    "g.map(sns.pointplot, 'is_home', 'win_pct').set(ylim=(0, 1));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(It'd be great if there was a library built on top of matplotlib that auto-labeled each point decently well. Apparently this is a difficult problem to do in general)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11df22978>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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A+q9Tt0IJ+/b94PT0m7B0cDNtgNVUTcqRByGEQPaNw0g8ug6ZV/YYjLN2bo76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cAAAg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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11df22c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(wins.reset_index(), col='team', hue='team', col_wrap=5, size=2)\n",
    "g.map(sns.pointplot, 'is_home', 'win_pct')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Those two graphs show that most teams have a higher win-percent at home than away. So we can continue to investigate.\n",
    "Let's aggregate over home / away to get an overall win percent per team."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "team\n",
       "Atlanta Hawks        0.585366\n",
       "Boston Celtics       0.585366\n",
       "Brooklyn Nets        0.256098\n",
       "Charlotte Hornets    0.585366\n",
       "Chicago Bulls        0.512195\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "win_percent = (\n",
    "    # Use sum(games) / sum(games) instead of mean\n",
    "    # since I don't know if teams play the same\n",
    "    # number of games at home as away\n",
    "    wins.groupby(level='team', as_index=True)\n",
    "        .apply(lambda x: x.n_wins.sum() / x.n_games.sum())\n",
    ")\n",
    "win_percent.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11ee74400>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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urq7lbur33XcfKSkpHDp0iJKSEsrKyq7qe+DAgUyePJmaNWuWCykXFxdGjRpFVFQUtWvX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\niGkppERExLQUUiIiYloKKRERMa2/AAIkroHJPPRyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f02a198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "win_percent.sort_values().plot.barh(figsize=(6, 12), width=.85, color='k')\n",
    "plt.tight_layout()\n",
    "sns.despine()\n",
    "plt.xlabel(\"Win Percent\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is there a relationship between overall team strength and their home-court advantage?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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511XbMpZrt3qqsMBdmcNNp4ohh4ioCGRFRTgSR2gqXlY9Ntn4x8LpkKKRFQW+0RBcjpqC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bba9470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "(wins.win_pct\n",
    "    .unstack()\n",
    "    .assign(**{'Home Win % - Away %': lambda x: x.home_team - x.away_team,\n",
    "               'Overall %': lambda x: (x.home_team + x.away_team) / 2})\n",
    "     .pipe((sns.regplot, 'data'), x='Overall %', y='Home Win % - Away %')\n",
    ")\n",
    "sns.despine()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get the team strength back into `df`.\n",
    "You could you `pd.merge`, but I prefer `.map` when joining a `Series`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>home_win</th>\n",
       "      <th>rest_spread</th>\n",
       "      <th>away_strength</th>\n",
       "      <th>home_strength</th>\n",
       "      <th>point_diff</th>\n",
       "      <th>rest_diff</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>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.536585</td>\n",
       "      <td>0.585366</td>\n",
       "      <td>-12.0</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>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.695122</td>\n",
       "      <td>0.512195</td>\n",
       "      <td>2.0</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>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.365854</td>\n",
       "      <td>0.890244</td>\n",
       "      <td>16.0</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>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.121951</td>\n",
       "      <td>0.585366</td>\n",
       "      <td>17.0</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>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.512195</td>\n",
       "      <td>0.256098</td>\n",
       "      <td>-15.0</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  home_win  rest_spread  \\\n",
       "game_id date                                                                   \n",
       "1       2015-10-27         94.0        NaN        NaN     False          NaN   \n",
       "2       2015-10-27         97.0        NaN        NaN      True          NaN   \n",
       "3       2015-10-27        111.0        NaN        NaN      True          NaN   \n",
       "4       2015-10-28        112.0        NaN        NaN      True          NaN   \n",
       "5       2015-10-28        100.0        0.0        NaN     False          NaN   \n",
       "\n",
       "                    away_strength  home_strength  point_diff  rest_diff  \n",
       "game_id date                                                             \n",
       "1       2015-10-27       0.536585       0.585366       -12.0        NaN  \n",
       "2       2015-10-27       0.695122       0.512195         2.0        NaN  \n",
       "3       2015-10-27       0.365854       0.890244        16.0        NaN  \n",
       "4       2015-10-28       0.121951       0.585366        17.0        NaN  \n",
       "5       2015-10-28       0.512195       0.256098       -15.0        NaN  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.assign(away_strength=df['away_team'].map(win_percent),\n",
    "               home_strength=df['home_team'].map(win_percent),\n",
    "               point_diff=df['home_points'] - df['away_points'],\n",
    "               rest_diff=df['home_rest'] - df['away_rest'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as sm\n",
    "\n",
    "df['home_win'] = df.home_win.astype(int)  # for statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.552792\n",
      "         Iterations 6\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>home_win</td>     <th>  No. Observations:  </th>  <td>  1213</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>               <td>Logit</td>      <th>  Df Residuals:      </th>  <td>  1208</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     4</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Sun, 03 Sep 2017</td> <th>  Pseudo R-squ.:     </th>  <td>0.1832</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>07:24:59</td>     <th>  Log-Likelihood:    </th> <td> -670.54</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -820.91</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th> <td>7.479e-64</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>     <td>    0.0707</td> <td>    0.314</td> <td>    0.225</td> <td> 0.822</td> <td>   -0.546</td> <td>    0.687</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>home_strength</th> <td>    5.4204</td> <td>    0.465</td> <td>   11.647</td> <td> 0.000</td> <td>    4.508</td> <td>    6.333</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>away_strength</th> <td>   -4.7445</td> <td>    0.452</td> <td>  -10.506</td> <td> 0.000</td> <td>   -5.630</td> <td>   -3.859</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>home_rest</th>     <td>    0.0894</td> <td>    0.079</td> <td>    1.137</td> <td> 0.255</td> <td>   -0.065</td> <td>    0.243</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>away_rest</th>     <td>   -0.0422</td> <td>    0.067</td> <td>   -0.629</td> <td> 0.529</td> <td>   -0.174</td> <td>    0.089</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:               home_win   No. Observations:                 1213\n",
       "Model:                          Logit   Df Residuals:                     1208\n",
       "Method:                           MLE   Df Model:                            4\n",
       "Date:                Sun, 03 Sep 2017   Pseudo R-squ.:                  0.1832\n",
       "Time:                        07:24:59   Log-Likelihood:                -670.54\n",
       "converged:                       True   LL-Null:                       -820.91\n",
       "                                        LLR p-value:                 7.479e-64\n",
       "=================================================================================\n",
       "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------\n",
       "Intercept         0.0707      0.314      0.225      0.822      -0.546       0.687\n",
       "home_strength     5.4204      0.465     11.647      0.000       4.508       6.333\n",
       "away_strength    -4.7445      0.452    -10.506      0.000      -5.630      -3.859\n",
       "home_rest         0.0894      0.079      1.137      0.255      -0.065       0.243\n",
       "away_rest        -0.0422      0.067     -0.629      0.529      -0.174       0.089\n",
       "=================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod = sm.logit('home_win ~ home_strength + away_strength + home_rest + away_rest', df)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The strength variables both have large coefficeints (really we should be using some independent measure of team strength here, `win_percent` is showing up on the left and right side of the equation). The rest variables don't seem to matter as much.\n",
    "\n",
    "With `.assign` we can quickly explore variations in formula."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.553499\n",
      "         Iterations 6\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>home_win</td>     <th>  No. Observations:  </th>  <td>  1213</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>               <td>Logit</td>      <th>  Df Residuals:      </th>  <td>  1210</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     2</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Sun, 03 Sep 2017</td> <th>  Pseudo R-squ.:     </th>  <td>0.1821</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>07:25:00</td>     <th>  Log-Likelihood:    </th> <td> -671.39</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -820.91</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th> <td>1.165e-65</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>     <td>    0.4610</td> <td>    0.068</td> <td>    6.756</td> <td> 0.000</td> <td>    0.327</td> <td>    0.595</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>strength_diff</th> <td>    5.0671</td> <td>    0.349</td> <td>   14.521</td> <td> 0.000</td> <td>    4.383</td> <td>    5.751</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rest_spread</th>   <td>    0.0566</td> <td>    0.062</td> <td>    0.912</td> <td> 0.362</td> <td>   -0.065</td> <td>    0.178</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:               home_win   No. Observations:                 1213\n",
       "Model:                          Logit   Df Residuals:                     1210\n",
       "Method:                           MLE   Df Model:                            2\n",
       "Date:                Sun, 03 Sep 2017   Pseudo R-squ.:                  0.1821\n",
       "Time:                        07:25:00   Log-Likelihood:                -671.39\n",
       "converged:                       True   LL-Null:                       -820.91\n",
       "                                        LLR p-value:                 1.165e-65\n",
       "=================================================================================\n",
       "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------\n",
       "Intercept         0.4610      0.068      6.756      0.000       0.327       0.595\n",
       "strength_diff     5.0671      0.349     14.521      0.000       4.383       5.751\n",
       "rest_spread       0.0566      0.062      0.912      0.362      -0.065       0.178\n",
       "=================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(sm.Logit.from_formula('home_win ~ strength_diff + rest_spread',\n",
    "                       df.assign(strength_diff=df.home_strength - df.away_strength))\n",
    "    .fit().summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.676549\n",
      "         Iterations 4\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>home_win</td>     <th>  No. Observations:  </th>  <td>  1213</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>               <td>Logit</td>      <th>  Df Residuals:      </th>  <td>  1210</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     2</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Sun, 03 Sep 2017</td> <th>  Pseudo R-squ.:     </th> <td>0.0003107</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>07:25:00</td>     <th>  Log-Likelihood:    </th> <td> -820.65</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -820.91</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th>  <td>0.7749</td>  \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    0.3667</td> <td>    0.094</td> <td>    3.889</td> <td> 0.000</td> <td>    0.182</td> <td>    0.552</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>home_rest</th> <td>    0.0338</td> <td>    0.069</td> <td>    0.486</td> <td> 0.627</td> <td>   -0.102</td> <td>    0.170</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>away_rest</th> <td>   -0.0420</td> <td>    0.061</td> <td>   -0.693</td> <td> 0.488</td> <td>   -0.161</td> <td>    0.077</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:               home_win   No. Observations:                 1213\n",
       "Model:                          Logit   Df Residuals:                     1210\n",
       "Method:                           MLE   Df Model:                            2\n",
       "Date:                Sun, 03 Sep 2017   Pseudo R-squ.:               0.0003107\n",
       "Time:                        07:25:00   Log-Likelihood:                -820.65\n",
       "converged:                       True   LL-Null:                       -820.91\n",
       "                                        LLR p-value:                    0.7749\n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      0.3667      0.094      3.889      0.000       0.182       0.552\n",
       "home_rest      0.0338      0.069      0.486      0.627      -0.102       0.170\n",
       "away_rest     -0.0420      0.061     -0.693      0.488      -0.161       0.077\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod = sm.Logit.from_formula('home_win ~ home_rest + away_rest', df)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall not seeing to much support for rest mattering, but we got to see some more tidy data.\n",
    "\n",
    "That's it for today.\n",
    "Next time we'll look at data visualization."
   ]
  }
 ],
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