{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Tidy Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "pd.options.display.max_rows = 6\n",
    "pd.options.display.max_columns = 6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Structuring datasets to facilitate analysis [(Wickham 2014)](http://www.jstatsoft.org/v59/i10/paper)\n",
    "\n",
    "If there's one maxim I can impart it's that your tools shouldn't get in the way of your analysis. Your problem is already difficult enough, don't let the data or your tools make it any harder."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "In a tidy dataset...\n",
    "\n",
    "1. Each variable forms a column\n",
    "2. Each observation forms a row\n",
    "3. Each type of observational unit forms a table\n",
    "\n",
    "We'll cover a few methods that help you get there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "url = \"http://www.basketball-reference.com/leagues/NBA_2015_games.html\"\n",
    "tables = pd.read_html(url)\n",
    "games = tables[0]\n",
    "games.to_csv('data/games.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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 28, 2014\",10:30 pm,Box Score,Houston Rockets,108.0,Los Angeles Lakers,90.0,,\r\n",
      "\"Tue, Oct 28, 2014\",8:00 pm,Box Score,Orlando Magic,84.0,New Orleans Pelicans,101.0,,\r\n",
      "\"Tue, Oct 28, 2014\",8:00 pm,Box Score,Dallas Mavericks,100.0,San Antonio Spurs,101.0,,\r\n"
     ]
    }
   ],
   "source": [
    "!head -n 5 data/games.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The Question:\n",
    "> **How many days of rest did each team get between each game?**\n",
    "\n",
    "Whether or not your dataset is tidy depends on your question. Given our question, what is an observation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>away_team</th>\n",
       "      <th>away_points</th>\n",
       "      <th>home_team</th>\n",
       "      <th>home_points</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th>2014-10-28 22:30:00</th>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>108</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>2014-10-28 20:00:00</th>\n",
       "      <td>Orlando Magic</td>\n",
       "      <td>84</td>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>2014-10-28 20:00:00</th>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>100</td>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1233</th>\n",
       "      <th>2015-04-15 20:00:00</th>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>112</td>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234</th>\n",
       "      <th>2015-04-15 20:00:00</th>\n",
       "      <td>Miami Heat</td>\n",
       "      <td>105</td>\n",
       "      <td>Philadelphia 76ers</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1235</th>\n",
       "      <th>2015-04-15 19:00:00</th>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>87</td>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1236 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  away_team  away_points             home_team  home_points\n",
       "     date                                                                                  \n",
       "0    2014-10-28 22:30:00    Houston Rockets          108    Los Angeles Lakers           90\n",
       "1    2014-10-28 20:00:00      Orlando Magic           84  New Orleans Pelicans          101\n",
       "2    2014-10-28 20:00:00   Dallas Mavericks          100     San Antonio Spurs          101\n",
       "...                                     ...          ...                   ...          ...\n",
       "1233 2015-04-15 20:00:00    Detroit Pistons          112       New York Knicks           90\n",
       "1234 2015-04-15 20:00:00         Miami Heat          105    Philadelphia 76ers          101\n",
       "1235 2015-04-15 19:00:00  Charlotte Hornets           87       Toronto Raptors           92\n",
       "\n",
       "[1236 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# parse and convert\n",
    "column_names = ['date1', 'time1', '_', 'away_team', 'away_points', \n",
    "                'home_team', 'home_points', 'n_ot', 'notes']\n",
    "games = (pd.read_csv('data/games.csv', names=column_names, \n",
    "                      header=None, skiprows=2)\n",
    "           .assign(date=lambda x: \n",
    "                  pd.to_datetime(x['date1'] + ' ' + x['time1'], \n",
    "                                 errors='coerce'))\n",
    "           .drop(['_', 'date1', 'time1', 'notes', 'n_ot'], axis='columns')\n",
    "           .set_index('date', append=True)\n",
    "        )\n",
    "games"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>away_team</th>\n",
       "      <th>away_points</th>\n",
       "      <th>home_team</th>\n",
       "      <th>home_points</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>game_id</th>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th>2014-10-28 22:30:00</th>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>108</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>2014-10-28 20:00:00</th>\n",
       "      <td>Orlando Magic</td>\n",
       "      <td>84</td>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>2014-10-28 20:00:00</th>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>100</td>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1233</th>\n",
       "      <th>2015-04-15 20:00:00</th>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>112</td>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234</th>\n",
       "      <th>2015-04-15 20:00:00</th>\n",
       "      <td>Miami Heat</td>\n",
       "      <td>105</td>\n",
       "      <td>Philadelphia 76ers</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1235</th>\n",
       "      <th>2015-04-15 19:00:00</th>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>87</td>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1236 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     away_team  away_points             home_team  home_points\n",
       "game_id date                                                                                  \n",
       "0       2014-10-28 22:30:00    Houston Rockets          108    Los Angeles Lakers           90\n",
       "1       2014-10-28 20:00:00      Orlando Magic           84  New Orleans Pelicans          101\n",
       "2       2014-10-28 20:00:00   Dallas Mavericks          100     San Antonio Spurs          101\n",
       "...                                        ...          ...                   ...          ...\n",
       "1233    2015-04-15 20:00:00    Detroit Pistons          112       New York Knicks           90\n",
       "1234    2015-04-15 20:00:00         Miami Heat          105    Philadelphia 76ers          101\n",
       "1235    2015-04-15 19:00:00  Charlotte Hornets           87       Toronto Raptors           92\n",
       "\n",
       "[1236 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games.index.names = ['game_id', 'date']\n",
    "games       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Is `games` a tidy dataset, given our question? No, we have multiple observations (teams) per row. We'll use `pd.melt` to fix that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# wide to long\n",
    "tidy = pd.melt(games.reset_index(),\n",
    "               id_vars=['game_id', 'date'], \n",
    "               value_vars=['away_team', 'home_team'],\n",
    "               value_name='team')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>game_id</th>\n",
       "      <th>date</th>\n",
       "      <th>variable</th>\n",
       "      <th>team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2014-10-28 22:30:00</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Houston Rockets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-10-28 20:00:00</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Orlando Magic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2014-10-28 20:00:00</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Dallas Mavericks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2469</th>\n",
       "      <td>1233</td>\n",
       "      <td>2015-04-15 20:00:00</td>\n",
       "      <td>home_team</td>\n",
       "      <td>New York Knicks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2470</th>\n",
       "      <td>1234</td>\n",
       "      <td>2015-04-15 20:00:00</td>\n",
       "      <td>home_team</td>\n",
       "      <td>Philadelphia 76ers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2471</th>\n",
       "      <td>1235</td>\n",
       "      <td>2015-04-15 19:00:00</td>\n",
       "      <td>home_team</td>\n",
       "      <td>Toronto Raptors</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2472 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      game_id                date   variable                team\n",
       "0           0 2014-10-28 22:30:00  away_team     Houston Rockets\n",
       "1           1 2014-10-28 20:00:00  away_team       Orlando Magic\n",
       "2           2 2014-10-28 20:00:00  away_team    Dallas Mavericks\n",
       "...       ...                 ...        ...                 ...\n",
       "2469     1233 2015-04-15 20:00:00  home_team     New York Knicks\n",
       "2470     1234 2015-04-15 20:00:00  home_team  Philadelphia 76ers\n",
       "2471     1235 2015-04-15 19:00:00  home_team     Toronto Raptors\n",
       "\n",
       "[2472 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tidy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>game_id</th>\n",
       "      <th>date</th>\n",
       "      <th>variable</th>\n",
       "      <th>team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2014-10-28 22:30:00</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Houston Rockets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1236</th>\n",
       "      <td>0</td>\n",
       "      <td>2014-10-28 22:30:00</td>\n",
       "      <td>home_team</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      game_id                date   variable                team\n",
       "0           0 2014-10-28 22:30:00  away_team     Houston Rockets\n",
       "1236        0 2014-10-28 22:30:00  home_team  Los Angeles Lakers"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tidy[tidy.game_id==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>game_id</th>\n",
       "      <th colspan=\"2\" halign=\"left\">date</th>\n",
       "      <th colspan=\"2\" halign=\"left\">team</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variable</th>\n",
       "      <th></th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_team</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2014-10-28 22:30:00</td>\n",
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       "      <td>Houston Rockets</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-10-28 20:00:00</td>\n",
       "      <td>2014-10-28 20:00:00</td>\n",
       "      <td>Orlando Magic</td>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2014-10-28 20:00:00</td>\n",
       "      <td>2014-10-28 20:00:00</td>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>San Antonio Spurs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1233</th>\n",
       "      <td>1233</td>\n",
       "      <td>2015-04-15 20:00:00</td>\n",
       "      <td>2015-04-15 20:00:00</td>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>New York Knicks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234</th>\n",
       "      <td>1234</td>\n",
       "      <td>2015-04-15 20:00:00</td>\n",
       "      <td>2015-04-15 20:00:00</td>\n",
       "      <td>Miami Heat</td>\n",
       "      <td>Philadelphia 76ers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1235</th>\n",
       "      <td>1235</td>\n",
       "      <td>2015-04-15 19:00:00</td>\n",
       "      <td>2015-04-15 19:00:00</td>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>Toronto Raptors</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1236 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         game_id                date                                   team                      \n",
       "variable                   away_team           home_team          away_team             home_team\n",
       "0              0 2014-10-28 22:30:00 2014-10-28 22:30:00    Houston Rockets    Los Angeles Lakers\n",
       "1              1 2014-10-28 20:00:00 2014-10-28 20:00:00      Orlando Magic  New Orleans Pelicans\n",
       "2              2 2014-10-28 20:00:00 2014-10-28 20:00:00   Dallas Mavericks     San Antonio Spurs\n",
       "...          ...                 ...                 ...                ...                   ...\n",
       "1233        1233 2015-04-15 20:00:00 2015-04-15 20:00:00    Detroit Pistons       New York Knicks\n",
       "1234        1234 2015-04-15 20:00:00 2015-04-15 20:00:00         Miami Heat    Philadelphia 76ers\n",
       "1235        1235 2015-04-15 19:00:00 2015-04-15 19:00:00  Charlotte Hornets       Toronto Raptors\n",
       "\n",
       "[1236 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# long to wide\n",
    "(tidy\n",
    "     .pivot(index='game_id',columns='variable')\n",
    "     .reset_index()\n",
    " )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Now the translation from question to operation is direct:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>game_id</th>\n",
       "      <th>date</th>\n",
       "      <th>variable</th>\n",
       "      <th>team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2014-10-28 22:30:00</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Houston Rockets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-10-28 20:00:00</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Orlando Magic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2014-10-28 20:00:00</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Dallas Mavericks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2469</th>\n",
       "      <td>1233</td>\n",
       "      <td>2015-04-15 20:00:00</td>\n",
       "      <td>home_team</td>\n",
       "      <td>New York Knicks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2470</th>\n",
       "      <td>1234</td>\n",
       "      <td>2015-04-15 20:00:00</td>\n",
       "      <td>home_team</td>\n",
       "      <td>Philadelphia 76ers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2471</th>\n",
       "      <td>1235</td>\n",
       "      <td>2015-04-15 19:00:00</td>\n",
       "      <td>home_team</td>\n",
       "      <td>Toronto Raptors</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2472 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      game_id                date   variable                team\n",
       "0           0 2014-10-28 22:30:00  away_team     Houston Rockets\n",
       "1           1 2014-10-28 20:00:00  away_team       Orlando Magic\n",
       "2           2 2014-10-28 20:00:00  away_team    Dallas Mavericks\n",
       "...       ...                 ...        ...                 ...\n",
       "2469     1233 2015-04-15 20:00:00  home_team     New York Knicks\n",
       "2470     1234 2015-04-15 20:00:00  home_team  Philadelphia 76ers\n",
       "2471     1235 2015-04-15 19:00:00  home_team     Toronto Raptors\n",
       "\n",
       "[2472 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tidy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      NaN\n",
       "1      NaN\n",
       "2      NaN\n",
       "        ..\n",
       "2469     4\n",
       "2470     1\n",
       "2471     9\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For each team... get number of dates between games\n",
    "tidy.groupby('team')['date'].diff().dt.days - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>game_id</th>\n",
       "      <th>date</th>\n",
       "      <th>variable</th>\n",
       "      <th>team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>2014-10-29 22:00:00</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>2014-11-01 22:30:00</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>105</td>\n",
       "      <td>2014-11-11 20:00:00</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2422</th>\n",
       "      <td>1186</td>\n",
       "      <td>2015-04-10 22:30:00</td>\n",
       "      <td>home_team</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2439</th>\n",
       "      <td>1203</td>\n",
       "      <td>2015-04-12 21:30:00</td>\n",
       "      <td>home_team</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2464</th>\n",
       "      <td>1228</td>\n",
       "      <td>2015-04-15 22:30:00</td>\n",
       "      <td>home_team</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      game_id                date   variable                team\n",
       "10         10 2014-10-29 22:00:00  away_team  Los Angeles Lakers\n",
       "30         30 2014-11-01 22:30:00  away_team  Los Angeles Lakers\n",
       "105       105 2014-11-11 20:00:00  away_team  Los Angeles Lakers\n",
       "...       ...                 ...        ...                 ...\n",
       "2422     1186 2015-04-10 22:30:00  home_team  Los Angeles Lakers\n",
       "2439     1203 2015-04-12 21:30:00  home_team  Los Angeles Lakers\n",
       "2464     1228 2015-04-15 22:30:00  home_team  Los Angeles Lakers\n",
       "\n",
       "[82 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tidy.groupby('team').get_group('Los Angeles Lakers')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10                 NaT\n",
       "30     3 days 00:30:00\n",
       "105    9 days 21:30:00\n",
       "             ...      \n",
       "2422   5 days 01:00:00\n",
       "2439   1 days 23:00:00\n",
       "2464   3 days 01:00:00\n",
       "Name: date, dtype: timedelta64[ns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tidy.groupby('team').get_group('Los Angeles Lakers')['date'].diff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>game_id</th>\n",
       "      <th>date</th>\n",
       "      <th>variable</th>\n",
       "      <th>team</th>\n",
       "      <th>rest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>2014-10-29 22:00:00</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>2014-10-29 21:00:00</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>2014-10-30 20:00:00</td>\n",
       "      <td>away_team</td>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2469</th>\n",
       "      <td>1233</td>\n",
       "      <td>2015-04-15 20:00:00</td>\n",
       "      <td>home_team</td>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2470</th>\n",
       "      <td>1234</td>\n",
       "      <td>2015-04-15 20:00:00</td>\n",
       "      <td>home_team</td>\n",
       "      <td>Philadelphia 76ers</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2471</th>\n",
       "      <td>1235</td>\n",
       "      <td>2015-04-15 19:00:00</td>\n",
       "      <td>home_team</td>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2430 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      game_id                date   variable                team  rest\n",
       "10         10 2014-10-29 22:00:00  away_team  Los Angeles Lakers    -1\n",
       "14         14 2014-10-29 21:00:00  away_team     Houston Rockets    -1\n",
       "15         15 2014-10-30 20:00:00  away_team     New York Knicks     0\n",
       "...       ...                 ...        ...                 ...   ...\n",
       "2469     1233 2015-04-15 20:00:00  home_team     New York Knicks     1\n",
       "2470     1234 2015-04-15 20:00:00  home_team  Philadelphia 76ers     1\n",
       "2471     1235 2015-04-15 19:00:00  home_team     Toronto Raptors    -1\n",
       "\n",
       "[2430 rows x 5 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tidy['rest'] = (tidy\n",
    "                    .sort_values('date')\n",
    "                    .groupby('team')\n",
    "                    .date.diff()\n",
    "                    .dt\n",
    "                    .days - 1\n",
    ")\n",
    "tidy.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11095fb00>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+lJz9QVW+7sfOL1Gq3tCr+fg8yv2xfhIR/0W5T9fLq+f+C9gvytKGF1Aa7X9E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yXwXMrnMlj8ftsU7NTSDbouPyLiYMr+4sDMvA0gMzdEmdHoHtVj9qccwywbXG9m/u+IdT2bso+5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\ngWdl5jEUxbSxhta9utwvEdFXHve/G9ZfBLwqM3+V4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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11095f1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(tidy.dropna()\n",
    "     .pipe(sns.FacetGrid, col='team', col_wrap=9, hue='team')\n",
    "     .map(sns.barplot, \"variable\", \"rest\")\n",
    " )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "What are we doing?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10fda1eb8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x106e1ca90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(tidy\n",
    "     .dropna()\n",
    "     .query('team == \"Los Angeles Lakers\"')\n",
    "     .pipe(sns.FacetGrid, col='team', hue='team')\n",
    "     .map(sns.barplot, \"variable\", \"rest\")\n",
    " )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "variable\n",
       "away_team    0.195122\n",
       "home_team    1.175000\n",
       "Name: rest, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = (tidy\n",
    "        .dropna()\n",
    "        .query('team == \"Los Angeles Lakers\"')\n",
    "        .groupby('variable')\n",
    "     )\n",
    "g.rest.sum()/g.rest.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Stack / Unstack"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An \"observation\" depends on the question. Home team advantage?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Home win percent: 0.572006472492\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jreback/miniconda/envs/python3/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": 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alr0haun0i8CrGJCewOJ9iYh/A3weuCwz/6q+52fg+9LEYwxQXxZxYg8GNoQt\norEXYwze6wOAiFgL3AhclZmfj4g/aFg8sH0ByMz3RMSLga8DL2hYNIh9eS+wEBEXAK8ErgUmG5YX\n6klXHprMzA2ZuTkzNwP/D7gwMw8DcxFxdv3Q5YXAniVX1Eci4kMR8e761ceBpwe9JwAR8XJq/2r9\n+cz8EkBmPsaA96UZ+wJ4rttW7o6IjfXLFzF4rw/qIeM24IOZubt+s32JeHdEfKh+9QngGPCNQe5L\nZm7MzE31rPJN4BeBW5fbk8r3iBXQ+GuD9wN/BpwCfCkz/66akipxDXBtfS/hKTxzjs5B7gnUxs0N\nA39cP4z9aGb+LPbluAV8DzXyXLfNHX+NXAbsiohh4F7ghupKqsw2aoeTroyI42PFtlL7jBnkvtwI\nfDYi7qQ2HncrcD++XhotcBLvIc81KUmSVJGuPDQpSZI0CAxikiRJFTGISZIkVcQgJkmSVBGDmCRJ\nUkUMYpIkSRUxiEmSJFXk/wNYudJt92ULtQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11091d390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "home_adv = games.home_points - games.away_points\n",
    "ax = home_adv.plot(kind='hist', bins=80, figsize=(10, 5))\n",
    "ax.set_xlim(-40, 40)\n",
    "ax.vlines(home_adv.mean(), *ax.get_ylim(), color='red', linewidth=3)\n",
    "print('Home win percent:', (home_adv > 0).mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Team Strength\n",
    "\n",
    "### Mini Project: Home Court Advantage?\n",
    "\n",
    "What's the effect (in terms of probability to win) of being\n",
    "the home team.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1. Calculate Win %\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": 19,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>away_team</th>\n",
       "      <th>away_points</th>\n",
       "      <th>home_team</th>\n",
       "      <th>home_points</th>\n",
       "      <th>home_win</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>game_id</th>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th>2014-10-28 22:30:00</th>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>108</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>90</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>2014-10-28 20:00:00</th>\n",
       "      <td>Orlando Magic</td>\n",
       "      <td>84</td>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>101</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>2014-10-28 20:00:00</th>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>100</td>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>101</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1233</th>\n",
       "      <th>2015-04-15 20:00:00</th>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>112</td>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>90</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234</th>\n",
       "      <th>2015-04-15 20:00:00</th>\n",
       "      <td>Miami Heat</td>\n",
       "      <td>105</td>\n",
       "      <td>Philadelphia 76ers</td>\n",
       "      <td>101</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1235</th>\n",
       "      <th>2015-04-15 19:00:00</th>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>87</td>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>92</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1236 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     away_team  away_points             home_team  home_points home_win\n",
       "game_id date                                                                                           \n",
       "0       2014-10-28 22:30:00    Houston Rockets          108    Los Angeles Lakers           90    False\n",
       "1       2014-10-28 20:00:00      Orlando Magic           84  New Orleans Pelicans          101     True\n",
       "2       2014-10-28 20:00:00   Dallas Mavericks          100     San Antonio Spurs          101     True\n",
       "...                                        ...          ...                   ...          ...      ...\n",
       "1233    2015-04-15 20:00:00    Detroit Pistons          112       New York Knicks           90    False\n",
       "1234    2015-04-15 20:00:00         Miami Heat          105    Philadelphia 76ers          101    False\n",
       "1235    2015-04-15 19:00:00  Charlotte Hornets           87       Toronto Raptors           92     True\n",
       "\n",
       "[1236 rows x 5 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games['home_win'] = games['home_points'] > games['away_points']\n",
    "games"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Step 2: Find the win percent for each team\n",
    "\n",
    "Teams are split across two columns. It's easiest to calculate the number of wins and number of games as away, and the number of wins and number of games as home. Then combine those two results to get the win percent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_wins</th>\n",
       "      <th>n_games</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>away_team</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Atlanta Hawks</th>\n",
       "      <td>60</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boston Celtics</th>\n",
       "      <td>40</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brooklyn Nets</th>\n",
       "      <td>38</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toronto Raptors</th>\n",
       "      <td>49</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah Jazz</th>\n",
       "      <td>38</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington Wizards</th>\n",
       "      <td>46</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    n_wins  n_games\n",
       "away_team                          \n",
       "Atlanta Hawks           60       82\n",
       "Boston Celtics          40       82\n",
       "Brooklyn Nets           38       82\n",
       "...                    ...      ...\n",
       "Toronto Raptors         49       82\n",
       "Utah Jazz               38       82\n",
       "Washington Wizards      46       82\n",
       "\n",
       "[30 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wins_as_away = games.groupby('away_team').home_win.agg(\n",
    "    {'n_games': 'count', 'n_wins': lambda x: (~x).sum()}\n",
    ")\n",
    "wins_as_home = games.groupby('home_team').home_win.agg(\n",
    "    {'n_games': 'count', 'n_wins': 'sum'}\n",
    ")\n",
    "wins = (wins_as_away + wins_as_home)\n",
    "wins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Finally, calculate the win percent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "team\n",
       "Atlanta Hawks         0.731707\n",
       "Boston Celtics        0.487805\n",
       "Brooklyn Nets         0.463415\n",
       "                        ...   \n",
       "Toronto Raptors       0.597561\n",
       "Utah Jazz             0.463415\n",
       "Washington Wizards    0.560976\n",
       "Name: strength, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strength = wins.n_wins / wins.n_games\n",
    "strength.index.name = 'team'\n",
    "strength.name = 'strength'\n",
    "strength"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10ff650b8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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yT4xIS11wkjQPfTXBt5X/KixAaH8/lPQlYjj9R8AEYpj/rMB1hGL3DkJl8L6k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\nY/teSV8EkLQm8H3ivsPswHa2H5N0OKH+nYFwwFxDJNpXgKu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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110951f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "strength.sort_values().plot.barh(figsize=(4,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Bring the `strength` values in for each team, for each game."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "For SQL people\n",
    "\n",
    "```sql\n",
    "SELECT *\n",
    "FROM games NATURAL JOIN strength\n",
    "```\n",
    "\n",
    "We just need to get the names worked out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>away_team</th>\n",
       "      <th>away_strength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>0.731707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.487805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>0.463415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>0.402439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>0.609756</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           away_team  away_strength\n",
       "0      Atlanta Hawks       0.731707\n",
       "1     Boston Celtics       0.487805\n",
       "2      Brooklyn Nets       0.463415\n",
       "3  Charlotte Hornets       0.402439\n",
       "4      Chicago Bulls       0.609756"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(strength\n",
    "         .head()\n",
    "         .reset_index()\n",
    "         .rename(columns=lambda x: 'away_' + x)\n",
    " )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>away_team</th>\n",
       "      <th>away_points</th>\n",
       "      <th>home_team</th>\n",
       "      <th>...</th>\n",
       "      <th>home_win</th>\n",
       "      <th>away_strength</th>\n",
       "      <th>home_strength</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>game_id</th>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th>2014-10-28 22:30:00</th>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>108</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>0.682927</td>\n",
       "      <td>0.256098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <th>2015-01-25 21:30:00</th>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>99</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>0.682927</td>\n",
       "      <td>0.256098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <th>2015-01-09 22:30:00</th>\n",
       "      <td>Orlando Magic</td>\n",
       "      <td>84</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>0.304878</td>\n",
       "      <td>0.256098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <th>2014-12-31 19:00:00</th>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>83</td>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>0.402439</td>\n",
       "      <td>0.682927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <th>2014-12-18 20:30:00</th>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>99</td>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>0.548780</td>\n",
       "      <td>0.682927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <th>2015-04-12 19:00:00</th>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>114</td>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>0.548780</td>\n",
       "      <td>0.682927</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1230 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        away_team  away_points           home_team      ...        home_win away_strength  home_strength\n",
       "game_id date                                                                            ...                                             \n",
       "0       2014-10-28 22:30:00       Houston Rockets          108  Los Angeles Lakers      ...           False      0.682927       0.256098\n",
       "664     2015-01-25 21:30:00       Houston Rockets           99  Los Angeles Lakers      ...           False      0.682927       0.256098\n",
       "544     2015-01-09 22:30:00         Orlando Magic           84  Los Angeles Lakers      ...            True      0.304878       0.256098\n",
       "...                                           ...          ...                 ...      ...             ...           ...            ...\n",
       "478     2014-12-31 19:00:00     Charlotte Hornets           83     Houston Rockets      ...            True      0.402439       0.682927\n",
       "382     2014-12-18 20:30:00  New Orleans Pelicans           99     Houston Rockets      ...           False      0.548780       0.682927\n",
       "1201    2015-04-12 19:00:00  New Orleans Pelicans          114     Houston Rockets      ...            True      0.548780       0.682927\n",
       "\n",
       "[1230 rows x 7 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(pd.merge(games.reset_index(), \n",
    "          strength.reset_index().add_prefix('away_'))\n",
    "   .pipe(pd.merge, \n",
    "         strength.reset_index().add_prefix('home_'))\n",
    "   .set_index(['game_id', 'date'])\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "For python people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>away_team</th>\n",
       "      <th>away_points</th>\n",
       "      <th>home_team</th>\n",
       "      <th>...</th>\n",
       "      <th>home_win</th>\n",
       "      <th>away_strength</th>\n",
       "      <th>home_strength</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>game_id</th>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th>2014-10-28 22:30:00</th>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>108</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>0.682927</td>\n",
       "      <td>0.256098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>2014-10-28 20:00:00</th>\n",
       "      <td>Orlando Magic</td>\n",
       "      <td>84</td>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>0.304878</td>\n",
       "      <td>0.548780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>2014-10-28 20:00:00</th>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>100</td>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>0.609756</td>\n",
       "      <td>0.670732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1233</th>\n",
       "      <th>2015-04-15 20:00:00</th>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>112</td>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>0.390244</td>\n",
       "      <td>0.207317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234</th>\n",
       "      <th>2015-04-15 20:00:00</th>\n",
       "      <td>Miami Heat</td>\n",
       "      <td>105</td>\n",
       "      <td>Philadelphia 76ers</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>0.451220</td>\n",
       "      <td>0.219512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1235</th>\n",
       "      <th>2015-04-15 19:00:00</th>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>87</td>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>0.402439</td>\n",
       "      <td>0.597561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1236 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     away_team  away_points             home_team      ...        home_win away_strength  home_strength\n",
       "game_id date                                                                           ...                                             \n",
       "0       2014-10-28 22:30:00    Houston Rockets          108    Los Angeles Lakers      ...           False      0.682927       0.256098\n",
       "1       2014-10-28 20:00:00      Orlando Magic           84  New Orleans Pelicans      ...            True      0.304878       0.548780\n",
       "2       2014-10-28 20:00:00   Dallas Mavericks          100     San Antonio Spurs      ...            True      0.609756       0.670732\n",
       "...                                        ...          ...                   ...      ...             ...           ...            ...\n",
       "1233    2015-04-15 20:00:00    Detroit Pistons          112       New York Knicks      ...           False      0.390244       0.207317\n",
       "1234    2015-04-15 20:00:00         Miami Heat          105    Philadelphia 76ers      ...           False      0.451220       0.219512\n",
       "1235    2015-04-15 19:00:00  Charlotte Hornets           87       Toronto Raptors      ...            True      0.402439       0.597561\n",
       "\n",
       "[1236 rows x 7 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games = games.assign(away_strength=games.away_team.map(strength),\n",
    "                     home_strength=games.home_team.map(strength))\n",
    "games"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Pivoting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>game_id</th>\n",
       "      <th>date</th>\n",
       "      <th>variable</th>\n",
       "      <th>team</th>\n",
       "      <th>rest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2014-10-28 22:30:00</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-10-28 20:00:00</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Orlando Magic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2014-10-28 20:00:00</td>\n",
       "      <td>away_team</td>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2469</th>\n",
       "      <td>1233</td>\n",
       "      <td>2015-04-15 20:00:00</td>\n",
       "      <td>home_team</td>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2470</th>\n",
       "      <td>1234</td>\n",
       "      <td>2015-04-15 20:00:00</td>\n",
       "      <td>home_team</td>\n",
       "      <td>Philadelphia 76ers</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2471</th>\n",
       "      <td>1235</td>\n",
       "      <td>2015-04-15 19:00:00</td>\n",
       "      <td>home_team</td>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2472 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      game_id                date   variable                team  rest\n",
       "0           0 2014-10-28 22:30:00  away_team     Houston Rockets   NaN\n",
       "1           1 2014-10-28 20:00:00  away_team       Orlando Magic   NaN\n",
       "2           2 2014-10-28 20:00:00  away_team    Dallas Mavericks   NaN\n",
       "...       ...                 ...        ...                 ...   ...\n",
       "2469     1233 2015-04-15 20:00:00  home_team     New York Knicks     1\n",
       "2470     1234 2015-04-15 20:00:00  home_team  Philadelphia 76ers     1\n",
       "2471     1235 2015-04-15 19:00:00  home_team     Toronto Raptors    -1\n",
       "\n",
       "[2472 rows x 5 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tidy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>team</th>\n",
       "      <th>Atlanta Hawks</th>\n",
       "      <th>Boston Celtics</th>\n",
       "      <th>Brooklyn Nets</th>\n",
       "      <th>...</th>\n",
       "      <th>Toronto Raptors</th>\n",
       "      <th>Utah Jazz</th>\n",
       "      <th>Washington Wizards</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variable</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>away_team</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.634146</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.707317</td>\n",
       "      <td>0.463415</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home_team</th>\n",
       "      <td>1.097561</td>\n",
       "      <td>0.775000</td>\n",
       "      <td>0.707317</td>\n",
       "      <td>...</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1.097561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "team       Atlanta Hawks  Boston Celtics  Brooklyn Nets         ...          Toronto Raptors  Utah Jazz  Washington Wizards\n",
       "variable                                                        ...                                                        \n",
       "away_team       0.500000        0.634146       0.750000         ...                 0.707317   0.463415            0.400000\n",
       "home_team       1.097561        0.775000       0.707317         ...                 0.800000   1.100000            1.097561\n",
       "\n",
       "[2 rows x 30 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summarizing pivot\n",
    "\n",
    "un = (pd.pivot_table(tidy,\n",
    "                     values='rest',\n",
    "                     index='variable',\n",
    "                     columns='team')\n",
    ")\n",
    "un"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>team</th>\n",
       "      <th>Atlanta Hawks</th>\n",
       "      <th>Boston Celtics</th>\n",
       "      <th>Brooklyn Nets</th>\n",
       "      <th>...</th>\n",
       "      <th>Toronto Raptors</th>\n",
       "      <th>Utah Jazz</th>\n",
       "      <th>Washington Wizards</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variable</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>away_team</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.634146</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.707317</td>\n",
       "      <td>0.463415</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home_team</th>\n",
       "      <td>1.097561</td>\n",
       "      <td>0.775000</td>\n",
       "      <td>0.707317</td>\n",
       "      <td>...</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1.097561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "team       Atlanta Hawks  Boston Celtics  Brooklyn Nets         ...          Toronto Raptors  Utah Jazz  Washington Wizards\n",
       "variable                                                        ...                                                        \n",
       "away_team       0.500000        0.634146       0.750000         ...                 0.707317   0.463415            0.400000\n",
       "home_team       1.097561        0.775000       0.707317         ...                 0.800000   1.100000            1.097561\n",
       "\n",
       "[2 rows x 30 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# equivalent\n",
    "tidy.groupby(['team','variable']).rest.mean().unstack('team')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# transformational pivot\n",
    "\n",
    "un = (pd.pivot_table(tidy,\n",
    "                     values='rest',\n",
    "                     index=['game_id','date'],\n",
    "                     columns='variable',\n",
    "                     aggfunc='mean')\n",
    "        .rename(columns={'away_team': 'away_rest', 'home_team': 'home_rest'})\n",
    ")\n",
    "un.columns.name = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>away_rest</th>\n",
       "      <th>home_rest</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>game_id</th>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <th>2014-10-30 20:30:00</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <th>2014-10-30 20:00:00</th>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <th>2014-10-30 19:00:00</th>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1233</th>\n",
       "      <th>2015-04-15 20:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234</th>\n",
       "      <th>2015-04-15 20:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1235</th>\n",
       "      <th>2015-04-15 19:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1213 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             away_rest  home_rest\n",
       "game_id date                                     \n",
       "16      2014-10-30 20:30:00         -1          1\n",
       "18      2014-10-30 20:00:00         -1          0\n",
       "19      2014-10-30 19:00:00         -1          0\n",
       "...                                ...        ...\n",
       "1233    2015-04-15 20:00:00          1          1\n",
       "1234    2015-04-15 20:00:00          1          1\n",
       "1235    2015-04-15 19:00:00          1         -1\n",
       "\n",
       "[1213 rows x 2 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "un.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>away_team</th>\n",
       "      <th>away_points</th>\n",
       "      <th>...</th>\n",
       "      <th>home_strength</th>\n",
       "      <th>away_rest</th>\n",
       "      <th>home_rest</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>game_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-10-28 22:30:00</td>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>108</td>\n",
       "      <td>...</td>\n",
       "      <td>0.256098</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-10-28 20:00:00</td>\n",
       "      <td>Orlando Magic</td>\n",
       "      <td>84</td>\n",
       "      <td>...</td>\n",
       "      <td>0.548780</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-10-28 20:00:00</td>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>0.670732</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1233</th>\n",
       "      <td>2015-04-15 20:00:00</td>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>112</td>\n",
       "      <td>...</td>\n",
       "      <td>0.207317</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234</th>\n",
       "      <td>2015-04-15 20:00:00</td>\n",
       "      <td>Miami Heat</td>\n",
       "      <td>105</td>\n",
       "      <td>...</td>\n",
       "      <td>0.219512</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1235</th>\n",
       "      <td>2015-04-15 19:00:00</td>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>87</td>\n",
       "      <td>...</td>\n",
       "      <td>0.597561</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1236 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       date          away_team  away_points    ...    home_strength  away_rest home_rest\n",
       "game_id                                                        ...                                      \n",
       "0       2014-10-28 22:30:00    Houston Rockets          108    ...         0.256098        NaN       NaN\n",
       "1       2014-10-28 20:00:00      Orlando Magic           84    ...         0.548780        NaN       NaN\n",
       "2       2014-10-28 20:00:00   Dallas Mavericks          100    ...         0.670732        NaN       NaN\n",
       "...                     ...                ...          ...    ...              ...        ...       ...\n",
       "1233    2015-04-15 20:00:00    Detroit Pistons          112    ...         0.207317          1         1\n",
       "1234    2015-04-15 20:00:00         Miami Heat          105    ...         0.219512          1         1\n",
       "1235    2015-04-15 19:00:00  Charlotte Hornets           87    ...         0.597561          1        -1\n",
       "\n",
       "[1236 rows x 10 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# glue\n",
    "res = pd.concat([games, un], axis=1).reset_index('date')\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "res.to_hdf('data/games.hdf','df',mode='w',format='table')"
   ]
  }
 ],
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