{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f14e95ab",
   "metadata": {},
   "source": [
    "# Class 12: Constructing Datasets from Multiple Sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bbca4d7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94f89de0",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "Sometimes we use data from multiple sources. For example data may be provided across a bunch of tables and we want to put them together. Or we may want to build our own dataset to answer the questions that we want to ask.\n",
    "For example we have a dataset of drivers who have been pulled over by the police, and need to compare distribution of them between day and night times. If we only have time of being pulled over without mentioning day/night, we can use another dataset including time of sunset for every day and build our own dataset out of these two dataset to find the answer.\n",
    "Later we will also learn about getting data out of databses with some small database operations through SQLite and some python libararies.\n",
    "\n",
    "\n",
    "To use relative paths as in class (`data/2018-games.csv`) instead of a full url `https://raw.githubusercontent.com/rhodyprog4ds/inclass-data/main/2018-games.csv`, download data from [this GitHub repo](https://github.com/rhodyprog4ds/inclass-data) by clicking on the green code button and choosing .zip. Then unzip the data and save it in a data folder in the same folder as the notebook. For the class notes, the urls make it so that the notebook can run without having to store the data in another place."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d5646ba0",
   "metadata": {},
   "outputs": [],
   "source": [
    "games_df18 = pd.read_csv('https://raw.githubusercontent.com/rhodyprog4ds/inclass-data/main/2018-games.csv')\n",
    "games_df19 = pd.read_csv('https://raw.githubusercontent.com/rhodyprog4ds/inclass-data/main/2019-games.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70effb06",
   "metadata": {},
   "source": [
    "<!-- annotate: Stacking DataFrames -->\n",
    "## Stacking DataFrames\n",
    "Let's look at the first couple of rows to see how the configuration of data is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ac6fe4f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>GAME_DATE_EST</th>\n",
       "      <th>GAME_ID</th>\n",
       "      <th>GAME_STATUS_TEXT</th>\n",
       "      <th>HOME_TEAM_ID</th>\n",
       "      <th>VISITOR_TEAM_ID</th>\n",
       "      <th>SEASON</th>\n",
       "      <th>TEAM_ID_home</th>\n",
       "      <th>PTS_home</th>\n",
       "      <th>FG_PCT_home</th>\n",
       "      <th>...</th>\n",
       "      <th>AST_home</th>\n",
       "      <th>REB_home</th>\n",
       "      <th>TEAM_ID_away</th>\n",
       "      <th>PTS_away</th>\n",
       "      <th>FG_PCT_away</th>\n",
       "      <th>FT_PCT_away</th>\n",
       "      <th>FG3_PCT_away</th>\n",
       "      <th>AST_away</th>\n",
       "      <th>REB_away</th>\n",
       "      <th>HOME_TEAM_WINS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16196</td>\n",
       "      <td>2019-06-13</td>\n",
       "      <td>41800406</td>\n",
       "      <td>Final</td>\n",
       "      <td>1610612744</td>\n",
       "      <td>1610612761</td>\n",
       "      <td>2018</td>\n",
       "      <td>1610612744</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.488</td>\n",
       "      <td>...</td>\n",
       "      <td>28.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1610612761</td>\n",
       "      <td>114.0</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0.793</td>\n",
       "      <td>0.394</td>\n",
       "      <td>25.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16197</td>\n",
       "      <td>2019-06-10</td>\n",
       "      <td>41800405</td>\n",
       "      <td>Final</td>\n",
       "      <td>1610612761</td>\n",
       "      <td>1610612744</td>\n",
       "      <td>2018</td>\n",
       "      <td>1610612761</td>\n",
       "      <td>105.0</td>\n",
       "      <td>0.447</td>\n",
       "      <td>...</td>\n",
       "      <td>19.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1610612744</td>\n",
       "      <td>106.0</td>\n",
       "      <td>0.463</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.476</td>\n",
       "      <td>27.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 GAME_DATE_EST   GAME_ID GAME_STATUS_TEXT  HOME_TEAM_ID  \\\n",
       "0       16196    2019-06-13  41800406            Final    1610612744   \n",
       "1       16197    2019-06-10  41800405            Final    1610612761   \n",
       "\n",
       "   VISITOR_TEAM_ID  SEASON  TEAM_ID_home  PTS_home  FG_PCT_home  ...  \\\n",
       "0       1610612761    2018    1610612744     110.0        0.488  ...   \n",
       "1       1610612744    2018    1610612761     105.0        0.447  ...   \n",
       "\n",
       "   AST_home  REB_home  TEAM_ID_away  PTS_away  FG_PCT_away  FT_PCT_away  \\\n",
       "0      28.0      42.0    1610612761     114.0        0.476        0.793   \n",
       "1      19.0      43.0    1610612744     106.0        0.463        0.714   \n",
       "\n",
       "   FG3_PCT_away  AST_away  REB_away  HOME_TEAM_WINS  \n",
       "0         0.394      25.0      39.0               0  \n",
       "1         0.476      27.0      37.0               0  \n",
       "\n",
       "[2 rows x 22 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games_df18.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "47ccc58e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>GAME_DATE_EST</th>\n",
       "      <th>GAME_ID</th>\n",
       "      <th>GAME_STATUS_TEXT</th>\n",
       "      <th>HOME_TEAM_ID</th>\n",
       "      <th>VISITOR_TEAM_ID</th>\n",
       "      <th>SEASON</th>\n",
       "      <th>TEAM_ID_home</th>\n",
       "      <th>PTS_home</th>\n",
       "      <th>FG_PCT_home</th>\n",
       "      <th>...</th>\n",
       "      <th>AST_home</th>\n",
       "      <th>REB_home</th>\n",
       "      <th>TEAM_ID_away</th>\n",
       "      <th>PTS_away</th>\n",
       "      <th>FG_PCT_away</th>\n",
       "      <th>FT_PCT_away</th>\n",
       "      <th>FG3_PCT_away</th>\n",
       "      <th>AST_away</th>\n",
       "      <th>REB_away</th>\n",
       "      <th>HOME_TEAM_WINS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>21900895</td>\n",
       "      <td>Final</td>\n",
       "      <td>1610612766</td>\n",
       "      <td>1610612749</td>\n",
       "      <td>2019</td>\n",
       "      <td>1610612766</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.354</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1610612749</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0.402</td>\n",
       "      <td>0.762</td>\n",
       "      <td>0.226</td>\n",
       "      <td>20.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>21900896</td>\n",
       "      <td>Final</td>\n",
       "      <td>1610612750</td>\n",
       "      <td>1610612742</td>\n",
       "      <td>2019</td>\n",
       "      <td>1610612750</td>\n",
       "      <td>91.0</td>\n",
       "      <td>0.364</td>\n",
       "      <td>...</td>\n",
       "      <td>19.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>1610612742</td>\n",
       "      <td>111.0</td>\n",
       "      <td>0.468</td>\n",
       "      <td>0.632</td>\n",
       "      <td>0.275</td>\n",
       "      <td>28.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 GAME_DATE_EST   GAME_ID GAME_STATUS_TEXT  HOME_TEAM_ID  \\\n",
       "0           0    2020-03-01  21900895            Final    1610612766   \n",
       "1           1    2020-03-01  21900896            Final    1610612750   \n",
       "\n",
       "   VISITOR_TEAM_ID  SEASON  TEAM_ID_home  PTS_home  FG_PCT_home  ...  \\\n",
       "0       1610612749    2019    1610612766      85.0        0.354  ...   \n",
       "1       1610612742    2019    1610612750      91.0        0.364  ...   \n",
       "\n",
       "   AST_home  REB_home  TEAM_ID_away  PTS_away  FG_PCT_away  FT_PCT_away  \\\n",
       "0      22.0      47.0    1610612749      93.0        0.402        0.762   \n",
       "1      19.0      57.0    1610612742     111.0        0.468        0.632   \n",
       "\n",
       "   FG3_PCT_away  AST_away  REB_away  HOME_TEAM_WINS  \n",
       "0         0.226      20.0      61.0               0  \n",
       "1         0.275      28.0      56.0               0  \n",
       "\n",
       "[2 rows x 22 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games_df19.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0d22345",
   "metadata": {},
   "source": [
    "As we can see, both datasets have the same columns, and just for two different years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f4801a18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1378, 22)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games_df18.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a9a126b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(965, 22)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games_df19.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b1dbc92",
   "metadata": {},
   "source": [
    "Let's concatenate two dataframes to make one dataframe out of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a758bdb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2343, 22)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games_df = pd.concat([games_df18,games_df19])\n",
    "games_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bfc9139",
   "metadata": {},
   "source": [
    "As we can see rows are added up but we have the same numbe of rcolums."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c1e2463d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'GAME_DATE_EST', 'GAME_ID', 'GAME_STATUS_TEXT',\n",
       "       'HOME_TEAM_ID', 'VISITOR_TEAM_ID', 'SEASON', 'TEAM_ID_home', 'PTS_home',\n",
       "       'FG_PCT_home', 'FT_PCT_home', 'FG3_PCT_home', 'AST_home', 'REB_home',\n",
       "       'TEAM_ID_away', 'PTS_away', 'FG_PCT_away', 'FT_PCT_away',\n",
       "       'FG3_PCT_away', 'AST_away', 'REB_away', 'HOME_TEAM_WINS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8df40c57",
   "metadata": {},
   "source": [
    "Let's drop the column we do not need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "872952bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GAME_DATE_EST</th>\n",
       "      <th>GAME_ID</th>\n",
       "      <th>GAME_STATUS_TEXT</th>\n",
       "      <th>HOME_TEAM_ID</th>\n",
       "      <th>VISITOR_TEAM_ID</th>\n",
       "      <th>SEASON</th>\n",
       "      <th>TEAM_ID_home</th>\n",
       "      <th>PTS_home</th>\n",
       "      <th>FG_PCT_home</th>\n",
       "      <th>FT_PCT_home</th>\n",
       "      <th>...</th>\n",
       "      <th>AST_home</th>\n",
       "      <th>REB_home</th>\n",
       "      <th>TEAM_ID_away</th>\n",
       "      <th>PTS_away</th>\n",
       "      <th>FG_PCT_away</th>\n",
       "      <th>FT_PCT_away</th>\n",
       "      <th>FG3_PCT_away</th>\n",
       "      <th>AST_away</th>\n",
       "      <th>REB_away</th>\n",
       "      <th>HOME_TEAM_WINS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-06-13</td>\n",
       "      <td>41800406</td>\n",
       "      <td>Final</td>\n",
       "      <td>1610612744</td>\n",
       "      <td>1610612761</td>\n",
       "      <td>2018</td>\n",
       "      <td>1610612744</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.488</td>\n",
       "      <td>0.700</td>\n",
       "      <td>...</td>\n",
       "      <td>28.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1610612761</td>\n",
       "      <td>114.0</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0.793</td>\n",
       "      <td>0.394</td>\n",
       "      <td>25.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-06-10</td>\n",
       "      <td>41800405</td>\n",
       "      <td>Final</td>\n",
       "      <td>1610612761</td>\n",
       "      <td>1610612744</td>\n",
       "      <td>2018</td>\n",
       "      <td>1610612761</td>\n",
       "      <td>105.0</td>\n",
       "      <td>0.447</td>\n",
       "      <td>0.778</td>\n",
       "      <td>...</td>\n",
       "      <td>19.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1610612744</td>\n",
       "      <td>106.0</td>\n",
       "      <td>0.463</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.476</td>\n",
       "      <td>27.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  GAME_DATE_EST   GAME_ID GAME_STATUS_TEXT  HOME_TEAM_ID  VISITOR_TEAM_ID  \\\n",
       "0    2019-06-13  41800406            Final    1610612744       1610612761   \n",
       "1    2019-06-10  41800405            Final    1610612761       1610612744   \n",
       "\n",
       "   SEASON  TEAM_ID_home  PTS_home  FG_PCT_home  FT_PCT_home  ...  AST_home  \\\n",
       "0    2018    1610612744     110.0        0.488        0.700  ...      28.0   \n",
       "1    2018    1610612761     105.0        0.447        0.778  ...      19.0   \n",
       "\n",
       "   REB_home  TEAM_ID_away  PTS_away  FG_PCT_away  FT_PCT_away  FG3_PCT_away  \\\n",
       "0      42.0    1610612761     114.0        0.476        0.793         0.394   \n",
       "1      43.0    1610612744     106.0        0.463        0.714         0.476   \n",
       "\n",
       "   AST_away  REB_away  HOME_TEAM_WINS  \n",
       "0      25.0      39.0               0  \n",
       "1      27.0      37.0               0  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games_df.drop(columns= 'Unnamed: 0',inplace=True,)\n",
    "games_df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e243ed3",
   "metadata": {},
   "source": [
    "<!-- annotate: Merging Data Frames -->\n",
    "## Merging Data Frames\n",
    "Now we read another dataset which some of its columns are the same as dataframe \"games_df\" and some are different."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2df04016",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LEAGUE_ID</th>\n",
       "      <th>TEAM_ID</th>\n",
       "      <th>MIN_YEAR</th>\n",
       "      <th>MAX_YEAR</th>\n",
       "      <th>ABBREVIATION</th>\n",
       "      <th>NICKNAME</th>\n",
       "      <th>YEARFOUNDED</th>\n",
       "      <th>CITY</th>\n",
       "      <th>ARENA</th>\n",
       "      <th>ARENACAPACITY</th>\n",
       "      <th>OWNER</th>\n",
       "      <th>GENERALMANAGER</th>\n",
       "      <th>HEADCOACH</th>\n",
       "      <th>DLEAGUEAFFILIATION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1610612737</td>\n",
       "      <td>1949</td>\n",
       "      <td>2019</td>\n",
       "      <td>ATL</td>\n",
       "      <td>Hawks</td>\n",
       "      <td>1949</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>State Farm Arena</td>\n",
       "      <td>18729.0</td>\n",
       "      <td>Tony Ressler</td>\n",
       "      <td>Travis Schlenk</td>\n",
       "      <td>Lloyd Pierce</td>\n",
       "      <td>Erie Bayhawks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1610612738</td>\n",
       "      <td>1946</td>\n",
       "      <td>2019</td>\n",
       "      <td>BOS</td>\n",
       "      <td>Celtics</td>\n",
       "      <td>1946</td>\n",
       "      <td>Boston</td>\n",
       "      <td>TD Garden</td>\n",
       "      <td>18624.0</td>\n",
       "      <td>Wyc Grousbeck</td>\n",
       "      <td>Danny Ainge</td>\n",
       "      <td>Brad Stevens</td>\n",
       "      <td>Maine Red Claws</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   LEAGUE_ID     TEAM_ID  MIN_YEAR  MAX_YEAR ABBREVIATION NICKNAME  \\\n",
       "0          0  1610612737      1949      2019          ATL    Hawks   \n",
       "1          0  1610612738      1946      2019          BOS  Celtics   \n",
       "\n",
       "   YEARFOUNDED     CITY             ARENA  ARENACAPACITY          OWNER  \\\n",
       "0         1949  Atlanta  State Farm Arena        18729.0   Tony Ressler   \n",
       "1         1946   Boston         TD Garden        18624.0  Wyc Grousbeck   \n",
       "\n",
       "   GENERALMANAGER     HEADCOACH DLEAGUEAFFILIATION  \n",
       "0  Travis Schlenk  Lloyd Pierce      Erie Bayhawks  \n",
       "1     Danny Ainge  Brad Stevens    Maine Red Claws  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "teams_df = pd.read_csv('https://raw.githubusercontent.com/rhodyprog4ds/inclass-data/main/teams.csv')\n",
    "teams_df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdaefef6",
   "metadata": {},
   "source": [
    "We use left_on='TEAM_ID' and right_on = 'HOME_TEAM_ID' to match two dataframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f0d13ec3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LEAGUE_ID</th>\n",
       "      <th>TEAM_ID</th>\n",
       "      <th>MIN_YEAR</th>\n",
       "      <th>MAX_YEAR</th>\n",
       "      <th>ABBREVIATION</th>\n",
       "      <th>NICKNAME</th>\n",
       "      <th>YEARFOUNDED</th>\n",
       "      <th>CITY</th>\n",
       "      <th>ARENA</th>\n",
       "      <th>ARENACAPACITY</th>\n",
       "      <th>...</th>\n",
       "      <th>AST_home</th>\n",
       "      <th>REB_home</th>\n",
       "      <th>TEAM_ID_away</th>\n",
       "      <th>PTS_away</th>\n",
       "      <th>FG_PCT_away</th>\n",
       "      <th>FT_PCT_away</th>\n",
       "      <th>FG3_PCT_away</th>\n",
       "      <th>AST_away</th>\n",
       "      <th>REB_away</th>\n",
       "      <th>HOME_TEAM_WINS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1610612737</td>\n",
       "      <td>1949</td>\n",
       "      <td>2019</td>\n",
       "      <td>ATL</td>\n",
       "      <td>Hawks</td>\n",
       "      <td>1949</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>State Farm Arena</td>\n",
       "      <td>18729.0</td>\n",
       "      <td>...</td>\n",
       "      <td>29.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1610612754</td>\n",
       "      <td>135.0</td>\n",
       "      <td>0.459</td>\n",
       "      <td>0.846</td>\n",
       "      <td>0.400</td>\n",
       "      <td>22.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1610612737</td>\n",
       "      <td>1949</td>\n",
       "      <td>2019</td>\n",
       "      <td>ATL</td>\n",
       "      <td>Hawks</td>\n",
       "      <td>1949</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>State Farm Arena</td>\n",
       "      <td>18729.0</td>\n",
       "      <td>...</td>\n",
       "      <td>29.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1610612755</td>\n",
       "      <td>122.0</td>\n",
       "      <td>0.459</td>\n",
       "      <td>0.579</td>\n",
       "      <td>0.323</td>\n",
       "      <td>27.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   LEAGUE_ID     TEAM_ID  MIN_YEAR  MAX_YEAR ABBREVIATION NICKNAME  \\\n",
       "0          0  1610612737      1949      2019          ATL    Hawks   \n",
       "1          0  1610612737      1949      2019          ATL    Hawks   \n",
       "\n",
       "   YEARFOUNDED     CITY             ARENA  ARENACAPACITY  ... AST_home  \\\n",
       "0         1949  Atlanta  State Farm Arena        18729.0  ...     29.0   \n",
       "1         1949  Atlanta  State Farm Arena        18729.0  ...     29.0   \n",
       "\n",
       "  REB_home TEAM_ID_away PTS_away FG_PCT_away  FT_PCT_away FG3_PCT_away  \\\n",
       "0     61.0   1610612754    135.0       0.459        0.846        0.400   \n",
       "1     44.0   1610612755    122.0       0.459        0.579        0.323   \n",
       "\n",
       "   AST_away  REB_away  HOME_TEAM_WINS  \n",
       "0      22.0      43.0               0  \n",
       "1      27.0      57.0               1  \n",
       "\n",
       "[2 rows x 35 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge1_df = pd.merge(teams_df,games_df,left_on='TEAM_ID', right_on = 'HOME_TEAM_ID')\n",
    "merge1_df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "793c5964",
   "metadata": {},
   "source": [
    "We want information for each game and append the team info onto that. So, lets try another settings in our mergging."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b53b7b7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2343, 35)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge1_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3bf49b69",
   "metadata": {},
   "outputs": [],
   "source": [
    "merge2_df = pd.merge(teams_df, games_df,left_on='TEAM_ID', right_on = 'HOME_TEAM_ID', how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2986c10c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LEAGUE_ID</th>\n",
       "      <th>TEAM_ID</th>\n",
       "      <th>MIN_YEAR</th>\n",
       "      <th>MAX_YEAR</th>\n",
       "      <th>ABBREVIATION</th>\n",
       "      <th>NICKNAME</th>\n",
       "      <th>YEARFOUNDED</th>\n",
       "      <th>CITY</th>\n",
       "      <th>ARENA</th>\n",
       "      <th>ARENACAPACITY</th>\n",
       "      <th>...</th>\n",
       "      <th>AST_home</th>\n",
       "      <th>REB_home</th>\n",
       "      <th>TEAM_ID_away</th>\n",
       "      <th>PTS_away</th>\n",
       "      <th>FG_PCT_away</th>\n",
       "      <th>FT_PCT_away</th>\n",
       "      <th>FG3_PCT_away</th>\n",
       "      <th>AST_away</th>\n",
       "      <th>REB_away</th>\n",
       "      <th>HOME_TEAM_WINS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1610612737</td>\n",
       "      <td>1949</td>\n",
       "      <td>2019</td>\n",
       "      <td>ATL</td>\n",
       "      <td>Hawks</td>\n",
       "      <td>1949</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>State Farm Arena</td>\n",
       "      <td>18729.0</td>\n",
       "      <td>...</td>\n",
       "      <td>29.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1610612754</td>\n",
       "      <td>135.0</td>\n",
       "      <td>0.459</td>\n",
       "      <td>0.846</td>\n",
       "      <td>0.400</td>\n",
       "      <td>22.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1610612737</td>\n",
       "      <td>1949</td>\n",
       "      <td>2019</td>\n",
       "      <td>ATL</td>\n",
       "      <td>Hawks</td>\n",
       "      <td>1949</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>State Farm Arena</td>\n",
       "      <td>18729.0</td>\n",
       "      <td>...</td>\n",
       "      <td>29.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1610612755</td>\n",
       "      <td>122.0</td>\n",
       "      <td>0.459</td>\n",
       "      <td>0.579</td>\n",
       "      <td>0.323</td>\n",
       "      <td>27.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   LEAGUE_ID     TEAM_ID  MIN_YEAR  MAX_YEAR ABBREVIATION NICKNAME  \\\n",
       "0          0  1610612737      1949      2019          ATL    Hawks   \n",
       "1          0  1610612737      1949      2019          ATL    Hawks   \n",
       "\n",
       "   YEARFOUNDED     CITY             ARENA  ARENACAPACITY  ... AST_home  \\\n",
       "0         1949  Atlanta  State Farm Arena        18729.0  ...     29.0   \n",
       "1         1949  Atlanta  State Farm Arena        18729.0  ...     29.0   \n",
       "\n",
       "  REB_home TEAM_ID_away PTS_away FG_PCT_away  FT_PCT_away FG3_PCT_away  \\\n",
       "0     61.0   1610612754    135.0       0.459        0.846        0.400   \n",
       "1     44.0   1610612755    122.0       0.459        0.579        0.323   \n",
       "\n",
       "   AST_away  REB_away  HOME_TEAM_WINS  \n",
       "0      22.0      43.0               0  \n",
       "1      27.0      57.0               1  \n",
       "\n",
       "[2 rows x 35 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge2_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8217507f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2343, 35)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge2_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc6b7830",
   "metadata": {},
   "source": [
    "We can group by \"ARENA\" and then look at the \"mean\" statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2cf706c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LEAGUE_ID</th>\n",
       "      <th>TEAM_ID</th>\n",
       "      <th>MIN_YEAR</th>\n",
       "      <th>MAX_YEAR</th>\n",
       "      <th>YEARFOUNDED</th>\n",
       "      <th>ARENACAPACITY</th>\n",
       "      <th>GAME_ID</th>\n",
       "      <th>HOME_TEAM_ID</th>\n",
       "      <th>VISITOR_TEAM_ID</th>\n",
       "      <th>SEASON</th>\n",
       "      <th>...</th>\n",
       "      <th>AST_home</th>\n",
       "      <th>REB_home</th>\n",
       "      <th>TEAM_ID_away</th>\n",
       "      <th>PTS_away</th>\n",
       "      <th>FG_PCT_away</th>\n",
       "      <th>FT_PCT_away</th>\n",
       "      <th>FG3_PCT_away</th>\n",
       "      <th>AST_away</th>\n",
       "      <th>REB_away</th>\n",
       "      <th>HOME_TEAM_WINS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARENA</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>AT&amp;T Center</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1976.000000</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1976.000000</td>\n",
       "      <td>18694.0</td>\n",
       "      <td>2.184021e+07</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2018.397436</td>\n",
       "      <td>...</td>\n",
       "      <td>25.141026</td>\n",
       "      <td>45.782051</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>108.448718</td>\n",
       "      <td>0.449333</td>\n",
       "      <td>0.770808</td>\n",
       "      <td>0.348231</td>\n",
       "      <td>23.987179</td>\n",
       "      <td>43.833333</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Airlines Center</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1980.000000</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1980.000000</td>\n",
       "      <td>19200.0</td>\n",
       "      <td>2.130985e+07</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2018.426667</td>\n",
       "      <td>...</td>\n",
       "      <td>23.920000</td>\n",
       "      <td>46.093333</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>108.960000</td>\n",
       "      <td>0.456093</td>\n",
       "      <td>0.777320</td>\n",
       "      <td>0.347707</td>\n",
       "      <td>22.960000</td>\n",
       "      <td>43.720000</td>\n",
       "      <td>0.546667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AmericanAirlines Arena</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1988.000000</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1988.000000</td>\n",
       "      <td>19600.0</td>\n",
       "      <td>2.105314e+07</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2018.421053</td>\n",
       "      <td>...</td>\n",
       "      <td>25.039474</td>\n",
       "      <td>46.276316</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>106.618421</td>\n",
       "      <td>0.442684</td>\n",
       "      <td>0.771987</td>\n",
       "      <td>0.344250</td>\n",
       "      <td>23.157895</td>\n",
       "      <td>42.131579</td>\n",
       "      <td>0.644737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amway Center</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1989.000000</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1989.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.171458e+07</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2018.423077</td>\n",
       "      <td>...</td>\n",
       "      <td>24.525641</td>\n",
       "      <td>45.935897</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>105.538462</td>\n",
       "      <td>0.454372</td>\n",
       "      <td>0.772487</td>\n",
       "      <td>0.359962</td>\n",
       "      <td>22.987179</td>\n",
       "      <td>44.769231</td>\n",
       "      <td>0.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bankers Life Fieldhouse</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1976.000000</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1976.000000</td>\n",
       "      <td>18345.0</td>\n",
       "      <td>2.197454e+07</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2018.441558</td>\n",
       "      <td>...</td>\n",
       "      <td>26.363636</td>\n",
       "      <td>44.597403</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>103.051948</td>\n",
       "      <td>0.435494</td>\n",
       "      <td>0.761805</td>\n",
       "      <td>0.329442</td>\n",
       "      <td>23.272727</td>\n",
       "      <td>44.662338</td>\n",
       "      <td>0.675325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barclays Center</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1976.000000</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1976.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.197516e+07</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2018.413333</td>\n",
       "      <td>...</td>\n",
       "      <td>23.880000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>110.293333</td>\n",
       "      <td>0.448133</td>\n",
       "      <td>0.773533</td>\n",
       "      <td>0.338547</td>\n",
       "      <td>22.640000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Capital One Arena</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1961.000000</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1961.000000</td>\n",
       "      <td>20647.0</td>\n",
       "      <td>2.130190e+07</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2018.418919</td>\n",
       "      <td>...</td>\n",
       "      <td>25.891892</td>\n",
       "      <td>43.932432</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>115.310811</td>\n",
       "      <td>0.472041</td>\n",
       "      <td>0.765959</td>\n",
       "      <td>0.353324</td>\n",
       "      <td>24.554054</td>\n",
       "      <td>46.121622</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chase Center</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1946.000000</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1946.000000</td>\n",
       "      <td>19596.0</td>\n",
       "      <td>2.350492e+07</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2018.377778</td>\n",
       "      <td>...</td>\n",
       "      <td>28.444444</td>\n",
       "      <td>44.722222</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>112.833333</td>\n",
       "      <td>0.461144</td>\n",
       "      <td>0.782122</td>\n",
       "      <td>0.374233</td>\n",
       "      <td>25.377778</td>\n",
       "      <td>43.900000</td>\n",
       "      <td>0.511111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chesapeake Energy Arena</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1967.000000</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1967.000000</td>\n",
       "      <td>19163.0</td>\n",
       "      <td>2.171802e+07</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2018.425000</td>\n",
       "      <td>...</td>\n",
       "      <td>23.450000</td>\n",
       "      <td>45.387500</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>109.250000</td>\n",
       "      <td>0.455088</td>\n",
       "      <td>0.772437</td>\n",
       "      <td>0.351600</td>\n",
       "      <td>23.175000</td>\n",
       "      <td>44.700000</td>\n",
       "      <td>0.637500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FedExForum</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>18119.0</td>\n",
       "      <td>2.131630e+07</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2018.421053</td>\n",
       "      <td>...</td>\n",
       "      <td>25.394737</td>\n",
       "      <td>44.578947</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>108.236842</td>\n",
       "      <td>0.445092</td>\n",
       "      <td>0.786355</td>\n",
       "      <td>0.362816</td>\n",
       "      <td>23.473684</td>\n",
       "      <td>44.986842</td>\n",
       "      <td>0.526316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fiserv Forum</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1968.000000</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1968.000000</td>\n",
       "      <td>17500.0</td>\n",
       "      <td>2.328482e+07</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2018.385542</td>\n",
       "      <td>...</td>\n",
       "      <td>26.819277</td>\n",
       "      <td>51.590361</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>106.421687</td>\n",
       "      <td>0.417289</td>\n",
       "      <td>0.758819</td>\n",
       "      <td>0.347325</td>\n",
       "      <td>23.253012</td>\n",
       "      <td>44.385542</td>\n",
       "      <td>0.843373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Golden 1 Center</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1948.000000</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1948.000000</td>\n",
       "      <td>17500.0</td>\n",
       "      <td>2.142553e+07</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2018.416667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.125000</td>\n",
       "      <td>45.208333</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>112.361111</td>\n",
       "      <td>0.459417</td>\n",
       "      <td>0.772444</td>\n",
       "      <td>0.349514</td>\n",
       "      <td>23.805556</td>\n",
       "      <td>46.708333</td>\n",
       "      <td>0.527778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Little Caesars Arena</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1948.000000</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1948.000000</td>\n",
       "      <td>21000.0</td>\n",
       "      <td>2.171695e+07</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2018.430380</td>\n",
       "      <td>...</td>\n",
       "      <td>23.974684</td>\n",
       "      <td>44.670886</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>108.797468</td>\n",
       "      <td>0.469127</td>\n",
       "      <td>0.767203</td>\n",
       "      <td>0.348127</td>\n",
       "      <td>24.151899</td>\n",
       "      <td>43.417722</td>\n",
       "      <td>0.493671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madison Square Garden</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1946.000000</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1946.000000</td>\n",
       "      <td>19763.0</td>\n",
       "      <td>2.105316e+07</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2018.421053</td>\n",
       "      <td>...</td>\n",
       "      <td>21.500000</td>\n",
       "      <td>45.447368</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>112.105263</td>\n",
       "      <td>0.463842</td>\n",
       "      <td>0.756461</td>\n",
       "      <td>0.365237</td>\n",
       "      <td>24.263158</td>\n",
       "      <td>45.263158</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moda Center</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1970.000000</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1970.000000</td>\n",
       "      <td>19980.0</td>\n",
       "      <td>2.316313e+07</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2018.373494</td>\n",
       "      <td>...</td>\n",
       "      <td>22.361446</td>\n",
       "      <td>47.831325</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>112.060241</td>\n",
       "      <td>0.448145</td>\n",
       "      <td>0.793880</td>\n",
       "      <td>0.371578</td>\n",
       "      <td>22.939759</td>\n",
       "      <td>44.048193</td>\n",
       "      <td>0.662651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pepsi Center</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1976.000000</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1976.000000</td>\n",
       "      <td>19099.0</td>\n",
       "      <td>2.369186e+07</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2018.395062</td>\n",
       "      <td>...</td>\n",
       "      <td>27.419753</td>\n",
       "      <td>46.419753</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>103.777778</td>\n",
       "      <td>0.448531</td>\n",
       "      <td>0.742173</td>\n",
       "      <td>0.323988</td>\n",
       "      <td>23.320988</td>\n",
       "      <td>42.172840</td>\n",
       "      <td>0.790123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quicken Loans Arena</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1970.000000</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1970.000000</td>\n",
       "      <td>20562.0</td>\n",
       "      <td>2.132390e+07</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2018.428571</td>\n",
       "      <td>...</td>\n",
       "      <td>22.038961</td>\n",
       "      <td>44.025974</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>113.584416</td>\n",
       "      <td>0.489766</td>\n",
       "      <td>0.776896</td>\n",
       "      <td>0.384247</td>\n",
       "      <td>25.584416</td>\n",
       "      <td>42.597403</td>\n",
       "      <td>0.298701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scotiabank Arena</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>19800.0</td>\n",
       "      <td>2.428270e+07</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2018.377778</td>\n",
       "      <td>...</td>\n",
       "      <td>25.433333</td>\n",
       "      <td>45.100000</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>106.144444</td>\n",
       "      <td>0.432111</td>\n",
       "      <td>0.770211</td>\n",
       "      <td>0.347544</td>\n",
       "      <td>24.155556</td>\n",
       "      <td>45.300000</td>\n",
       "      <td>0.744444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Smoothie King Center</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2002.000000</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2002.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.156903e+07</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2018.424658</td>\n",
       "      <td>...</td>\n",
       "      <td>27.232877</td>\n",
       "      <td>46.945205</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>116.808219</td>\n",
       "      <td>0.474096</td>\n",
       "      <td>0.771562</td>\n",
       "      <td>0.362822</td>\n",
       "      <td>25.109589</td>\n",
       "      <td>44.561644</td>\n",
       "      <td>0.438356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spectrum Center</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1988.000000</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1988.000000</td>\n",
       "      <td>19026.0</td>\n",
       "      <td>2.104182e+07</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2018.413333</td>\n",
       "      <td>...</td>\n",
       "      <td>23.746667</td>\n",
       "      <td>44.293333</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>109.386667</td>\n",
       "      <td>0.463920</td>\n",
       "      <td>0.761573</td>\n",
       "      <td>0.350853</td>\n",
       "      <td>25.066667</td>\n",
       "      <td>45.266667</td>\n",
       "      <td>0.493333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staples Center</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1959.210191</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1959.210191</td>\n",
       "      <td>19060.0</td>\n",
       "      <td>2.139604e+07</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2018.414013</td>\n",
       "      <td>...</td>\n",
       "      <td>25.942675</td>\n",
       "      <td>47.286624</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>110.477707</td>\n",
       "      <td>0.443783</td>\n",
       "      <td>0.775064</td>\n",
       "      <td>0.338796</td>\n",
       "      <td>24.095541</td>\n",
       "      <td>45.133758</td>\n",
       "      <td>0.636943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State Farm Arena</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1949.000000</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1949.000000</td>\n",
       "      <td>18729.0</td>\n",
       "      <td>2.131766e+07</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2018.434211</td>\n",
       "      <td>...</td>\n",
       "      <td>25.736842</td>\n",
       "      <td>45.960526</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>117.894737</td>\n",
       "      <td>0.463447</td>\n",
       "      <td>0.767184</td>\n",
       "      <td>0.343553</td>\n",
       "      <td>25.500000</td>\n",
       "      <td>46.276316</td>\n",
       "      <td>0.421053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TD Garden</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1946.000000</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1946.000000</td>\n",
       "      <td>18624.0</td>\n",
       "      <td>2.235308e+07</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2018.397436</td>\n",
       "      <td>...</td>\n",
       "      <td>25.717949</td>\n",
       "      <td>45.974359</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>106.358974</td>\n",
       "      <td>0.442769</td>\n",
       "      <td>0.743064</td>\n",
       "      <td>0.335808</td>\n",
       "      <td>23.282051</td>\n",
       "      <td>44.153846</td>\n",
       "      <td>0.717949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Talking Stick Resort Arena</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1968.000000</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1968.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.132518e+07</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2018.441558</td>\n",
       "      <td>...</td>\n",
       "      <td>25.298701</td>\n",
       "      <td>41.519481</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>113.064935</td>\n",
       "      <td>0.474922</td>\n",
       "      <td>0.762377</td>\n",
       "      <td>0.363143</td>\n",
       "      <td>24.285714</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0.311688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Target Center</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1989.000000</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1989.000000</td>\n",
       "      <td>19356.0</td>\n",
       "      <td>2.156304e+07</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2018.402778</td>\n",
       "      <td>...</td>\n",
       "      <td>24.458333</td>\n",
       "      <td>46.583333</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>113.208333</td>\n",
       "      <td>0.462306</td>\n",
       "      <td>0.763181</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>24.402778</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>0.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toyota Center</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1967.000000</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1967.000000</td>\n",
       "      <td>18104.0</td>\n",
       "      <td>2.283925e+07</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2018.387500</td>\n",
       "      <td>...</td>\n",
       "      <td>21.712500</td>\n",
       "      <td>44.850000</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>110.275000</td>\n",
       "      <td>0.454438</td>\n",
       "      <td>0.759188</td>\n",
       "      <td>0.339250</td>\n",
       "      <td>24.200000</td>\n",
       "      <td>45.700000</td>\n",
       "      <td>0.737500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Center</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1966.000000</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1966.000000</td>\n",
       "      <td>21711.0</td>\n",
       "      <td>2.107486e+07</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2018.435897</td>\n",
       "      <td>...</td>\n",
       "      <td>22.987179</td>\n",
       "      <td>43.512821</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>110.705128</td>\n",
       "      <td>0.475372</td>\n",
       "      <td>0.750628</td>\n",
       "      <td>0.355808</td>\n",
       "      <td>25.602564</td>\n",
       "      <td>46.192308</td>\n",
       "      <td>0.307692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vivint Smart Home Arena</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1974.000000</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1974.000000</td>\n",
       "      <td>20148.0</td>\n",
       "      <td>2.197424e+07</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2018.421053</td>\n",
       "      <td>...</td>\n",
       "      <td>24.526316</td>\n",
       "      <td>47.592105</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>105.723684</td>\n",
       "      <td>0.443618</td>\n",
       "      <td>0.767803</td>\n",
       "      <td>0.351961</td>\n",
       "      <td>20.539474</td>\n",
       "      <td>42.276316</td>\n",
       "      <td>0.684211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wells Fargo Center</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1949.000000</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1949.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.282764e+07</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2018.395062</td>\n",
       "      <td>...</td>\n",
       "      <td>27.395062</td>\n",
       "      <td>47.617284</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>106.753086</td>\n",
       "      <td>0.445346</td>\n",
       "      <td>0.757247</td>\n",
       "      <td>0.343272</td>\n",
       "      <td>22.086420</td>\n",
       "      <td>41.160494</td>\n",
       "      <td>0.802469</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            LEAGUE_ID       TEAM_ID     MIN_YEAR  MAX_YEAR  \\\n",
       "ARENA                                                                        \n",
       "AT&T Center                       0.0  1.610613e+09  1976.000000    2019.0   \n",
       "American Airlines Center          0.0  1.610613e+09  1980.000000    2019.0   \n",
       "AmericanAirlines Arena            0.0  1.610613e+09  1988.000000    2019.0   \n",
       "Amway Center                      0.0  1.610613e+09  1989.000000    2019.0   \n",
       "Bankers Life Fieldhouse           0.0  1.610613e+09  1976.000000    2019.0   \n",
       "Barclays Center                   0.0  1.610613e+09  1976.000000    2019.0   \n",
       "Capital One Arena                 0.0  1.610613e+09  1961.000000    2019.0   \n",
       "Chase Center                      0.0  1.610613e+09  1946.000000    2019.0   \n",
       "Chesapeake Energy Arena           0.0  1.610613e+09  1967.000000    2019.0   \n",
       "FedExForum                        0.0  1.610613e+09  1995.000000    2019.0   \n",
       "Fiserv Forum                      0.0  1.610613e+09  1968.000000    2019.0   \n",
       "Golden 1 Center                   0.0  1.610613e+09  1948.000000    2019.0   \n",
       "Little Caesars Arena              0.0  1.610613e+09  1948.000000    2019.0   \n",
       "Madison Square Garden             0.0  1.610613e+09  1946.000000    2019.0   \n",
       "Moda Center                       0.0  1.610613e+09  1970.000000    2019.0   \n",
       "Pepsi Center                      0.0  1.610613e+09  1976.000000    2019.0   \n",
       "Quicken Loans Arena               0.0  1.610613e+09  1970.000000    2019.0   \n",
       "Scotiabank Arena                  0.0  1.610613e+09  1995.000000    2019.0   \n",
       "Smoothie King Center              0.0  1.610613e+09  2002.000000    2019.0   \n",
       "Spectrum Center                   0.0  1.610613e+09  1988.000000    2019.0   \n",
       "Staples Center                    0.0  1.610613e+09  1959.210191    2019.0   \n",
       "State Farm Arena                  0.0  1.610613e+09  1949.000000    2019.0   \n",
       "TD Garden                         0.0  1.610613e+09  1946.000000    2019.0   \n",
       "Talking Stick Resort Arena        0.0  1.610613e+09  1968.000000    2019.0   \n",
       "Target Center                     0.0  1.610613e+09  1989.000000    2019.0   \n",
       "Toyota Center                     0.0  1.610613e+09  1967.000000    2019.0   \n",
       "United Center                     0.0  1.610613e+09  1966.000000    2019.0   \n",
       "Vivint Smart Home Arena           0.0  1.610613e+09  1974.000000    2019.0   \n",
       "Wells Fargo Center                0.0  1.610613e+09  1949.000000    2019.0   \n",
       "\n",
       "                            YEARFOUNDED  ARENACAPACITY       GAME_ID  \\\n",
       "ARENA                                                                  \n",
       "AT&T Center                 1976.000000        18694.0  2.184021e+07   \n",
       "American Airlines Center    1980.000000        19200.0  2.130985e+07   \n",
       "AmericanAirlines Arena      1988.000000        19600.0  2.105314e+07   \n",
       "Amway Center                1989.000000            0.0  2.171458e+07   \n",
       "Bankers Life Fieldhouse     1976.000000        18345.0  2.197454e+07   \n",
       "Barclays Center             1976.000000            NaN  2.197516e+07   \n",
       "Capital One Arena           1961.000000        20647.0  2.130190e+07   \n",
       "Chase Center                1946.000000        19596.0  2.350492e+07   \n",
       "Chesapeake Energy Arena     1967.000000        19163.0  2.171802e+07   \n",
       "FedExForum                  1995.000000        18119.0  2.131630e+07   \n",
       "Fiserv Forum                1968.000000        17500.0  2.328482e+07   \n",
       "Golden 1 Center             1948.000000        17500.0  2.142553e+07   \n",
       "Little Caesars Arena        1948.000000        21000.0  2.171695e+07   \n",
       "Madison Square Garden       1946.000000        19763.0  2.105316e+07   \n",
       "Moda Center                 1970.000000        19980.0  2.316313e+07   \n",
       "Pepsi Center                1976.000000        19099.0  2.369186e+07   \n",
       "Quicken Loans Arena         1970.000000        20562.0  2.132390e+07   \n",
       "Scotiabank Arena            1995.000000        19800.0  2.428270e+07   \n",
       "Smoothie King Center        2002.000000            NaN  2.156903e+07   \n",
       "Spectrum Center             1988.000000        19026.0  2.104182e+07   \n",
       "Staples Center              1959.210191        19060.0  2.139604e+07   \n",
       "State Farm Arena            1949.000000        18729.0  2.131766e+07   \n",
       "TD Garden                   1946.000000        18624.0  2.235308e+07   \n",
       "Talking Stick Resort Arena  1968.000000            NaN  2.132518e+07   \n",
       "Target Center               1989.000000        19356.0  2.156304e+07   \n",
       "Toyota Center               1967.000000        18104.0  2.283925e+07   \n",
       "United Center               1966.000000        21711.0  2.107486e+07   \n",
       "Vivint Smart Home Arena     1974.000000        20148.0  2.197424e+07   \n",
       "Wells Fargo Center          1949.000000            NaN  2.282764e+07   \n",
       "\n",
       "                            HOME_TEAM_ID  VISITOR_TEAM_ID       SEASON  ...  \\\n",
       "ARENA                                                                   ...   \n",
       "AT&T Center                 1.610613e+09     1.610613e+09  2018.397436  ...   \n",
       "American Airlines Center    1.610613e+09     1.610613e+09  2018.426667  ...   \n",
       "AmericanAirlines Arena      1.610613e+09     1.610613e+09  2018.421053  ...   \n",
       "Amway Center                1.610613e+09     1.610613e+09  2018.423077  ...   \n",
       "Bankers Life Fieldhouse     1.610613e+09     1.610613e+09  2018.441558  ...   \n",
       "Barclays Center             1.610613e+09     1.610613e+09  2018.413333  ...   \n",
       "Capital One Arena           1.610613e+09     1.610613e+09  2018.418919  ...   \n",
       "Chase Center                1.610613e+09     1.610613e+09  2018.377778  ...   \n",
       "Chesapeake Energy Arena     1.610613e+09     1.610613e+09  2018.425000  ...   \n",
       "FedExForum                  1.610613e+09     1.610613e+09  2018.421053  ...   \n",
       "Fiserv Forum                1.610613e+09     1.610613e+09  2018.385542  ...   \n",
       "Golden 1 Center             1.610613e+09     1.610613e+09  2018.416667  ...   \n",
       "Little Caesars Arena        1.610613e+09     1.610613e+09  2018.430380  ...   \n",
       "Madison Square Garden       1.610613e+09     1.610613e+09  2018.421053  ...   \n",
       "Moda Center                 1.610613e+09     1.610613e+09  2018.373494  ...   \n",
       "Pepsi Center                1.610613e+09     1.610613e+09  2018.395062  ...   \n",
       "Quicken Loans Arena         1.610613e+09     1.610613e+09  2018.428571  ...   \n",
       "Scotiabank Arena            1.610613e+09     1.610613e+09  2018.377778  ...   \n",
       "Smoothie King Center        1.610613e+09     1.610613e+09  2018.424658  ...   \n",
       "Spectrum Center             1.610613e+09     1.610613e+09  2018.413333  ...   \n",
       "Staples Center              1.610613e+09     1.610613e+09  2018.414013  ...   \n",
       "State Farm Arena            1.610613e+09     1.610613e+09  2018.434211  ...   \n",
       "TD Garden                   1.610613e+09     1.610613e+09  2018.397436  ...   \n",
       "Talking Stick Resort Arena  1.610613e+09     1.610613e+09  2018.441558  ...   \n",
       "Target Center               1.610613e+09     1.610613e+09  2018.402778  ...   \n",
       "Toyota Center               1.610613e+09     1.610613e+09  2018.387500  ...   \n",
       "United Center               1.610613e+09     1.610613e+09  2018.435897  ...   \n",
       "Vivint Smart Home Arena     1.610613e+09     1.610613e+09  2018.421053  ...   \n",
       "Wells Fargo Center          1.610613e+09     1.610613e+09  2018.395062  ...   \n",
       "\n",
       "                             AST_home   REB_home  TEAM_ID_away    PTS_away  \\\n",
       "ARENA                                                                        \n",
       "AT&T Center                 25.141026  45.782051  1.610613e+09  108.448718   \n",
       "American Airlines Center    23.920000  46.093333  1.610613e+09  108.960000   \n",
       "AmericanAirlines Arena      25.039474  46.276316  1.610613e+09  106.618421   \n",
       "Amway Center                24.525641  45.935897  1.610613e+09  105.538462   \n",
       "Bankers Life Fieldhouse     26.363636  44.597403  1.610613e+09  103.051948   \n",
       "Barclays Center             23.880000  48.000000  1.610613e+09  110.293333   \n",
       "Capital One Arena           25.891892  43.932432  1.610613e+09  115.310811   \n",
       "Chase Center                28.444444  44.722222  1.610613e+09  112.833333   \n",
       "Chesapeake Energy Arena     23.450000  45.387500  1.610613e+09  109.250000   \n",
       "FedExForum                  25.394737  44.578947  1.610613e+09  108.236842   \n",
       "Fiserv Forum                26.819277  51.590361  1.610613e+09  106.421687   \n",
       "Golden 1 Center             24.125000  45.208333  1.610613e+09  112.361111   \n",
       "Little Caesars Arena        23.974684  44.670886  1.610613e+09  108.797468   \n",
       "Madison Square Garden       21.500000  45.447368  1.610613e+09  112.105263   \n",
       "Moda Center                 22.361446  47.831325  1.610613e+09  112.060241   \n",
       "Pepsi Center                27.419753  46.419753  1.610613e+09  103.777778   \n",
       "Quicken Loans Arena         22.038961  44.025974  1.610613e+09  113.584416   \n",
       "Scotiabank Arena            25.433333  45.100000  1.610613e+09  106.144444   \n",
       "Smoothie King Center        27.232877  46.945205  1.610613e+09  116.808219   \n",
       "Spectrum Center             23.746667  44.293333  1.610613e+09  109.386667   \n",
       "Staples Center              25.942675  47.286624  1.610613e+09  110.477707   \n",
       "State Farm Arena            25.736842  45.960526  1.610613e+09  117.894737   \n",
       "TD Garden                   25.717949  45.974359  1.610613e+09  106.358974   \n",
       "Talking Stick Resort Arena  25.298701  41.519481  1.610613e+09  113.064935   \n",
       "Target Center               24.458333  46.583333  1.610613e+09  113.208333   \n",
       "Toyota Center               21.712500  44.850000  1.610613e+09  110.275000   \n",
       "United Center               22.987179  43.512821  1.610613e+09  110.705128   \n",
       "Vivint Smart Home Arena     24.526316  47.592105  1.610613e+09  105.723684   \n",
       "Wells Fargo Center          27.395062  47.617284  1.610613e+09  106.753086   \n",
       "\n",
       "                            FG_PCT_away  FT_PCT_away  FG3_PCT_away   AST_away  \\\n",
       "ARENA                                                                           \n",
       "AT&T Center                    0.449333     0.770808      0.348231  23.987179   \n",
       "American Airlines Center       0.456093     0.777320      0.347707  22.960000   \n",
       "AmericanAirlines Arena         0.442684     0.771987      0.344250  23.157895   \n",
       "Amway Center                   0.454372     0.772487      0.359962  22.987179   \n",
       "Bankers Life Fieldhouse        0.435494     0.761805      0.329442  23.272727   \n",
       "Barclays Center                0.448133     0.773533      0.338547  22.640000   \n",
       "Capital One Arena              0.472041     0.765959      0.353324  24.554054   \n",
       "Chase Center                   0.461144     0.782122      0.374233  25.377778   \n",
       "Chesapeake Energy Arena        0.455088     0.772437      0.351600  23.175000   \n",
       "FedExForum                     0.445092     0.786355      0.362816  23.473684   \n",
       "Fiserv Forum                   0.417289     0.758819      0.347325  23.253012   \n",
       "Golden 1 Center                0.459417     0.772444      0.349514  23.805556   \n",
       "Little Caesars Arena           0.469127     0.767203      0.348127  24.151899   \n",
       "Madison Square Garden          0.463842     0.756461      0.365237  24.263158   \n",
       "Moda Center                    0.448145     0.793880      0.371578  22.939759   \n",
       "Pepsi Center                   0.448531     0.742173      0.323988  23.320988   \n",
       "Quicken Loans Arena            0.489766     0.776896      0.384247  25.584416   \n",
       "Scotiabank Arena               0.432111     0.770211      0.347544  24.155556   \n",
       "Smoothie King Center           0.474096     0.771562      0.362822  25.109589   \n",
       "Spectrum Center                0.463920     0.761573      0.350853  25.066667   \n",
       "Staples Center                 0.443783     0.775064      0.338796  24.095541   \n",
       "State Farm Arena               0.463447     0.767184      0.343553  25.500000   \n",
       "TD Garden                      0.442769     0.743064      0.335808  23.282051   \n",
       "Talking Stick Resort Arena     0.474922     0.762377      0.363143  24.285714   \n",
       "Target Center                  0.462306     0.763181      0.363625  24.402778   \n",
       "Toyota Center                  0.454438     0.759188      0.339250  24.200000   \n",
       "United Center                  0.475372     0.750628      0.355808  25.602564   \n",
       "Vivint Smart Home Arena        0.443618     0.767803      0.351961  20.539474   \n",
       "Wells Fargo Center             0.445346     0.757247      0.343272  22.086420   \n",
       "\n",
       "                             REB_away  HOME_TEAM_WINS  \n",
       "ARENA                                                  \n",
       "AT&T Center                 43.833333        0.666667  \n",
       "American Airlines Center    43.720000        0.546667  \n",
       "AmericanAirlines Arena      42.131579        0.644737  \n",
       "Amway Center                44.769231        0.538462  \n",
       "Bankers Life Fieldhouse     44.662338        0.675325  \n",
       "Barclays Center             45.000000        0.533333  \n",
       "Capital One Arena           46.121622        0.500000  \n",
       "Chase Center                43.900000        0.511111  \n",
       "Chesapeake Energy Arena     44.700000        0.637500  \n",
       "FedExForum                  44.986842        0.526316  \n",
       "Fiserv Forum                44.385542        0.843373  \n",
       "Golden 1 Center             46.708333        0.527778  \n",
       "Little Caesars Arena        43.417722        0.493671  \n",
       "Madison Square Garden       45.263158        0.250000  \n",
       "Moda Center                 44.048193        0.662651  \n",
       "Pepsi Center                42.172840        0.790123  \n",
       "Quicken Loans Arena         42.597403        0.298701  \n",
       "Scotiabank Arena            45.300000        0.744444  \n",
       "Smoothie King Center        44.561644        0.438356  \n",
       "Spectrum Center             45.266667        0.493333  \n",
       "Staples Center              45.133758        0.636943  \n",
       "State Farm Arena            46.276316        0.421053  \n",
       "TD Garden                   44.153846        0.717949  \n",
       "Talking Stick Resort Arena  45.000000        0.311688  \n",
       "Target Center               46.500000        0.444444  \n",
       "Toyota Center               45.700000        0.737500  \n",
       "United Center               46.192308        0.307692  \n",
       "Vivint Smart Home Arena     42.276316        0.684211  \n",
       "Wells Fargo Center          41.160494        0.802469  \n",
       "\n",
       "[29 rows x 25 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge1_df.groupby('ARENA').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "742c3e08",
   "metadata": {},
   "source": [
    "## How to combine the same data for different outcomes\n",
    "\n",
    "We can combine datasets in different ways to learn different things about the data.\n",
    "Let's now read info about the players."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "20436050",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>PLAYER_NAME</th>\n",
       "      <th>TEAM_ID</th>\n",
       "      <th>PLAYER_ID</th>\n",
       "      <th>SEASON</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>626</td>\n",
       "      <td>Kawhi Leonard</td>\n",
       "      <td>1610612761</td>\n",
       "      <td>202695</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>627</td>\n",
       "      <td>Pascal Siakam</td>\n",
       "      <td>1610612761</td>\n",
       "      <td>1627783</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>628</td>\n",
       "      <td>Marc Gasol</td>\n",
       "      <td>1610612761</td>\n",
       "      <td>201188</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>629</td>\n",
       "      <td>Danny Green</td>\n",
       "      <td>1610612761</td>\n",
       "      <td>201980</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>630</td>\n",
       "      <td>Kyle Lowry</td>\n",
       "      <td>1610612761</td>\n",
       "      <td>200768</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    PLAYER_NAME     TEAM_ID  PLAYER_ID  SEASON\n",
       "0         626  Kawhi Leonard  1610612761     202695    2018\n",
       "1         627  Pascal Siakam  1610612761    1627783    2018\n",
       "2         628     Marc Gasol  1610612761     201188    2018\n",
       "3         629    Danny Green  1610612761     201980    2018\n",
       "4         630     Kyle Lowry  1610612761     200768    2018"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players18 = pd.read_csv('https://raw.githubusercontent.com/rhodyprog4ds/inclass-data/main/2018-players.csv')\n",
    "players19 = pd.read_csv('https://raw.githubusercontent.com/rhodyprog4ds/inclass-data/main/2019-players.csv')\n",
    "players18.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1cd8f53",
   "metadata": {},
   "source": [
    "First let's look at the shape of each of them to have a reference for what happens when we try different merges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "aef18b85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((748, 5), (626, 5))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players18.shape, players19.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cbed9bc",
   "metadata": {},
   "source": [
    "One thing we might want to do is to put all of the information into one long DataFrame. We can do this with `concat`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "01e95e00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>PLAYER_NAME</th>\n",
       "      <th>TEAM_ID</th>\n",
       "      <th>PLAYER_ID</th>\n",
       "      <th>SEASON</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>626</td>\n",
       "      <td>Kawhi Leonard</td>\n",
       "      <td>1610612761</td>\n",
       "      <td>202695</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>627</td>\n",
       "      <td>Pascal Siakam</td>\n",
       "      <td>1610612761</td>\n",
       "      <td>1627783</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>628</td>\n",
       "      <td>Marc Gasol</td>\n",
       "      <td>1610612761</td>\n",
       "      <td>201188</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>629</td>\n",
       "      <td>Danny Green</td>\n",
       "      <td>1610612761</td>\n",
       "      <td>201980</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>630</td>\n",
       "      <td>Kyle Lowry</td>\n",
       "      <td>1610612761</td>\n",
       "      <td>200768</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>621</td>\n",
       "      <td>Anthony Bennett</td>\n",
       "      <td>1610612745</td>\n",
       "      <td>203461</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>622</td>\n",
       "      <td>Ray Spalding</td>\n",
       "      <td>1610612737</td>\n",
       "      <td>1629034</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>623</td>\n",
       "      <td>Devyn Marble</td>\n",
       "      <td>1610612744</td>\n",
       "      <td>203906</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>624</td>\n",
       "      <td>Hassani Gravett</td>\n",
       "      <td>1610612753</td>\n",
       "      <td>1629755</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>625</td>\n",
       "      <td>JaKeenan Gant</td>\n",
       "      <td>1610612754</td>\n",
       "      <td>1629721</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1374 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0      PLAYER_NAME     TEAM_ID  PLAYER_ID  SEASON\n",
       "0           626    Kawhi Leonard  1610612761     202695    2018\n",
       "1           627    Pascal Siakam  1610612761    1627783    2018\n",
       "2           628       Marc Gasol  1610612761     201188    2018\n",
       "3           629      Danny Green  1610612761     201980    2018\n",
       "4           630       Kyle Lowry  1610612761     200768    2018\n",
       "..          ...              ...         ...        ...     ...\n",
       "621         621  Anthony Bennett  1610612745     203461    2019\n",
       "622         622     Ray Spalding  1610612737    1629034    2019\n",
       "623         623     Devyn Marble  1610612744     203906    2019\n",
       "624         624  Hassani Gravett  1610612753    1629755    2019\n",
       "625         625    JaKeenan Gant  1610612754    1629721    2019\n",
       "\n",
       "[1374 rows x 5 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([players18,players19])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7357d24d",
   "metadata": {},
   "source": [
    "This allows us to see all the players for each year, we could do groupby and count to see how many players played each year for example.\n",
    "\n",
    "We can check that this is the size we expected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9c658050",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1374, 5)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([players18,players19]).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf57de1e",
   "metadata": {},
   "source": [
    "If we use the default merge settings we get an empty result because the two DataFrames have the same columns so pandas tries to merge `on` all of the columns, but there are no rows that have the same value in all of the columns, so there's nothing left."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "05ea2718",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>PLAYER_NAME</th>\n",
       "      <th>TEAM_ID</th>\n",
       "      <th>PLAYER_ID</th>\n",
       "      <th>SEASON</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Unnamed: 0, PLAYER_NAME, TEAM_ID, PLAYER_ID, SEASON]\n",
       "Index: []"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(players18,players19,)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b591650",
   "metadata": {},
   "source": [
    "If we merge with on='PLAYER_ID', it only requires that one column to be the same to match rows from the two DataFrames together.  With the default value for `how` or explicitly setting `how='inner'` we get the info of players who played both seasons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "264de540",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>Unnamed: 0_x</th>\n",
       "      <th>PLAYER_NAME_x</th>\n",
       "      <th>TEAM_ID_x</th>\n",
       "      <th>PLAYER_ID</th>\n",
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       "      <th>Unnamed: 0_y</th>\n",
       "      <th>PLAYER_NAME_y</th>\n",
       "      <th>TEAM_ID_y</th>\n",
       "      <th>SEASON_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>626</td>\n",
       "      <td>Kawhi Leonard</td>\n",
       "      <td>1610612761</td>\n",
       "      <td>202695</td>\n",
       "      <td>2018</td>\n",
       "      <td>299</td>\n",
       "      <td>Kawhi Leonard</td>\n",
       "      <td>1610612746</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>627</td>\n",
       "      <td>Pascal Siakam</td>\n",
       "      <td>1610612761</td>\n",
       "      <td>1627783</td>\n",
       "      <td>2018</td>\n",
       "      <td>275</td>\n",
       "      <td>Pascal Siakam</td>\n",
       "      <td>1610612761</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>628</td>\n",
       "      <td>Marc Gasol</td>\n",
       "      <td>1610612761</td>\n",
       "      <td>201188</td>\n",
       "      <td>2018</td>\n",
       "      <td>276</td>\n",
       "      <td>Marc Gasol</td>\n",
       "      <td>1610612761</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1157</td>\n",
       "      <td>Marc Gasol</td>\n",
       "      <td>1610612763</td>\n",
       "      <td>201188</td>\n",
       "      <td>2018</td>\n",
       "      <td>276</td>\n",
       "      <td>Marc Gasol</td>\n",
       "      <td>1610612761</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>629</td>\n",
       "      <td>Danny Green</td>\n",
       "      <td>1610612761</td>\n",
       "      <td>201980</td>\n",
       "      <td>2018</td>\n",
       "      <td>202</td>\n",
       "      <td>Danny Green</td>\n",
       "      <td>1610612747</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>533</th>\n",
       "      <td>1339</td>\n",
       "      <td>Abdul Gaddy</td>\n",
       "      <td>1610612760</td>\n",
       "      <td>203583</td>\n",
       "      <td>2018</td>\n",
       "      <td>561</td>\n",
       "      <td>Abdul Gaddy</td>\n",
       "      <td>1610612760</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>1342</td>\n",
       "      <td>Andre Roberson</td>\n",
       "      <td>1610612760</td>\n",
       "      <td>203460</td>\n",
       "      <td>2018</td>\n",
       "      <td>566</td>\n",
       "      <td>Andre Roberson</td>\n",
       "      <td>1610612760</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>1348</td>\n",
       "      <td>Norvel Pelle</td>\n",
       "      <td>1610612755</td>\n",
       "      <td>203658</td>\n",
       "      <td>2018</td>\n",
       "      <td>24</td>\n",
       "      <td>Norvel Pelle</td>\n",
       "      <td>1610612755</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>1350</td>\n",
       "      <td>Denzel Valentine</td>\n",
       "      <td>1610612741</td>\n",
       "      <td>1627756</td>\n",
       "      <td>2018</td>\n",
       "      <td>58</td>\n",
       "      <td>Denzel Valentine</td>\n",
       "      <td>1610612741</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>1353</td>\n",
       "      <td>C.J. Wilcox</td>\n",
       "      <td>1610612754</td>\n",
       "      <td>203912</td>\n",
       "      <td>2018</td>\n",
       "      <td>573</td>\n",
       "      <td>C.J. Wilcox</td>\n",
       "      <td>1610612754</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>538 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0_x     PLAYER_NAME_x   TEAM_ID_x  PLAYER_ID  SEASON_x  \\\n",
       "0             626     Kawhi Leonard  1610612761     202695      2018   \n",
       "1             627     Pascal Siakam  1610612761    1627783      2018   \n",
       "2             628        Marc Gasol  1610612761     201188      2018   \n",
       "3            1157        Marc Gasol  1610612763     201188      2018   \n",
       "4             629       Danny Green  1610612761     201980      2018   \n",
       "..            ...               ...         ...        ...       ...   \n",
       "533          1339       Abdul Gaddy  1610612760     203583      2018   \n",
       "534          1342    Andre Roberson  1610612760     203460      2018   \n",
       "535          1348      Norvel Pelle  1610612755     203658      2018   \n",
       "536          1350  Denzel Valentine  1610612741    1627756      2018   \n",
       "537          1353       C.J. Wilcox  1610612754     203912      2018   \n",
       "\n",
       "     Unnamed: 0_y     PLAYER_NAME_y   TEAM_ID_y  SEASON_y  \n",
       "0             299     Kawhi Leonard  1610612746      2019  \n",
       "1             275     Pascal Siakam  1610612761      2019  \n",
       "2             276        Marc Gasol  1610612761      2019  \n",
       "3             276        Marc Gasol  1610612761      2019  \n",
       "4             202       Danny Green  1610612747      2019  \n",
       "..            ...               ...         ...       ...  \n",
       "533           561       Abdul Gaddy  1610612760      2019  \n",
       "534           566    Andre Roberson  1610612760      2019  \n",
       "535            24      Norvel Pelle  1610612755      2019  \n",
       "536            58  Denzel Valentine  1610612741      2019  \n",
       "537           573       C.J. Wilcox  1610612754      2019  \n",
       "\n",
       "[538 rows x 9 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(players18,players19,on='PLAYER_ID', how='inner')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13c0b233",
   "metadata": {},
   "source": [
    "When we use `outer` we get one row for each player who played in either season or both seasons.  From this we can for example see who changed teams, who are the rookies in 2019 and who retired or was unsigned in 2019."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8b3bd2b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>626.0</td>\n",
       "      <td>Kawhi Leonard</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>202695</td>\n",
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       "      <td>299.0</td>\n",
       "      <td>Kawhi Leonard</td>\n",
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       "      <th>1</th>\n",
       "      <td>627.0</td>\n",
       "      <td>Pascal Siakam</td>\n",
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       "      <td>275.0</td>\n",
       "      <td>Pascal Siakam</td>\n",
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       "      <td>Marc Gasol</td>\n",
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       "      <td>Marc Gasol</td>\n",
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       "      <td>2019.0</td>\n",
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       "      <td>1157.0</td>\n",
       "      <td>Marc Gasol</td>\n",
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       "      <td>201188</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>Marc Gasol</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2019.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>629.0</td>\n",
       "      <td>Danny Green</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>201980</td>\n",
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       "      <td>202.0</td>\n",
       "      <td>Danny Green</td>\n",
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       "      <th>922</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1629097</td>\n",
       "      <td>NaN</td>\n",
       "      <td>619.0</td>\n",
       "      <td>Terry Larrier</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>923</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>203461</td>\n",
       "      <td>NaN</td>\n",
       "      <td>621.0</td>\n",
       "      <td>Anthony Bennett</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>924</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>203906</td>\n",
       "      <td>NaN</td>\n",
       "      <td>623.0</td>\n",
       "      <td>Devyn Marble</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2019.0</td>\n",
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       "    <tr>\n",
       "      <th>925</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1629755</td>\n",
       "      <td>NaN</td>\n",
       "      <td>624.0</td>\n",
       "      <td>Hassani Gravett</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>926</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1629721</td>\n",
       "      <td>NaN</td>\n",
       "      <td>625.0</td>\n",
       "      <td>JaKeenan Gant</td>\n",
       "      <td>1.610613e+09</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>927 rows × 9 columns</p>\n",
       "</div>"
      ],
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       "     Unnamed: 0_x  PLAYER_NAME_x     TEAM_ID_x  PLAYER_ID  SEASON_x  \\\n",
       "0           626.0  Kawhi Leonard  1.610613e+09     202695    2018.0   \n",
       "1           627.0  Pascal Siakam  1.610613e+09    1627783    2018.0   \n",
       "2           628.0     Marc Gasol  1.610613e+09     201188    2018.0   \n",
       "3          1157.0     Marc Gasol  1.610613e+09     201188    2018.0   \n",
       "4           629.0    Danny Green  1.610613e+09     201980    2018.0   \n",
       "..            ...            ...           ...        ...       ...   \n",
       "922           NaN            NaN           NaN    1629097       NaN   \n",
       "923           NaN            NaN           NaN     203461       NaN   \n",
       "924           NaN            NaN           NaN     203906       NaN   \n",
       "925           NaN            NaN           NaN    1629755       NaN   \n",
       "926           NaN            NaN           NaN    1629721       NaN   \n",
       "\n",
       "     Unnamed: 0_y    PLAYER_NAME_y     TEAM_ID_y  SEASON_y  \n",
       "0           299.0    Kawhi Leonard  1.610613e+09    2019.0  \n",
       "1           275.0    Pascal Siakam  1.610613e+09    2019.0  \n",
       "2           276.0       Marc Gasol  1.610613e+09    2019.0  \n",
       "3           276.0       Marc Gasol  1.610613e+09    2019.0  \n",
       "4           202.0      Danny Green  1.610613e+09    2019.0  \n",
       "..            ...              ...           ...       ...  \n",
       "922         619.0    Terry Larrier  1.610613e+09    2019.0  \n",
       "923         621.0  Anthony Bennett  1.610613e+09    2019.0  \n",
       "924         623.0     Devyn Marble  1.610613e+09    2019.0  \n",
       "925         624.0  Hassani Gravett  1.610613e+09    2019.0  \n",
       "926         625.0    JaKeenan Gant  1.610613e+09    2019.0  \n",
       "\n",
       "[927 rows x 9 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(players18,players19,on='PLAYER_ID', how='outer')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ef712ed",
   "metadata": {},
   "source": [
    "Using  `left` gives us the `'PLAYER_ID'` that are in the left(`players18`) DataFrame, including those that are in both DataFrame\n",
    "`right` would give players in the `players19` DataFrame or both DataFrames. With this result, we can see who retired, but not the 2019 rookies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9e47539c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>630</td>\n",
       "      <td>Kyle Lowry</td>\n",
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       "      <td>2018</td>\n",
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       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>1369</td>\n",
       "      <td>Tyrius Walker</td>\n",
       "      <td>1610612752</td>\n",
       "      <td>1629246</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>1370</td>\n",
       "      <td>Marcus Lee</td>\n",
       "      <td>1610612748</td>\n",
       "      <td>1629159</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>1371</td>\n",
       "      <td>Trey Lewis</td>\n",
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       "      <td>1629163</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>1372</td>\n",
       "      <td>Emanuel Terry</td>\n",
       "      <td>1610612743</td>\n",
       "      <td>1629150</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>1373</td>\n",
       "      <td>Justin Bibbs</td>\n",
       "      <td>1610612738</td>\n",
       "      <td>1629167</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>754 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0_x  PLAYER_NAME_x   TEAM_ID_x  PLAYER_ID  SEASON_x  \\\n",
       "0             626  Kawhi Leonard  1610612761     202695      2018   \n",
       "1             627  Pascal Siakam  1610612761    1627783      2018   \n",
       "2             628     Marc Gasol  1610612761     201188      2018   \n",
       "3             629    Danny Green  1610612761     201980      2018   \n",
       "4             630     Kyle Lowry  1610612761     200768      2018   \n",
       "..            ...            ...         ...        ...       ...   \n",
       "749          1369  Tyrius Walker  1610612752    1629246      2018   \n",
       "750          1370     Marcus Lee  1610612748    1629159      2018   \n",
       "751          1371     Trey Lewis  1610612762    1629163      2018   \n",
       "752          1372  Emanuel Terry  1610612743    1629150      2018   \n",
       "753          1373   Justin Bibbs  1610612738    1629167      2018   \n",
       "\n",
       "     Unnamed: 0_y  PLAYER_NAME_y     TEAM_ID_y  SEASON_y  \n",
       "0           299.0  Kawhi Leonard  1.610613e+09    2019.0  \n",
       "1           275.0  Pascal Siakam  1.610613e+09    2019.0  \n",
       "2           276.0     Marc Gasol  1.610613e+09    2019.0  \n",
       "3           202.0    Danny Green  1.610613e+09    2019.0  \n",
       "4           451.0     Kyle Lowry  1.610613e+09    2019.0  \n",
       "..            ...            ...           ...       ...  \n",
       "749           NaN            NaN           NaN       NaN  \n",
       "750           NaN            NaN           NaN       NaN  \n",
       "751           NaN            NaN           NaN       NaN  \n",
       "752           NaN            NaN           NaN       NaN  \n",
       "753           NaN            NaN           NaN       NaN  \n",
       "\n",
       "[754 rows x 9 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(players18,players19,on='PLAYER_ID', how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fa8fb6e",
   "metadata": {},
   "source": [
    "## Try it yourself\n",
    "\n",
    "Try different merges and inspect them:\n",
    "- how many rows & columns?\n",
    "- Where are NaN values inserted?\n",
    "- What rows from the original datasets are not included?\n",
    "- describe each type of merge in your own words\n",
    "\n",
    "\n",
    "Split a DataFrame into separate data frames by subsetting the columns and indexing the rows with `loc`, then use concat to put it back together. Programmatically check that it's back together correctly."
   ]
  }
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