LAB1.ipynb

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   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "NBA Data Analysis in Pandas: The Power of Multi-Indexed Data Frames"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "SUMMARY: In this post I create data frames that summarize and/or aggregate various NBA statistics based on permutations of categorical variables (not combinations because order matters!)."
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "TAKEAWAY: Muli-indexed data frames combined with grouping, aggregating, and sorting functions can serve as powerful tools for exploratory data analysis.  "
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "MAIN STEPS: $$\\\\$$\n",
      "(1) Split - I use the groupby() function to the split data into chunks based on a permutation of variables. $$\\\\$$\n",
      "(2) Apply - I input the values in each chunk into an aggregating function that outputs a summary data object. $$\\\\$$\n",
      "(3) Combine - All the summary data objects are stored in a multi-indexed data frame. $$\\\\$$\n",
      "(4) Explore - The new data frame is examined and its contents is used to create data visualizations. $$\\\\$$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I've been working on my data mining project for the past couple of days, and have learned quite a bit about multi-indexed data frames. As a way to solidify my understanding, I created a short summary of some of the things I've been able to accomplish. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Import all necessary modules:\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline\n",
      "#from ggplot import *\n",
      "\n",
      "#Change some IPython settings:\n",
      "pd.set_option('display.max_columns', 100)\n",
      "\n",
      "#Read in data:\n",
      "teams = pd.read_csv('teams.csv', index_col = [0])\n",
      "games = pd.read_csv('games.csv',index_col = False, parse_dates = [1])\n",
      "players = pd.read_csv('players.csv', index_col = False)\n",
      "gameStats = pd.read_csv('gameStats.csv', index_col = False, parse_dates = [2])\n",
      "games.index = games.game_id"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "1) Comparing Monthly Player Statistics"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here, I create a dataframe that contains a bunch of descriptive statistics (aka mean, std, etc.) of player statistics, broken down by month. After creating this data frame, I can efficiently manipulate the player comparison visualization shown below. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#poss_agg_funcs = ['count', 'median','mean','std','var','sum','max','min', 'first','last', 'mode'] #possible aggregate functions\n",
      "agg_funcs = ['mean','std','max','sum']\n",
      "pl_vars1 = ['game_id', 'player', 'player_id', 'team','team_id']\n",
      "pl_vars2 = ['AST', 'BLK', 'DREB','MIN', 'OREB', 'PF', '+/-', 'PTS', 'REB', 'STL', 'TO', \n",
      "            '3PM', '3PA','FGM', 'FGA', 'FTM', 'FTA']\n",
      "\n",
      "#Create aggregator (aka a dictionary defining the aggregate function to be applied onto each column):\n",
      "dict1 = {'team': lambda col: list(set(col)),\n",
      "         'team_id': lambda col: list(set(col)),\n",
      "         'game_id': 'count'}\n",
      "dict2 = dict([tuple([col_name, agg_funcs]) for col_name in pl_vars2])\n",
      "dict1.update(dict2)\n",
      "pl_mapping = dict1\n",
      "\n",
      "#Create Player Stats Summary Data Frame for 2015:\n",
      "tempids = games.loc[games.season == 2015, 'game_id'].values\n",
      "crit1 = players.game_id.map(lambda x: x in tempids)\n",
      "players.index = players.game_id\n",
      "players['date'] = games.date\n",
      "players = players.reset_index(drop = True)\n",
      "players['period'] = players.date.dt.to_period('M')\n",
      "\n",
      "pl_groupPerm = ['period','player']\n",
      "playerStats2015_bymonth = players[crit1].groupby(pl_groupPerm).agg(pl_mapping)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "playerStats2015 = players[crit1].groupby('player').agg(pl_mapping)\n",
      "playerStats2015.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th colspan=\"4\" halign=\"left\">TO</th>\n",
        "      <th colspan=\"4\" halign=\"left\">MIN</th>\n",
        "      <th colspan=\"4\" halign=\"left\">STL</th>\n",
        "      <th colspan=\"4\" halign=\"left\">FTA</th>\n",
        "      <th>team_id</th>\n",
        "      <th colspan=\"4\" halign=\"left\">3PM</th>\n",
        "      <th colspan=\"4\" halign=\"left\">BLK</th>\n",
        "      <th colspan=\"4\" halign=\"left\">3PA</th>\n",
        "      <th colspan=\"4\" halign=\"left\">REB</th>\n",
        "      <th>game_id</th>\n",
        "      <th colspan=\"4\" halign=\"left\">DREB</th>\n",
        "      <th colspan=\"4\" halign=\"left\">FTM</th>\n",
        "      <th colspan=\"4\" halign=\"left\">+/-</th>\n",
        "      <th colspan=\"4\" halign=\"left\">AST</th>\n",
        "      <th colspan=\"4\" halign=\"left\">OREB</th>\n",
        "      <th colspan=\"4\" halign=\"left\">FGM</th>\n",
        "      <th colspan=\"4\" halign=\"left\">PF</th>\n",
        "      <th>team</th>\n",
        "      <th colspan=\"4\" halign=\"left\">PTS</th>\n",
        "      <th colspan=\"4\" halign=\"left\">FGA</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "      <th>std</th>\n",
        "      <th>max</th>\n",
        "      <th>sum</th>\n",
        "      <th>mean</th>\n",
        "      <th>std</th>\n",
        "      <th>max</th>\n",
        "      <th>sum</th>\n",
        "      <th>mean</th>\n",
        "      <th>std</th>\n",
        "      <th>max</th>\n",
        "      <th>sum</th>\n",
        "      <th>mean</th>\n",
        "      <th>std</th>\n",
        "      <th>max</th>\n",
        "      <th>sum</th>\n",
        "      <th>&lt;lambda&gt;</th>\n",
        "      <th>mean</th>\n",
        "      <th>std</th>\n",
        "      <th>max</th>\n",
        "      <th>sum</th>\n",
        "      <th>mean</th>\n",
        "      <th>std</th>\n",
        "      <th>max</th>\n",
        "      <th>sum</th>\n",
        "      <th>mean</th>\n",
        "      <th>std</th>\n",
        "      <th>max</th>\n",
        "      <th>sum</th>\n",
        "      <th>mean</th>\n",
        "      <th>std</th>\n",
        "      <th>max</th>\n",
        "      <th>sum</th>\n",
        "      <th>count</th>\n",
        "      <th>mean</th>\n",
        "      <th>std</th>\n",
        "      <th>max</th>\n",
        "      <th>sum</th>\n",
        "      <th>mean</th>\n",
        "      <th>std</th>\n",
        "      <th>max</th>\n",
        "      <th>sum</th>\n",
        "      <th>mean</th>\n",
        "      <th>std</th>\n",
        "      <th>max</th>\n",
        "      <th>sum</th>\n",
        "      <th>mean</th>\n",
        "      <th>std</th>\n",
        "      <th>max</th>\n",
        "      <th>sum</th>\n",
        "      <th>mean</th>\n",
        "      <th>std</th>\n",
        "      <th>max</th>\n",
        "      <th>sum</th>\n",
        "      <th>mean</th>\n",
        "      <th>std</th>\n",
        "      <th>max</th>\n",
        "      <th>sum</th>\n",
        "      <th>mean</th>\n",
        "      <th>std</th>\n",
        "      <th>max</th>\n",
        "      <th>sum</th>\n",
        "      <th>&lt;lambda&gt;</th>\n",
        "      <th>mean</th>\n",
        "      <th>std</th>\n",
        "      <th>max</th>\n",
        "      <th>sum</th>\n",
        "      <th>mean</th>\n",
        "      <th>std</th>\n",
        "      <th>max</th>\n",
        "      <th>sum</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>player</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",
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        "      <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",
        "      <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",
        "      <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",
        "      <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",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>a.j.-price</th>\n",
        "      <td> 0.541667</td>\n",
        "      <td> 0.721060</td>\n",
        "      <td> 2</td>\n",
        "      <td>  13</td>\n",
        "      <td> 12.833333</td>\n",
        "      <td> 7.475564</td>\n",
        "      <td> 25</td>\n",
        "      <td>  308</td>\n",
        "      <td> 0.291667</td>\n",
        "      <td> 0.550033</td>\n",
        "      <td> 2</td>\n",
        "      <td>  7</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.841549</td>\n",
        "      <td> 7</td>\n",
        "      <td>  24</td>\n",
        "      <td> [11, 5, 23]</td>\n",
        "      <td> 0.625000</td>\n",
        "      <td> 0.969648</td>\n",
        "      <td> 3</td>\n",
        "      <td>  15</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0</td>\n",
        "      <td> 2.333333</td>\n",
        "      <td> 1.857222</td>\n",
        "      <td> 5</td>\n",
        "      <td>  56</td>\n",
        "      <td> 1.291667</td>\n",
        "      <td> 1.301476</td>\n",
        "      <td>  5</td>\n",
        "      <td>  31</td>\n",
        "      <td> 32</td>\n",
        "      <td> 1.041667</td>\n",
        "      <td> 1.232853</td>\n",
        "      <td>  5</td>\n",
        "      <td>  25</td>\n",
        "      <td> 0.666667</td>\n",
        "      <td> 1.274044</td>\n",
        "      <td> 5</td>\n",
        "      <td>  16</td>\n",
        "      <td>-2.166667</td>\n",
        "      <td>  6.578401</td>\n",
        "      <td> 17</td>\n",
        "      <td> -52</td>\n",
        "      <td> 1.708333</td>\n",
        "      <td> 1.627993</td>\n",
        "      <td>  5</td>\n",
        "      <td>  41</td>\n",
        "      <td> 0.250000</td>\n",
        "      <td> 0.531610</td>\n",
        "      <td> 2</td>\n",
        "      <td>  6</td>\n",
        "      <td> 2.083333</td>\n",
        "      <td> 2.263446</td>\n",
        "      <td>  8</td>\n",
        "      <td>  50</td>\n",
        "      <td> 0.583333</td>\n",
        "      <td> 0.717282</td>\n",
        "      <td> 2</td>\n",
        "      <td>  14</td>\n",
        "      <td> [Phoenix Suns, Indiana Pacers, Cleveland Caval...</td>\n",
        "      <td>  5.458333</td>\n",
        "      <td> 6.227423</td>\n",
        "      <td> 22</td>\n",
        "      <td> 131</td>\n",
        "      <td> 5.541667</td>\n",
        "      <td> 3.977974</td>\n",
        "      <td> 12</td>\n",
        "      <td> 133</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>aaron-brooks</th>\n",
        "      <td> 1.863014</td>\n",
        "      <td> 1.272738</td>\n",
        "      <td> 5</td>\n",
        "      <td> 136</td>\n",
        "      <td> 22.369863</td>\n",
        "      <td> 7.032675</td>\n",
        "      <td> 37</td>\n",
        "      <td> 1633</td>\n",
        "      <td> 0.561644</td>\n",
        "      <td> 0.686907</td>\n",
        "      <td> 3</td>\n",
        "      <td> 41</td>\n",
        "      <td> 2.164384</td>\n",
        "      <td> 2.179537</td>\n",
        "      <td> 8</td>\n",
        "      <td> 158</td>\n",
        "      <td>         [4]</td>\n",
        "      <td> 1.452055</td>\n",
        "      <td> 1.190718</td>\n",
        "      <td> 4</td>\n",
        "      <td> 106</td>\n",
        "      <td> 0.191781</td>\n",
        "      <td> 0.517943</td>\n",
        "      <td> 3</td>\n",
        "      <td> 14</td>\n",
        "      <td> 3.698630</td>\n",
        "      <td> 2.132324</td>\n",
        "      <td> 8</td>\n",
        "      <td> 270</td>\n",
        "      <td> 1.958904</td>\n",
        "      <td> 1.295702</td>\n",
        "      <td>  6</td>\n",
        "      <td> 143</td>\n",
        "      <td> 73</td>\n",
        "      <td> 1.602740</td>\n",
        "      <td> 1.187197</td>\n",
        "      <td>  5</td>\n",
        "      <td> 117</td>\n",
        "      <td> 1.821918</td>\n",
        "      <td> 1.835926</td>\n",
        "      <td> 7</td>\n",
        "      <td> 133</td>\n",
        "      <td> 2.027397</td>\n",
        "      <td> 10.333297</td>\n",
        "      <td> 26</td>\n",
        "      <td> 148</td>\n",
        "      <td> 3.123288</td>\n",
        "      <td> 2.107824</td>\n",
        "      <td> 10</td>\n",
        "      <td> 228</td>\n",
        "      <td> 0.356164</td>\n",
        "      <td> 0.653407</td>\n",
        "      <td> 3</td>\n",
        "      <td> 26</td>\n",
        "      <td> 4.136986</td>\n",
        "      <td> 2.293197</td>\n",
        "      <td> 12</td>\n",
        "      <td> 302</td>\n",
        "      <td> 2.342466</td>\n",
        "      <td> 1.356253</td>\n",
        "      <td> 6</td>\n",
        "      <td> 171</td>\n",
        "      <td>                                   [Chicago Bulls]</td>\n",
        "      <td> 11.547945</td>\n",
        "      <td> 6.162253</td>\n",
        "      <td> 31</td>\n",
        "      <td> 843</td>\n",
        "      <td> 9.794521</td>\n",
        "      <td> 4.092537</td>\n",
        "      <td> 23</td>\n",
        "      <td> 715</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>aaron-gordon</th>\n",
        "      <td> 0.842105</td>\n",
        "      <td> 0.886119</td>\n",
        "      <td> 3</td>\n",
        "      <td>  32</td>\n",
        "      <td> 15.210526</td>\n",
        "      <td> 5.827047</td>\n",
        "      <td> 30</td>\n",
        "      <td>  578</td>\n",
        "      <td> 0.368421</td>\n",
        "      <td> 0.541322</td>\n",
        "      <td> 2</td>\n",
        "      <td> 14</td>\n",
        "      <td> 1.368421</td>\n",
        "      <td> 1.403106</td>\n",
        "      <td> 5</td>\n",
        "      <td>  52</td>\n",
        "      <td>        [21]</td>\n",
        "      <td> 0.289474</td>\n",
        "      <td> 0.565107</td>\n",
        "      <td> 2</td>\n",
        "      <td>  11</td>\n",
        "      <td> 0.447368</td>\n",
        "      <td> 0.554947</td>\n",
        "      <td> 2</td>\n",
        "      <td> 17</td>\n",
        "      <td> 0.921053</td>\n",
        "      <td> 0.969301</td>\n",
        "      <td> 3</td>\n",
        "      <td>  35</td>\n",
        "      <td> 3.131579</td>\n",
        "      <td> 1.988766</td>\n",
        "      <td>  8</td>\n",
        "      <td> 119</td>\n",
        "      <td> 43</td>\n",
        "      <td> 2.394737</td>\n",
        "      <td> 1.763588</td>\n",
        "      <td>  6</td>\n",
        "      <td>  91</td>\n",
        "      <td> 0.973684</td>\n",
        "      <td> 1.150484</td>\n",
        "      <td> 4</td>\n",
        "      <td>  37</td>\n",
        "      <td>-3.526316</td>\n",
        "      <td>  8.554237</td>\n",
        "      <td> 20</td>\n",
        "      <td>-134</td>\n",
        "      <td> 0.552632</td>\n",
        "      <td> 0.724004</td>\n",
        "      <td>  3</td>\n",
        "      <td>  21</td>\n",
        "      <td> 0.736842</td>\n",
        "      <td> 0.759951</td>\n",
        "      <td> 2</td>\n",
        "      <td> 28</td>\n",
        "      <td> 1.868421</td>\n",
        "      <td> 1.378818</td>\n",
        "      <td>  6</td>\n",
        "      <td>  71</td>\n",
        "      <td> 1.578947</td>\n",
        "      <td> 1.222129</td>\n",
        "      <td> 5</td>\n",
        "      <td>  60</td>\n",
        "      <td>                                   [Orlando Magic]</td>\n",
        "      <td>  5.000000</td>\n",
        "      <td> 3.734427</td>\n",
        "      <td> 17</td>\n",
        "      <td> 190</td>\n",
        "      <td> 3.842105</td>\n",
        "      <td> 2.422042</td>\n",
        "      <td>  9</td>\n",
        "      <td> 146</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>adreian-payne</th>\n",
        "      <td> 1.095238</td>\n",
        "      <td> 1.410842</td>\n",
        "      <td> 5</td>\n",
        "      <td>  23</td>\n",
        "      <td> 21.428571</td>\n",
        "      <td> 8.634648</td>\n",
        "      <td> 37</td>\n",
        "      <td>  450</td>\n",
        "      <td> 0.523810</td>\n",
        "      <td> 0.749603</td>\n",
        "      <td> 2</td>\n",
        "      <td> 11</td>\n",
        "      <td> 1.523810</td>\n",
        "      <td> 1.631534</td>\n",
        "      <td> 4</td>\n",
        "      <td>  32</td>\n",
        "      <td>     [0, 17]</td>\n",
        "      <td> 0.047619</td>\n",
        "      <td> 0.218218</td>\n",
        "      <td> 1</td>\n",
        "      <td>   1</td>\n",
        "      <td> 0.238095</td>\n",
        "      <td> 0.538958</td>\n",
        "      <td> 2</td>\n",
        "      <td>  5</td>\n",
        "      <td> 0.380952</td>\n",
        "      <td> 0.740013</td>\n",
        "      <td> 2</td>\n",
        "      <td>   8</td>\n",
        "      <td> 5.476190</td>\n",
        "      <td> 3.370742</td>\n",
        "      <td> 15</td>\n",
        "      <td> 115</td>\n",
        "      <td> 31</td>\n",
        "      <td> 3.857143</td>\n",
        "      <td> 2.434866</td>\n",
        "      <td> 10</td>\n",
        "      <td>  81</td>\n",
        "      <td> 1.047619</td>\n",
        "      <td> 1.321975</td>\n",
        "      <td> 4</td>\n",
        "      <td>  22</td>\n",
        "      <td>-5.190476</td>\n",
        "      <td>  7.839764</td>\n",
        "      <td> 10</td>\n",
        "      <td>-109</td>\n",
        "      <td> 0.571429</td>\n",
        "      <td> 0.810643</td>\n",
        "      <td>  2</td>\n",
        "      <td>  12</td>\n",
        "      <td> 1.619048</td>\n",
        "      <td> 1.564487</td>\n",
        "      <td> 5</td>\n",
        "      <td> 34</td>\n",
        "      <td> 2.761905</td>\n",
        "      <td> 1.868282</td>\n",
        "      <td>  7</td>\n",
        "      <td>  58</td>\n",
        "      <td> 2.571429</td>\n",
        "      <td> 1.567528</td>\n",
        "      <td> 5</td>\n",
        "      <td>  54</td>\n",
        "      <td>           [Atlanta Hawks, Minnesota Timberwolves]</td>\n",
        "      <td>  6.619048</td>\n",
        "      <td> 4.329852</td>\n",
        "      <td> 16</td>\n",
        "      <td> 139</td>\n",
        "      <td> 6.666667</td>\n",
        "      <td> 3.439961</td>\n",
        "      <td> 15</td>\n",
        "      <td> 140</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>al-farouq-aminu</th>\n",
        "      <td> 0.718750</td>\n",
        "      <td> 0.999504</td>\n",
        "      <td> 4</td>\n",
        "      <td>  46</td>\n",
        "      <td> 17.390625</td>\n",
        "      <td> 7.947481</td>\n",
        "      <td> 31</td>\n",
        "      <td> 1113</td>\n",
        "      <td> 0.984375</td>\n",
        "      <td> 1.385780</td>\n",
        "      <td> 6</td>\n",
        "      <td> 63</td>\n",
        "      <td> 1.390625</td>\n",
        "      <td> 1.839748</td>\n",
        "      <td> 7</td>\n",
        "      <td>  89</td>\n",
        "      <td>         [6]</td>\n",
        "      <td> 0.437500</td>\n",
        "      <td> 0.639320</td>\n",
        "      <td> 2</td>\n",
        "      <td>  28</td>\n",
        "      <td> 0.812500</td>\n",
        "      <td> 1.139131</td>\n",
        "      <td> 6</td>\n",
        "      <td> 52</td>\n",
        "      <td> 1.687500</td>\n",
        "      <td> 1.390158</td>\n",
        "      <td> 5</td>\n",
        "      <td> 108</td>\n",
        "      <td> 4.218750</td>\n",
        "      <td> 2.924819</td>\n",
        "      <td> 12</td>\n",
        "      <td> 270</td>\n",
        "      <td> 70</td>\n",
        "      <td> 2.906250</td>\n",
        "      <td> 2.106066</td>\n",
        "      <td> 10</td>\n",
        "      <td> 186</td>\n",
        "      <td> 1.015625</td>\n",
        "      <td> 1.527444</td>\n",
        "      <td> 6</td>\n",
        "      <td>  65</td>\n",
        "      <td> 2.359375</td>\n",
        "      <td>  7.756957</td>\n",
        "      <td> 26</td>\n",
        "      <td> 151</td>\n",
        "      <td> 0.687500</td>\n",
        "      <td> 0.731925</td>\n",
        "      <td>  2</td>\n",
        "      <td>  44</td>\n",
        "      <td> 1.312500</td>\n",
        "      <td> 1.446122</td>\n",
        "      <td> 5</td>\n",
        "      <td> 84</td>\n",
        "      <td> 1.921875</td>\n",
        "      <td> 1.645626</td>\n",
        "      <td>  6</td>\n",
        "      <td> 123</td>\n",
        "      <td> 1.859375</td>\n",
        "      <td> 1.270760</td>\n",
        "      <td> 5</td>\n",
        "      <td> 119</td>\n",
        "      <td>                                [Dallas Mavericks]</td>\n",
        "      <td>  5.296875</td>\n",
        "      <td> 4.395812</td>\n",
        "      <td> 17</td>\n",
        "      <td> 339</td>\n",
        "      <td> 4.625000</td>\n",
        "      <td> 2.687419</td>\n",
        "      <td> 11</td>\n",
        "      <td> 296</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "                       TO                           MIN                      \\\n",
        "                     mean       std max  sum       mean       std max   sum   \n",
        "player                                                                        \n",
        "a.j.-price       0.541667  0.721060   2   13  12.833333  7.475564  25   308   \n",
        "aaron-brooks     1.863014  1.272738   5  136  22.369863  7.032675  37  1633   \n",
        "aaron-gordon     0.842105  0.886119   3   32  15.210526  5.827047  30   578   \n",
        "adreian-payne    1.095238  1.410842   5   23  21.428571  8.634648  37   450   \n",
        "al-farouq-aminu  0.718750  0.999504   4   46  17.390625  7.947481  31  1113   \n",
        "\n",
        "                      STL                         FTA                     \\\n",
        "                     mean       std max sum      mean       std max  sum   \n",
        "player                                                                     \n",
        "a.j.-price       0.291667  0.550033   2   7  1.000000  1.841549   7   24   \n",
        "aaron-brooks     0.561644  0.686907   3  41  2.164384  2.179537   8  158   \n",
        "aaron-gordon     0.368421  0.541322   2  14  1.368421  1.403106   5   52   \n",
        "adreian-payne    0.523810  0.749603   2  11  1.523810  1.631534   4   32   \n",
        "al-farouq-aminu  0.984375  1.385780   6  63  1.390625  1.839748   7   89   \n",
        "\n",
        "                     team_id       3PM                          BLK            \\\n",
        "                    <lambda>      mean       std max  sum      mean       std   \n",
        "player                                                                          \n",
        "a.j.-price       [11, 5, 23]  0.625000  0.969648   3   15  0.000000  0.000000   \n",
        "aaron-brooks             [4]  1.452055  1.190718   4  106  0.191781  0.517943   \n",
        "aaron-gordon            [21]  0.289474  0.565107   2   11  0.447368  0.554947   \n",
        "adreian-payne        [0, 17]  0.047619  0.218218   1    1  0.238095  0.538958   \n",
        "al-farouq-aminu          [6]  0.437500  0.639320   2   28  0.812500  1.139131   \n",
        "\n",
        "                              3PA                          REB                \\\n",
        "                max sum      mean       std max  sum      mean       std max   \n",
        "player                                                                         \n",
        "a.j.-price        0   0  2.333333  1.857222   5   56  1.291667  1.301476   5   \n",
        "aaron-brooks      3  14  3.698630  2.132324   8  270  1.958904  1.295702   6   \n",
        "aaron-gordon      2  17  0.921053  0.969301   3   35  3.131579  1.988766   8   \n",
        "adreian-payne     2   5  0.380952  0.740013   2    8  5.476190  3.370742  15   \n",
        "al-farouq-aminu   6  52  1.687500  1.390158   5  108  4.218750  2.924819  12   \n",
        "\n",
        "                     game_id      DREB                          FTM            \\\n",
        "                 sum   count      mean       std max  sum      mean       std   \n",
        "player                                                                          \n",
        "a.j.-price        31      32  1.041667  1.232853   5   25  0.666667  1.274044   \n",
        "aaron-brooks     143      73  1.602740  1.187197   5  117  1.821918  1.835926   \n",
        "aaron-gordon     119      43  2.394737  1.763588   6   91  0.973684  1.150484   \n",
        "adreian-payne    115      31  3.857143  2.434866  10   81  1.047619  1.321975   \n",
        "al-farouq-aminu  270      70  2.906250  2.106066  10  186  1.015625  1.527444   \n",
        "\n",
        "                               +/-                           AST            \\\n",
        "                max  sum      mean        std max  sum      mean       std   \n",
        "player                                                                       \n",
        "a.j.-price        5   16 -2.166667   6.578401  17  -52  1.708333  1.627993   \n",
        "aaron-brooks      7  133  2.027397  10.333297  26  148  3.123288  2.107824   \n",
        "aaron-gordon      4   37 -3.526316   8.554237  20 -134  0.552632  0.724004   \n",
        "adreian-payne     4   22 -5.190476   7.839764  10 -109  0.571429  0.810643   \n",
        "al-farouq-aminu   6   65  2.359375   7.756957  26  151  0.687500  0.731925   \n",
        "\n",
        "                              OREB                         FGM                \\\n",
        "                max  sum      mean       std max sum      mean       std max   \n",
        "player                                                                         \n",
        "a.j.-price        5   41  0.250000  0.531610   2   6  2.083333  2.263446   8   \n",
        "aaron-brooks     10  228  0.356164  0.653407   3  26  4.136986  2.293197  12   \n",
        "aaron-gordon      3   21  0.736842  0.759951   2  28  1.868421  1.378818   6   \n",
        "adreian-payne     2   12  1.619048  1.564487   5  34  2.761905  1.868282   7   \n",
        "al-farouq-aminu   2   44  1.312500  1.446122   5  84  1.921875  1.645626   6   \n",
        "\n",
        "                            PF                     \\\n",
        "                 sum      mean       std max  sum   \n",
        "player                                              \n",
        "a.j.-price        50  0.583333  0.717282   2   14   \n",
        "aaron-brooks     302  2.342466  1.356253   6  171   \n",
        "aaron-gordon      71  1.578947  1.222129   5   60   \n",
        "adreian-payne     58  2.571429  1.567528   5   54   \n",
        "al-farouq-aminu  123  1.859375  1.270760   5  119   \n",
        "\n",
        "                                                              team        PTS  \\\n",
        "                                                          <lambda>       mean   \n",
        "player                                                                          \n",
        "a.j.-price       [Phoenix Suns, Indiana Pacers, Cleveland Caval...   5.458333   \n",
        "aaron-brooks                                       [Chicago Bulls]  11.547945   \n",
        "aaron-gordon                                       [Orlando Magic]   5.000000   \n",
        "adreian-payne              [Atlanta Hawks, Minnesota Timberwolves]   6.619048   \n",
        "al-farouq-aminu                                 [Dallas Mavericks]   5.296875   \n",
        "\n",
        "                                         FGA                     \n",
        "                      std max  sum      mean       std max  sum  \n",
        "player                                                           \n",
        "a.j.-price       6.227423  22  131  5.541667  3.977974  12  133  \n",
        "aaron-brooks     6.162253  31  843  9.794521  4.092537  23  715  \n",
        "aaron-gordon     3.734427  17  190  3.842105  2.422042   9  146  \n",
        "adreian-payne    4.329852  16  139  6.666667  3.439961  15  140  \n",
        "al-farouq-aminu  4.395812  17  339  4.625000  2.687419  11  296  "
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Then, by changing the two lists (player_to_compare and stats_to_compare) below, I can quickly change the players and the statistics that I would like to visually compare. Below, I compare three players' monthly averages of rebounds, points, and assists. Some things I found interesting from these graphs: (1) Although Chris Paul may not score as many points as Westbrook or James, he makes up for it in assists. (2) As of late, Russell Westbrook has been dominant, improving in all three stats in question. This is likely because the Thunder picked up their pace at the end of the season in the hopes of making the playoffs. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "players_to_compare = ['russell-westbrook', 'lebron-james','chris-paul']\n",
      "stats_to_compare = {'PTS':'mean', 'REB':'mean','AST':'mean'}\n",
      "\n",
      "for key,val in stats_to_compare.iteritems():\n",
      "    ax = playerStats2015_bymonth.unstack()[key][val][players_to_compare].plot(figsize = (12,4))\n",
      "    ax.set_ylabel(key + ' ' + val)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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yJaUl7MrcxYbDG9zheuPhjWTmZzI0cijDI4dXql538u/k9JBFWpTM/ExW7Fnh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\ninAzMzuo4Il0RLzEVxgJnz4dfvlLKC+HLl2OX1x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       "text": [
        "<matplotlib.figure.Figure at 0x105d798d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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txdyhGVVKij6/PMDy5bbbe6OU4ssvv2T9+vXs3r2byMhI3NzcePPNNzMcFxwc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\n/NCyTxMORUTaZ/hw+Mtf4Nhjw2ofTz8dzzjKyso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EpJAoTIuI5BAzKwbmAC8B\nRwFvABcDhwG3AH2BDcCl7v6+mSWAV4DjgfvNrB+wzd1vMbNxwB1AL2A58GV3rzazCcBdhJ0En8re\ndyci0vmozUNEJPeUAjPd/TBgC3A5cBtwnrtPBO4Gfh7d14Hu7n60u/9nyjmAe4AfuPuRwGLgJ9H5\nu4F/cfdxmf9WREQ6N1WmRURyz3vu/rfo+u+AHwFjgblmBtAVWJ1y/wcbP4GZ9QcGuPuz0alZwB/M\nbEB0/rno/L3AtI7/FkRECoPCtIhI7vGU60aoTr/h7p9u4v7bW/Cc1srzIiLSAmrzEBHJPSPNbFJ0\n/QLgBWBI8pyZdTezw5p5vLn7FmCTmR0fnbsISLj7ZqDazI6Lzl+YgfGLiBQMhWkRkdzzFvAvZvYP\nYABRvzRwo5lVEiYcfqqZxycr25cAvzSzV4FPAj+Lzl8GzDSzVxrdX0REWsnc9R4qIpIrotU8HnX3\nI2IeioiItIAq0yIiuUdVDhGRPKHKtIiIiIhIG6kyLSIiIiLSRgrTIiIiIiJtpDAtIiIiItJGCtMi\nIiIiIm2kMC0iIiIi0kYK0yIiIiIibfR/ig1qaWAQmuUAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1147de350>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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6NerjVcnL1W/tLOmZ6Sz9eylzI+eyYu8Krml7DWFdwri89eXFGu2WiiM9Hd5+\nG154Aa6+2l7WrnlzV1clImWhNK0dxQnSUZzdymGMMa1KcsIzr/kYMA67VeQP4HZjzOk89ytIV1An\nTsCTT8KCBfDSSxAWVvoJPRkZcODA2QE7+/qxY9CixdkBO/u6n59j3pszGWNISksqMhRnHx9LPUb9\nGvXPaq3IDsXZYbmhT0N8qvq4+q05zJGTR/hkyyeEbwonJimG0Z1HExYURpeGXVxdmjhBREREzihU\nUbKy4LPP7PWf27e3t/Tuoh8JkQrFqUHaFRSkK6ZvvoH774dBg2DGjLJbk/XkSYiKOvdo9p494OVV\neMhu0cK5fZPpmekcTD5Y5MoV2eG5qlfVs8Lwua7Xq1HPLUePy9L2w9uZt2ke8zbNo16NeoR1CePm\nzjfT0Kehq0sTBylOkF6xwu5/tiz7D/eBA8umNhFxLwrS4tH27YMHHoBt2+Ddd6EYg0hlxhhITCx8\nNHvfPvD3P3fLSMuW0KTJ2W0jxhiOpR47OxgnxxOfkr/VIiktiQY1G5y3taJhzYbUrFrTNd8kD5aZ\nlUlEVARzI+ey6K9F9G3el3FB4xjSbgjVK2tmWXkVGWkH6L//tls5brihYrV3iUh+CtLikTIzYdYs\nePZZeyR64kSo5mF7XGZm5raN7NyTxpa9B9kZG0dUQjyxJ+JJseKo2SieanXjsXzjSK8WTzLxVPfy\nppFPI5rULrq9wr+GP5Us/YYvC8npyXy57UvCN4XzZ/yf3NDxBsKCwujdtLeW0isnYmJgyhRYtgz+\n8x97HkbVqq6uSkRczSlB2rKs5saYmFJVVkIK0uXfhUwmdCVjDImnEs+7pFvciTiS05Np6NMwfxCu\n1ogqaY3IOt6YU4cbkbS/EYf3NiJmjzd79tjnKGw0OzAQvLW/iEvEJMUwf9N85kbOJctkEdYljLFB\nYwn0C3R1aVJMeVs7EhNh2jT48EO45x549FGoVcu19YmI+3BWkN5ojOlaqspKSEG6/HLGZMKSSM1I\nzTcRr7D2ioMpB6lRpUaxVq6o6133gkaPjYGjRwtvG4mJgbp1C19tpEkTu39bnMcYw7oD6wiPDOfT\nrZ/SqUEnxgWNY3jH4dSqpiTmziIiIujVK5Q33rD/rbn+epg61V6lR0QkLwVp8QjOnkyYZbJyRo/P\ntzHIydMnaVizYbFWrnBVr2xmpr0pRGGTIBMS7OW5CpsIWbeutjB2pLSMNL79+1vmRs7lp6ifuPai\nawkLCmMYX7qLAAAgAElEQVRQy0EVfvKmu8nMhPnz7TaObt3s0ej27V1dlYi4K2cF6UPAJ9g7GRZU\nqi3Cz1uUgnS54ojJhMnpyWw+uLnI9oqDyQfxqepTrJUr6nrX9fi+19RUiI4+92j2nj32sl6FjWYH\nBkKNGq5+B57rcMphFm5ZSHhkOHHJcYzpPIZxwePoWL+jq0srF9LSICkJjh/PveQ9Pt99iYlw8cX2\nSPSll7r63YiIu3NWkI4GnuTsLcEt7CA9tyQnLFZRCtLlgqMmEy7+azH3fHePPTnPt0mh7RUNazak\nWmUPm63oRNltI+cazY6Ohjp1Ch/NbtpUbSPFteXQFuZFzmP+5vk09mlMWFAYozqNon7N+q4urcyl\np1946D3XfcZA7dp2H3P21+xLUcd5r2/ZEsHAgaGu/paIiAdQa4e4HUdMJjyccpgHv3+QdQfW8f6Q\n9xnYUou8OkpWFsTFFT6affiwHaYLmwhZr57aRgrKzMpkxd4VzI2cy5KdSwgNDGVc0DgGtx3s9n/g\nnT5d8tCb93pmZuHBtrgBuFYtx6zNXtwNWUREnBWkfzXG9CpVZSWkIO25HDGZ0BjDwi0LeXjZw4zt\nMpanBz5NjSrqQyhLaWmFt43s3WsHr8JGswMDoWYFX9L6eNpxvtj2BeGR4Ww5tIWbLr6JsKAwejTp\n4dCWooyMkofevMenT5c89OY9rl5df2CJiOdxVpAOBI4ZY46dOf4H8E8gCphljEkvyQmLVZSCtEdy\nxGTCfUn7uPvbu4lJimH20NmENAlxfKFSaseOFd02UqtW4aPZzZpB5cqufgdlZ+/RvczfNJ/wTeF4\nWV5268fFY/GzmpV6FDg9PX+oLekosLe3ArCIVFzOCtLrgH8aY2ItywoGVgAvAEFAujHm9pIWfN6i\nFKQ9iiMmE2aZLN7b8B5TfpzCAz0e4PG+j1PVSzsleKKsLIiPL3w0++BBe+m+wka069d331CXmWl/\n6nKhI79JSZB03HDEey1JgeFkXPQ5XgnB1I4aR8Mj11Onpk+JRoFr1HDf75WrqbVDRIrLWUF6kzGm\ny5nr/wWyjDGPWZZVCYg0xnQuccXnK0pB2iM4ajLh30f+5o7Fd5CakcrsobO5uMHFji9W3EZamr1G\ndmHrZ6el2e0hhY1o+/hc+DmzsvIH4JK2Qpw8aZ+/pK0P2de9qqWyZOdi5kbO5eeYn7mu/XWEdQlj\nYMuB2snSQRSkRaS4nBWkN2eHZcuyNgKTjDHfF7zPGRSk3Z8jJhNmZGXwytpXmL5mOk/0f4L7e9yv\n9XiFpKTccF0wbO/da//M5Q3YVauePxCnpNh926WZAFe7tv0alRycc+OT41m4eSHhm8I5cvIIY7qM\nISwojPb1tPCxiEhZcFaQfh1oDMQBQ4B2xph0y7ICgEXGmO4lLfi8RSlIuy1H7UwYGR/JbYtuw6+6\nH+8NeY9WdVo5vlgpd7Ky7NaQvAE7I+P8gdjHx/EB2Bk2HdxEeGQ4H2/+mOa1mzMuaBw3XXwT/jX8\nXV2aiEi55awgXQm4CWgEfGaMOXDm9q5AA2PMshLWe/6iFKTdkiMmE6ZlpPHcqud4d8O7vHjZi9wS\nfIvHb4wi4mgZWRn8sPsH5kbOZemupVzW6jLCuoRxddurNXegmNTaIdmMMcQkxbAhbgMp6SlUrlQZ\nr0peVK5UOefiZeUe572vsNvP9xz9XvMsTgnSRZysHzDKGHNPSU5YzHMoSLsRR0wmBFi7by23LbqN\ndvXa8eY1bxLgG+DQOkXKo2Opx/h86+eEbwrnr4S/GNlpJGFBYXRr3E2/rIugIF1xHU45zPrY9aw/\nsJ51setYf2A9XpW8CAkIoXb12mRkZZCRlUFmVmbudZN5ztuLui/v7XnvyzSZVLIqOS2kO/MPgHyP\nK8brXchz3Hn+h9ODtGVZlwCjgBuBvcCXxpg3SnLCYhWlIO0WHDWZMDk9mSdWPsGnWz/l9ateZ0TH\nEQoAIiWwO3E38zbNIzwyHO8q3oR1CWNMlzE0qdXE1aWJuMSJtBNsiNvA+gPrWR+7nnUH1nEs9Rjd\nA7rTo0kPQgJCCGkSQhPfJmX2e8cYQ5bJOm/gLklId0Toz/cc48TXLnCfhVX2wb6Yj3u4z8NOae1o\nhx2ebwIOA58Djxpjmpf0h6vYRSlIu5wjJhMC/LD7B+5ccif9W/Tn5SteVq+niANkmSzWxKwhPDKc\nL7d/SfeA7owLGsc/2/+TmlUr+G44Um6lZaSx6eAme7T5TGiOOhZFUMOgnMDco0kP2tRt49ajnxVV\nvj8uHBzSS/tHyKtXv+qUIJ0FLAHuM8bEnLltrzGmZYm/i8UtSkHaZRw1mfDoqaM8svwRVuxdwbvX\nvstVba5yfLEiwqnTp/jmr2+YGzmXX/f/yrD2wwgLCqN/i/4VOkyotcOzZWZl8teRv+z2jAPrWB+7\nnq2Ht9KmbhtCAkJyRps7NehEFa8qri5XPFxpWjuK2l/seuwR6VWWZX2PPSJd6s9Fzox0f5LnplbA\nFGPM66V9bSmdvJMJt24t2WRCgK+2f8X9S+9nWPthbLl7C77VfB1bqIjk8K7izchOIxnZaSRxJ+L4\nePPH3L/0fk6knWBsl7GEBYXR1r+tq8sUKVT2ZMDswLw+dj0bYjfQoGaDnMA8qvMoujbqqk9cxO2c\nt0fasiwf4DrsUD0QCAf+zxizvNQnt1cGOQD0MMbsy3O7RqTLkKMmE8Ynx3Pfd/ex+dBmPhjyAf1a\n9HNonSJSPMYY/oz/k/DIcBZsWUDrOq0ZFzSOGy++kTredVxdnlRwhU0GzA7NPZr0oHtAd+p613V1\nqVJBlNmqHZZl1QVGACONMf8oyQkLvN4VwJPGmL4FbleQLgOOmkxojCE8MpxHf3iU2y+5nScHPEn1\nytUdX7CIXLDTmadZtnsZcyPnsnz3cq5sfSVhQWFc2fpKfSQuTnci7QR/xP2Rb7T56KmjLp0MKFJQ\nmS5/50iWZX0I/G6MeavA7QrSTuaoyYRRx6K4a8ldHEo5xIdDP6Rr466OLVREHOboqaN8uvVTwiPD\n2X10Nzd3uplxweMIahhU7kKMeqTLniYDiqfyyCBtWVZV7LaOjsaYwwXuU5B2EkdNJswyWby57k2e\n/ulpHun9CP/u82+Nbol4kJ1HdjIvch7hm8KpXa0244LGcXPnm2ns29jVpTmEgrRzaTKglCfOmmzo\nbFcDGwqG6Gzjx48nMDAQAD8/P4KDg3P+UYyIiADQ8QUeJyWFcv/90LFjBO+9B9ddV7LXm/v1XGb8\nMgO/9n78fOvPxG+JZ83qNS5/fzrWsY4v7PjZfzzLQGsgmw5uIvJwJB3e7MBFJy7iqjZXMWnMJLyr\neLtVvTp2zbExhlZdW7HuwDq+XPolOxJ2sKf2HhrUbECLoy1oX689Lw95ma6NurL+l/X287u5T/06\n1nHB4+zrUVFRlJYrR6Q/AZYaY+ae4z6NSDuQoyYTns48zYxfZvDy2pd5OvRp7g65Wx/PiZQjKekp\n/N+O/yM8MpzfY39neIfhjAsex6XNLi13rR9SOHeZDKifOXGGc+VLj2vtsCyrJhANtDTGnDjH/QrS\nDuCoyYQAf8T9wa3f3Eojn0a8e+27tPBr4dhiRcSt7D++n483fczcyLmkZablLKXXqk4rV5dWLBER\nETmjUFI4d54MeCbclOk5pXwr7GfK44L0+ShIl56jJhOeOn2Kp396mo/+/IgZl89gbJexGiUQqUCM\nMWyI20B4ZDgLtyykfb32jAsaxw0db6B29dquLq9QCtJnK2wyYJeGXegR0IOQJiGEBITQ1r+tW3za\nqCAtjqYgLeflqMmEAKujV3P74tsJahjEG1e/QUOfho4tVkQ8SnpmOkv/XsrcyLms2LuCa9peQ1iX\nMC5vfTmVK7lyyo0UdL7JgNktGu48GVBBWhxNQVqKlHdnwhkzSr4z4fG040z63yS+/utrZl09i2Ed\nhjm2UBHxeEdOHuGTLZ8QvimcmKQYRncezbigcXRu2NnVpVU4xdkZMKRJiMftDKggLY6mIC3n5KjJ\nhABL/17KhG8ncHmry5lx+QztgiYi57X98HbmbZrHvE3zqFejHuOCxjGq0yiXfopVnls73GUyoLMp\nSNurTcycOZNu3bq5upRywRlBWp/FebCCkwk/+aTkkwkTTibw0LKHWBOzhtlDZ3NZq8scW6yIlFsd\n6nfghUEv8OzAZ4mIimBu5FymRkylX4t+hHUJY0i7IdrttISKmgwYEhDC7V1v591r39XOgOWUZVlO\n/e+alZVFpUqu74f3ZPrueagNG6BnT/j6a1izBp56quTbe3+29TM6v92Zet712Hz3ZoVoESkRr0pe\nDGo1iPBh4ex/eD8jOozgnQ3v0OTlJkxYMoFf9v1SZiOMnjganZaRxvoD63lr/Vvc8s0tXPzWxTSa\n2YiJKyay//h+hrYbyvejvyfx8UT+F/Y/pl02jWEdhtG0VlOFaA8XFRVF+/btGTNmDB07duSGG27g\n1KlT+R5zzz33EBISQqdOnZg6dSoAK1euZNiw3PbLH374geuvvx6A5cuX06dPH7p168aNN95ISkoK\nAIGBgUycOJFu3brxxRdflM0bLMfU2uFhHDmZMPZELPd8ew9/J/7N7KGz6dW0l2OLFREBYpJimL9p\nPnMj55JlsgjrEsbYoLEE+gW6ujSXKQ+TAZ2tIrV2REVF0apVK9asWUPv3r257bbb6NixI0uWLGHm\nzJlccsklHD16lDp16pCZmclll13GG2+8QadOnejQoQM///wz/v7+3HzzzYwePZqePXsyfPhwvv/+\ne7y9vZk+fTrp6elMmTKFli1bcu+99/Lvf//b1W+7zKm1o4LLO5lw69aSTyY0xjB742wmrZjEPd3v\n4dMRn1Ktcgl7QkREzqN57eZM7jeZSX0nse7AOsIjw+n+Xnc6N+xMWJcwRnQcgW81X4ee0516pIua\nDBjSJIQeAT0Y1XmUx00GFMdq1qwZvXv3BmDMmDG8/vrr+e7/9NNPef/998nIyCAuLo5t27bRqVMn\nxo4dy7x58xg/fjy//vor8+fP57vvvmPbtm306dMHgPT09JzrADfddFPZvbFyTkHaA+SdTBgeXrrJ\nhLsTd3Pnkjs5nnacFWEr6NKwi8PqFBEpimVZ9Gzak55Ne/LylS/z7d/fMjdyLg8te4hrL7qWsKAw\nBrUchFclL1eXWirnmww48dKJdA/ojn8Nf1eXKm4kb3uOMSbf8d69e5k5cya///47tWvX5pZbbslp\n/bjlllsYMmQI1atX58Ybb8zpeb788stZsGDBOc9Vs6b+YHMUBWk35sjJhJlZmbz222u8sPoFJvad\nyL96/UvrvoqIy1SrXI3rO1zP9R2u53DKYRZuWcjkFZOJS45jTOcxjAseR8f6HUv8+mU1Gq3JgOIo\nMTEx/Prrr/Tq1YsFCxbQt29fFi9ejDGG48ePU7NmTWrVqsXBgwdZunQpAwcOBKBx48YEBATw3HPP\nsWLFCgB69uzJvffey+7du2ndujUpKSnExsbStm1bV77FcklJyk3l3ZlwzZqS70wIsOXQFm5bdBve\nlb359fZfaVO3jeMKFREppfo16/NAzwd4oOcDbD20lfDIcC6fdzmNfRozLmgcIzuNpH7N+q4u87w7\nAw5tN5RnBz7rNjsDimdp164db775JrfeeisXX3wxd999N4sXL8ayLIKCgujatSvt27enWbNm9O3b\nN99zb775ZhISEmh3JizUr1+fOXPmMGrUKNLS0gB4/vnnFaSdQJMN3YwjJxOmZ6YzbfU0Zq2fxfP/\neJ7bL7ld/7iLiEfIzMpkxd4VzI2cy7c7vyU0MJSwoDAGtx1crDkdpe2R1mRA16tokw2HDBnC5s2b\nS/T8++67j27dunHLLbc4uLLyRZMNyzlHTSYEWHdgHbctuo1Av0A23rWRprWaOq5QEREn86rkxRWt\nr+CK1ldwPO04X277ktd/e507F9/JTRffxLjgcYQEhDikXUKTAcUdlPRnuVu3bvj6+vLKK684uCIp\nDo1Iu4G8kwnfeQfOtD2VyMnTJ5mycgofb/6YV658hZGdRqovT0TKjahjUcyLnEf4pnAqV6pMWJcw\nxnQZQ7PazYr9GuebDBgSEKLJgG6gIo1IS9nQFuHlTMHJhBMnlnwyIcCPe3/k9sW306tpL1698lW3\n6CkUEXEGYwxr968lPDKcz7d9TtdGXQkLCuP6DtfjU9Un53HnmwzYo0kPQpqEaDKgG1KQFkdTkC5H\n8k4mfOed0k0mPJZ6jMd+eIylu5by9uC3ufaiax1XqIiIm0vNSGXxX4uZGzmXn2N+Zmi7oRzaeoh9\ndfflmwwY0sQebdZkQM+gIC2OpiBdDjhyMiHAor8Wcc+39zDkoiG8eNmL1K5e23HFioh4mIPJB/ls\n62fs/XMvY4eO1WRAD6YgLY6mIO3h8k4mnDGjdJMJD6Uc4oGlD7AhbgMfDPmAAYEDHFeoiIiIiylI\ni6M5I0jrs60ysG8fDBsGjz0Gc+fCRx+Vbnvvjzd9TJe3u9C8dnMiJ0QqRIuIiIi4gIK0E2Vmwmuv\nQdeuEBwMmzaVbkWOfUn7uHbhtUxfM50lNy/hpctfokaVGo4rWESknIiIiHB1CSJOERUVRaVKlcjK\nygLsXTxnz57t4qpyjR8/nilTppTJudzhvbskSFuW5WdZ1heWZW23LGubZVm9XFGHM23YAD17wtdf\n2zsTPvVUyVfkyDJZvL3+bS557xJ6N+3N73f+TveA7o4tWERERDyOZVkuW3Fmzpw59OvXz2X1uPK9\nZ3PVhiyvAd8ZY0ZYllUZKDcr3Dt6MuHOIzu5fdHtnM46zU/jf6Jj/Y6OK1ZEpJwqza6GIhciIyOD\nypW1v11exe1tLw/fuzIfkbYsqzbQzxjzIYAxJsMYk1TWdTjDN9/AxRfDsWP2zoTjxpU8RGdkZfDS\nmpfoM7sPwzsM5+dbflaIFhERcQOBgYG89NJLBAUF4ePjQ6VKldizZ0/O/XnbGxISErj22mupU6cO\n/v7+9O/fP+dx06dPp2nTptSqVYv27duzcuVKwA6iL774Im3atKFevXrcdNNNHD169ILrfOqpp3jg\ngQcAOH36NDVr1uSxxx4D4NSpU1SvXp1jx44B8Ouvv9KnTx/q1KlDcHAwP/30U87rzJkzh9atW1Or\nVi1atWrFggUL2LFjBxMmTGDt2rX4+vpSt27dnMcnJCRwxRVXUKtWLUJDQ4mJicm5r1KlSrz11lu0\nbduWdmfW/n3//fdp27Yt/v7+XHfddcTFxeU8/pdffiEkJAQ/Pz969OjB2rVrz/le4+Li6NKlCzNn\nzrzg71OpGGPK9AIEA78BHwF/AO8DNQo8xniSmBhj/vlPYy66yJiVK0v/en/G/WkuefcSc1n4ZWZP\n4p7Sv6CISAXz448/uroEKSV3zgItWrQwXbt2Nfv37zenTp0ylmWZ3bt359w/fvx4M2XKFGOMMRMn\nTjQTJkwwGRkZJiMjw/z888/GGGN27NhhmjVrZuLi4owxxkRHR+e8xquvvmp69+5tDhw4YNLT081d\nd91lRo0aZYwxZu/evcayLJOZmWmMMSY0NNTMnj37nHWuXLnSdO7c2RhjzJo1a0zr1q1Nz549jTHG\nrFixwgQHBxtjjNm/f7/x9/c3S5cuNcYY88MPPxh/f3+TkJBgkpOTTa1atczOnTuNMcbEx8ebrVu3\nGmOMmTNnjunbt2++c44bN874+vqa1atXm7S0NPPggw/me4xlWeaKK64wR48eNampqWbFihWmXr16\nZuPGjSYtLc3cf//9pn///sYYY44cOWL8/PzM/PnzTWZmplm4cKGpU6eOSUxMzPfe9+zZYy666CLz\n/vvvF/nfrbCfqTO3lyjXumI8vTJwCXCfMWa9ZVmvAhOBJ/M+aPz48QQGBgLg5+dHcHBwzkd12ZNI\nXH3cr18os2bBk09GMGwYbNoUSrVqJX+9Xn178dyq55j12Szu6nYXL455Ecuy3Ob96ljHOtaxpxxn\nc5d6dFyy46I4qjW2JCvsWZbFAw88QJMmTc772KpVqxIXF0dUVBStW7fm0ksvBcDLy4u0tDS2bt2K\nv78/zZs3z3nOu+++y6xZswgICADskeUWLVowf/78C6qzV69e/P333yQmJrJ69Wpuu+023nrrLVJS\nUvjpp58YMMBe9Wv+/Plcc801XHXVVQBcdtlldO/enW+//ZYRI0ZQqVIlNm/eTNOmTWnYsCENGzYE\nCm/huPbaa+nbty8Azz//PLVr1+bAgQM5369Jkybh5+cHwMcff8xtt91GcHAwANOmTaNOnTpER0ez\natUq2rVrx+jRowEYOXIkr7/+OosWLWLcuHEAbN26lWeffZYXX3yRm2666bzfk+yfsYiICKKioi7o\n+3lOJU3gJb0AjYC9eY77AksKPKbIvyjcwe+/G9OtmzGhocbs2FH611sTs8a0n9XeDPtkmIk9Hlv6\nFxQREfFg7pwFAgMDzf/+97+c43ONSD/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8+C/eBw2wbtc6Jq+czKCug5iXP4/ObZJrbEMR\nEWk8au1o/tTaIY0tZcaRdvd94Z9/MbPfAnnA+th9LrpoArt2ZXPXXZCVlUVubm71h2LVQyQns1x6\ntJSxD4zlrb1vsWT6Ei7rdVmDjteylrWsZS03v+UqyRKPluNbFmlsVddYQUFB9RCFX0TCK9Jm1gZI\nd/cyM2sLvATc5e4vxezToJkN6/Lcu89x06qbuOIfruC+i++j/antv/A5RUREpOmpIi2NLVUq0mcA\nvw17k1sBv4xNohvDB+UfMG31NDbv28zya5bzjR7faMzTi4iIiIgkftQOd9/l7rnhT46739uI52bZ\n1mV87dGvkd0hmy1TtiiJFhFpgWq2eIiINIVmO0V4TcUfFzPlhSmUlJXw4pgXGXD2gKhDEhEREZEU\nlvCKdGM75sdY8NYC+i/sz+Bug9k4eaOSaBGRFk4Pq0kUnnjiCYYOHdqgYy699FKWLl3aRBHFJzs7\nm7Vr10YdRrPQrCvSOw7uYPLKyXx27DNem/Aa53U5L+qQRERERE7aqlWrog7hb9Q2RbjUrllWpD87\n9hn3vX4fgx8fzKjeo1g/cb2SaBERqaYeaUl27q5RSVJAs0uki/YXccGiC1i3ax0br9/I1Aumkp6W\nHnVYIiIi0oLs2bOHq6++mtNPP53OnTszderU6irubbfdRqdOnejZsyerV6+uPmb48OHccccdDB48\nmHbt2vHee+8xfPhwFi9eDMDOnTsZNmwYWVlZdOnShdGjR9f590+YMIEpU6YwYsQI2rdvz/Dhwyku\nLq7ePn36dLp3706HDh0YMGAAr7/++nHHzpw5s3q5oKCAbt26Ndp/m5ak2STSRz87yk/X/pQRS0cw\nNW8qa/5lDdlZ2VGHJSIiSUg90tKUKisrGTlyJOeeey5//vOfKSkpYfTo0bg7GzZs4Ctf+Qoffvgh\nP/7xj5k0adJxxy5btoxFixZRVlZGjx49jmujmDlzJvn5+ZSWllJSUsK0adPqjWP58uXceeedHDx4\nkNzcXMaOHVu9LS8vjy1btnDo0CHGjBnDqFGjqKioANS60ZiaRSJdWFxI7qO5vPvhu2z9wVYm5E7Q\nBSAiItKSmTXOTxzefPNN9u3bx9y5c8nIyODUU09l8ODBAPTo0YNJkyZhZowfP559+/Zx4MCBMGRj\nwoQJnHfeeaSlpdGq1fGPqrVu3Zrdu3dTUlJC69atufDCC+uNY+TIkQwZMoTWrVsza9Ys3njjDUpK\nSgAYO3YsHTt2JC0tjVtvvZVPP/2UHTt2VB+rtpLGkdSJdHlFOdP+exqjVoxi1jdn8ex1z3JmuzOj\nDktERJKceqRbAPfG+YnDnj176NGjB2lpf5tGnXnm53lKmzZtACgvL69eV18LxZw5c3B38vLyyMnJ\nYcmSJQDMnj2bzMxMMjMzufHGG4EgKe/atWv1sW3btqVTp07s3bsXgPvvv5/evXuTlZVFx44d+fjj\njzl48GBc71fqlrSjdqzZuYYbXriBi869iLdvfJtOGZ2iDklERESEbt26UVxcTGVlJenpDXtOq747\n6meccQYLFy4EoLCwkIsvvphhw4YxY8YMZsyYcdy+7s6ePXuql8vLy/noo484++yzWb9+PXPnzmXd\nunWcf/75AHTq1Km6Ct22bVuOHDlSfez+/fsb9B7kc0lbkZ7y4hQWXr6QJVcuURItIiINoh5paUoX\nXHABZ511FrfffjtHjhzh6NGjFBYWntSx9bVUrFixgvfffx+ArKwszKzWqneVVatWUVhYSEVFBTNn\nzmTQoEGcc845lJWV0apVKzp37kxFRQV33303hw8frj4uNzeXVatWcejQIfbv38+8efNO8p1LTUmb\nSG/7wTZG/N2IqMMQEREROU5aWhorV65k586ddO/enW7durFixYpaH+I70XKsjRs3MnDgQDIzM7ny\nyiuZP38+2dnZte5rZowZM4a77rqL0047jc2bN7Ns2TIA8vPzyc/Pp1evXmRnZ5ORkUH37t2rjx03\nbhx9+vQhOzub/Px8Ro8erWfP4mTJ2GxuZp6McYmISPNQUFCgqnQzZ2Z6IK4eEydOpGvXrtxzzz1R\nh9Js1HVNhevj+iaRtBVpEREREamdvmQkByXSIiKSclSNllSnsaCTg1o7REREJOmotUMam1o7RERE\nToLGkRaRRFAiLSIiIiISB7V2iIiISNJRa4c0tqZo7UjamQ1FRESkZdPDdJLsIkukzSwd2Ai87+6X\nRxWHiIikHo0j3fypGi3NQZQ90tOB7YD+TxERkUZVVFQUdQgi0gJEkkibWVfgUmARoPs2IiLSqEpL\nS6MOQURagKgq0v8B3AYci+jvl2ZIw1lJbXRdSG12794ddQiShPR5IY0t4Ym0mY0EDrj7ZlSNlgbQ\nB6DURteF1EatHVIbfV5IY0v48HdmNhsYB3wGfAloDzzr7uNj9lHftIiIiIgkRLzD30U6jrSZDQN+\npFE7RERERKS5SYaZDVV9FhEREZFmJylnNhQRERERSXaRVaTNrKuZPWdmfzKznWY2z8xOqWf/fzWz\njETGKIljZsfM7P6Y5R+Z2c+ijEmiZ2aVZrbZzN42syIzu9U01ZnEMLPyqGOQ5BHzmVH1072efQvM\nrH8i45PEC/OLpTHLrczsL2a2sjHOH9U40gb8BviNu/cCegHtgFn1HDYdaJOA8CQaFcBVZnZauKxb\nJQJwxN37unsO8G3gEkBfsCSWPiskVtVnRtVPcT376tppGT4BzjezL4XL3wbepwH//mZW50zgUVWk\nvwn81d2fBHD3Y8AtwPfMrI2Z3W9m28xsi5ndbGZTgbOBV81sbUQxS9P6P2AhwXVwHDPLNrN14fXw\nipl1M7MOZrY7Zp+2ZlYcTj0vKcjd/wJcD9wMYGbpZjbXzN4Mr43rq/Y1s5+Y2dawin1vVDFLYoT/\n/79iZn8I/92vCNdnm9kfzWxheFdjTcwvU2khzKx/WH3eaGarzezMmM3jwsr1NjP7emRBSlNbBVwW\nvv5n4FeEQzCbWZ6Z/d7MNplZoZn1CtdPMLPnw7zz5bpOHFUifT7wh9gV7l4GFAPfB3oAfdy9D/BL\nd38I2AsMd/dvJTpYSZgFwFgza19j/UPAkqrrAZjv7h8DRWY2PNxnJLDa3SsTFq0knLvvAtLN7HRg\nElDq7nlAHjA5TJwuAa4A8tw9F5gTXcSSIH8FrnL3/gSFmgditn0ZeDi8q1EKXBNBfJI4GTFtHc+G\nlcSHgGvcfQCwhM/vfhuQ4e59gRuBx6MJWRLgGWC0mZ0KfBXYELPtj8BQd+9HcMdzdsy2vgTXzkV1\nnbjOUnUTq6ucbsBw4JGwSo27H0pUUBItdy8zs6eAaQS/GKsMBL4Tvl7G54nRM8A/AQXAaODhxEQq\nSWIE8FUzuzZcbg/8PfAt4HF3Pwr6DGkh0oB7zWwowYy5Z4dftgB2ufvW8PUfgOwI4pPE+WuYGANg\nZjkExbtXwscr0gkKcxDkIr8CcPf1ZtbezNq7++EExyxNzN23mVk2QTX6xRqbs4CnzOzLBNdEbG78\nkruX1nfuqBLp7cC1sSvCKmQ34D0042FLNg/YRFA1iFXbNbESmG1mHYF+wLomjk0iZmY9gUp3PxD+\nUrzZ3V+usc8/os+QlmYs0Bno5+6VZraLYMIvgE9j9qsE9NB6y2LAO+5+4Unur77p1PU8cD8wDOgS\ns/4eYK27X2VmPQiKc1WOnOikkbR2uPtaoI2ZjYOg15HgVtwS4CXghqpe1zBJAigjqDhJCgurh78m\nuG1f9YH2e4KKMwS/MF8L9y0H3gLmAytdYzmmNDPrAjxKcJsWYA1wY9VDIGbWy8zaEPSyTawa5Sfm\nM0RSVwfgQJhEX0TQHigCsAPoYmYDAczsFDPrHW4zgruamNkQglaxsmjClAR4HPi5u79TY317Pr9L\nMbGhJ41yQpargFFm9ieCC/0IMANYRNArvdXMigjK8BA8iLZaDxumrNgk+AGC6lKVqQSJ0RaCRHp6\nzLZngDHhn5J6qvod3yZIkFcDd4fbFhHc3dpkZtuA/wTS3X0NQeVho5ltBn4YQdySAOGXqE8Jnp0Y\nYGZbgXEEPY9Van7B1hfu1Hbcv6+7VxDcAf9FmFNsBgbF7HvUzDYRPKMzKZGBSsI4gLuXuPvDMeuq\nrpU5BK1hmwhaf7yWfeqkCVlERKRZMrM+wGPuPjDqWESkZUqGKcJFREQaxMymAMuBO6KORURaLlWk\nRURERETioIq0iIiIiEgclEiLiEjSC2c0fdXM3glnKZwWru9kZi+b2Z/M7CUzy4pZ/6qZlZnZQzXO\nVWBm78ZM3NG5tr9TRORE1NohIiJJL5zW+Ux3LzKzdgSTq3yHYLiqg+4+x8x+AnR099vDoRD7AjlA\njrtPjTnXq8AP3X1T4t+JiKQSVaRFRCTpuft+dy8KX5cTDHF3DsF08E+Guz1JOAuqux9x90KOn5Al\nlibtEZEvTIm0iIg0K+FUv32BDcAZ7v5BuOkD4Iwau9d12/XJsK1Do36ISNyUSIuISLMRtnU8C0yv\nOQtdOLvpyfQrjnX3HGAoMLRqll0RkYZSIi0iIs2CmZ1CkEQvdfffhas/CPunMbOzgAMnOo+77w3/\nLCcYizqvaSIWkVSnRFpERJKemRmwGNju7vNiNj0PfDd8/V3gdzUPrXGe9KpROsLE/HJgW5MELSIp\nT6N2iIhI0jOzIcBrwFY+b9/4N+BN4NdAd2A3cJ27l4bH7AYygdZAKfBtoDg8zylAOvAycKvrl6GI\nxEGJtIiIiIhIHNTaISIiIiISByXSIiIiIiJxUCItIiIiIhIHJdIiIiIiInFQIi0iIiIiEgcl0iIi\nIiIicVAiLSKSQszshoZMeW1m2WamCUlEROLQKuoARESkcZhZurs/FnUcIiIthRJpEZEkYmbZwGpg\nI9APeAcYD/QGHgDaAQeBCe6+38wKgM3AEOBXZpYJlLv7A2aWCzwKZAD/C3zP3UvNrD/wOMEMgS8l\n7t2JiKQWtXaIiCSfXsAj7t4bOAzcDMwHrnX3AcASYFa4rwOnuPvX3f3fY9YBPAXc5u59gG3Az8L1\nS4Cb3D236d+KiEjqUkVaRCT57HH3N8LXy4CfAjnAy2YGkA7sjdn/mZonMLP2QAd3Xx+uehJYYWYd\nwvWvh+uXApc0/lsQEUl9SqRFRJKPx7w2gqr0O+5+YR37f3IS57QGrhcRkRNQa4eISPLpbmYDw9dj\ngP8BulStM7NTzKx3Pcebux8GDpnZkHDdOKDA3T8GSs1scLh+bBPELyLSIiiRFhFJPjuAm8xsO9CB\nsD8a+IWZFRE8XDionuOrKtrfBeaa2Rbga8Dd4fqJwCNmtrnG/iIi0gDmrs9PEZFkEY7asdLdvxpx\nKCIicgKqSIuIJB9VOEREmgFVpEVERERE4qCKtIiIiIhIHJRIi4iIiIjEQYm0iIiIiEgclEiLiIiI\niMRBibSIiIiISByUSIuIiIiIxOH/AZmq0F6KCH0qAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x105d79e50>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "2) Comparing Yearly Team Statistics"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I performed a similar exercise on team statistics, which allowed me to compare teams' performance over the past ten years. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Creating a summary data frame with all of the game metrics and their various descriptive statistics:\n",
      "agg_funcs = ['mean','std','max','sum']\n",
      "\n",
      "gs_vars1 = ['game_id', 'h_or_a', 'date', 'season', 'team', 'team_id']\n",
      "gs_vars2 = ['home_01', 'win_01']\n",
      "gs_vars3 = ['PTSF', 'PTSA', 'AST', 'BLK', 'DREB', 'OREB', 'REB', 'MIN', 'PF', 'STL', 'TO', \n",
      "            '3PM', '3PA', 'FGM', 'FGA', 'FTM', 'FTA','+/-']\n",
      "gs_vars4 = ['PTS_diff', 'PACE', 'AST_ratio', 'DEF_eff', 'OFF_eff', 'FGP_adj', 'TO_rate', \n",
      "            'DREB_p', 'OREB_p', 'FT_rate']\n",
      "\n",
      "dict1 = {'game_id': 'count',\n",
      "         'win_01': ['mean','sum']}\n",
      "dict2 = dict([tuple([col_name, agg_funcs]) for col_name in gs_vars3])\n",
      "dict3 = dict([tuple([col_name, agg_funcs]) for col_name in gs_vars4])\n",
      "dict1.update(dict2)\n",
      "dict1.update(dict3)\n",
      "gs_mapping = dict1\n",
      "\n",
      "#Create gameStats_derivs data frame\n",
      "gs_groupPerm\n",
      "gameStats_derivs = gameStats.groupby(gs_groupPerm).agg(gs_mapping)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 334
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Below, I compare the yearly performance of four teams across three metrics: defensive rebound percentage, assist ratio, and number of wins. Some interesting things to notice: (1) The recent improvement of the performance of the Golden State Warriors, (2) the drop in the  performance of the Cleveland Cavaliers after Lebron James left in 2010, and (3) the dominance of the Spurs over the past ten years"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "teams_to_compare = ['Detroit Pistons', 'Cleveland Cavaliers', 'San Antonio Spurs', 'Golden State Warriors']\n",
      "stats_to_compare = {'win_01':'sum', 'DREB_p':'mean','AST_ratio':'mean'}\n",
      "\n",
      "for key,val in stats_to_compare.iteritems():\n",
      "    ax = gameStats_derivs.unstack()[key][val][teams_to_compare].plot(figsize = (15,5))\n",
      "    ax.set_ylabel(key + ' ' + val)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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o8XENXDK44H/en8wpMlOzRk37+Xli8fGkFZOYdHASRcsXZWqdqdw4dcOu4pPH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+y1nrXWvfB2w72fYdFqxw4Ode6Mj5U8d7UqScEnaiop+MRNk3ko1+eP8+d5OCEBHzs7PmrenCY3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\nbqTgk3XBRTm15VlSo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       "text": [
        "<matplotlib.figure.Figure at 0x13185fed0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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XHnlEL8Xct8+sFjy+lZnJ8JAQ5tWqZZIk7n6MmsgppforpTyUUjZKqapKqcVK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\nIYQQQgghhHAx/w9s4QgNW7M0BwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x139890e10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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XyykpzD95ktBjx6hatCjjqlenV6VK+Fj4MAHL78itWaMkgRM2s9vuXMaD2auP\nLpsEzKYdrxwSMFWmDMk+ZTh2uSwnk8pQuU4Z7m5WhuJ32rgbZa8EzELsMpZAKV3mY8Pf0c2L51kf\n/R31fO6krk/lv98vr4QwP7uEkhC6hNS0VOp+Upev+3xtvxNLL1/WJ/YtXKh34IYOlTdAsyUm6gt+\nixfrssvBg13zxChnk/43YbbNm3VS9+mn0KvX337L7P43W7hsaWXDhkoSOJFvdumdyyzzFcQnntBX\ndC5ftj0ps0cJYtmyXC9SisVLfVxy/pszZJxY+9NPjs1/lFIMWDYAHy8fFvZciJG1FCi7hLAgpasF\nSQgrVoRWrfQOs3CqqeFT2XpiK1/3+bpwD6SUHiEQHKxLzSZNkr9Pq9mxQ++S+vjo0sCmTc2OyJqk\n/01Yyfbt+mTfjz+GgQMt0/9mC5dN5FJSlCRwdmCZWmUnsuvuXIaMPro//sjfTkshd8Dy1f/mguy1\nPlNT9YW2ypVh7lzHtVpM3DyRZXuWsWnwJooXceCLvlL69Jr8JICnTuk3qwoVoF07PRerXTv9QbOI\ngw/icEP5WZuXrl/CL9SPP174gxplaxTsCQ8e1KclJiToft2OHQv2OMLx0tL0C82bb8LTT+v+j9Kl\nnRqCZd/bpf/N41l1babGxPDDm28y5fnnOVa+vCX632zhsj1yksSJgrJb71xmGX10TrJ3r94IXLwY\nevfW89+aNHHa07scb2998bdjRz3s3BFjCVbuXcnMbTOJHBrp2CQO9CK+4w79lZ+5EWlpenhgeLj+\n+s9/dG9gy5Y6qcv4kl0euypTrAzPNHuGmdtmEtI5JH8/fP263nmbNg3Gj9e7cZJ4W5uXl35z6dlT\n/501aqQHsvfp47kJi/S/CYu61f92+TJVX3mFcaGh9GrVCp/x480OrVAsvyNn5fiE63DI7pyDuPv8\nN2c4flznKR9/rD9L2EvsmVg6LejEyv4r7dcH5SwXL8LWrX8ld5GRsmvnAIcuHCJgTgBHgo9QsmhJ\n235o3Tr9wtS4sf4QXLOmY4MUjrF5sy63rFEDPvlED2n3BNL/Jiwsx/6348d1m0yvXvDBB5a/+OKy\npZVWjk+4Hrv3ztnRjRt/zX+7cUP63worOlq/Rq9cqfOUwjp/9TwBcwJ4q+NbDGo+qPAPaLasu3YR\nEbJrZye9lvTiodoPMaLNiNzveOKELjnbuhWmT4dHH3VOgMJxbt7Uu6ohIXru3Pjx7tsPJv1vwsJs\n6n87cwa2fTl3AAAgAElEQVS6dtVjCaZNs3S/Sk6JnHUjFnYVFhZmdgiW4OsLq1bp95uHHtIHwt24\nYW5M587Bv/8Nfn7w5Zf6/X/3bnj+ec9J4hyxPps318l6795w+HDhHislLYV+S/vxeP3H3SOJA/2G\n1agRDBmi+3x279Z9WW+8oRfe7Nn62PU6dfQVhRkzdO/dzZtmR+5UBVmbwW2DmRY5jTSVlv0dUlL0\nh4ZmzfSuze7dksS5iyJF4OWXISoKdu7UtfBr1zrs6Ux5bz99Wr95+vnpEsrQUL0T9+yzksSJW8xY\nm6lKsezMGe7bsYO+MTEElC5NfGAgU+rWzf4Qk8qVdQlUVBQ895x+bXYxksgJj5PROxcVpS+EBwTo\n3Rtn27tXV+HUrQuHDun+t7Vr4eGHLX1RyKU88gi8/roe+3TxYsEf5+W1L+NtePNhlw/tF5wVlSun\nr3D861+wZo2+yrByJdx/v/5HMmiQLsfs1Ek3IP7wg/5QJ/6mY62OlChSgjUH1tz+mxER0KYNrFgB\nv/6qS3pKlHB+kMKxatTQg4inTdNlhv366VIQVxYTo68w1q+vd5M3bNBzDTt3tnxZmnBvl1NSmH7s\nGPUiI/koIYGXqlfnQNu2jK1RI+9DTMqW1R/A/vwTnnxS9yu7ECmtFB7N2b1z0v9mjsKMJZgXNY+Q\nX0OIHBpJ+eLlHROgK5FeO5ss/GMhX+78krVPp+/GnDunX2BWr9bNm08+KR9+PcXVq3rm3KxZesd7\n9GjXmeMp/W/Cwuw6/+36dV0anJysx79Y7AKb9MgJkQtH985l7X8bNw6eespzSifNVtCxBFsSttBz\ncU82PbuJBpUaODZIV5W11y48XPfatWrl0b1211Ouc/e0u1n71Boa/7RdJ3F9++rB3hYbNCucZO9e\nfcXwzBldunzvvWZHlDPpfxMW5rD5bykpusQyPl5Xo1jotVoSOQ9n1XkeVuKI3blz5/TJ7zNn6laJ\nsWN1X61ciP87Z6zPK1f0WIK+fW0bS5CQmEDgZ4HMeWwO3et1d2hsbie3XbuMryZNXGLXrjBr89P5\no3jgo6XUK13rr3lawrMpBUuWwD//qWu+Q0KgYsUCP5zdXztl/puwE3uvzVSl+OHsWaYkJHDs+nXH\nzX9LS9O75pGRusWgUiX7Pn4ByWEnQuTBnr1z2fW//fyz7n+T90NzlCqlL7DNmgXffJP7fZNvJtNz\nSU+C2wZLElcQufXaRUXp4cnly7tvr92VK/Dyyzz/6mJmNbjEmXU/SBInNMPQZbWxsbp0q1EjmDdP\nf3g0k/S/CYsqVP9bQXh56cO9unTR71EnTtj/OexIduSEyEZBduek/8015DWWQCnFgGUD8PHyYWHP\nhRjyIcYx3GjX7haldG/F2LHwwAMwaRJDI17Hr5wfb3Z80+zohBXt2KGv+vn46F2wpk2d99zS/yYs\nzK79bwU1caLux1i/Hu6+27nPnYWUVgpRALb0zkn/m+tZvVpffP7tt9tfmydunsiyPcvYNHgTxYvI\nX6LTuHqv3cGDuhznyBH9gbxjRwB2ndpF1y+7Eh8cT1FvB56kJFxXWpr+sPjmm3q3+p13oHRpxz2f\n9L8JC3NY/1tBzZypS6DXroWGDU0LQ0orPZzMkSuY3ObOZZ7/9tVX+t95TAwMHSpJXH45e33mNJZg\n5d6VzNw2k+/7fS9JnLNlnWsXEwPHjuU8127mTL2b4eC5dnmuzevX9QEmbdtCUJDe8k1P4gCaVG2C\nfxV/luxe4tA4hQvz8tJXDHfvhvPn9b+DpUv1jlke8vXaKfPfhBPlZ23me/6bM40cqcfEPPCAfs+x\nGBc5/1YI82T0znXurN9rAwL0Z7ZvvtHDpn/+WVeACdcyahQcOAB9+uixBAcSYxmyYggr+q/At4yv\n2eEJ+KvX7qGH9O2su3azZpm7a7dunX6Tb9xYv8HXrJnt3YLbBvPWhrcY2HSglOqKnFWpAvPnw+bN\nutzys8/gk090s3VhxMTopG3pUn3a04YNpu4sCJHhckoK80+eJPTYMaoWLcq46tXpVakSPlYbpjto\nkG60f/hhWL4c2rc3O6JbHF5aaRhGPHAJSAVuKqUCDMOoACwBagHxQF+l1G3jeqW0UliNUnr37fBh\nndRJ/5tryxhLUKbqeSKaBPBWx7cY1HyQ2WGJ/Lh4UffXhYfrYdvZ9do1bWrfuV0nTuga6q1bYfp0\nePTRXO+eptJoOLMhcx+bS4daHewXh3BfN2/qYeIhIbpkd/z4/O2aSf+bsDBL9L8VxM8/64qQRYt0\ns70TmdYjZxjGYaCVUup8pu9NAs4qpSYZhjEeKK+Uuu1AcEnkhBCOdvFSCjVf60azak3Z/OZks8MR\nhZVdr93Ro3rXLmNgeUF37VJSdEnnBx/A8OG6PtfGobGzts1i/aH1LOu3LP/PKzxXQgIEB8POnXrt\nZexO50T634SFWa7/rSA2b9alPJ9+qq8EO4nZiVxrpdS5TN/bA3RSSp0yDONOIEwpddu0XUnk7Efm\nyAkrM3N9Bq8JJvr4Hg6+t4rJH/nQt68pYQhHKsSu3a21GRGhy90qVNAlnfXr5yuEKzeu4Bfqx9bn\nt1K7fG07/cGEx/jxR10P3qaNTtJ8dfn3rfUp89+ExWSsTafNf3Om7dt1s/3HH+sdOifIKZFzxv9F\nBaw3DCMV+FQpNQeoqpQ6lf77pwApUBNCON28qHn8uP9HIodGcqSVD1266Dan7MYSCBdWrhx07aq/\nIPteu5x27RITdR316tX6TfvJJwv04bhU0VI81+I5Pon8hKkPT7XzH1C4ve7dda/bxInQrJk+BGj0\naF3n/9VX0v8mLCc5NZXpx45Zv/+tIFq1gv/9T7+nXL6sL/KZxBk7ctWUUn8ahlEZWAeMBlYopcpn\nus95pVSFbH5WduQK6UbqDZbsXsKyPctISUsxOxy3cU+FexgVMIq7y5s7V0QU3JaELfRc3JNNz26i\nQSVdEJDbWALh5nLatbtyBfr10ydTFrKH42jiUZr/pznxwfGUKVbGToELj7N3rz5kJy5OX5SQ/jdh\nsjSliL92jZikJGKTk9mdlMSP5865Vv9bQRw6pE/CGz5c97E6kGk7ckqpP9P/e8YwjOVAAHDKMIw7\nlVInDcOoBpzO6ecHDx6Mn58fAOXKlaN58+a3SrAyjjaV27ffPpd8jvFzx7M8bjkt2rXguRbPER8V\nD0CTtvqIxV2Ru+R2AW43btuYX4/+SrMJzWhRrQUhQ0JoV6Odpf7+5XbutxMSE+gxsQcv3/vyrSQu\nLCyMkiXh9deD6N4dPvoojFKlrBGv3HbC7ehoKFaMoHfe0bf/9z84epSgwEBo0MBuz9elThfmR82n\n2bVm1vrzy23XuV2/PmFvvAH79hE0aBDccYe14pPbbnu7Y6dOxF+7xuK1a4m/do1rTZsSk5REzG+/\nUcbHh5b33Yd/yZJUi41lZqlSPHnffZaK3yG3N28m7N57Ydcugr74AgzDLo8fHR3NxfT5SPHx8eTE\noTtyhmGUALyVUpcNwygJrAXeBToD55RSHxqGMQEoJ4ed2Me+c/sIjQjl691f07NBT4LbBtPszmaE\nhYXdWiDCPi5fv8z86PmERoRSpWQVxrUbR++GvfHxcuG6b5M4c30m30ymw/wO9PPvx6vtX832PsHB\nsGuXHktQtKhTwhIWZe+1GXEsggHfDWD/6P14e3nb7XGFZ5L3duEIWXfYYpKSiElKYk9yMhWLFKFR\niRL4lyyJf8mSNCpRgkYlS97W8+ZRa/PMGV1m2b69Pm3Wy8vuT2HWjlxVYHn63Bwf4Cul1FrDMH4H\nvjEMYwjp4wccHIdbU0oRFh/GlIgpRB6LZHir4cS+GEu10tXMDs2tlS5WmjFtxzCyzUhW7F3BlIgp\nvLLuFV5q+xJDWgyh7B1uWkrgwpRSDFkxhIaVGvLKva/keL/Jk/VhVCNG6NnUcl6AsJfA6oFUKVmF\nlftW0rNBT7PDEUJ4MFsTtqBy5XjxrruyTdgEuqx5wwZ9AMpzz+kPDk76/+TwHrnCkB253GX0v02J\nmMK1lGuMDRzLwKYDKVHEtuOwhf1tPb6VqRFT+fnAzwxqNogxbcdIH52FTNw8kWV7lrFp8CaKF8n9\nyOMrV6BjR31+wITb6gWEKLjFuxcz+/fZbBy80exQhBAewB47bMIGSUnQuzeULq0PISpWzG4Pbdr4\ngcKQRC5755LP8en2T5mxdQaNKjdiXLtxPFz3YbwM+2/lioI5mniUGVtn8FnUZzxw9wOMCxxHuxrt\nzA7Lo63cu5IRq0cQOTQS3zK+Nv3M8eP68MKPP0bGEgi7uZl6k9rTa/PDkz/QslpLs8MRQriJjIQt\nNimJmPSELTY9YasgCZtzXL+uZzcmJ8OyZTbPGs2LJHJuIKf+N1t4VK2yhUgfnW0cvT5jz8QStCCI\nFf1XEFg9f7MFoqOhSxdYuVLGEngiR63ND3/9kNizsXze83O7P7bwHPLe7plyS9jKFymCf3rC1qhk\nSfxNStg8em2mpOgSy/h4/eHBDqd2mjlHThRC1v63F1q/QNzIOO4sdafZoQkbSB+d+c5fPU+Pr3vw\nUZeP8p3EATRvDgsW6GoJGUsg7OX5Vs9TZ3od/rz8p/QzCyGyZWvC1kl62KzFx0d/cBg9Gh58ENas\ngUqVHPJUsiNnUdn1vz3d9Ok8+3qE9UkfnfOkpKXQ7atuNK3SlMldJxfqsWbMgJkz9ZixcuXsFKDw\naC+ufpFKJSrx3v3vmR2KEMJErrDDJgpAKXj9dVixAtatg7vuKvBDSWmli8jc/+ZfxZ9xgePoWrer\n9L+5Iemjc7zgNcHsObuHVQNW2aWcVcYSCHvae3YvHRd05EjwEe7wucPscIQQDiYJm4eaOFGfZLl+\nfYHLeiSRs7is/W9jA8fStGpTuz2+R9cqW1xGH920yGlULlHZI/voHLE+50XNI+TXECKHRlK+eHm7\nPGZqqh5LULmyjCXwFI5+7Xxk0SP0btCbIS2HOOw5hPuS93ZrkoRN1uZtZs6EkBBYuxYaNsz3j0uP\nnAVJ/5sA6aNzhC0JW5iwfgKbnt1ktyQOwNsbFi3SYwk+/FDGEojCC24bzLi143iuxXMYcmVACJci\nPWzCZiNHQqlS8MADsHo1tLTPicWyI2cC6X8Tedl2fBtTI6ay5sAa6aPLp4TEBAI/C2TOY3PoXq+7\nQ55DxhIIe1FK0WR2E0IfDqVz7c5mhyOEyIbssAm7+e47GDECli+H9u1t/jEprbQA6X8T+ZWQmMAn\nWz+RPjobJd9MpsP8DvTz78er7V916HPJWAJhL3N3zOX7Pd+zasAqs0MRwqNJwiac4uefYeBAXeLT\npYtNPyKJnIkc3f9mC6lVdm2Xr19mQfQCQiND3bKPzh7rUynFgGUD8Da8+aLXF04pU1u9Gp5/XsYS\nuDNnvHZevXmVWqG1+PW5X7mn4j0OfS7hXuS93T6iL19m2L59xCYlScJmJ7I287B5M/TpA59+qpvv\nc3DqyimeX/k8KweszDaRk60gB1FKseHwBh77+jHum3cflUpUIm5kHPMfn+/0JE64vtLFSjO67Wj2\njdrH+Pbj+WTrJ9SZXocp4VNIvJZodniWEPJrCAfOH2DOY3Oc1mv0yCP6ZOHu3eHiRac8pXBDxYsU\nZ3ir4UyLmGZ2KEJ4nCspKfSNjeW5O+/kxL33ktCuHWuaNWNy3boMqVaNwLJlJYkT9tehg54v9+KL\n8OWX2d4lPCGc1nNa55o3yI6cnUn/m3AW6aP7y8q9KxmxegSRQyPxLePr9OeXsQSisP68/Cf+s/w5\nOOagXQ/oEULkbuiePaQoxYICnCQoRKHFxkLXrvqq8IgRgN4MmrltJu9vep/PenzGo/c8KqWVjib9\nb8Isnt5HF3smlqAFQazov4LA6uY0q8lYAmEPzyx/hsZVGju8v1MIoX17+jSvHz7MjlatKC27bsIs\nhw5B584wfDhJY0cxbNUwYk7HsKzfMmqXrw3k3CMnWUYh7Tu3jxdXv0jdT+qy//x+1gxcw7qn19Gt\nXjdLJXFhYWFmhyAcpEbZGkzqMon4l+LpWLMjA5cPJHBuIN/EfENKWorZ4dmkoOvz/NXz9Pi6Bx91\n+ci0JA7+GksQFaXHEgj34czXzpfavsSMrTO4mXrTac8pXJu8txfckWvXGLl/P4saNpQkzgFkbeZD\n7dqweTPX583hqx5+FDF8CB8SfiuJy41NmYZhGLUNw5hqGMZywzBWpn+tKHTgLkr634QVeVofXUpa\nCv2W9uPx+o8zqPkgs8OhVCl9guWsWfDNN2ZHI1xRq7ta4VfOj+V7lpsdihBuLVUpBsbF8c8aNWhT\npozZ4QjB8ktbaf7kRXrFF2f+L6Up7l3Mpp+zqbTSMIydwFxgN5CW/m2llNpY0IBtYbXSSul/E67G\nnfvogtcEs+fsHlYNWGWp0ztlLIEojOVxy5m0ZRLhQ8LNDkUIt/VefDwbL15kXbNmeEktvDBRSloK\nb/zyBotjFrP0iaW0KXWPPkmtbl3dq5G+W1yoHjnDMLYqpQLsH36ez2uJRE7634Src7c+unlR8wj5\nNYTIoZGWPBhCxhKIgkpNS6XuJ3X5us/XppYLC+GufktMpM/u3Wxv3RrfYrbtegjhCKeunKL/d/3x\n8fJhUZ9FVCpRSf9GUhL07g2lS8NXX0GxYoXukfvEMIx3DMNoZxhGy4wvO/5ZLMlV+t9sIbXKns3q\nfXT5WZ9bErYwYf0EVvRfYckkDmQsgTtx9munt5c3YwLGEBoR6tTnFa5J3tvzJzElhYFxcXxav74k\ncQ4mazN3GaMF2tdoz09P/fRXEgdQsiSsWAFpafD445CcnOPj2JqN+APPAyHA5Exfbkf634Q7c/U+\nuoTEBJ749gkW9FxAg0oNzA4nV6NG6ROF+/SBGzfMjka4kiEth7D24FoSEhPMDkUIt6GU4oV9++hW\noQKPV6qU9w8I4QBKKWZsncHjix9nVvdZvP/A+3h7ed9+x2LFdMN9lSrw8MM5Pp6tpZUHgYZKKad+\nHHFmaaX0vwlP5Sp9dMk3k+kwvwP9/Pu5zPHsMpZAFFTwmmDu8LmDkM4hZocihFv4/ORJJh09yu+t\nWlHcO5sPzkI4WNKNpFujBb7r+x11KtTJ+4fS0uD//g/jrbcK1SP3PTBcKXWqIIEXlDMSOel/E0Kz\nch+dUooBywbgbXjzRa8vMFwoI7pyBTp2hL59YcIEs6MRruLQhUMEzAngSPARShYtaXY4Qri0A8nJ\ntIuK4pdmzWhaqpTZ4QgPtO/cPnov6U3ru1oz+5HZ+d4oKmyPXHlgj2EYa91l/IA79b/ZQmqVRV7M\n7KPLa32G/BrCgfMHmPPYHJdK4kDGErg6s147a5evTYdaHVj4x0JTnl+4Bnlvz9uNtDT6x8Xxdq1a\nksQ5kazNvyyPW8598+5jdMBo5j8+367Vfrae2f0vuz2jiZRShMWHMSViCpHHInmh9QvEjYzjzlJ3\nmh2aEJaR0Uf3YpsXWbF3BVMipvDKuld4qe1LDGkxhLJ3lHVqPCv3rmTmtplEDo102VJnX1/dt9yl\nC9SsKWMJhG2C2wYzbNUwhrce7pYXGIVwhrcPH6ZqkSKM8vU1OxThYTKPFlg9YDVtfNvY/TlsKq00\ni71KK7P2v40LHMfApgNd9kOhEM5mrz46pRQnb9wgJimJmORkYpOS2H/1KiN9felTufJt9489E0vQ\ngiBW9F/hFkexy1gCkR9KKVr9txUfPPAB3et1NzscIVzOLxcu8ExcHNGtW1O5aFGzwxEeJMfRAgVU\n2DlyV4CMOxYFigBXlFJlChVV3s9bqERO+t+EsK/MfXT3+93PuHbjaFe93W3ljtklbDFJScQmJ+MF\n+JcseeurSpEiBB84wPiaNRlTvfqtxzh/9TwBcwJ4q+NbDGo+yMl/UseZMQNmzoTwcChXzuxohNUt\n/GMhX+78krVPrzU7FCFcytkbN2j+++/Ma9CAhypUMDsc4UHCE8Lpu7Qvg5sN5p2gd7I/lTKfCpXI\nZXkgL6AHEKiUcmjrfkETuX3n9hEaEcrXu7+mV4NeBAcGe/zogLCwMIKCgswOQ7iJy9cvsyB6AVMj\nQylXujbdm79IhUot2Zt8LceErVGJEjpxy+aq6OKff+adcuXoUakSIbVrk6ZS6fZVN5pWacrkru43\n6SQ4GHbtgp9+ArlIbG1mv3ZeT7nO3dPuZu3Ta2lcpbFpcQhrMnt9WpVSip67d3NPiRJ8VMeGkwGF\n3Xni2lRKMXPbTN7b+B6f9fiMx+o/ZrfHzimRs7VH7halVBrwvWEY7wCWOYNN+t+EcJxsd9iKtOdi\nyxacTb3BJyePkHZwLl2q1mP8PQ/QtnyVbBO2nNxZrBi/tWxJj127GBgXR4Wjc/E2vPmwy4cO/FOZ\nZ/JkPZZgxAgZSyByV8ynGCNaj2BaxDTm9JhjdjhCuIT/nDjBsevX+dbf3+xQhIfIPFogfEi4baMF\n7MDW0so+mW56Aa2ATkoph55NbsuOnPS/CWE/uZVEehsG/iVK0Chjly391xkJmz366K6mptI+fA17\nLh4lrtMT1CrtvkNbZSyBsNXppNPUn1GffaP2Ubnk7b2kQoi/xCQlERQdza8tWlC/RAmzwxEeIPNo\ngVmPzKJEEfuvu8L2yC3grx65FCAemKOUOm3HGLN73hwTOel/E6LgCpOw5cXWPrrsbEnYwuOLe9Gl\n60pirhv82LQpvsWKFfaPa1nHj0O7dvDxxzqhEyInQ1cMxa+cH292fNPsUISwrGupqQTs2MFL1asz\npFo1s8MRHmB53HKGrxrO+/e/z7BWwxw2IsluPXLOlF0iJ/1vBeOJtcrCsQlbXjL66EIjQ6lcojLj\n2o2jd8Pe+HjdXtEdFhZGnRZ1CPwskDmPzaFb3W58ePQos0+c4KemTWlU0n0HIkdH67EEK1fKWAIr\nsspr565Tu+j6ZVfig+Mp6i2NlUKzyvq0ijH793Pyxg2WNGrkcjNH3Y27r83MowWWPrHUIaMFMitU\nj5xhGJOAD4CrwBqgGTBWKfWFXaPMgfS/CZE7WxO2pqVK0b9KFbsmbDnJaR7dmIAxDG059G/z6K6l\nXKPnkp681PalW8esT6hVC99ixbg/Opql/v50cNMjHps3hwULoHdvGUsgctakahP8q/izZPcSnm72\ntNnhCGE5q8+d44ezZ4lu3VqSOOFQmUcLbB+2vdCjBQrD1tLKP5RSzQzD6AU8CowDNiulHLoVZhiG\nWhi9UPrfhEhn5g6bPWTXR+dXzo8BywbgbXjzRa8vbnsDXnf+PE/FxTGrXj3+UaWKSZE7nowlEHlZ\nvW81b214i+3DtssHVSEy+fP6dVpu3843jRq57UU/YQ2OGC1gi8L2yMUopfwNw/gMWKqU+ikjuXNE\nsJmeV3Ve2Fn634THsSVh8y9ZUidt6b92pWGnmfvo6pSvg0KxafCmHC/SRF2+zKO7dt02a87dyFgC\nkZs0lUbDmQ2Z+9hcOtTqYHY4QlhCmlI8vHMn7cqU4V0paRAO4sjRArYobCIXAvQErgEBQDlgpVKq\nrb0DzfK8hRoILv7i7rXKrsrdE7a8XL5+mSUxSyh3shz/6P6PXO8bf/UqD+/ceWvWnJcb7kikpuqx\nBJUry1gCq7Daa+esbbNYf2g9y/otMzsUYQFWW59mmJyQwHdnzrCpeXN8vOSCv1W409rMPFrgu77f\nOW20QGaF6pFTSk0wDOMj4KJSKtUwjCTgcXsHKYS7sjVhy+hhc7eELSeli5VmaMuhhIWF5Xlfv+LF\n/zZrbn6DBhRzszdtb29YtEiPJfjwQxlLIG73TLNneHvD2xy6cIja5WubHY4Qptp++TIfHj3K1pYt\nJYkTDpF5tMCWIVscMlqgMFzu1EohrMzTd9ic4WpqKgPi4riUksKyxo0p62PT9SiXImMJRG5eXfcq\nN1NvMvXhqWaHIoRprqSk0HL7dt7z8+PJqlXNDke4IWeNFrCF24wfEMIKJGEzV6pSjNm/n18TE912\n1pyMJRA5OZp4lOb/aU58cDxlipUxOxwhTDF0zx5SlGJBw4ZmhyLcjLNHC9hCEjkP5061ymY6kJzM\nB0eOsOLcOUnY7Kgg61Mp5faz5lavhuefl7EEZrLqa2e/pf24t/q9vBT4ktmhCBNZdX062renT/P6\n4cPsaNWK0m5YleEOXHVtZh4tsKjPIlNHC2SWUyJX4IJiwzAa5OO+3oZhRBmGsTL9dgXDMNYZhrHP\nMIy1hmHIWbHC0g4kJzM4Lo52UVHULl6c2DZtONO+PWEtWjDznnsY6etLUPnyksQ5kWEYTKhViw/u\nvpv7o6PZfPGi2SHZ3SOPwOuvQ/fu4IZ/PFEIYwPHMi1yGqlpqWaHIoRTHbl2jZH797OoYUNJ4oRd\nhSeE03pOa9rXaM9PT/1kmSQuNwXekTMM46hSqqaN9x0HtAJKK6V6pA8YP6uUmmQYxnigvFLqtrZ+\n2ZETZsvYgVt9/jyjfX0Z4+tLuSJFzA5LZOHus+ZkLIHITuDcQCbcN4GeDXqaHYoQTpGqFEHR0Txa\nsSLja9r0EVSIPJk9WsAWBSqtNAzjk1wec7BSqrQNT1wdWAD8GxinlHrMMIw9QCel1CnDMO4EwpRS\nt+3wSSInzCIJnOtx51lzMpZAZGfx7sXM/n02GwdvNDsUIZzivfh4Nl68yLpmzdxyBI1wPiuMFrBF\nQUsrBwO7ge3A75m+tgM3bHzuqcArQFqm71VVSp1K//UpQI4bcjBbjncXt5dQ7g8I4G0/P0niHMwe\n67NF6dL81qIFs44f59WDB0lzo4tAGWMJoqL0WALhPFZ+7ezTsA+HLhxix587zA5FmMTK69PefktM\nZNbx4yxs2FCSOBfgCmtz37l9tJ3bliJeRdgyZItlk7jc5FVc/DuwWyn1W9bfMAzjnbwe3DCMR4HT\nSqkowzCCsruPUkoZhpHjJ67Bgwfj5+cHQLly5WjevPmt5smMRSK35XZhbx9ITmbUt98SfukS/3zs\nMds7eK0AACAASURBVPYHBBD9229Ex8dbIj65bfvt39q3p8euXXT5/HPG16zJQw88YKn4Cnr799/D\neO01+Oc/g6hdG6pUsVZ87no7g1XiyXp7VJtRTIucxrPlnrVEPHLbubczWCUeR91e9csvDN27l0/7\n9sW3WDHT45Hbed+Ojo62VDxZb28+spnpp6fzwf0fcM/le9j621ZLxRcdHc3F9Ob4+Ph4cpJXaWUF\n4JpSKjnHO+XCMIz/A54GUoA7gDLAMqANEKSUOmkYRjVgg5RWCjNICaV7cudZczKWQGR2/up56kyv\nQ+yLsVQrXc3scISwO6UUA+LiKO/jw6x77jE7HOHiMo8W+PaJbwnwDTA7JJsUqLRSKXW+oElc+s+/\nrpSqoZS6G3gS+J9S6mlgBTAo/W6DgO8L+hxCFISUULq34t7eLPX3p0GJEnSMiuL49etmh2Q3zZvD\nggXQuzccPmx2NMJsFYpXoH/j/sz+fbbZoQjhEAtPnWLnlStMruN6ZW/CWk5dOcVDXzxE1Mkotg/b\n7jJJXG5yTeQMwyhnGEaIYRh7DMO4YBjG+fRfhxRwZEDG9loI0MUwjH3AA+m3hQNlLcPwVJLAWZMj\n1qe3YTCjXj36V6nCvTt2EJuUZPfnMIuMJXAeV3jtfKntS3y6/VOu3rxqdijCyVxhfRbGgeRkXj54\nkK8bNaK4t7fZ4Yh8sNradMXRArbINZEDvgEuAEFABaVUBeB+4GL679lMKbVRKdUj/dfnlVKdlVL3\nKKUeUkrJRxHhUJLAeSZ3njU3ahR07Qp9+sANW4+eEm6pfqX6tL6rNYt2LTI7FCHs5kZaGv3j4ni7\nVi2alipldjjCRSmlmLF1Bo8vfpxZ3Wfx/gPv4+3lPhcF8uqR26eUyrYgObffsxfpkROFJT1wIoM7\nzpqTsQQiw7qD6xi3dhw7X9iJIQtBuIEJBw+yOymJlU2ayJoWBeIqowVsUdDxA0cMw3jVMIxb4wEM\nw7gzfYj3UXsHKYS9yA6cyKpLhQr83LQpLx04wPRjx8wOxy5kLIHI0Ll2Z5RS/HL4F7NDEaLQfrlw\ngS9OnWJ+gwaSxIkCcYfRArbIK5HrB1QCNqb3yF0AwoCKQF8HxybsyGq1yo4iCZxrctb6dMdZc6VK\n6RMsZ82Cb/JV8C5s4SqvnYZhEBwYTGhEqNmhCCdylfWZH2dv3GBQXBzzGzSgctGiZocjCsjMtbk8\nbjnt57VndMBo5j8+nxJFSpgWi6PZcmrlq0qpBkqp8ulfDdK/dz7jfoZhDMrtcYRwNEnghK38ihfn\nt5Yt+S0xkYFxcVxPSzM7pELz9YUVK2DkSIiIMDsaYZanmjzF1uNb2Xdun9mhCFEgSimG7N1L/6pV\neahCBbPDES4mJS2F8evGE/xzMKsHrGZ46+Fuv6Oba4+czQ9iGFFKqRZ2iCfr40qPnMiV9MCJgrqa\nmspTcXEkutGsudWr4bnnYPJkeOop6ZnzRG/97y3OXz3PzEdmmh2KEPk2+/hx5v75J+EtW1LUK6+i\nMSH+curKKfp/1x8fLx8W9VnkNqdSZihoj5wQliQ7cKKwint7862/Pw3daNbcI4/onbnJk+HBByEu\nzuyIhLONaDOCRbsXceHqBbNDESJfdl+5wtvx8Sxq1EiSOJEv7jpawBbyL8VDuEsdvSRw7sms9elt\nGHxSrx4DqlZ1m1lzbdvCtm3Qsyd07KjnzSUnmx2V63K11867St/Fo/c8ypwdc8wORTiBq63PnFxN\nTaV/XBwf1q5N/RLu28/kSZyxNt19tIAtJJETLkESOOEohmEwvmZN/u1Gs+Z8fGDMGNi5Ew4fBn9/\nfSCK8AzBbYOZsXUGN1Nvmh2KEDYZf+gQDUuU4Nk77zQ7FOEikm4kMXD5QObumEv4kHAeq/+Y2SGZ\nwl49cjOUUqPsEE/Wx5UeOQ8nPXDCmdxx1hzA+vX6IJQGDWD6dKhVy+yIhKN1nN+RUQGj6OsvB0wL\na1t19iwj9+8nunVrysv7u7DBvnP76L2kN63vas2sR2a59amUGXLqkbMpkTMM4w6gD+AHZJwIoJRS\n79kzyGyeVxI5DyUJnDBL9OXLPLprF6/WrMmY6tXNDsdurl+Hjz6C0FB4+WUYNw7kZG/3tTxuOZO2\nTCJ8SLjZoQiRoz+vX6fF77/zrb8/HcqVMzsc4QKWxy1n2KphfHD/BwxrNcztT6XMUNjDTn4AegA3\ngSvpX67fTOJBXKWOXkooPZOV1mfz0qX5tUULZp844Taz5gCKFYM334StW2HzZmjeHCz0v92yrLQ2\n86NH/R6cvHKSiGMyj8Kduer6BEhTikF79jD8rrskiXND9l6bnjhawBa2nrftq5Tq6tBIhEfLugO3\nPyBAkjdhGr/ixfmtRQt67NrFwPTBtMXc5BS12rVh1Sr4/nt45hno1Ak+/hiqVjU7MmFP3l7ejAkY\nQ2hEKIv/sdjscIS4zdRjx0hKTeUtqfUWecgYLeDt5c32Yds96lTKvNhaWvlfYIZSaqfjQ/rb80pp\npZuTEkphZe44ay6zK1fgvfdg/nx4910YPhy8PevAL7d26fol/EL9+OOFP6hRtobZ4Qhxy/bLl+m2\ncydbW7bEr3hxs8MRFhaeEE7fpX0Z1GwQ7wa963GnUmYobI9cHFAXOAxkDFtSSqmmdo3y9ueVRM5N\nSQInXEWqUry0fz+bExP5sWlTfIsVMzsku9u9G0aMgGvXYPZsaN3a7IiEvQSvCeYOnzsI6RxiahxX\nU1OZ++efNClZkqDy5U2NRZjrSkoKLbdv5z0/P56UUgCRA6UUM7fN5L2N7/FZj8889lTKDIXtkesG\n1AMeAh5L/+phv/CEo1mljl564ER2rLI+s+OOs+ayatwYNm3SJ1s++iiMGgVuMIXBLqy8Nm0xpu0Y\n5u6YS9INc9ZtmlJ8efIkDbZuZd2FC/SPiyPkyBHkIq19uOL6DD5wgHvLlJEkzs0VZm3KaAHb5ZrI\nGYZRJv2Xl3L4EsImksAJV+aOs+ayMgwYPBhiYyElBRo1gq++Avm87dpql69Nh1odWPjHQqc/d9iF\nCwRs386M48dZ1KgRK5o0YVvLlnx/9iy9du8mMSXF6TEJc317+jQbExP5pF49s0MRFrXv3D7azm1L\nEa8ibBmyhToV6pgdkqXlWlppGMZqpdQjhmHEA1nvqJRStR0anJRWujwpoRTuxl1nzWUVEaHLLcuX\nh5kzoWFDsyMSBbUxfiPDVg0jbmQcXobjD+3Zk5TE+EOH2JmUREjt2vStXPlvp8vdSEtj3IED/Hzh\nAsv8/WlSqpTDYxLmO3LtGm22b2d1kya0KVMm7x8QHsdTRwvYolA9cmaRRM51SQIn3Jm7zprLKiUF\nZs2C99+H55/X4wtKuP/cVbejlKLVf1vxwQMf0L1ed4c9z5kbN3g3Pp4lZ84wvkYNRlevnutpr1+d\nOkXwgQOE1q3LU1Jm59ZSlSIoOppHK1ZkfM2aZocjLCYlLYU3fnmDxTGL+faJbwnwDTA7JMspVI+c\nYRhfGobxvGEYDewfmnAGZ9XRSwmlKAhX6/Nw11lzWfn4wJgxsHMnHD4M/v6wcqXZUTmXq63N7BiG\nQXBgMKERoQ55/KupqYQcOULDrVvxMQz2BATwcs2aeY7seKpqVf7XrBnvxMczat8+bqSlOSQ+d+Yq\n6/PfR45Q1DB4pYacnuopbF2bp66c4qEvHmLHyR1sH7Zdkrh8srXGYh5wF/CJYRiHDcP4zjCMYAfG\nJVyMJHDC02TMmtuSmMjAuDiuu/GH0GrV4OuvYc4cePllePxxOHLE7KhEfvTz78fu07vZfXq33R4z\n80Emv1++THjLloTWq0fFfLzuNyn1/+zdeVhUZfvA8e8BRVB2cWVVURFcE80llMylRcold8x6fevX\nYrmVaWqamWm59pZWtrgvmWmlpZaFZrmkhguCKIogKoIgm7LJ8/sDmURBQYaZYbg/1zUXzsyZc+5D\nd8Pc8zznuW052LYt57Oy6BoayvnMTL3FJ0zDnykpLI6LY0WzZljIVDlxi72xe/Ff6k8n905sG7ZN\n+sPdhxJPrdQ0rQrgD3QDXgSuK6WalmNsMrWyApAplKKyM/dec7fLyoIPP4SFC/OLunHjwMrK2FGJ\nknh317vEpMSw9MmlZd5XSHIyr0dFUUXTmOftTWcHhzLtL08pPoiJYVFcHKubNaObtCgwCym5ubQ+\neJCF3t485SIf0kU+aS1QemXtI7cTqAHsBfYAfyilLus9yjuPK4WciZICToh/VYZec7c7cwZefTV/\nyuXixRAYaOyIxL1czrhM04+bEjkqklo1at3XPu61kElZ7UxOJjg8nNGurrzp4SGLHVRgSimGhofj\nXKUKnzRpYuxwhInIyM7ghS0vEHY5jI0DN8qqlCVU1j5yR4EcoDnQEmiuaZqNHuMT5Uxf8+hlCqUo\nDxXlOo/iVIZec7dr2BC2bIH33oNnnoHhwyE+3thR6V9Fz81b1a5Rm/7N+vPZoc9K/dqE7GxGRUYS\nEBpKgIMDEe3bM6h2bb0XWo84OUmLglIw5fxcER/P0fR05jaSD+qVUVG5Ka0F9K9EhZxSaqxSKgDo\nByQCXwPm10hJFEsKOCHurjL0mrudpkHfvvm95+rVy28svngx3Lhh7MhEcUY/OJrFfy8m+0Z2iba/\n34VMysLN2prdbdrgVq0a/ocOcSw9vdyOJcrH6WvXeD0qinW+vthYWho7HGECNoVvovNXnXm1/at8\n/dTXVK8qSyDrQ0mnVr4KBABtgbPAH+RPr/ytXIOTqZVGJ1MohSi9ytJr7nbHj+f3nsvMhCVLwN/f\n2BGJonRf0Z0RrUYwvNXwYrfJU4o18fFMPnuWdnZ2vN+wIY2N0HtCWhRUPNl5eXT+5x9G1KnDKDNu\nzyJKRloL6EdZr5F7A9gNHFZK5ZRDfMUdVwo5I5ECToiyqSy95m6nFCxfDhMnwtNPw8yZ4Oho7KjE\nrbZGbmXq71M59MKhIqdG6nshk7I6lp5Ov7Awejk5Md/bG6tyHA0UZTcxKorjGRn82KKFXONYycWn\nxzNk4xAsLSxZ23+trEpZBmW6Rk4p9aFSar8hizihXyWdRy9TKIUxmPJ1HversvSau52mwbPP5k+3\nzM0FX19YvTq/wKuIzDE3H2v8GBk5GeyJ2VPo8YiMDJ46doznTp7kDQ8P9j7wgNGLOJAWBXdjavm5\nMzmZlfHxfO3jI0VcJffJN59IawEDkK+1BCAFnBDloTL1mrudszN8+il89x3MnQuPPALh4caOSgBY\naBaMfnA0C/YtAAy3kElZOFSpwnfNm/NUzZq0O3yY35KTjR2SuE1idjYjwsNZ5uNDLelJUmmlZqXy\n7q53mfzbZBY/vpiZ3WZiaSHXSZaXEveRMwaZWln+ZAqlEOWvsvWau11ubv4iKO++C88/D1OmgBEu\ntxK3SM9Ox3NRE0YGbeWrxAyC69RhqpdXqZp5G4u0KDA9Sin6HD9Ok+rV+VBWqayUUrNS+fjAxyzc\nt5CejXry7sPv0sCpgbHDMhtlukbOWKSQKz9SwAlhWJWx19ztLl7MbyC+bx989BEESQ9YoyhYyOSV\n8H+onZfMTx37GGUhk7I4n5nJ02Fh1LWyYnmzZpXuyxFTsyQuji8uXmTvAw/INYyVzO0F3JQuU/Bx\n8TF2WGanrH3kRAVXMI9eplAKU2Rq13mUh8rYa+529erB2rWwdCm8/jo89RScO2fsqO7O3HIzJDmZ\n9ocO8XFcHF81acSVg6OoY1nxerW5WVuzq00bXCt5iwJTyM/j6em8HR3NGl9fKeIqkdSsVGb9MQvv\nj7w5kXCC3c/tZlW/VboizhRyszKQ/+MqibjMTCnghDCyythrrijdu8PRo9CuHbRtC7NnQ3bJ2pqJ\n+1TUQib9XZvQo1EPvv7na2OHd1+qWVjwSZMmTPP0pNuRI6w2x470Ju76jRsMCQ9nTsOGNK1go7ri\n/tyrgBOGZfJTK/n9d2OHYRYcq1RhrJubTKEUwkRU1l5ztztzBl59Fc6ezb+OLjDQ2BGZl4TsbN6J\njmZ9QgITPTwY5epaqJn3vvP7GLpxKKdePVWhFySQFgXG8dqpU1zKzma9r69cq2jmZAqlcZnVNXLy\nZiEqMlP+f04YVmXtNXc7pWDzZhg9Grp2zV/lUno/l831GzdYdP48c2Nj77mQSYcvOjDxoYn08elj\n4Cj1KyU3lxHh4cTn5LDB1xc3a2tjh2TWtiQm8sqpU4T6++MkXxCbLSngTIPZXSOnlJKb3CrcTRSt\nss6lr6y95m6nadC3b37vuXr1oHnz/NG5GzeMHVnFy808pVh16RI+Bw5wMC2NvQ88wMLGje+6GuWY\nDmN0rQgqssrYosBY+XkxK4v/njzJqmbNpIgzU2WdQlnR3jsrqgpbyAkhhDmozL3mbmdrCx98AL//\nDuvWQYcOcPCgsaOqOG5dyGSNry/fNm9eotUo+zfrz5nkMxy+eNgAUZYvC01joqcnq5o1Y1h4OLPP\nnZMv0fQsTylGRETwf/XrE+DoaOxwhJ7JNXAVS4WdWmnKcQtRHMldUZyCXnNXc3PZVAl7zd1OKVix\nAt58E55+GmbOBPnMWLSIjAzePHOGoxkZzG7YkIG1apX6EoQ5e+ZwIvEEy/ssL6coDU9aFJSPuTEx\nbEpMZFfr1lSRaxHNhkyhNG1GmVqpaZq1pmn7NU0L1TTthKZp79983FnTtF80TYvUNG2Hpmny51kI\nUanZWFqywc8P3+rVCfjnH+KysowdklFpGowYkT/dMjcXfH1h9er8Ak/kS8jOZlRkJAGhoXRxdCSi\nfXsG1a59X9eRP9/2eX44+QMX0y6WQ6TGIS0K9O9QWhpzYmNZ3ayZFHFmQkbgKrZy/b9QKZUJPKyU\nag20BB7WNO0hYCLwi1KqCbDz5n1RDlJSUliyZImxwxDirmQufb6CXnPDbvaaC6uEveZu5+wMn34K\n332XvwjKI49AeLjhjm+KuXn9xg1mnztHswMHqKJpRLRvz3h390KrUZaWs40zQ5oPYclB8/p7Ye4t\nCgyZn+m5uQw5cYL/eXvjZWNjsOOK8lHeBZwpvneao3L/OkUpde3mP60ASyAZeBIomL+xHKjYS2WZ\nsOTkZBYvXmzsMIQQJXRrr7luoaHsrqS95m7XoQP8/Tf06QNdusBbb8G1a/d+nTm5n4VMSmP0g6P5\n7NBnXM+5rpf9mZLgunXZ2aoV06OjGRUZSXYlvhb1fo0+fZpO9vYMliVlKzQZgTMv5V7IaZpmoWla\nKBAP/K6UCgPqKKUKvhaLB+RdoZxMnDiRqKgo2rRpw4QJE5g7dy7t27enVatWTJ8+Xbdd37598ff3\np3nz5ixdulT3uK2tLRMmTKB58+b06NGDffv20bVrVxo1asSPP/5ohDMS5ihQmofdIbhuXVY1a8bT\nYWFsuHzZ2OGYhCpV4LXX8puJnz0Lfn5Q3m9DppKb97uQSWk0dWmKf31/1hxbo9f9moqWtrYcbNuW\n81lZdA0N5XxmprFDKjND5ec3ly+zOyWF/zVubJDjCf0zdAFnKu+d5s5gi51omuYAbAcmAd8ppZxu\neS5JKeVcxGtksZMyOnfuHL179+bYsWPs2LGDjRs38tlnn5GXl8dTTz3FhAkTCAgIIDk5GScnJ65f\nv0779u3ZvXs3Tk5OWFhY8PPPP9OrVy/69etHeno6P//8M2FhYYwYMYJ//vnH2KdYoUjuitIq6DX3\nhocHoytxr7mi/PorvPIK+PjARx+Bp6exI9I/fSxkUhq/RP3CuB3jOPri0TIf58YNiIrKv84xLCx/\nSqynJwwYAK1a5V8HaQx5SvFBTAyL4uJY3awZ3Zyc7v2iSuxcZibtDh1ia4sWtLO3N3Y4opRkERPz\nUNxiJwZbwkkplaJp2lagLRCvaVpdpdQlTdPqAcV+3fzss8/i5eUFgKOjI61btzZIvObi1qJhx44d\n7NixgzZt2gCQkZHB6dOnCQgIYNGiRWzevBmA2NhYTp06Rfv27bGysqJXr14AtGjRAmtraywtLWne\nvDnR0dEGPx9zUjB/vOBbq8p8/9a59KYQj6nd39OmDV2WLeMve3vWDh2KhaaZVHzGul+lChw9GsiH\nH0LLliEMGgQffxyIlZX+jlfwmKHPb/Mvv7D80iX2NGzIRA8PXklIwOrECbRyPn73rt1RSjF/7Xza\n1m9botffuAFr1oRw7hxoWiBhYXDgQAixseDqGoifH9jahuDpCbm5gfTtC9nZITz8MLz+eiCtWsGu\nXYb7/VpoGh3OnqVqWhrDwsMZ7erKg2fOoGmaSeV3Se4XPFZe+3+oSxeCw8Ppc/48GTk5YGLnL/eL\nv5+RncERmyMs3LeQVtdbMbfVXJ556hmDHT80NJQxY8aYzO+jot0PDQ3l6s1LK+72ebtcR+Q0TXMB\ncpVSVzVNsyF/RO4doBdwRSk1R9O0iYCjUuqOBU9kRK7soqOjCQoK4tixY7z++us0adKEF154odA2\nISEhTJ06lV9++QVra2sefvhh3nnnHbp06YKdnR1paWkAvPPOO9ja2jJ+/HiAQs+JkpHcLVpISIju\nDUwULSknhyePHcPd2pplPj5lWtjCHJ05A6++mj/lcvFi3efNMjN0bl6/cYNF588zNzaW4Dp1mOrl\npbdr4Erqi8NfsDliM1uGbin0+O0jbGFh+f+OjIS6dfOnuvr65v/088sfKa1R4879KwWHDsE338CG\nDflTZgcONM5IXUVvUVDe+TkjOppdV6/yS6tWWBhrCFWUiqmMwMnfdf0qbkSuvAu5FuQvZmJx87ZS\nKfWhpmnOwDeABxANDFRK3XFFvxRyZXflyhXatm1LdHQ0O3bs4O2332bnzp3UqFGDuLg4rKys2Lt3\nL1988QU//PADERERtGnThu3bt0shVw4kd0VZSK+5u1MKNm+G0aOha9f8VS4ryroMeUqxJj6eyWfP\n0s7OjvcbNtT7NXAllZ55Hc+Fnszw+oOrUU3vu2ArCVMo6rLy8hh3+jQ7kpP5zs+PFra25X/QCuDP\nlBT6Hz/OIX9/XKtVM3Y44h5MpYAT5cMohVxZSSGnH8OGDePo0aM89thjuLm58cUXXwD5hdiqVatw\ndXWlT58+REdH07RpU1JSUpg+fTpdunTB3t6e1NRUIL+Qs7OzY9y4cQCFnhMlI7kryuqGUow+dYrd\nKSn83LKlfMAqQno6zJgBX38N77wD//d/YGlp7KiKF5KczOtRUVTRNOZ5e9PZwcEgx73bCFu1R6fi\nUC+JQbaf6KVgKwljF3WrLl1ibFQUC729GVZRvgEoJ1dzcmhz6BALvb15ysXF2OEYRHp6xexTmZqV\nyuehH7P48EK6efZkQocpNHE2fgFXvbppv+9WNFLICWECJHeLJlMwSkcpxQexsSyOi+Onli3xK89P\n1xXY8ePw8suQmQlLlkDbtqXfR3nmpqEWMrmfKZEpeRfwW+zHmdfO4GRj+MVAjFXUHU1Pp39YGL2c\nnJjv7Y2ViU9hLo/8VEox5MQJalatyidNmuh136YkLQ1+/x22b8+/xcXl51lFoaxSyWnzMTltF2IZ\n3ROrfVOwSDJ+AQf5//9mZoZQr14g7u7g5kaRP+vWlWKvpIy+2IkQQgj9KOg152plRbfQUDb4+dHF\n0dHYYZmc5s1h1y5YsQKeeAKefhpmzgRj/6oSsrN5Jzqa9QkJTPTw4Bs/P71c81jSgu3RR2H8+LuP\nsNWgPr2b9Gbp4aVM6DyhzLGVlqaBv3/+bc6cf4u6vn3Lt6hraWvL3w88wLMREXQNDWWDry9u1tb6\nO0AFsCI+nmMZGRy8n28+TFheHhw58m/hdvAgPPgg9OoF330HLVoYbyXV0rhzCuVuk5xC+csv0KQJ\nxMbC+fP5t+ho2LPn38cSE/Onv0uxd/9kRE4IA5LcFfr2S1ISw8LD+aRxYwbUrm3scExWUlJ+E/Ef\nfoAPP4ShQw3/oU1fC5kUFGwFhVpB0XbqlH6vYTt04RB91/cl6rUoqloadsGV4hhqpK6ytig4de0a\nnf75h99atTKLawUvX4YdO/ILtx078r/E6dUr/xYYWL5ThfXNHK+By86GixcLF3sF/5ZirzCZWimE\nCZDcFeVBes2V3L598NJL4OQEn3wCzZqV/zHvdyETQxVsd9Pl6y680u4VBjUfpN8d64EhirqdyckE\n32xR8KaHR7n28DO27Lw8Ov/zDyPq1GFUBX0fyc6GvXth27b84u3MGejW7d/i7WY3qwrFHAu40pBi\nL58UckKYAMndosk1cmUXff06jx07RjdHR56oWRO3atVwq1YNpypVzPrD5/3Izc1vUfDuu/D88zBl\nSv6F+UUpa26WZCETUyjYirMpfBMf/PUBe0fuNcwB71N5FnWm3KJAn++dE6OiOJ6RwY8tWlSo94yo\nqH+nS4aE5E/nKyjcOnQAA3fv0JuKXsAZ8u96SYu9unXzC7uKWOxJISeECZDcLZoUcvqRlJPDtOho\nTl27xvmsLGKzsshVCrdq1XC/Wdi5W1sXvl+tGo6VtNi7eBHGjcsfpfvoIwgKunOb+83NohYyycvT\nTLZgK86NvBt4/8+btf3X0sGtg3GDKaHyKOpMtUWBvt47dyYn80x4OKH+/tSysip7YOXo9kVKMjL+\nLdx69ICKvshmRS/gCpja3/WKXuxJISeECZDcFYaWmpvL+aws3S224GdmphR7N/36K7zySn7h9NFH\n4Ol5//tKyM5m+tlo1sYn8GS6Bw1CXTl53MLkC7a7WbB3Afvj9rPu6XXGDqXU9F3UmWOLgsTsbFof\nPMjXPj70cHY2djh3uHWRkm3b8v97tm+fv2hPr14VZ5GSezGXAq4iM+ViTwo5IUyA5K4wRVLsQVZW\n/iIoCxfC66/nj9Tda2Di1imRRyJusNniPMf9YuHXOrju9KKlV9UKVbAVJzUrFa+FXhx58QjuDu7G\nDue+6auoq2gtCu5GKUWf48dpUr06HzZqZOxwdMxpkZJ7kQKuYiltsVdQ4JW12JNCrhJ5//333fmm\nUwAAIABJREFUOXPmDEuXLi3zvl566SVcXV2ZMmWKHiITkrtFM7UpGOJOlaXYO3MGXn0Vzp7Nv44O\nQggICCx2SmSdegrH/vGcefgsTZQdb9VqSE/f6mb1QRNgzLYxWFexZnb32cYORS/KWtRdzcnh2YgI\n4nNyjNqioKzvnUvi4vji4kX2PvCAUQvS7Gz4669/p0uawyIl92LuBVxl/rteHsWeFHIG4uXlxeXL\nl6lSpQqWlpb4+vryzDPP8MILL5Tow0tISAjDhw8nNjZWL/FER0fTsGFDcnNzsSjmTXr69Om89957\nWFtbU6VKFXx9fZk3bx4dOpT8eggvLy+++uorunXrppe4zZUp564xVeY3fHNiLsWeUrB5M4weDRBC\nYmJgkVMiL9dPZmrc3RcyMRdnks/Qfml7zo05Rw0r86pS77eoM4UWBWV57zyens7DR47wZ5s2NCnB\nKqr6Zq6LlNyLuRdwBeTv+t2Vttjbu1cKOYNo0KABX375Jd26dSMtLY2QkBBGjx5NYGAgX3311T1f\nX5JC7saNG1iWcDy2oJDLyckp9jXvvPMOUVFRrFixgtzcXCZPnszKlSu5cOFCiY4B+ef9xRdf8Mgj\nj5T4NZWRKeeuEIZQ0mKvoLAzZrGXkZHfTLtJk8JTuYpayMSURxn1pe/6vvRs2JOX2r1k7FDKzf0U\ndRWxRcH1Gzdof/gwY93c+E+9egY5prkvUnIvlaWAE/pza7EXEFB0IYdSymRv+eHdqbjHTYGXl5fa\nuXNnoccOHDigLCws1PHjx5VSSmVmZqrx48crDw8PVadOHfXiiy+q69evq/T0dGVtba0sLCyUra2t\nsrOzUxcuXFDTpk1T/fv3V8HBwcre3l59+eWXKi4uTgUFBSlnZ2fl7e2tli5dqjvetGnTVHBwsFJK\nKXd3d6VpmrK1tVW2trZq3759d8R86/ZKKXX8+HGlaZpKTExUI0aMUFOmTFFKKZWQkKCeeOIJ5ejo\nqJydnVVAQIDKy8tTwcHBysLCQtnY2ChbW1v14YcfKqWU+v7775Wvr69ydHRUgYGBKjw8XHcMT09P\nNXfuXNWyZUvl4OCgBg0apDIzM+96HHNgyrkrhKlIyclRYenpavuVK+qLCxfU9LNn1X8jIlSv0FDl\nt3+/st+9W1XftUs13bdPPfLPP2rEiRNqypkz6tO4OLU1MVEdSUtTSdnZen/fuJyVpV45eVK57Nmj\n5sbEqMwbN/S6f1MXcjZENflfE3Ujr3Kcd16eUgcOKPXGG0p5eSnl7a3UW28p9c8/+c/dKvb6dfXg\nwYPqqaNH1dWcHOMEXAqvRkaqAcePl+vf1hs3lDp0SKlZs5Tq2lUpW1ulunVTas4cpUJD7/wdmquU\nzBT13u73VK0PaqlhG4ep8ITwe79IiNvc/Px4R61kOs1QzFi7du1wc3Njz549+Pn5MXHiRM6ePcuR\nI0eoUqUKQ4cOZcaMGcyaNYtt27YRHBx8x4jcDz/8wLfffsvKlSvJzMykZ8+etGzZkm+//Zbw8HB6\n9OhBo0aNePjhhwt9G/jHH3/QoEEDUlJSip1aeausrCyWLVuGh4cHNWvWRNM03f7mzZuHu7s7iYmJ\nAOzbtw9N01i5ciV79uzRjUQCREZGMnToUL7//nsCAwOZP38+QUFBhIeHU+XmN+kbNmxg+/btVKtW\njc6dO7Ns2TL+7//+r9jjCPMlUzDEreyrVMG3ShV873Kh2a0jewWjegfT0tiUkKDXkb2QkBAeDAhg\n0fnzzI2NJbhOHSLat6emuc77uosunl2oUbUG205v4/HGjxs7nHKnadCuXf5tzhw4eDB/lK5v3ztH\n6tysrdnVpg3jTp/G/9Ahg7UouJ/3zi2JiXyfmEiov7/e/7YWt0jJG2+Y3yIl93L7CNzu53ZXqhE4\n+btuGFLIGUj9+vVJSkpCKcXSpUs5evQojo6OAEyaNIlhw4Yxa9asYqfdderUiSeffBKAhIQE/vrr\nL37++WesrKxo1aoV//3vf1mxYgUPP/xwoX0Ut7/bffPNN2zZsgUrKytatGjBpk2b7tjGysqKixcv\nEh0dTaNGjejcuXOx+1u/fj29e/fWTbV8/fXXWbRoEX/99RddunQB4LXXXqNu3boABAUFERoaWurj\nCCEqJ0MVe78kJTHiwAHa2dmx94EHaGyEa4lMhaZpjOkwhoX7FlaKQu5WJSvqLPi4VRNWx1+i25Ej\nJtmi4GJWFv89eZINfn446eHLiKIWKXn44fzibcYMaNBAD0FXMJW9gBOGZZaFnL6+YNLnpUznz5/H\n2dmZxMRErl27Rtu2bW85jiIvL++ur3dzc9P9+8KFCzg7O1Pjlg8wHh4eHDx48L7jGzRoECtWrCjy\nuYJi8I033mD69On07NkTgBdeeIE333yzyNdcvHgRDw8P3X1N03B3dycuLk73WEERB2BjY6O7Jq80\nxxHmQb61E+VBH8Wec4MGrG7QgIdufvFW2Q3yG8TEXydy/PJxmtdubuxwjOLeRV1dPulny+ToMPam\npJRri4LSvHfmKcWIiAj+r359AsqQzwWLlGzbBrt2/btIyaJF5r1Iyb1IAVeY/F03DLMs5ExtLYm/\n//6bCxcu8NBDD1GzZk1sbGw4ceIE9Yq4wLioaQ63Tm+Ef0f30tPTsb05dSMmJqZQsXe3/RW1TUlG\n7mxtbZk7dy5z584lLCyMbt260b59+zumcxbEeOzYMd19pRSxsbG4uroWG8PdjtOuXTtZEVMIoXcl\nKfbEv6pVqcZL/i+xaN8ilj5Z9hY3FV1xRd2bT9ui2T3AL1Mi8I8PZWs7X9yN1KKgwPzYWDJu3GBq\nKTveF7VISc+eMGQIfPkl1KpVTgFXEFLACWOquF0sTVhBUZSamsqWLVsYMmQIw4cPx8/PDwsLC55/\n/nnGjBlDQkICAHFxcezYsQOAOnXqcOXKFVJTU+/YXwF3d3c6derEpEmTyMrK4ujRo3z11VcEBwff\nEUutWrWwsLAgKirqnvHe67ktW7Zw+vRplFLY29tjaWmpu+6uTp06hY4xcOBAtm7dym+//UZOTg7z\n5s3D2tqaTp063ddxSrpKp6iYQkJCjB2CEEWS3LzT//n/H9+Gf0tCRoKxQzEpBUXdBx/kTzFc/2VV\nnjzYnNh1NWmw7TDD5iYTGqrfL5tLmp+H0tKYExvL6mbNqHKP0cG8PDh8GN5/P/+6tvr180fbPD1h\n40a4cAGWL88v5CpzEZealcqsP2bh/ZE3JxJOsPu53azqt0qKuJvkvdMwpJArB0FBQdjb2+Ph4cH7\n77/P+PHj+frrr3XPz5kzB29vbzp06ICDgwM9evQgMjISAB8fH4YMGULDhg1xdnbm4sWLd4zIAaxd\nu5bo6Gjq169Pv379mDFjhm7E6tbtq1evzuTJk+ncuTNOTk4cOHDgjniL2n9Rz50+fZoePXpgZ2dH\np06deOWVV+jatSuQf53fzJkzcXJyYv78+TRp0oRVq1bx6quvUqtWLbZu3cqPP/5IlSpFDwKX9DhC\nCCGMq3aN2vRv1p/PDn1m7FBMVkFR9+EHGkkfe7KobjO+bx7Ow0vP0biJYvJk9F7UFSc9N5chJ07w\nP29vvGxsitzm8mVYtQqGD4d69WDw4Pxlz994Ay5dgp07YcKEkjVLN3dSwAlTIn3khDAgyV0hhDk4\nFn+MXqt6ET0mGitLK2OHUyGcz8zk6bAwql2zovXPzfhhbZUSNx8vi5EREeQBX/v8W2jcbZGSXr0q\n5yIl9yJ94IQx3fz8KA3BhTAmyV0hhLnovqI7I1qNYHir4cYOpcLIystj3OnT7EhOZqOvH1kRtmzY\nUPLm46X1zeXLTD57lsNt23L5XBW2bcsv3G5dpKRXr8q9SMm9SAEnTEFxhZxMrRRCGJ3MpRemSnKz\neGM7jGXBvgXy5VQpVLOw4JMmTZjm6ckjR48Q6RGvu6ZuzRrIyclf/bJJE0o0/fJu+RmWmMkLx0/R\n6sdmtPGpwkMPwYED+dMmT5+Gv/+GmTMhIECKuKLIFMqykfdOwzDLVSuFEEIIUb4ea/wY43aM44+Y\nP+ji2cXY4VQowXXr0tLWlv5h/7YoaNfOokTNx4sbqcvLyy/8tm+Hbb/ksad/OA0uutPe3p6pG6Fl\nS7m+rSRkFUpRkcjUSiEMSHJXCGFOFv+9mF/P/Mp3g74zdigV0tWcHJ6NiCA+J4cNvr643daiQKl/\ni7qipl8mJMCOHfk93X75BRwc8qdKJgdFc77mVX57oBUWUr2ViEyhFKZMrpETwgRI7gohzEl6djpe\nC7048PwBGjo1NHY4FVKeUsyJieGjuDhWN2tGNyenIre7vai7fh0yM+9cpOTPlBT6Hz/OYX9/6ler\nZuCzqRgyczM5mXiSEwknCEsIIywhjD0xe+jVqJcUcMIkSSEnhAmQ3C1aSEgIgYGBxg5DiDtIbt7b\nhF8mkHMjhwWPLjB2KBXazuRkgsPDGe3qypseHsW2BYL8oi4qCqKjQ+jePVD3+NWcHNocOsQib2+e\ndHExQNSmraiC7UTCCWJSYmjo1BDfWr741fLDr5Yf7Vzb4eXoZeyQzYa8d+pXcYWcXCMnhBBCiPs2\nqv0oWn/amncefgf7avbGDqfCesTJib8feICnw8LYl5rK8mbNcCi29yp4e8P58/8+ppTixchIHnd2\nrnRFXEkLtqHNh+Jby5fGNRtL2wxhFmRETggDktwVQpijQd8OoqNbR8Z0GGPsUCq8W1sUfOfnRwtb\n2xK9bvmlS3wYE8PfbdtiY2lZzlEaR2lG2KRgE+ZEplaaiOnTpxMVFcXKlSvL7RghISEMHz6c2NhY\nve/bEPHfj8DAQIYPH87IkSNZvXo1K1asYPv27cYO6w4VOXeFEKI4+87vY+jGoZx69RSWFuZZRBja\nqkuXGBsVxUJvb4bVqXPXbU9du0anf/7ht1atSlz4mTIp2IQoTKZWGtCaNWuYP38+J0+exM7Ojtat\nWzN58mQ6d+581znvFcG94k9NTeXtt99m06ZNJCUlUadOHYKCgpgyZQo1a9Ys17gKYhs2bBjDhg0r\nt2MJ/ZO59MJUSW6WTAe3DtSuUZsfI3+kj08fY4djFopqUWBlUbj9b0hICJ26dGFoeDjTPD0rXBGX\nmZtJ5JVIwi7/W6yFJYTJlEgzIO+dhiGFnJ7Nnz+fOXPm8Nlnn9GrVy+srKzYtm0bP/74I507dzbr\n0Zjs7GweeeQRnJ2d2b59Oz4+PiQkJPD5559z4MABHnvsMWOHeE95eXlY3PaHUgghxL2N6TCGBfsW\nSCGnRy1tbfn7gQd4NiKCrqGhRbYoePvsWepaWfGKq6uRory3khZsQ5oPkYJNiFKQT6x6lJKSwrRp\n01i8eDF9+vTBxsYGS0tLnnjiCWbPnl3ka/bt20enTp1wcnKidevW7Nq1C4D169fTrl27QtsuWLCA\np556CoCsrCxef/11PD09qVu3Li+99BKZmZlFHmP27Nl4e3tjb2+Pn58fmzdv1j23bNkyHnroId54\n4w2cnZ1p2LAh27Zt0z1/9uxZunbtir29PT179iQxMbHY81+xYgWxsbFs2rQJH5/8pXtr1arF5MmT\ndUVccbFkZWXh6OhIWFiYbn8JCQlUr16dxMREkpOT6d27N7Vr18bZ2ZmgoCDi4uKKjGPZsmUEBATo\n7kdERNCjRw9q1qyJj48PGzZs0D337LPP8tJLL/H4449ja2tLSEgIP/30E76+vtjb2+Pm5sa8efOK\nPWehH/KtnTBVkpsl179Zf84kn+HwxcPGDsWsOFatynfNm/NkzZq0O3yY35KTdc/daNWKlfHxfNW0\nqUnM+MnMzeRo/FHWHlvLlN+m0G99P5p+3BSnOU4M2TiE7yK+o4pFFYY0H8J3A78jZWIKYS+HsWHA\nBqYHTmeA3wD8avtJEWcG5L3TMKSQ06O9e/eSmZlJ3759S7R9XFwcvXv35u233yY5OZm5c+fSv39/\nrly5QlBQECdPnuT06dO67desWaObMjhx4kROnz7NkSNHOH36NHFxccyYMaPI43h7e7Nnzx5SU1OZ\nNm0awcHBxMfH654/cOAAPj4+XLlyhQkTJjBy5Ejdc0OHDqVdu3ZcuXKFqVOnsnz58mL/WPz66688\n9thjVK9evdhzLi6WatWq0b9/f9auXavb9ptvviEwMBAXFxeUUowcOZKYmBhiYmKwsbFh1KhR9/wd\nZ2Rk0KNHD4KDg0lISGDdunW8/PLLhIeH67ZZu3YtU6dOJT09nU6dOjFy5EiWLl1KamoqYWFhdOvW\n7Z7HEUKIyq6qZVVGtRvFov2LjB2K2bHQNCZ5erKqWTOGhYcz+9w5ErKzGREezjIfH2pZGbbwkYJN\nCNMghZweXblyBRcXlxJPzVu1ahWPP/44jz76KADdu3fH39+frVu3Ur16dZ566ildYXPq1ClOnjzJ\nk08+iVKKpUuXMn/+fBwdHbG1tWXSpEmsW7euyOM8/fTT1K1bF4CBAwfSuHFj9u/fr3ve09OTkSNH\nomkazzzzDBcvXuTy5cvExMRw8OBB3n33XapWrUpAQABBQUHFTg9NSkqiXr16dz3nu8UydOjQQuew\nZs0ahg4dCoCzszN9+/bF2toaW1tb3nrrLd3o5d1s2bKFBg0aMGLECCwsLGjdujX9+vUrNCrXp08f\nOnbsCIC1tTVWVlaEhYWRmpqKg4MDbdq0uedxRNmEhIQYOwQhiiS5WTrPt32eH07+wMW0i8YOxSwV\ntCjYnJhIswMH6Hz2LD2cncvteFKwifsl752GYZbXyGnv6Gd6gZpWuuvZatasSWJiYomvszp37hwb\nNmzgxx9/1D2Wm5urGwEaOnQo48ePZ+rUqaxZs0ZXyFy+fJlr167Rtm3bf2NViry8vCKPs2LFChYs\nWEB0dDQA6enpXLlyRfd8QWEF6EbT0tPTuXz5Mk5OTtjY2Oie9/T0LHY1zJo1a3LhwoW7nvPdYgkM\nDOTatWscOHCA2rVrc+TIEd3o5rVr1xg7dizbt28n+ea0kvT0dJRSd51Ocu7cOfbv34+Tk5Pusdzc\nXJ555hkgf5EUNze3Qq/ZuHEjM2fOZOLEibRs2ZLZs2fToUOHu56XEEIIcLZxZkjzISw5uIQZDxc9\nS0SUjZu1NbvbtGFlfDwe2dl62adcwyZExWSWhVxpCzB96dixI9WqVWPTpk3079//ntt7eHgwfPhw\nPv/88yKf7969OwkJCRw5coR169axcOFCAFxcXLCxseHEiRP3HAE7d+4cL7zwAr/99hsdO3ZE0zTa\ntGlTokVX6tWrR3JyMteuXdMVeOfOncOymP403bt3Z8qUKYW2L00slpaWDBw4kLVr11K7dm2CgoKo\nUaMGAPPmzSMyMlJX5IWGhvLAAw/cs5Dz8PCga9eu7Nix457nW8Df35/Nmzdz48YN/ve//zFw4EBi\nYmJK/HpRejKXXpgqyc3SG/3gaLos68KkhyZhU9Xm3i8QpWZlYcHIevXgHp8BbicFmzAUee80DJla\nqUcODg7MmDGDV155he+//55r166Rk5PDzz//zJtvvnnH9sHBwfz444/s2LGDGzdukJmZSUhIiG4R\nj6pVqzJgwABef/11kpOT6dGjBwAWFhY8//zzjBkzhoSEBCD/eruiipWMjAw0TcPFxYW8vDy+/vpr\njh8/XqLz8fT0xN/fn2nTppGTk8OePXvYsmVLsdsPHz4cd3d3+vfvz8mTJ8nLy+PKlSvMmjWLn3/+\nuUSxFEyvvHVaJeSPvtnY2ODg4EBSUhLvvPNOic7hiSeeIDIyklWrVpGTk0NOTg5///03ERERAHcU\ntDk5OaxevZqUlBQsLS2xs7MrtnAVQghxp6YuTfGv78+aY2uMHUqlJVMihagcpJDTs3HjxjF//nxm\nzpxJ7dq18fDwYPHixbopgrf2O3Nzc+P7779n1qxZum3nzZtXaIrk0KFD2blzJwMGDCg0XXPOnDl4\ne3vToUMHHBwc6NGjB5GRkbrnC47h6+vL+PHj6dixI3Xr1uX48eM89NBDhba7fUTr1vtr1qxh//79\nODs7M2PGDEaMGFHsuVtZWfHrr7/i4+NDjx49cHBw4MEHHyQpKYkOHTrcMxaA9u3bY2try8WLFwu1\nKxgzZgzXr1/HxcWFTp068dhjjxU7EnfrOdnZ2bFjxw7WrVuHq6sr9erVY9KkSWTfnI5S1PmvWrWK\nBg0a4ODgwOeff87q1auLPWehHzKXXpgqyc37M+bBMSzcv9CsW+6Ygh07d0jBJkySvHcahmbKb7Ka\npqmi4rvZ3dwIEQlRNpK7RZPGocJUSW7eH6UULZa0YOGjC+nesLuxw6mQlFIkZyZzPvU851PPE5sS\nm/8zNf9nTEoM0aHRNG7bWDcl0q+Wn0yJFCZB3jv16+bnxztGMMq1kNM0zR1YAdQGFPC5UuojTdOc\ngfWAJxANDFRKXS3i9VLICbMiuSuEqCy+OPwFmyM2s2Vo8VPyK6t7FWkFP6taVMXdwR03ezfc7W/7\n6eBOQ6eGUrAJUQkYq5CrC9RVSoVqmmYLHAL6AM8BiUqpDzRNexNwUkpNLOL1UsgJsyK5K4SoLK7n\nXMdzoSd/PPcHTV2aGjscg9FHkVZws6tmZ+zTEUKYAKMUckUEsRn4+Oatq1Iq/maxF6KU8ilieynk\nhFmR3C2aTMEQpkpys2ym/jaVpOtJfPLEJ8YORS9MrUiT/BSmSnJTv4or5AzWfkDTNC+gDbAfqKOU\nir/5VDxQx1BxCCGEEMIwXmr3En6L/ZjZbSZONk73foERlaVI6+rZVUbShBAGZ5BC7ua0yo3AaKVU\n2q2rBCqllKZpxQ5RPPvss3h5eQHg6OhI69atyzlaIQyjYEWngm+sKvP9wMBAk4pH7st9ua+/+72b\n9GbiFxMZ0mKI0eL5/fffSctOw6u1F+dTz/Prb7+SkJGAZUNLzqee5+ShkyRkJGDtbY27gzs2522o\nXaM27Tu3p6tnV66cuEItt1r0e6wfdtXsij5eCjQLbGaU85P7ct8U7xcwlXgq0v3Q0FCuXs1fPiQ6\nOprilPvUSk3TqgJbgJ+VUgtvPhYBBCqlLmmaVg/4XaZWispAclcIUdkcunCIvuv7EvVaFFUtq+p9\n/6Y23VEIIfTNKFMrtfyhty+BEwVF3E0/ACOAOTd/bi7POIQQpi0kJET3TZQQpkRys+za1m+Ll6MX\n34V/x6Dmg0r1WpnueHeSn8JUSW4aRnlPrewMBANHNU375+Zjk4DZwDeapo3kZvuBco5DCCGEEEYy\ntsNYPvjrg0KFnBRpQghRNtIQXOhF8+bNWbx4MV26dDF2KCZNclcIURndyLuB9/+8aVe/HUnXk2S6\noxBClIJJtB8orYpYyO3Zs4cJEyZw4sQJLC0tadasGQsXLsTf379cjhcSEkK3bt2YPXs2EyZMKPHr\npk+fTlRUFCtXriyXuEoqOzubSZMm8c0333D16lVcXFzo06cPCxYsMGpc5cWUc1cIIcrT4YuHOXLp\niBRpQghRSsUVchbGCMZcpaam0rt3b0aPHk1ycjJxcXFMmzaNatWqldsxly9fTvPmzVmxYkW5HaM8\nvf/++xw+fJi///6btLQ0QkJCaNu2bbkcKy8vr1z2K8ru9hWuhDAVkpv680C9B3iuzXP0aNSDZrWa\nSRGnB5KfwlRJbhqGFHJ6FBkZiaZpDBo0CE3TsLa2pkePHrRo0QKAqKgounXrhouLC7Vq1SI4OJiU\nlBTd6728vJg3bx6tWrXC0dGRwYMHk5WVVezxMjIy2LhxI59++ikxMTEcOnRI91x0dDQWFhasWLEC\nT09PatWqxaxZswDYtm0b77//PuvXr8fOzo42bdoAcOHCBZ588klq1qxJ48aN+eKLL3T7mz59OgMH\nDmTEiBHY29vTvHnzQsfz8vJi586dAGRlZTFmzBhcXV1xdXVl7NixZGdnF3kOBw8epE+fPtStWxcA\nT09PgoODC+139uzZ+Pn54ezszH/+8x/d72TZsmUEBAQU2p+FhQVnzpwB8ltXvPTSSzz++OPY2toS\nEhLCTz/9hK+vL/b29ri5uTFv3rxif79CCCGEEEKYKink9Khp06ZYWlry7LPPsm3bNpKTk+/YZvLk\nyVy8eJHw8HBiY2OZPn267jlN09iwYQPbt2/n7NmzHD16lGXLlhV7vO+++446derQqVMngoKCWL58\n+R3b/Pnnn0RGRrJz505mzJjByZMnefTRR3nrrbcYPHgwaWlp/PNP/jo0gwcPxsPDg4sXL/Ltt9/y\n1ltv8fvvv+v29eOPPzJkyBBSUlJ48sknGTVqVKHYC/oDvvfeexw4cIAjR45w5MgRDhw4wMyZM4s8\nhw4dOjB//nyWLFnCsWPHipx2uGbNGnbs2EFUVBSRkZHF7qsoa9euZerUqaSnp9OpUydGjhzJ0qVL\nSU1NJSwsjG7dupV4X6L8yMpWwlRJbgpTJvkpTJXkpmFIIadHdnZ27NmzB03TeP7556lduzZPPfUU\nly9fBqBRo0Y88sgjVK1aFRcXF8aOHcuuXbsK7eO1116jbt26ODk5ERQURGhoaLHHW758OQMGDABg\nwIABrFu3jtzc3ELbFEztbNmyJa1ateLIkSNA/mphtxZNsbGx/PXXX8yZMwcrKytatWrFf//730JT\nNgMCAnj00UfRNI3g4GDdvm63Zs0a3n77bVxcXHBxcWHatGnFXos3adIk3nzzTVavXk27du1wc3Mr\ndExN0xg1ahSurq44OTkxefJk1q5dW+zv5HZ9+vShY8eOAFhbW2NlZUVYWBipqak4ODjoRiOFEEII\nIYSoSMyzkNM0/dzug4+PD19//TWxsbEcP36cCxcuMGbMGADi4+MZPHgwbm5uODg4MHz4cK5cuVLo\n9QVTDAFsbGxIT08v8jixsbGEhIToCrlHH32UzMxMtm7dWuz+qlevXuz+Lly4gLOzMzVq1NA95uHh\nQVxcnO5+nTp1Cu0rMzOzyOvOLly4gKenZ6H9XLhwocjjWlhY8PLLL7Nnzx5SUlKYPHn1HrJ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       "text": [
        "<matplotlib.figure.Figure at 0x13173b110>"
       ]
      }
     ],
     "prompt_number": 335
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Comparing the Statistics of The Decade's Best Teams"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The mult-index format of the data frame containing all these summary data objects makes it very convenient to find patterns across combinations of categorical and/or numerical variables.\n",
      "\n",
      "Say I wanted to compare the statistics of the top five best teams from the previous ten years. \n",
      "\n",
      "I first sort the data frame by a team's number of wins in a season. This allows me to easily compare the statistics of the decade's top teams. A popular comparison is between the 2008 Boston Celtics (back when they had KG, Pierce, and Allen) and the 2013 Miami Heat (with Lebron, Wade, and Bosh). The table below allows you to compare various season statistics to determine each team's relatives strengths and weaknesses. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gameStats_derivs.sort(('win_01','sum'),ascending = False).applymap(lambda x: round(x,2)).head(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th colspan=\"4\" halign=\"left\">AST_ratio</th>\n",
        "      <th colspan=\"4\" halign=\"left\">MIN</th>\n",
        "      <th colspan=\"4\" halign=\"left\">STL</th>\n",
        "      <th colspan=\"4\" halign=\"left\">FGP_adj</th>\n",
        "      <th colspan=\"4\" halign=\"left\">FTA</th>\n",
        "      <th colspan=\"4\" halign=\"left\">DEF_eff</th>\n",
        "      <th colspan=\"4\" halign=\"left\">FGM</th>\n",
        "      <th colspan=\"4\" halign=\"left\">BLK</th>\n",
        "      <th colspan=\"4\" halign=\"left\">3PA</th>\n",
        "      <th colspan=\"4\" halign=\"left\">REB</th>\n",
        "      <th>game_id</th>\n",
        "      <th colspan=\"4\" halign=\"left\">DREB</th>\n",
        "      <th colspan=\"4\" halign=\"left\">FTM</th>\n",
        "      <th>OFF_eff</th>\n",
        "      <th>...</th>\n",
        "      <th colspan=\"4\" halign=\"left\">FT_rate</th>\n",
        "      <th colspan=\"4\" halign=\"left\">TO_rate</th>\n",
        "      <th colspan=\"2\" halign=\"left\">win_01</th>\n",
        "      <th colspan=\"4\" halign=\"left\">PTS_diff</th>\n",
        "      <th colspan=\"4\" halign=\"left\">DREB_p</th>\n",
        "      <th colspan=\"4\" halign=\"left\">TO</th>\n",
        "      <th colspan=\"4\" halign=\"left\">PF</th>\n",
        "      <th colspan=\"4\" halign=\"left\">PTSF</th>\n",
        "      <th colspan=\"4\" halign=\"left\">PTSA</th>\n",
        "      <th colspan=\"4\" halign=\"left\">OREB</th>\n",
        "      <th colspan=\"4\" halign=\"left\">FGA</th>\n",
        "      <th colspan=\"4\" halign=\"left\">OREB_p</th>\n",
        "      <th colspan=\"4\" halign=\"left\">3PM</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "      <th>std</th>\n",
        "      <th>max</th>\n",
        "      <th>sum</th>\n",
        "      <th>mean</th>\n",
        "      <th>std</th>\n",
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        "      <th>sum</th>\n",
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        "      <th>sum</th>\n",
        "      <th>mean</th>\n",
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        "      <th>max</th>\n",
        "      <th>sum</th>\n",
        "      <th>mean</th>\n",
        "      <th>std</th>\n",
        "      <th>max</th>\n",
        "      <th>sum</th>\n",
        "      <th>mean</th>\n",
        "      <th>std</th>\n",
        "      <th>max</th>\n",
        "      <th>sum</th>\n",
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        "      <th>sum</th>\n",
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        "      <th>std</th>\n",
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        "      <th>mean</th>\n",
        "      <th>std</th>\n",
        "      <th>max</th>\n",
        "      <th>sum</th>\n",
        "      <th>count</th>\n",
        "      <th>mean</th>\n",
        "      <th>std</th>\n",
        "      <th>max</th>\n",
        "      <th>sum</th>\n",
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        "      <th>std</th>\n",
        "      <th>max</th>\n",
        "      <th>sum</th>\n",
        "      <th>mean</th>\n",
        "      <th>std</th>\n",
        "      <th>max</th>\n",
        "      <th>sum</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>season</th>\n",
        "      <th>team</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <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",
        "      <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",
        "      <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",
        "      <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",
        "      <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",
        "      <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",
        "      <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",
        "      <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>2013</th>\n",
        "      <th>Miami Heat</th>\n",
        "      <td> 18.57</td>\n",
        "      <td> 2.77</td>\n",
        "      <td> 25.82</td>\n",
        "      <td> 1522.45</td>\n",
        "      <td> 242.46</td>\n",
        "      <td> 9.35</td>\n",
        "      <td> 290</td>\n",
        "      <td> 19882</td>\n",
        "      <td> 8.66</td>\n",
        "      <td> 2.70</td>\n",
        "      <td> 15</td>\n",
        "      <td> 710</td>\n",
        "      <td> 0.55</td>\n",
        "      <td> 0.06</td>\n",
        "      <td> 0.71</td>\n",
        "      <td> 45.43</td>\n",
        "      <td> 23.01</td>\n",
        "      <td> 6.57</td>\n",
        "      <td> 41</td>\n",
        "      <td> 1887</td>\n",
        "      <td> 104.77</td>\n",
        "      <td> 10.97</td>\n",
        "      <td> 124.55</td>\n",
        "      <td> 8590.76</td>\n",
        "      <td> 38.39</td>\n",
        "      <td> 4.49</td>\n",
        "      <td> 56</td>\n",
        "      <td> 3148</td>\n",
        "      <td> 5.38</td>\n",
        "      <td> 2.59</td>\n",
        "      <td> 14</td>\n",
        "      <td> 441</td>\n",
        "      <td> 22.06</td>\n",
        "      <td> 5.90</td>\n",
        "      <td> 41</td>\n",
        "      <td> 1809</td>\n",
        "      <td> 38.61</td>\n",
        "      <td> 7.09</td>\n",
        "      <td> 54</td>\n",
        "      <td> 3166</td>\n",
        "      <td> 82</td>\n",
        "      <td> 30.37</td>\n",
        "      <td> 5.53</td>\n",
        "      <td> 44</td>\n",
        "      <td> 2490</td>\n",
        "      <td> 17.35</td>\n",
        "      <td> 4.95</td>\n",
        "      <td> 29</td>\n",
        "      <td> 1423</td>\n",
        "      <td> 113.43</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.23</td>\n",
        "      <td> 0.07</td>\n",
        "      <td> 0.42</td>\n",
        "      <td> 18.61</td>\n",
        "      <td> 0.15</td>\n",
        "      <td> 0.04</td>\n",
        "      <td> 0.24</td>\n",
        "      <td> 12.00</td>\n",
        "      <td> 0.80</td>\n",
        "      <td> 66</td>\n",
        "      <td>  7.87</td>\n",
        "      <td> 11.56</td>\n",
        "      <td> 32</td>\n",
        "      <td> 645</td>\n",
        "      <td> 0.73</td>\n",
        "      <td> 0.09</td>\n",
        "      <td> 0.88</td>\n",
        "      <td> 60.09</td>\n",
        "      <td> 13.27</td>\n",
        "      <td> 3.70</td>\n",
        "      <td> 23</td>\n",
        "      <td> 1088</td>\n",
        "      <td> 18.70</td>\n",
        "      <td> 4.45</td>\n",
        "      <td> 29</td>\n",
        "      <td> 1533</td>\n",
        "      <td> 102.88</td>\n",
        "      <td> 10.53</td>\n",
        "      <td> 141</td>\n",
        "      <td> 8436</td>\n",
        "      <td> 95.01</td>\n",
        "      <td> 10.95</td>\n",
        "      <td> 129</td>\n",
        "      <td> 7791</td>\n",
        "      <td>  8.24</td>\n",
        "      <td> 3.37</td>\n",
        "      <td> 17</td>\n",
        "      <td> 676</td>\n",
        "      <td> 77.41</td>\n",
        "      <td> 7.24</td>\n",
        "      <td> 101</td>\n",
        "      <td> 6348</td>\n",
        "      <td> 0.21</td>\n",
        "      <td> 0.07</td>\n",
        "      <td> 0.35</td>\n",
        "      <td> 17.24</td>\n",
        "      <td> 8.74</td>\n",
        "      <td> 3.33</td>\n",
        "      <td> 17</td>\n",
        "      <td> 717</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007</th>\n",
        "      <th>Dallas Mavericks</th>\n",
        "      <td> 16.09</td>\n",
        "      <td> 3.54</td>\n",
        "      <td> 26.11</td>\n",
        "      <td> 1286.86</td>\n",
        "      <td> 238.88</td>\n",
        "      <td> 7.84</td>\n",
        "      <td> 286</td>\n",
        "      <td> 19110</td>\n",
        "      <td> 6.85</td>\n",
        "      <td> 2.66</td>\n",
        "      <td> 14</td>\n",
        "      <td> 548</td>\n",
        "      <td> 0.51</td>\n",
        "      <td> 0.06</td>\n",
        "      <td> 0.63</td>\n",
        "      <td> 40.86</td>\n",
        "      <td> 24.81</td>\n",
        "      <td> 7.34</td>\n",
        "      <td> 45</td>\n",
        "      <td> 1985</td>\n",
        "      <td> 104.83</td>\n",
        "      <td> 13.00</td>\n",
        "      <td> 149.52</td>\n",
        "      <td> 8386.13</td>\n",
        "      <td> 36.30</td>\n",
        "      <td> 4.91</td>\n",
        "      <td> 48</td>\n",
        "      <td> 2904</td>\n",
        "      <td> 5.03</td>\n",
        "      <td> 2.16</td>\n",
        "      <td> 11</td>\n",
        "      <td> 402</td>\n",
        "      <td> 16.73</td>\n",
        "      <td> 5.26</td>\n",
        "      <td> 33</td>\n",
        "      <td> 1338</td>\n",
        "      <td> 41.51</td>\n",
        "      <td> 6.55</td>\n",
        "      <td> 58</td>\n",
        "      <td> 3321</td>\n",
        "      <td> 80</td>\n",
        "      <td> 30.46</td>\n",
        "      <td> 5.33</td>\n",
        "      <td> 41</td>\n",
        "      <td> 2437</td>\n",
        "      <td> 20.04</td>\n",
        "      <td> 6.18</td>\n",
        "      <td> 35</td>\n",
        "      <td> 1603</td>\n",
        "      <td> 112.81</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.26</td>\n",
        "      <td> 0.09</td>\n",
        "      <td> 0.46</td>\n",
        "      <td> 21.03</td>\n",
        "      <td> 0.15</td>\n",
        "      <td> 0.04</td>\n",
        "      <td> 0.25</td>\n",
        "      <td> 11.70</td>\n",
        "      <td> 0.81</td>\n",
        "      <td> 65</td>\n",
        "      <td>  7.03</td>\n",
        "      <td> 13.12</td>\n",
        "      <td> 36</td>\n",
        "      <td> 562</td>\n",
        "      <td> 0.75</td>\n",
        "      <td> 0.09</td>\n",
        "      <td> 0.93</td>\n",
        "      <td> 60.09</td>\n",
        "      <td> 13.01</td>\n",
        "      <td> 4.00</td>\n",
        "      <td> 23</td>\n",
        "      <td> 1041</td>\n",
        "      <td> 22.31</td>\n",
        "      <td> 3.80</td>\n",
        "      <td> 32</td>\n",
        "      <td> 1785</td>\n",
        "      <td>  99.84</td>\n",
        "      <td> 11.11</td>\n",
        "      <td> 127</td>\n",
        "      <td> 7987</td>\n",
        "      <td> 92.81</td>\n",
        "      <td> 12.16</td>\n",
        "      <td> 129</td>\n",
        "      <td> 7425</td>\n",
        "      <td> 11.05</td>\n",
        "      <td> 4.39</td>\n",
        "      <td> 27</td>\n",
        "      <td> 884</td>\n",
        "      <td> 77.54</td>\n",
        "      <td> 8.09</td>\n",
        "      <td> 104</td>\n",
        "      <td> 6203</td>\n",
        "      <td> 0.26</td>\n",
        "      <td> 0.09</td>\n",
        "      <td> 0.49</td>\n",
        "      <td> 21.11</td>\n",
        "      <td> 6.39</td>\n",
        "      <td> 2.75</td>\n",
        "      <td> 15</td>\n",
        "      <td> 511</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
        "      <th>Boston Celtics</th>\n",
        "      <td> 17.68</td>\n",
        "      <td> 3.44</td>\n",
        "      <td> 24.41</td>\n",
        "      <td> 1414.22</td>\n",
        "      <td> 241.03</td>\n",
        "      <td> 5.16</td>\n",
        "      <td> 267</td>\n",
        "      <td> 19282</td>\n",
        "      <td> 8.47</td>\n",
        "      <td> 3.09</td>\n",
        "      <td> 20</td>\n",
        "      <td> 678</td>\n",
        "      <td> 0.52</td>\n",
        "      <td> 0.06</td>\n",
        "      <td> 0.69</td>\n",
        "      <td> 41.90</td>\n",
        "      <td> 26.68</td>\n",
        "      <td> 7.43</td>\n",
        "      <td> 46</td>\n",
        "      <td> 2134</td>\n",
        "      <td>  99.81</td>\n",
        "      <td> 12.12</td>\n",
        "      <td> 132.87</td>\n",
        "      <td> 7985.19</td>\n",
        "      <td> 36.35</td>\n",
        "      <td> 4.82</td>\n",
        "      <td> 49</td>\n",
        "      <td> 2908</td>\n",
        "      <td> 4.59</td>\n",
        "      <td> 2.23</td>\n",
        "      <td>  9</td>\n",
        "      <td> 367</td>\n",
        "      <td> 19.05</td>\n",
        "      <td> 5.00</td>\n",
        "      <td> 33</td>\n",
        "      <td> 1524</td>\n",
        "      <td> 41.91</td>\n",
        "      <td> 6.06</td>\n",
        "      <td> 55</td>\n",
        "      <td> 3353</td>\n",
        "      <td> 80</td>\n",
        "      <td> 31.84</td>\n",
        "      <td> 5.42</td>\n",
        "      <td> 43</td>\n",
        "      <td> 2547</td>\n",
        "      <td> 20.54</td>\n",
        "      <td> 6.18</td>\n",
        "      <td> 38</td>\n",
        "      <td> 1643</td>\n",
        "      <td> 111.23</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.27</td>\n",
        "      <td> 0.09</td>\n",
        "      <td> 0.48</td>\n",
        "      <td> 21.68</td>\n",
        "      <td> 0.16</td>\n",
        "      <td> 0.04</td>\n",
        "      <td> 0.25</td>\n",
        "      <td> 12.72</td>\n",
        "      <td> 0.80</td>\n",
        "      <td> 64</td>\n",
        "      <td> 10.29</td>\n",
        "      <td> 11.88</td>\n",
        "      <td> 45</td>\n",
        "      <td> 823</td>\n",
        "      <td> 0.75</td>\n",
        "      <td> 0.08</td>\n",
        "      <td> 0.90</td>\n",
        "      <td> 59.84</td>\n",
        "      <td> 14.40</td>\n",
        "      <td> 3.99</td>\n",
        "      <td> 23</td>\n",
        "      <td> 1152</td>\n",
        "      <td> 22.77</td>\n",
        "      <td> 4.29</td>\n",
        "      <td> 33</td>\n",
        "      <td> 1822</td>\n",
        "      <td> 100.49</td>\n",
        "      <td> 10.42</td>\n",
        "      <td> 119</td>\n",
        "      <td> 8039</td>\n",
        "      <td> 90.20</td>\n",
        "      <td> 12.13</td>\n",
        "      <td> 124</td>\n",
        "      <td> 7216</td>\n",
        "      <td> 10.07</td>\n",
        "      <td> 3.34</td>\n",
        "      <td> 22</td>\n",
        "      <td> 806</td>\n",
        "      <td> 76.45</td>\n",
        "      <td> 6.18</td>\n",
        "      <td>  99</td>\n",
        "      <td> 6116</td>\n",
        "      <td> 0.24</td>\n",
        "      <td> 0.07</td>\n",
        "      <td> 0.41</td>\n",
        "      <td> 19.24</td>\n",
        "      <td> 7.25</td>\n",
        "      <td> 3.01</td>\n",
        "      <td> 15</td>\n",
        "      <td> 580</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">2009</th>\n",
        "      <th>Cleveland Cavaliers</th>\n",
        "      <td> 16.47</td>\n",
        "      <td> 2.75</td>\n",
        "      <td> 23.07</td>\n",
        "      <td> 1317.31</td>\n",
        "      <td> 241.35</td>\n",
        "      <td> 5.53</td>\n",
        "      <td> 266</td>\n",
        "      <td> 19308</td>\n",
        "      <td> 7.17</td>\n",
        "      <td> 3.17</td>\n",
        "      <td> 18</td>\n",
        "      <td> 574</td>\n",
        "      <td> 0.52</td>\n",
        "      <td> 0.05</td>\n",
        "      <td> 0.64</td>\n",
        "      <td> 41.41</td>\n",
        "      <td> 24.76</td>\n",
        "      <td> 6.72</td>\n",
        "      <td> 41</td>\n",
        "      <td> 1981</td>\n",
        "      <td> 103.21</td>\n",
        "      <td> 11.37</td>\n",
        "      <td> 125.06</td>\n",
        "      <td> 8257.14</td>\n",
        "      <td> 36.63</td>\n",
        "      <td> 4.26</td>\n",
        "      <td> 48</td>\n",
        "      <td> 2930</td>\n",
        "      <td> 5.34</td>\n",
        "      <td> 2.31</td>\n",
        "      <td> 11</td>\n",
        "      <td> 427</td>\n",
        "      <td> 20.50</td>\n",
        "      <td> 4.83</td>\n",
        "      <td> 33</td>\n",
        "      <td> 1640</td>\n",
        "      <td> 42.31</td>\n",
        "      <td> 5.40</td>\n",
        "      <td> 53</td>\n",
        "      <td> 3385</td>\n",
        "      <td> 80</td>\n",
        "      <td> 31.45</td>\n",
        "      <td> 4.82</td>\n",
        "      <td> 44</td>\n",
        "      <td> 2516</td>\n",
        "      <td> 18.74</td>\n",
        "      <td> 5.49</td>\n",
        "      <td> 32</td>\n",
        "      <td> 1499</td>\n",
        "      <td> 112.97</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.24</td>\n",
        "      <td> 0.08</td>\n",
        "      <td> 0.41</td>\n",
        "      <td> 19.26</td>\n",
        "      <td> 0.13</td>\n",
        "      <td> 0.04</td>\n",
        "      <td> 0.23</td>\n",
        "      <td> 10.77</td>\n",
        "      <td> 0.80</td>\n",
        "      <td> 64</td>\n",
        "      <td>  8.64</td>\n",
        "      <td> 12.12</td>\n",
        "      <td> 36</td>\n",
        "      <td> 691</td>\n",
        "      <td> 0.74</td>\n",
        "      <td> 0.07</td>\n",
        "      <td> 0.88</td>\n",
        "      <td> 59.57</td>\n",
        "      <td> 11.96</td>\n",
        "      <td> 3.67</td>\n",
        "      <td> 21</td>\n",
        "      <td>  957</td>\n",
        "      <td> 20.32</td>\n",
        "      <td> 4.32</td>\n",
        "      <td> 31</td>\n",
        "      <td> 1626</td>\n",
        "      <td> 100.03</td>\n",
        "      <td> 10.18</td>\n",
        "      <td> 126</td>\n",
        "      <td> 8002</td>\n",
        "      <td> 91.39</td>\n",
        "      <td> 11.38</td>\n",
        "      <td> 123</td>\n",
        "      <td> 7311</td>\n",
        "      <td> 10.86</td>\n",
        "      <td> 3.28</td>\n",
        "      <td> 19</td>\n",
        "      <td> 869</td>\n",
        "      <td> 78.71</td>\n",
        "      <td> 6.57</td>\n",
        "      <td>  94</td>\n",
        "      <td> 6297</td>\n",
        "      <td> 0.26</td>\n",
        "      <td> 0.07</td>\n",
        "      <td> 0.39</td>\n",
        "      <td> 20.51</td>\n",
        "      <td> 8.04</td>\n",
        "      <td> 2.83</td>\n",
        "      <td> 17</td>\n",
        "      <td> 643</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Los Angeles Lakers</th>\n",
        "      <td> 17.47</td>\n",
        "      <td> 3.24</td>\n",
        "      <td> 25.73</td>\n",
        "      <td> 1397.65</td>\n",
        "      <td> 241.30</td>\n",
        "      <td> 6.73</td>\n",
        "      <td> 290</td>\n",
        "      <td> 19304</td>\n",
        "      <td> 8.79</td>\n",
        "      <td> 2.91</td>\n",
        "      <td> 16</td>\n",
        "      <td> 703</td>\n",
        "      <td> 0.51</td>\n",
        "      <td> 0.06</td>\n",
        "      <td> 0.62</td>\n",
        "      <td> 41.15</td>\n",
        "      <td> 25.48</td>\n",
        "      <td> 8.65</td>\n",
        "      <td> 46</td>\n",
        "      <td> 2038</td>\n",
        "      <td> 105.39</td>\n",
        "      <td> 10.26</td>\n",
        "      <td> 126.41</td>\n",
        "      <td> 8430.86</td>\n",
        "      <td> 40.26</td>\n",
        "      <td> 4.99</td>\n",
        "      <td> 54</td>\n",
        "      <td> 3221</td>\n",
        "      <td> 5.10</td>\n",
        "      <td> 2.67</td>\n",
        "      <td> 13</td>\n",
        "      <td> 408</td>\n",
        "      <td> 18.51</td>\n",
        "      <td> 4.23</td>\n",
        "      <td> 34</td>\n",
        "      <td> 1481</td>\n",
        "      <td> 43.76</td>\n",
        "      <td> 6.44</td>\n",
        "      <td> 67</td>\n",
        "      <td> 3501</td>\n",
        "      <td> 80</td>\n",
        "      <td> 31.40</td>\n",
        "      <td> 4.64</td>\n",
        "      <td> 41</td>\n",
        "      <td> 2512</td>\n",
        "      <td> 19.55</td>\n",
        "      <td> 6.99</td>\n",
        "      <td> 37</td>\n",
        "      <td> 1564</td>\n",
        "      <td> 113.54</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.23</td>\n",
        "      <td> 0.09</td>\n",
        "      <td> 0.46</td>\n",
        "      <td> 18.70</td>\n",
        "      <td> 0.14</td>\n",
        "      <td> 0.04</td>\n",
        "      <td> 0.26</td>\n",
        "      <td> 11.05</td>\n",
        "      <td> 0.79</td>\n",
        "      <td> 63</td>\n",
        "      <td>  7.65</td>\n",
        "      <td> 10.14</td>\n",
        "      <td> 38</td>\n",
        "      <td> 612</td>\n",
        "      <td> 0.73</td>\n",
        "      <td> 0.07</td>\n",
        "      <td> 0.93</td>\n",
        "      <td> 58.50</td>\n",
        "      <td> 12.97</td>\n",
        "      <td> 3.81</td>\n",
        "      <td> 25</td>\n",
        "      <td> 1038</td>\n",
        "      <td> 20.76</td>\n",
        "      <td> 4.49</td>\n",
        "      <td> 32</td>\n",
        "      <td> 1661</td>\n",
        "      <td> 106.75</td>\n",
        "      <td> 11.42</td>\n",
        "      <td> 132</td>\n",
        "      <td> 8540</td>\n",
        "      <td> 99.10</td>\n",
        "      <td> 11.78</td>\n",
        "      <td> 121</td>\n",
        "      <td> 7928</td>\n",
        "      <td> 12.36</td>\n",
        "      <td> 4.12</td>\n",
        "      <td> 28</td>\n",
        "      <td> 989</td>\n",
        "      <td> 85.00</td>\n",
        "      <td> 7.42</td>\n",
        "      <td>  99</td>\n",
        "      <td> 6800</td>\n",
        "      <td> 0.28</td>\n",
        "      <td> 0.07</td>\n",
        "      <td> 0.43</td>\n",
        "      <td> 22.33</td>\n",
        "      <td> 6.67</td>\n",
        "      <td> 2.35</td>\n",
        "      <td> 16</td>\n",
        "      <td> 534</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 115 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 344,
       "text": [
        "                           AST_ratio                           MIN             \\\n",
        "                                mean   std    max      sum    mean   std  max   \n",
        "season team                                                                     \n",
        "2013   Miami Heat              18.57  2.77  25.82  1522.45  242.46  9.35  290   \n",
        "2007   Dallas Mavericks        16.09  3.54  26.11  1286.86  238.88  7.84  286   \n",
        "2008   Boston Celtics          17.68  3.44  24.41  1414.22  241.03  5.16  267   \n",
        "2009   Cleveland Cavaliers     16.47  2.75  23.07  1317.31  241.35  5.53  266   \n",
        "       Los Angeles Lakers      17.47  3.24  25.73  1397.65  241.30  6.73  290   \n",
        "\n",
        "                                    STL                FGP_adj              \\\n",
        "                              sum  mean   std max  sum    mean   std   max   \n",
        "season team                                                                  \n",
        "2013   Miami Heat           19882  8.66  2.70  15  710    0.55  0.06  0.71   \n",
        "2007   Dallas Mavericks     19110  6.85  2.66  14  548    0.51  0.06  0.63   \n",
        "2008   Boston Celtics       19282  8.47  3.09  20  678    0.52  0.06  0.69   \n",
        "2009   Cleveland Cavaliers  19308  7.17  3.17  18  574    0.52  0.05  0.64   \n",
        "       Los Angeles Lakers   19304  8.79  2.91  16  703    0.51  0.06  0.62   \n",
        "\n",
        "                                     FTA                 DEF_eff         \\\n",
        "                              sum   mean   std max   sum    mean    std   \n",
        "season team                                                               \n",
        "2013   Miami Heat           45.43  23.01  6.57  41  1887  104.77  10.97   \n",
        "2007   Dallas Mavericks     40.86  24.81  7.34  45  1985  104.83  13.00   \n",
        "2008   Boston Celtics       41.90  26.68  7.43  46  2134   99.81  12.12   \n",
        "2009   Cleveland Cavaliers  41.41  24.76  6.72  41  1981  103.21  11.37   \n",
        "       Los Angeles Lakers   41.15  25.48  8.65  46  2038  105.39  10.26   \n",
        "\n",
        "                                               FGM                   BLK  \\\n",
        "                               max      sum   mean   std max   sum  mean   \n",
        "season team                                                                \n",
        "2013   Miami Heat           124.55  8590.76  38.39  4.49  56  3148  5.38   \n",
        "2007   Dallas Mavericks     149.52  8386.13  36.30  4.91  48  2904  5.03   \n",
        "2008   Boston Celtics       132.87  7985.19  36.35  4.82  49  2908  4.59   \n",
        "2009   Cleveland Cavaliers  125.06  8257.14  36.63  4.26  48  2930  5.34   \n",
        "       Los Angeles Lakers   126.41  8430.86  40.26  4.99  54  3221  5.10   \n",
        "\n",
        "                                             3PA                    REB        \\\n",
        "                             std max  sum   mean   std max   sum   mean   std   \n",
        "season team                                                                     \n",
        "2013   Miami Heat           2.59  14  441  22.06  5.90  41  1809  38.61  7.09   \n",
        "2007   Dallas Mavericks     2.16  11  402  16.73  5.26  33  1338  41.51  6.55   \n",
        "2008   Boston Celtics       2.23   9  367  19.05  5.00  33  1524  41.91  6.06   \n",
        "2009   Cleveland Cavaliers  2.31  11  427  20.50  4.83  33  1640  42.31  5.40   \n",
        "       Los Angeles Lakers   2.67  13  408  18.51  4.23  34  1481  43.76  6.44   \n",
        "\n",
        "                                     game_id   DREB                    FTM  \\\n",
        "                           max   sum   count   mean   std max   sum   mean   \n",
        "season team                                                                  \n",
        "2013   Miami Heat           54  3166      82  30.37  5.53  44  2490  17.35   \n",
        "2007   Dallas Mavericks     58  3321      80  30.46  5.33  41  2437  20.04   \n",
        "2008   Boston Celtics       55  3353      80  31.84  5.42  43  2547  20.54   \n",
        "2009   Cleveland Cavaliers  53  3385      80  31.45  4.82  44  2516  18.74   \n",
        "       Los Angeles Lakers   67  3501      80  31.40  4.64  41  2512  19.55   \n",
        "\n",
        "                                           OFF_eff ...  FT_rate              \\\n",
        "                             std max   sum    mean ...     mean   std   max   \n",
        "season team                                        ...                        \n",
        "2013   Miami Heat           4.95  29  1423  113.43 ...     0.23  0.07  0.42   \n",
        "2007   Dallas Mavericks     6.18  35  1603  112.81 ...     0.26  0.09  0.46   \n",
        "2008   Boston Celtics       6.18  38  1643  111.23 ...     0.27  0.09  0.48   \n",
        "2009   Cleveland Cavaliers  5.49  32  1499  112.97 ...     0.24  0.08  0.41   \n",
        "       Los Angeles Lakers   6.99  37  1564  113.54 ...     0.23  0.09  0.46   \n",
        "\n",
        "                                  TO_rate                    win_01      \\\n",
        "                              sum    mean   std   max    sum   mean sum   \n",
        "season team                                                               \n",
        "2013   Miami Heat           18.61    0.15  0.04  0.24  12.00   0.80  66   \n",
        "2007   Dallas Mavericks     21.03    0.15  0.04  0.25  11.70   0.81  65   \n",
        "2008   Boston Celtics       21.68    0.16  0.04  0.25  12.72   0.80  64   \n",
        "2009   Cleveland Cavaliers  19.26    0.13  0.04  0.23  10.77   0.80  64   \n",
        "       Los Angeles Lakers   18.70    0.14  0.04  0.26  11.05   0.79  63   \n",
        "\n",
        "                           PTS_diff                 DREB_p                     \\\n",
        "                               mean    std max  sum   mean   std   max    sum   \n",
        "season team                                                                     \n",
        "2013   Miami Heat              7.87  11.56  32  645   0.73  0.09  0.88  60.09   \n",
        "2007   Dallas Mavericks        7.03  13.12  36  562   0.75  0.09  0.93  60.09   \n",
        "2008   Boston Celtics         10.29  11.88  45  823   0.75  0.08  0.90  59.84   \n",
        "2009   Cleveland Cavaliers     8.64  12.12  36  691   0.74  0.07  0.88  59.57   \n",
        "       Los Angeles Lakers      7.65  10.14  38  612   0.73  0.07  0.93  58.50   \n",
        "\n",
        "                               TO                     PF                  \\\n",
        "                             mean   std max   sum   mean   std max   sum   \n",
        "season team                                                                \n",
        "2013   Miami Heat           13.27  3.70  23  1088  18.70  4.45  29  1533   \n",
        "2007   Dallas Mavericks     13.01  4.00  23  1041  22.31  3.80  32  1785   \n",
        "2008   Boston Celtics       14.40  3.99  23  1152  22.77  4.29  33  1822   \n",
        "2009   Cleveland Cavaliers  11.96  3.67  21   957  20.32  4.32  31  1626   \n",
        "       Los Angeles Lakers   12.97  3.81  25  1038  20.76  4.49  32  1661   \n",
        "\n",
        "                              PTSF                     PTSA                    \\\n",
        "                              mean    std  max   sum   mean    std  max   sum   \n",
        "season team                                                                     \n",
        "2013   Miami Heat           102.88  10.53  141  8436  95.01  10.95  129  7791   \n",
        "2007   Dallas Mavericks      99.84  11.11  127  7987  92.81  12.16  129  7425   \n",
        "2008   Boston Celtics       100.49  10.42  119  8039  90.20  12.13  124  7216   \n",
        "2009   Cleveland Cavaliers  100.03  10.18  126  8002  91.39  11.38  123  7311   \n",
        "       Los Angeles Lakers   106.75  11.42  132  8540  99.10  11.78  121  7928   \n",
        "\n",
        "                             OREB                   FGA                   \\\n",
        "                             mean   std max  sum   mean   std  max   sum   \n",
        "season team                                                                \n",
        "2013   Miami Heat            8.24  3.37  17  676  77.41  7.24  101  6348   \n",
        "2007   Dallas Mavericks     11.05  4.39  27  884  77.54  8.09  104  6203   \n",
        "2008   Boston Celtics       10.07  3.34  22  806  76.45  6.18   99  6116   \n",
        "2009   Cleveland Cavaliers  10.86  3.28  19  869  78.71  6.57   94  6297   \n",
        "       Los Angeles Lakers   12.36  4.12  28  989  85.00  7.42   99  6800   \n",
        "\n",
        "                           OREB_p                      3PM                 \n",
        "                             mean   std   max    sum  mean   std max  sum  \n",
        "season team                                                                \n",
        "2013   Miami Heat            0.21  0.07  0.35  17.24  8.74  3.33  17  717  \n",
        "2007   Dallas Mavericks      0.26  0.09  0.49  21.11  6.39  2.75  15  511  \n",
        "2008   Boston Celtics        0.24  0.07  0.41  19.24  7.25  3.01  15  580  \n",
        "2009   Cleveland Cavaliers   0.26  0.07  0.39  20.51  8.04  2.83  17  643  \n",
        "       Los Angeles Lakers    0.28  0.07  0.43  22.33  6.67  2.35  16  534  \n",
        "\n",
        "[5 rows x 115 columns]"
       ]
      }
     ],
     "prompt_number": 344
    }
   ],
   "metadata": {}
  }
 ]
}