Sesi 05.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "48c10a47",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cb305ffd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://raw.githubusercontent.com/ardhiraka/PFDS_sources/master/nbaallelo.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "380b60dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"https://raw.githubusercontent.com/ardhiraka/PFDS_sources/master/nbaallelo.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d010f617",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "126314"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9447b34b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(126314, 23)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "66cbaa79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>gameorder</th>\n",
       "      <th>game_id</th>\n",
       "      <th>lg_id</th>\n",
       "      <th>_iscopy</th>\n",
       "      <th>year_id</th>\n",
       "      <th>date_game</th>\n",
       "      <th>seasongame</th>\n",
       "      <th>is_playoffs</th>\n",
       "      <th>team_id</th>\n",
       "      <th>fran_id</th>\n",
       "      <th>...</th>\n",
       "      <th>win_equiv</th>\n",
       "      <th>opp_id</th>\n",
       "      <th>opp_fran</th>\n",
       "      <th>opp_pts</th>\n",
       "      <th>opp_elo_i</th>\n",
       "      <th>opp_elo_n</th>\n",
       "      <th>game_location</th>\n",
       "      <th>game_result</th>\n",
       "      <th>forecast</th>\n",
       "      <th>notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>194611010TRH</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/1/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>TRH</td>\n",
       "      <td>Huskies</td>\n",
       "      <td>...</td>\n",
       "      <td>40.294830</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>68</td>\n",
       "      <td>1300.0000</td>\n",
       "      <td>1306.7233</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.640065</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>194611010TRH</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/1/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>...</td>\n",
       "      <td>41.705170</td>\n",
       "      <td>TRH</td>\n",
       "      <td>Huskies</td>\n",
       "      <td>66</td>\n",
       "      <td>1300.0000</td>\n",
       "      <td>1293.2767</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>0.359935</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>194611020CHS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/2/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>CHS</td>\n",
       "      <td>Stags</td>\n",
       "      <td>...</td>\n",
       "      <td>42.012257</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>47</td>\n",
       "      <td>1306.7233</td>\n",
       "      <td>1297.0712</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.631101</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>194611020CHS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/2/1946</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>...</td>\n",
       "      <td>40.692783</td>\n",
       "      <td>CHS</td>\n",
       "      <td>Stags</td>\n",
       "      <td>63</td>\n",
       "      <td>1300.0000</td>\n",
       "      <td>1309.6521</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>0.368899</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>194611020DTF</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/2/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>DTF</td>\n",
       "      <td>Falcons</td>\n",
       "      <td>...</td>\n",
       "      <td>38.864048</td>\n",
       "      <td>WSC</td>\n",
       "      <td>Capitols</td>\n",
       "      <td>50</td>\n",
       "      <td>1300.0000</td>\n",
       "      <td>1320.3811</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.640065</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   gameorder       game_id lg_id  _iscopy  year_id  date_game  seasongame  \\\n",
       "0          1  194611010TRH   NBA        0     1947  11/1/1946           1   \n",
       "1          1  194611010TRH   NBA        1     1947  11/1/1946           1   \n",
       "2          2  194611020CHS   NBA        0     1947  11/2/1946           1   \n",
       "3          2  194611020CHS   NBA        1     1947  11/2/1946           2   \n",
       "4          3  194611020DTF   NBA        0     1947  11/2/1946           1   \n",
       "\n",
       "   is_playoffs team_id  fran_id  ...  win_equiv  opp_id  opp_fran  opp_pts  \\\n",
       "0            0     TRH  Huskies  ...  40.294830     NYK    Knicks       68   \n",
       "1            0     NYK   Knicks  ...  41.705170     TRH   Huskies       66   \n",
       "2            0     CHS    Stags  ...  42.012257     NYK    Knicks       47   \n",
       "3            0     NYK   Knicks  ...  40.692783     CHS     Stags       63   \n",
       "4            0     DTF  Falcons  ...  38.864048     WSC  Capitols       50   \n",
       "\n",
       "   opp_elo_i  opp_elo_n  game_location  game_result  forecast notes  \n",
       "0  1300.0000  1306.7233              H            L  0.640065   NaN  \n",
       "1  1300.0000  1293.2767              A            W  0.359935   NaN  \n",
       "2  1306.7233  1297.0712              H            W  0.631101   NaN  \n",
       "3  1300.0000  1309.6521              A            L  0.368899   NaN  \n",
       "4  1300.0000  1320.3811              H            L  0.640065   NaN  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "55e7bd77",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max.columns\",None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1d3ab89d",
   "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>gameorder</th>\n",
       "      <th>game_id</th>\n",
       "      <th>lg_id</th>\n",
       "      <th>_iscopy</th>\n",
       "      <th>year_id</th>\n",
       "      <th>date_game</th>\n",
       "      <th>seasongame</th>\n",
       "      <th>is_playoffs</th>\n",
       "      <th>team_id</th>\n",
       "      <th>fran_id</th>\n",
       "      <th>pts</th>\n",
       "      <th>elo_i</th>\n",
       "      <th>elo_n</th>\n",
       "      <th>win_equiv</th>\n",
       "      <th>opp_id</th>\n",
       "      <th>opp_fran</th>\n",
       "      <th>opp_pts</th>\n",
       "      <th>opp_elo_i</th>\n",
       "      <th>opp_elo_n</th>\n",
       "      <th>game_location</th>\n",
       "      <th>game_result</th>\n",
       "      <th>forecast</th>\n",
       "      <th>notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>194611010TRH</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/1/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>TRH</td>\n",
       "      <td>Huskies</td>\n",
       "      <td>66</td>\n",
       "      <td>1300.0000</td>\n",
       "      <td>1293.2767</td>\n",
       "      <td>40.294830</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>68</td>\n",
       "      <td>1300.0000</td>\n",
       "      <td>1306.7233</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.640065</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>194611010TRH</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/1/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>68</td>\n",
       "      <td>1300.0000</td>\n",
       "      <td>1306.7233</td>\n",
       "      <td>41.705170</td>\n",
       "      <td>TRH</td>\n",
       "      <td>Huskies</td>\n",
       "      <td>66</td>\n",
       "      <td>1300.0000</td>\n",
       "      <td>1293.2767</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>0.359935</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>194611020CHS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/2/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>CHS</td>\n",
       "      <td>Stags</td>\n",
       "      <td>63</td>\n",
       "      <td>1300.0000</td>\n",
       "      <td>1309.6521</td>\n",
       "      <td>42.012257</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>47</td>\n",
       "      <td>1306.7233</td>\n",
       "      <td>1297.0712</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.631101</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>194611020CHS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/2/1946</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>47</td>\n",
       "      <td>1306.7233</td>\n",
       "      <td>1297.0712</td>\n",
       "      <td>40.692783</td>\n",
       "      <td>CHS</td>\n",
       "      <td>Stags</td>\n",
       "      <td>63</td>\n",
       "      <td>1300.0000</td>\n",
       "      <td>1309.6521</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>0.368899</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>194611020DTF</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/2/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>DTF</td>\n",
       "      <td>Falcons</td>\n",
       "      <td>33</td>\n",
       "      <td>1300.0000</td>\n",
       "      <td>1279.6189</td>\n",
       "      <td>38.864048</td>\n",
       "      <td>WSC</td>\n",
       "      <td>Capitols</td>\n",
       "      <td>50</td>\n",
       "      <td>1300.0000</td>\n",
       "      <td>1320.3811</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.640065</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gameorder       game_id lg_id  _iscopy  year_id  date_game  seasongame  \\\n",
       "0          1  194611010TRH   NBA        0     1947  11/1/1946           1   \n",
       "1          1  194611010TRH   NBA        1     1947  11/1/1946           1   \n",
       "2          2  194611020CHS   NBA        0     1947  11/2/1946           1   \n",
       "3          2  194611020CHS   NBA        1     1947  11/2/1946           2   \n",
       "4          3  194611020DTF   NBA        0     1947  11/2/1946           1   \n",
       "\n",
       "   is_playoffs team_id  fran_id  pts      elo_i      elo_n  win_equiv opp_id  \\\n",
       "0            0     TRH  Huskies   66  1300.0000  1293.2767  40.294830    NYK   \n",
       "1            0     NYK   Knicks   68  1300.0000  1306.7233  41.705170    TRH   \n",
       "2            0     CHS    Stags   63  1300.0000  1309.6521  42.012257    NYK   \n",
       "3            0     NYK   Knicks   47  1306.7233  1297.0712  40.692783    CHS   \n",
       "4            0     DTF  Falcons   33  1300.0000  1279.6189  38.864048    WSC   \n",
       "\n",
       "   opp_fran  opp_pts  opp_elo_i  opp_elo_n game_location game_result  \\\n",
       "0    Knicks       68  1300.0000  1306.7233             H           L   \n",
       "1   Huskies       66  1300.0000  1293.2767             A           W   \n",
       "2    Knicks       47  1306.7233  1297.0712             H           W   \n",
       "3     Stags       63  1300.0000  1309.6521             A           L   \n",
       "4  Capitols       50  1300.0000  1320.3811             H           L   \n",
       "\n",
       "   forecast notes  \n",
       "0  0.640065   NaN  \n",
       "1  0.359935   NaN  \n",
       "2  0.631101   NaN  \n",
       "3  0.368899   NaN  \n",
       "4  0.640065   NaN  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "20cd691c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"display.precision\",2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "da01f23d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gameorder</th>\n",
       "      <th>game_id</th>\n",
       "      <th>lg_id</th>\n",
       "      <th>_iscopy</th>\n",
       "      <th>year_id</th>\n",
       "      <th>date_game</th>\n",
       "      <th>seasongame</th>\n",
       "      <th>is_playoffs</th>\n",
       "      <th>team_id</th>\n",
       "      <th>fran_id</th>\n",
       "      <th>pts</th>\n",
       "      <th>elo_i</th>\n",
       "      <th>elo_n</th>\n",
       "      <th>win_equiv</th>\n",
       "      <th>opp_id</th>\n",
       "      <th>opp_fran</th>\n",
       "      <th>opp_pts</th>\n",
       "      <th>opp_elo_i</th>\n",
       "      <th>opp_elo_n</th>\n",
       "      <th>game_location</th>\n",
       "      <th>game_result</th>\n",
       "      <th>forecast</th>\n",
       "      <th>notes</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>194611010TRH</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/1/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>TRH</td>\n",
       "      <td>Huskies</td>\n",
       "      <td>66</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1293.28</td>\n",
       "      <td>40.29</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>68</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1306.72</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.64</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>194611010TRH</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/1/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>68</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1306.72</td>\n",
       "      <td>41.71</td>\n",
       "      <td>TRH</td>\n",
       "      <td>Huskies</td>\n",
       "      <td>66</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1293.28</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>0.36</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>194611020CHS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/2/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>CHS</td>\n",
       "      <td>Stags</td>\n",
       "      <td>63</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1309.65</td>\n",
       "      <td>42.01</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>47</td>\n",
       "      <td>1306.72</td>\n",
       "      <td>1297.07</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.63</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>194611020CHS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/2/1946</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>47</td>\n",
       "      <td>1306.72</td>\n",
       "      <td>1297.07</td>\n",
       "      <td>40.69</td>\n",
       "      <td>CHS</td>\n",
       "      <td>Stags</td>\n",
       "      <td>63</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1309.65</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>0.37</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>194611020DTF</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/2/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>DTF</td>\n",
       "      <td>Falcons</td>\n",
       "      <td>33</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1279.62</td>\n",
       "      <td>38.86</td>\n",
       "      <td>WSC</td>\n",
       "      <td>Capitols</td>\n",
       "      <td>50</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1320.38</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.64</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gameorder       game_id lg_id  _iscopy  year_id  date_game  seasongame  \\\n",
       "0          1  194611010TRH   NBA        0     1947  11/1/1946           1   \n",
       "1          1  194611010TRH   NBA        1     1947  11/1/1946           1   \n",
       "2          2  194611020CHS   NBA        0     1947  11/2/1946           1   \n",
       "3          2  194611020CHS   NBA        1     1947  11/2/1946           2   \n",
       "4          3  194611020DTF   NBA        0     1947  11/2/1946           1   \n",
       "\n",
       "   is_playoffs team_id  fran_id  pts    elo_i    elo_n  win_equiv opp_id  \\\n",
       "0            0     TRH  Huskies   66  1300.00  1293.28      40.29    NYK   \n",
       "1            0     NYK   Knicks   68  1300.00  1306.72      41.71    TRH   \n",
       "2            0     CHS    Stags   63  1300.00  1309.65      42.01    NYK   \n",
       "3            0     NYK   Knicks   47  1306.72  1297.07      40.69    CHS   \n",
       "4            0     DTF  Falcons   33  1300.00  1279.62      38.86    WSC   \n",
       "\n",
       "   opp_fran  opp_pts  opp_elo_i  opp_elo_n game_location game_result  \\\n",
       "0    Knicks       68    1300.00    1306.72             H           L   \n",
       "1   Huskies       66    1300.00    1293.28             A           W   \n",
       "2    Knicks       47    1306.72    1297.07             H           W   \n",
       "3     Stags       63    1300.00    1309.65             A           L   \n",
       "4  Capitols       50    1300.00    1320.38             H           L   \n",
       "\n",
       "   forecast notes  \n",
       "0      0.64   NaN  \n",
       "1      0.36   NaN  \n",
       "2      0.63   NaN  \n",
       "3      0.37   NaN  \n",
       "4      0.64   NaN  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() # Isi n untuk tampilkan n baris pertama"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b897b941",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>gameorder</th>\n",
       "      <th>game_id</th>\n",
       "      <th>lg_id</th>\n",
       "      <th>_iscopy</th>\n",
       "      <th>year_id</th>\n",
       "      <th>date_game</th>\n",
       "      <th>seasongame</th>\n",
       "      <th>is_playoffs</th>\n",
       "      <th>team_id</th>\n",
       "      <th>fran_id</th>\n",
       "      <th>pts</th>\n",
       "      <th>elo_i</th>\n",
       "      <th>elo_n</th>\n",
       "      <th>win_equiv</th>\n",
       "      <th>opp_id</th>\n",
       "      <th>opp_fran</th>\n",
       "      <th>opp_pts</th>\n",
       "      <th>opp_elo_i</th>\n",
       "      <th>opp_elo_n</th>\n",
       "      <th>game_location</th>\n",
       "      <th>game_result</th>\n",
       "      <th>forecast</th>\n",
       "      <th>notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>126309</th>\n",
       "      <td>63155</td>\n",
       "      <td>201506110CLE</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>6/11/2015</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>82</td>\n",
       "      <td>1723.41</td>\n",
       "      <td>1704.39</td>\n",
       "      <td>60.31</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>103</td>\n",
       "      <td>1790.96</td>\n",
       "      <td>1809.98</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.55</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126310</th>\n",
       "      <td>63156</td>\n",
       "      <td>201506140GSW</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>6/14/2015</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>104</td>\n",
       "      <td>1809.98</td>\n",
       "      <td>1813.63</td>\n",
       "      <td>68.01</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>91</td>\n",
       "      <td>1704.39</td>\n",
       "      <td>1700.74</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.77</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126311</th>\n",
       "      <td>63156</td>\n",
       "      <td>201506140GSW</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>6/14/2015</td>\n",
       "      <td>101</td>\n",
       "      <td>1</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>91</td>\n",
       "      <td>1704.39</td>\n",
       "      <td>1700.74</td>\n",
       "      <td>60.01</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>104</td>\n",
       "      <td>1809.98</td>\n",
       "      <td>1813.63</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>0.23</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126312</th>\n",
       "      <td>63157</td>\n",
       "      <td>201506170CLE</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>6/16/2015</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>97</td>\n",
       "      <td>1700.74</td>\n",
       "      <td>1692.09</td>\n",
       "      <td>59.29</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>105</td>\n",
       "      <td>1813.63</td>\n",
       "      <td>1822.29</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.48</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126313</th>\n",
       "      <td>63157</td>\n",
       "      <td>201506170CLE</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>6/16/2015</td>\n",
       "      <td>103</td>\n",
       "      <td>1</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>105</td>\n",
       "      <td>1813.63</td>\n",
       "      <td>1822.29</td>\n",
       "      <td>68.52</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>97</td>\n",
       "      <td>1700.74</td>\n",
       "      <td>1692.09</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>0.52</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        gameorder       game_id lg_id  _iscopy  year_id  date_game  \\\n",
       "126309      63155  201506110CLE   NBA        0     2015  6/11/2015   \n",
       "126310      63156  201506140GSW   NBA        0     2015  6/14/2015   \n",
       "126311      63156  201506140GSW   NBA        1     2015  6/14/2015   \n",
       "126312      63157  201506170CLE   NBA        0     2015  6/16/2015   \n",
       "126313      63157  201506170CLE   NBA        1     2015  6/16/2015   \n",
       "\n",
       "        seasongame  is_playoffs team_id    fran_id  pts    elo_i    elo_n  \\\n",
       "126309         100            1     CLE  Cavaliers   82  1723.41  1704.39   \n",
       "126310         102            1     GSW   Warriors  104  1809.98  1813.63   \n",
       "126311         101            1     CLE  Cavaliers   91  1704.39  1700.74   \n",
       "126312         102            1     CLE  Cavaliers   97  1700.74  1692.09   \n",
       "126313         103            1     GSW   Warriors  105  1813.63  1822.29   \n",
       "\n",
       "        win_equiv opp_id   opp_fran  opp_pts  opp_elo_i  opp_elo_n  \\\n",
       "126309      60.31    GSW   Warriors      103    1790.96    1809.98   \n",
       "126310      68.01    CLE  Cavaliers       91    1704.39    1700.74   \n",
       "126311      60.01    GSW   Warriors      104    1809.98    1813.63   \n",
       "126312      59.29    GSW   Warriors      105    1813.63    1822.29   \n",
       "126313      68.52    CLE  Cavaliers       97    1700.74    1692.09   \n",
       "\n",
       "       game_location game_result  forecast notes  \n",
       "126309             H           L      0.55   NaN  \n",
       "126310             H           W      0.77   NaN  \n",
       "126311             A           L      0.23   NaN  \n",
       "126312             H           L      0.48   NaN  \n",
       "126313             A           W      0.52   NaN  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2b4ad9e",
   "metadata": {},
   "source": [
    "# Displaying Data Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c1341578",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 126314 entries, 0 to 126313\n",
      "Data columns (total 23 columns):\n",
      " #   Column         Non-Null Count   Dtype  \n",
      "---  ------         --------------   -----  \n",
      " 0   gameorder      126314 non-null  int64  \n",
      " 1   game_id        126314 non-null  object \n",
      " 2   lg_id          126314 non-null  object \n",
      " 3   _iscopy        126314 non-null  int64  \n",
      " 4   year_id        126314 non-null  int64  \n",
      " 5   date_game      126314 non-null  object \n",
      " 6   seasongame     126314 non-null  int64  \n",
      " 7   is_playoffs    126314 non-null  int64  \n",
      " 8   team_id        126314 non-null  object \n",
      " 9   fran_id        126314 non-null  object \n",
      " 10  pts            126314 non-null  int64  \n",
      " 11  elo_i          126314 non-null  float64\n",
      " 12  elo_n          126314 non-null  float64\n",
      " 13  win_equiv      126314 non-null  float64\n",
      " 14  opp_id         126314 non-null  object \n",
      " 15  opp_fran       126314 non-null  object \n",
      " 16  opp_pts        126314 non-null  int64  \n",
      " 17  opp_elo_i      126314 non-null  float64\n",
      " 18  opp_elo_n      126314 non-null  float64\n",
      " 19  game_location  126314 non-null  object \n",
      " 20  game_result    126314 non-null  object \n",
      " 21  forecast       126314 non-null  float64\n",
      " 22  notes          5424 non-null    object \n",
      "dtypes: float64(6), int64(7), object(10)\n",
      "memory usage: 22.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0723073d",
   "metadata": {},
   "source": [
    "# Showing Basics Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c5359d05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gameorder          int64\n",
       "game_id           object\n",
       "lg_id             object\n",
       "_iscopy            int64\n",
       "year_id            int64\n",
       "date_game         object\n",
       "seasongame         int64\n",
       "is_playoffs        int64\n",
       "team_id           object\n",
       "fran_id           object\n",
       "pts                int64\n",
       "elo_i            float64\n",
       "elo_n            float64\n",
       "win_equiv        float64\n",
       "opp_id            object\n",
       "opp_fran          object\n",
       "opp_pts            int64\n",
       "opp_elo_i        float64\n",
       "opp_elo_n        float64\n",
       "game_location     object\n",
       "game_result       object\n",
       "forecast         float64\n",
       "notes             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "71662d80",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>_iscopy</th>\n",
       "      <th>year_id</th>\n",
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       "      <td>126314.00</td>\n",
       "      <td>126314.00</td>\n",
       "      <td>126314.00</td>\n",
       "      <td>126314.00</td>\n",
       "      <td>126314.00</td>\n",
       "      <td>126314.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>31579.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1988.20</td>\n",
       "      <td>43.53</td>\n",
       "      <td>0.06</td>\n",
       "      <td>102.73</td>\n",
       "      <td>1495.24</td>\n",
       "      <td>1495.24</td>\n",
       "      <td>41.71</td>\n",
       "      <td>102.73</td>\n",
       "      <td>1495.24</td>\n",
       "      <td>1495.24</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>18231.93</td>\n",
       "      <td>0.5</td>\n",
       "      <td>17.58</td>\n",
       "      <td>25.38</td>\n",
       "      <td>0.24</td>\n",
       "      <td>14.81</td>\n",
       "      <td>112.14</td>\n",
       "      <td>112.46</td>\n",
       "      <td>10.63</td>\n",
       "      <td>14.81</td>\n",
       "      <td>112.14</td>\n",
       "      <td>112.46</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1947.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1091.64</td>\n",
       "      <td>1085.77</td>\n",
       "      <td>10.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1091.64</td>\n",
       "      <td>1085.77</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>15790.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1975.00</td>\n",
       "      <td>22.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>93.00</td>\n",
       "      <td>1417.24</td>\n",
       "      <td>1416.99</td>\n",
       "      <td>34.10</td>\n",
       "      <td>93.00</td>\n",
       "      <td>1417.24</td>\n",
       "      <td>1416.99</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>31579.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1990.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>103.00</td>\n",
       "      <td>1500.95</td>\n",
       "      <td>1500.95</td>\n",
       "      <td>42.11</td>\n",
       "      <td>103.00</td>\n",
       "      <td>1500.95</td>\n",
       "      <td>1500.95</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>47368.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2003.00</td>\n",
       "      <td>65.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>112.00</td>\n",
       "      <td>1576.06</td>\n",
       "      <td>1576.29</td>\n",
       "      <td>49.64</td>\n",
       "      <td>112.00</td>\n",
       "      <td>1576.06</td>\n",
       "      <td>1576.29</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>63157.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2015.00</td>\n",
       "      <td>108.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>186.00</td>\n",
       "      <td>1853.10</td>\n",
       "      <td>1853.10</td>\n",
       "      <td>71.11</td>\n",
       "      <td>186.00</td>\n",
       "      <td>1853.10</td>\n",
       "      <td>1853.10</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       gameorder   _iscopy    year_id  seasongame  is_playoffs        pts  \\\n",
       "count  126314.00  126314.0  126314.00   126314.00    126314.00  126314.00   \n",
       "mean    31579.00       0.5    1988.20       43.53         0.06     102.73   \n",
       "std     18231.93       0.5      17.58       25.38         0.24      14.81   \n",
       "min         1.00       0.0    1947.00        1.00         0.00       0.00   \n",
       "25%     15790.00       0.0    1975.00       22.00         0.00      93.00   \n",
       "50%     31579.00       0.5    1990.00       43.00         0.00     103.00   \n",
       "75%     47368.00       1.0    2003.00       65.00         0.00     112.00   \n",
       "max     63157.00       1.0    2015.00      108.00         1.00     186.00   \n",
       "\n",
       "           elo_i      elo_n  win_equiv    opp_pts  opp_elo_i  opp_elo_n  \\\n",
       "count  126314.00  126314.00  126314.00  126314.00  126314.00  126314.00   \n",
       "mean     1495.24    1495.24      41.71     102.73    1495.24    1495.24   \n",
       "std       112.14     112.46      10.63      14.81     112.14     112.46   \n",
       "min      1091.64    1085.77      10.15       0.00    1091.64    1085.77   \n",
       "25%      1417.24    1416.99      34.10      93.00    1417.24    1416.99   \n",
       "50%      1500.95    1500.95      42.11     103.00    1500.95    1500.95   \n",
       "75%      1576.06    1576.29      49.64     112.00    1576.06    1576.29   \n",
       "max      1853.10    1853.10      71.11     186.00    1853.10    1853.10   \n",
       "\n",
       "        forecast  \n",
       "count  126314.00  \n",
       "mean        0.50  \n",
       "std         0.22  \n",
       "min         0.02  \n",
       "25%         0.33  \n",
       "50%         0.50  \n",
       "75%         0.67  \n",
       "max         0.98  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd12e050",
   "metadata": {},
   "source": [
    "# Exploring Your Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5b23a520",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BOS    5997\n",
       "NYK    5769\n",
       "LAL    5078\n",
       "DET    4985\n",
       "PHI    4533\n",
       "       ... \n",
       "INJ      60\n",
       "PIT      60\n",
       "DTF      60\n",
       "TRH      60\n",
       "SDS      11\n",
       "Name: team_id, Length: 104, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"team_id\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3f4660c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['TRH', 'NYK', 'CHS', 'DTF', 'WSC', 'BOS', 'PRO', 'PIT', 'STB',\n",
       "       'CLR', 'PHW', 'BLB', 'INJ', 'FTW', 'MNL', 'ROC', 'TRI', 'DNN',\n",
       "       'INO', 'SHE', 'WAT', 'AND', 'SYR', 'MLH', 'STL', 'DET', 'CIN',\n",
       "       'LAL', 'CHP', 'CHZ', 'SFW', 'BAL', 'PHI', 'CHI', 'SEA', 'OAK',\n",
       "       'ANA', 'SDR', 'KEN', 'INA', 'DNR', 'DLC', 'HSM', 'NOB', 'MNM',\n",
       "       'NJA', 'PTP', 'ATL', 'MIL', 'PHO', 'NYA', 'MMF', 'MNP', 'LAS',\n",
       "       'CAR', 'WSA', 'BUF', 'CLE', 'UTS', 'FLO', 'PTC', 'POR', 'VIR',\n",
       "       'TEX', 'MMP', 'HOU', 'GSW', 'KCO', 'MMT', 'SDA', 'CAP', 'SAA',\n",
       "       'NOJ', 'DNA', 'SSL', 'MMS', 'WSB', 'KCK', 'SDS', 'IND', 'NYN',\n",
       "       'DEN', 'SAS', 'NJN', 'SDC', 'UTA', 'DAL', 'LAC', 'SAC', 'CHH',\n",
       "       'MIA', 'MIN', 'ORL', 'VAN', 'TOR', 'WAS', 'MEM', 'NOH', 'CHA',\n",
       "       'NOK', 'OKC', 'BRK', 'NOP', 'CHO'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"team_id\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "810388e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lakers          6024\n",
       "Celtics         5997\n",
       "Knicks          5769\n",
       "Warriors        5657\n",
       "Pistons         5650\n",
       "Sixers          5644\n",
       "Hawks           5572\n",
       "Kings           5475\n",
       "Wizards         4582\n",
       "Spurs           4309\n",
       "Bulls           4307\n",
       "Pacers          4227\n",
       "Thunder         4178\n",
       "Rockets         4154\n",
       "Nuggets         4120\n",
       "Nets            4106\n",
       "Suns            4080\n",
       "Bucks           4034\n",
       "Trailblazers    3870\n",
       "Cavaliers       3810\n",
       "Clippers        3733\n",
       "Jazz            3555\n",
       "Mavericks       3013\n",
       "Heat            2371\n",
       "Pelicans        2254\n",
       "Magic           2207\n",
       "Timberwolves    2131\n",
       "Grizzlies       1657\n",
       "Raptors         1634\n",
       "Hornets          894\n",
       "Colonels         846\n",
       "Squires          799\n",
       "Spirits          777\n",
       "Stars            756\n",
       "Sounds           697\n",
       "Baltimore        467\n",
       "Floridians       440\n",
       "Condors          430\n",
       "Capitols         291\n",
       "Olympians        282\n",
       "Sails            274\n",
       "Stags            260\n",
       "Bombers          249\n",
       "Steamrollers     168\n",
       "Packers           72\n",
       "Redskins          65\n",
       "Rebels            63\n",
       "Waterloo          62\n",
       "Denver            62\n",
       "Falcons           60\n",
       "Jets              60\n",
       "Ironmen           60\n",
       "Huskies           60\n",
       "Name: fran_id, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"fran_id\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6efae059",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LAL    5078\n",
       "MNL     946\n",
       "Name: team_id, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"fran_id\"] == \"Lakers\",\"team_id\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0b608ae3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1/1/1949'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"team_id\"] == \"MNL\",\"date_game\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "54b7ccdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4/9/1959'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"team_id\"] == \"MNL\",\"date_game\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a0e08e81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "min        1/1/1949\n",
       "max        4/9/1959\n",
       "nunique         946\n",
       "Name: date_game, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"team_id\"] == \"MNL\",\"date_game\"].agg([\"min\",\"max\",\"nunique\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "548c2245",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "626484"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"team_id\"] == \"BOS\",\"pts\"].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b52a793",
   "metadata": {},
   "source": [
    "# Pandas Data Structures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2db83f8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "revenues = pd.Series([5555,7000,1980])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "929ef28b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5555\n",
       "1    7000\n",
       "2    1980\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "revenues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2b2ea5e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5555, 7000, 1980], dtype=int64)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "revenues.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bd6b7f52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=3, step=1)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "revenues.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ccdf82c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(revenues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "009394c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Amsterdam    4200\n",
       "Toronto      8000\n",
       "Tokyo        6500\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_revenues = pd.Series([4200,8000,6500],index=[\"Amsterdam\",\"Toronto\",\"Tokyo\"])\n",
    "city_revenues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e900ff9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6500"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_revenues[\"Tokyo\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "26e0fbae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8000"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_revenues[\"Toronto\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "62d34dae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Amsterdam    5\n",
       "Tokyo        8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_employee_count = pd.Series({\"Amsterdam\":5,\"Tokyo\":8})\n",
    "city_employee_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "eb643880",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Amsterdam', 'Tokyo'], dtype='object')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_employee_count.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5f5f42fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"Tokyo\" in city_employee_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a3e0e24c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"New York\" in city_employee_count"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99ad0a42",
   "metadata": {},
   "source": [
    "# DataFrame Objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a4af0ebe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Amsterdam    4200\n",
       "Toronto      8000\n",
       "Tokyo        6500\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_revenues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "daf21112",
   "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>revenue</th>\n",
       "      <th>employee_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Amsterdam</th>\n",
       "      <td>4200</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokyo</th>\n",
       "      <td>6500</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toronto</th>\n",
       "      <td>8000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           revenue  employee_count\n",
       "Amsterdam     4200             5.0\n",
       "Tokyo         6500             8.0\n",
       "Toronto       8000             NaN"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_data = pd.DataFrame({\"revenue\":city_revenues,\"employee_count\":city_employee_count})\n",
    "city_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3c83cc5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Amsterdam', 'Tokyo', 'Toronto'], dtype='object')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6a05d4d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.2e+03, 5.0e+00],\n",
       "       [6.5e+03, 8.0e+00],\n",
       "       [8.0e+03,     nan]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_data.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2af20de9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Index(['Amsterdam', 'Tokyo', 'Toronto'], dtype='object'),\n",
       " Index(['revenue', 'employee_count'], dtype='object')]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_data.axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e6acff47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Amsterdam', 'Tokyo', 'Toronto'], dtype='object')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_data.axes[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6a7809d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['revenue', 'employee_count'], dtype='object')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_data.axes[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "181971c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['revenue', 'employee_count'], dtype='object')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "65eeddcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"Amsterdam\" in city_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "276b2003",
   "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>gameorder</th>\n",
       "      <th>game_id</th>\n",
       "      <th>lg_id</th>\n",
       "      <th>_iscopy</th>\n",
       "      <th>year_id</th>\n",
       "      <th>date_game</th>\n",
       "      <th>seasongame</th>\n",
       "      <th>is_playoffs</th>\n",
       "      <th>team_id</th>\n",
       "      <th>fran_id</th>\n",
       "      <th>pts</th>\n",
       "      <th>elo_i</th>\n",
       "      <th>elo_n</th>\n",
       "      <th>win_equiv</th>\n",
       "      <th>opp_id</th>\n",
       "      <th>opp_fran</th>\n",
       "      <th>opp_pts</th>\n",
       "      <th>opp_elo_i</th>\n",
       "      <th>opp_elo_n</th>\n",
       "      <th>game_location</th>\n",
       "      <th>game_result</th>\n",
       "      <th>forecast</th>\n",
       "      <th>notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>194611010TRH</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/1/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>TRH</td>\n",
       "      <td>Huskies</td>\n",
       "      <td>66</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1293.28</td>\n",
       "      <td>40.29</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>68</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1306.72</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.64</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>194611010TRH</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/1/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>68</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1306.72</td>\n",
       "      <td>41.71</td>\n",
       "      <td>TRH</td>\n",
       "      <td>Huskies</td>\n",
       "      <td>66</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1293.28</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>0.36</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>194611020CHS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/2/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>CHS</td>\n",
       "      <td>Stags</td>\n",
       "      <td>63</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1309.65</td>\n",
       "      <td>42.01</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>47</td>\n",
       "      <td>1306.72</td>\n",
       "      <td>1297.07</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.63</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>194611020CHS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/2/1946</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>47</td>\n",
       "      <td>1306.72</td>\n",
       "      <td>1297.07</td>\n",
       "      <td>40.69</td>\n",
       "      <td>CHS</td>\n",
       "      <td>Stags</td>\n",
       "      <td>63</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1309.65</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>0.37</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>194611020DTF</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/2/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>DTF</td>\n",
       "      <td>Falcons</td>\n",
       "      <td>33</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1279.62</td>\n",
       "      <td>38.86</td>\n",
       "      <td>WSC</td>\n",
       "      <td>Capitols</td>\n",
       "      <td>50</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1320.38</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.64</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gameorder       game_id lg_id  _iscopy  year_id  date_game  seasongame  \\\n",
       "0          1  194611010TRH   NBA        0     1947  11/1/1946           1   \n",
       "1          1  194611010TRH   NBA        1     1947  11/1/1946           1   \n",
       "2          2  194611020CHS   NBA        0     1947  11/2/1946           1   \n",
       "3          2  194611020CHS   NBA        1     1947  11/2/1946           2   \n",
       "4          3  194611020DTF   NBA        0     1947  11/2/1946           1   \n",
       "\n",
       "   is_playoffs team_id  fran_id  pts    elo_i    elo_n  win_equiv opp_id  \\\n",
       "0            0     TRH  Huskies   66  1300.00  1293.28      40.29    NYK   \n",
       "1            0     NYK   Knicks   68  1300.00  1306.72      41.71    TRH   \n",
       "2            0     CHS    Stags   63  1300.00  1309.65      42.01    NYK   \n",
       "3            0     NYK   Knicks   47  1306.72  1297.07      40.69    CHS   \n",
       "4            0     DTF  Falcons   33  1300.00  1279.62      38.86    WSC   \n",
       "\n",
       "   opp_fran  opp_pts  opp_elo_i  opp_elo_n game_location game_result  \\\n",
       "0    Knicks       68    1300.00    1306.72             H           L   \n",
       "1   Huskies       66    1300.00    1293.28             A           W   \n",
       "2    Knicks       47    1306.72    1297.07             H           W   \n",
       "3     Stags       63    1300.00    1309.65             A           L   \n",
       "4  Capitols       50    1300.00    1320.38             H           L   \n",
       "\n",
       "   forecast notes  \n",
       "0      0.64   NaN  \n",
       "1      0.36   NaN  \n",
       "2      0.63   NaN  \n",
       "3      0.37   NaN  \n",
       "4      0.64   NaN  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "80c84646",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=126314, step=1)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "658f7ed8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[RangeIndex(start=0, stop=126314, step=1),\n",
       " Index(['gameorder', 'game_id', 'lg_id', '_iscopy', 'year_id', 'date_game',\n",
       "        'seasongame', 'is_playoffs', 'team_id', 'fran_id', 'pts', 'elo_i',\n",
       "        'elo_n', 'win_equiv', 'opp_id', 'opp_fran', 'opp_pts', 'opp_elo_i',\n",
       "        'opp_elo_n', 'game_location', 'game_result', 'forecast', 'notes'],\n",
       "       dtype='object')]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2d16e94c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"pts\" in df.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79a5b4de",
   "metadata": {},
   "source": [
    "# Accessing Series Elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "22daf1c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Amsterdam    4200\n",
       "Toronto      8000\n",
       "Tokyo        6500\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_revenues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e01b7b0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8000"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_revenues[\"Toronto\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "98e2a589",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8000"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_revenues[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "270d5589",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6500"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_revenues[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ffb705e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Toronto    8000\n",
       "Tokyo      6500\n",
       "dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_revenues[\"Toronto\":]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "abaaa934",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1       red\n",
       "2    purple\n",
       "3      blue\n",
       "5     green\n",
       "8    yellow\n",
       "dtype: object"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colors = pd.Series(\n",
    "    [\"red\",\"purple\",\"blue\",\"green\",\"yellow\"],\n",
    "    index = [1,2,3,5,8]\n",
    ")\n",
    "colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "87fc393c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'red'"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colors[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c5ad664b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'green'"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colors.iloc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c92ecff3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'yellow'"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colors.loc[8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "55063421",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    purple\n",
       "3      blue\n",
       "dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colors.iloc[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "03071a5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5     green\n",
       "8    yellow\n",
       "dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colors.iloc[3:8]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86c88569",
   "metadata": {},
   "source": [
    "# Accessing DataFrame Elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "45e27d68",
   "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>revenue</th>\n",
       "      <th>employee_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Amsterdam</th>\n",
       "      <td>4200</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokyo</th>\n",
       "      <td>6500</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toronto</th>\n",
       "      <td>8000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           revenue  employee_count\n",
       "Amsterdam     4200             5.0\n",
       "Tokyo         6500             8.0\n",
       "Toronto       8000             NaN"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "33fd74dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Amsterdam    4200\n",
       "Tokyo        6500\n",
       "Toronto      8000\n",
       "Name: revenue, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_data[\"revenue\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "3b8be27e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Amsterdam    4200\n",
       "Tokyo        6500\n",
       "Toronto      8000\n",
       "Name: revenue, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "city_data.revenue"
   ]
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  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "d17b4d9b",
   "metadata": {},
   "outputs": [
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       "           name   shape\n",
       "0          ball  sphere\n",
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    "toys = pd.DataFrame([\n",
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    "])\n",
    "toys"
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   "execution_count": 64,
   "id": "bb1641c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    sphere\n",
       "1      cube\n",
       "Name: shape, dtype: object"
      ]
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  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "022c7452",
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>revenue</th>\n",
       "      <th>employee_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Amsterdam</th>\n",
       "      <td>4200</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokyo</th>\n",
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       "           revenue  employee_count\n",
       "Amsterdam     4200             5.0\n",
       "Tokyo         6500             8.0\n",
       "Toronto       8000             NaN"
      ]
     },
     "execution_count": 65,
     "metadata": {},
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    "city_data"
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  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "403437c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "revenue           4200.0\n",
       "employee_count       5.0\n",
       "Name: Amsterdam, dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_data.loc[\"Amsterdam\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a107a82e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "revenue           4200.0\n",
       "employee_count       5.0\n",
       "Name: Amsterdam, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "city_data.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "206135f9",
   "metadata": {},
   "outputs": [
    {
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       "      <th>employee_count</th>\n",
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       "      <th>Amsterdam</th>\n",
       "      <td>4200</td>\n",
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       "    <tr>\n",
       "      <th>Tokyo</th>\n",
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      "text/plain": [
       "           revenue  employee_count\n",
       "Amsterdam     4200             5.0\n",
       "Tokyo         6500             8.0"
      ]
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     "execution_count": 68,
     "metadata": {},
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    "city_data.loc[\"Amsterdam\":\"Tokyo\"]"
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   "cell_type": "code",
   "execution_count": 69,
   "id": "dc52f8d8",
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    {
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       "           revenue  employee_count\n",
       "Amsterdam     4200             5.0\n",
       "Tokyo         6500             8.0"
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     "execution_count": 69,
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   "cell_type": "code",
   "execution_count": 70,
   "id": "0985afa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Amsterdam    5.0\n",
       "Tokyo        8.0\n",
       "Name: employee_count, dtype: float64"
      ]
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     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "city_data.loc[\"Amsterdam\":\"Tokyo\",\"employee_count\"]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f3888059",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>194611010TRH</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/1/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>TRH</td>\n",
       "      <td>Huskies</td>\n",
       "      <td>66</td>\n",
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       "      <td>1293.28</td>\n",
       "      <td>40.29</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>68</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1306.72</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.64</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>194611010TRH</td>\n",
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       "      <td>1</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/1/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>68</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1306.72</td>\n",
       "      <td>41.71</td>\n",
       "      <td>TRH</td>\n",
       "      <td>Huskies</td>\n",
       "      <td>66</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1293.28</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>0.36</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>194611020CHS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/2/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>CHS</td>\n",
       "      <td>Stags</td>\n",
       "      <td>63</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1309.65</td>\n",
       "      <td>42.01</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>47</td>\n",
       "      <td>1306.72</td>\n",
       "      <td>1297.07</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.63</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>194611020CHS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/2/1946</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>47</td>\n",
       "      <td>1306.72</td>\n",
       "      <td>1297.07</td>\n",
       "      <td>40.69</td>\n",
       "      <td>CHS</td>\n",
       "      <td>Stags</td>\n",
       "      <td>63</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1309.65</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>0.37</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>194611020DTF</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/2/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>DTF</td>\n",
       "      <td>Falcons</td>\n",
       "      <td>33</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1279.62</td>\n",
       "      <td>38.86</td>\n",
       "      <td>WSC</td>\n",
       "      <td>Capitols</td>\n",
       "      <td>50</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1320.38</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.64</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gameorder       game_id lg_id  _iscopy  year_id  date_game  seasongame  \\\n",
       "0          1  194611010TRH   NBA        0     1947  11/1/1946           1   \n",
       "1          1  194611010TRH   NBA        1     1947  11/1/1946           1   \n",
       "2          2  194611020CHS   NBA        0     1947  11/2/1946           1   \n",
       "3          2  194611020CHS   NBA        1     1947  11/2/1946           2   \n",
       "4          3  194611020DTF   NBA        0     1947  11/2/1946           1   \n",
       "\n",
       "   is_playoffs team_id  fran_id  pts    elo_i    elo_n  win_equiv opp_id  \\\n",
       "0            0     TRH  Huskies   66  1300.00  1293.28      40.29    NYK   \n",
       "1            0     NYK   Knicks   68  1300.00  1306.72      41.71    TRH   \n",
       "2            0     CHS    Stags   63  1300.00  1309.65      42.01    NYK   \n",
       "3            0     NYK   Knicks   47  1306.72  1297.07      40.69    CHS   \n",
       "4            0     DTF  Falcons   33  1300.00  1279.62      38.86    WSC   \n",
       "\n",
       "   opp_fran  opp_pts  opp_elo_i  opp_elo_n game_location game_result  \\\n",
       "0    Knicks       68    1300.00    1306.72             H           L   \n",
       "1   Huskies       66    1300.00    1293.28             A           W   \n",
       "2    Knicks       47    1306.72    1297.07             H           W   \n",
       "3     Stags       63    1300.00    1309.65             A           L   \n",
       "4  Capitols       50    1300.00    1320.38             H           L   \n",
       "\n",
       "   forecast notes  \n",
       "0      0.64   NaN  \n",
       "1      0.36   NaN  \n",
       "2      0.63   NaN  \n",
       "3      0.37   NaN  \n",
       "4      0.64   NaN  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "4f3c3c2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>5559</th>\n",
       "      <td>Sixers</td>\n",
       "      <td>Kings</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fran_id  opp_fran  pts\n",
       "5555  Pistons  Warriors   83\n",
       "5556  Celtics    Knicks   95\n",
       "5557   Knicks   Celtics   74\n",
       "5558    Kings    Sixers   81\n",
       "5559   Sixers     Kings   86"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[5555:5559,[\"fran_id\",\"opp_fran\",\"pts\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dc827b5",
   "metadata": {},
   "source": [
    "# Querying Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "88aa076c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gameorder</th>\n",
       "      <th>game_id</th>\n",
       "      <th>lg_id</th>\n",
       "      <th>_iscopy</th>\n",
       "      <th>year_id</th>\n",
       "      <th>date_game</th>\n",
       "      <th>seasongame</th>\n",
       "      <th>is_playoffs</th>\n",
       "      <th>team_id</th>\n",
       "      <th>fran_id</th>\n",
       "      <th>pts</th>\n",
       "      <th>elo_i</th>\n",
       "      <th>elo_n</th>\n",
       "      <th>win_equiv</th>\n",
       "      <th>opp_id</th>\n",
       "      <th>opp_fran</th>\n",
       "      <th>opp_pts</th>\n",
       "      <th>opp_elo_i</th>\n",
       "      <th>opp_elo_n</th>\n",
       "      <th>game_location</th>\n",
       "      <th>game_result</th>\n",
       "      <th>forecast</th>\n",
       "      <th>notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>194611010TRH</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/1/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>TRH</td>\n",
       "      <td>Huskies</td>\n",
       "      <td>66</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1293.28</td>\n",
       "      <td>40.29</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>68</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1306.72</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.64</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>194611010TRH</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/1/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>68</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1306.72</td>\n",
       "      <td>41.71</td>\n",
       "      <td>TRH</td>\n",
       "      <td>Huskies</td>\n",
       "      <td>66</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1293.28</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>0.36</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>194611020CHS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/2/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>CHS</td>\n",
       "      <td>Stags</td>\n",
       "      <td>63</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1309.65</td>\n",
       "      <td>42.01</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>47</td>\n",
       "      <td>1306.72</td>\n",
       "      <td>1297.07</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.63</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>194611020CHS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/2/1946</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>47</td>\n",
       "      <td>1306.72</td>\n",
       "      <td>1297.07</td>\n",
       "      <td>40.69</td>\n",
       "      <td>CHS</td>\n",
       "      <td>Stags</td>\n",
       "      <td>63</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1309.65</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>0.37</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>194611020DTF</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/2/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>DTF</td>\n",
       "      <td>Falcons</td>\n",
       "      <td>33</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1279.62</td>\n",
       "      <td>38.86</td>\n",
       "      <td>WSC</td>\n",
       "      <td>Capitols</td>\n",
       "      <td>50</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1320.38</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.64</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gameorder       game_id lg_id  _iscopy  year_id  date_game  seasongame  \\\n",
       "0          1  194611010TRH   NBA        0     1947  11/1/1946           1   \n",
       "1          1  194611010TRH   NBA        1     1947  11/1/1946           1   \n",
       "2          2  194611020CHS   NBA        0     1947  11/2/1946           1   \n",
       "3          2  194611020CHS   NBA        1     1947  11/2/1946           2   \n",
       "4          3  194611020DTF   NBA        0     1947  11/2/1946           1   \n",
       "\n",
       "   is_playoffs team_id  fran_id  pts    elo_i    elo_n  win_equiv opp_id  \\\n",
       "0            0     TRH  Huskies   66  1300.00  1293.28      40.29    NYK   \n",
       "1            0     NYK   Knicks   68  1300.00  1306.72      41.71    TRH   \n",
       "2            0     CHS    Stags   63  1300.00  1309.65      42.01    NYK   \n",
       "3            0     NYK   Knicks   47  1306.72  1297.07      40.69    CHS   \n",
       "4            0     DTF  Falcons   33  1300.00  1279.62      38.86    WSC   \n",
       "\n",
       "   opp_fran  opp_pts  opp_elo_i  opp_elo_n game_location game_result  \\\n",
       "0    Knicks       68    1300.00    1306.72             H           L   \n",
       "1   Huskies       66    1300.00    1293.28             A           W   \n",
       "2    Knicks       47    1306.72    1297.07             H           W   \n",
       "3     Stags       63    1300.00    1309.65             A           L   \n",
       "4  Capitols       50    1300.00    1320.38             H           L   \n",
       "\n",
       "   forecast notes  \n",
       "0      0.64   NaN  \n",
       "1      0.36   NaN  \n",
       "2      0.63   NaN  \n",
       "3      0.37   NaN  \n",
       "4      0.64   NaN  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "8d970bfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
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       "      <td>10/26/2010</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>HOU</td>\n",
       "      <td>Rockets</td>\n",
       "      <td>110</td>\n",
       "      <td>1504.20</td>\n",
       "      <td>1502.60</td>\n",
       "      <td>40.90</td>\n",
       "      <td>LAL</td>\n",
       "      <td>Lakers</td>\n",
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       "    <tr>\n",
       "      <th>113659</th>\n",
       "      <td>56830</td>\n",
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       "      <td>110</td>\n",
       "      <td>1504.20</td>\n",
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       "      <td>W</td>\n",
       "      <td>0.80</td>\n",
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       "    <tr>\n",
       "      <th>113660</th>\n",
       "      <td>56831</td>\n",
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       "      <td>1630.62</td>\n",
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       "      <td>6/11/2015</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>82</td>\n",
       "      <td>1723.41</td>\n",
       "      <td>1704.39</td>\n",
       "      <td>60.31</td>\n",
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       "      <td>Warriors</td>\n",
       "      <td>103</td>\n",
       "      <td>1790.96</td>\n",
       "      <td>1809.98</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.55</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>126310</th>\n",
       "      <td>63156</td>\n",
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       "      <td>1813.63</td>\n",
       "      <td>68.01</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
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       "      <td>CLE</td>\n",
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       "      <td>91</td>\n",
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       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>97</td>\n",
       "      <td>1700.74</td>\n",
       "      <td>1692.09</td>\n",
       "      <td>59.29</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>105</td>\n",
       "      <td>1813.63</td>\n",
       "      <td>1822.29</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.48</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126313</th>\n",
       "      <td>63157</td>\n",
       "      <td>201506170CLE</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>6/16/2015</td>\n",
       "      <td>103</td>\n",
       "      <td>1</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>105</td>\n",
       "      <td>1813.63</td>\n",
       "      <td>1822.29</td>\n",
       "      <td>68.52</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>97</td>\n",
       "      <td>1700.74</td>\n",
       "      <td>1692.09</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>0.52</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12658 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        gameorder       game_id lg_id  _iscopy  year_id   date_game  \\\n",
       "113656      56829  201010260BOS   NBA        1     2011  10/26/2010   \n",
       "113657      56829  201010260BOS   NBA        0     2011  10/26/2010   \n",
       "113658      56830  201010260LAL   NBA        1     2011  10/26/2010   \n",
       "113659      56830  201010260LAL   NBA        0     2011  10/26/2010   \n",
       "113660      56831  201010260POR   NBA        1     2011  10/26/2010   \n",
       "...           ...           ...   ...      ...      ...         ...   \n",
       "126309      63155  201506110CLE   NBA        0     2015   6/11/2015   \n",
       "126310      63156  201506140GSW   NBA        0     2015   6/14/2015   \n",
       "126311      63156  201506140GSW   NBA        1     2015   6/14/2015   \n",
       "126312      63157  201506170CLE   NBA        0     2015   6/16/2015   \n",
       "126313      63157  201506170CLE   NBA        1     2015   6/16/2015   \n",
       "\n",
       "        seasongame  is_playoffs team_id    fran_id  pts    elo_i    elo_n  \\\n",
       "113656           1            0     MIA       Heat   80  1547.36  1543.16   \n",
       "113657           1            0     BOS    Celtics   88  1625.10  1629.30   \n",
       "113658           1            0     HOU    Rockets  110  1504.20  1502.60   \n",
       "113659           1            0     LAL     Lakers  112  1647.60  1649.20   \n",
       "113660           1            0     PHO       Suns   92  1643.02  1630.62   \n",
       "...            ...          ...     ...        ...  ...      ...      ...   \n",
       "126309         100            1     CLE  Cavaliers   82  1723.41  1704.39   \n",
       "126310         102            1     GSW   Warriors  104  1809.98  1813.63   \n",
       "126311         101            1     CLE  Cavaliers   91  1704.39  1700.74   \n",
       "126312         102            1     CLE  Cavaliers   97  1700.74  1692.09   \n",
       "126313         103            1     GSW   Warriors  105  1813.63  1822.29   \n",
       "\n",
       "        win_equiv opp_id      opp_fran  opp_pts  opp_elo_i  opp_elo_n  \\\n",
       "113656      45.14    BOS       Celtics       88    1625.10    1629.30   \n",
       "113657      53.75    MIA          Heat       80    1547.36    1543.16   \n",
       "113658      40.90    LAL        Lakers      112    1647.60    1649.20   \n",
       "113659      55.61    HOU       Rockets      110    1504.20    1502.60   \n",
       "113660      53.88    POR  Trailblazers      106    1552.81    1565.22   \n",
       "...           ...    ...           ...      ...        ...        ...   \n",
       "126309      60.31    GSW      Warriors      103    1790.96    1809.98   \n",
       "126310      68.01    CLE     Cavaliers       91    1704.39    1700.74   \n",
       "126311      60.01    GSW      Warriors      104    1809.98    1813.63   \n",
       "126312      59.29    GSW      Warriors      105    1813.63    1822.29   \n",
       "126313      68.52    CLE     Cavaliers       97    1700.74    1692.09   \n",
       "\n",
       "       game_location game_result  forecast notes  \n",
       "113656             A           L      0.26   NaN  \n",
       "113657             H           W      0.74   NaN  \n",
       "113658             A           L      0.20   NaN  \n",
       "113659             H           W      0.80   NaN  \n",
       "113660             A           L      0.49   NaN  \n",
       "...              ...         ...       ...   ...  \n",
       "126309             H           L      0.55   NaN  \n",
       "126310             H           W      0.77   NaN  \n",
       "126311             A           L      0.23   NaN  \n",
       "126312             H           L      0.48   NaN  \n",
       "126313             A           W      0.52   NaN  \n",
       "\n",
       "[12658 rows x 23 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_decade = df[df[\"year_id\"] > 2010].copy()\n",
    "current_decade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "d2b95143",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12658, 23)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_decade.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "0240df93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5424, 23)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games_with_notes = df[df[\"notes\"].notnull()].copy()\n",
    "games_with_notes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "c5990612",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27797, 23)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ers = df[df[\"fran_id\"].str.endswith(\"ers\")].copy()\n",
    "ers.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "26bb4261",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>gameorder</th>\n",
       "      <th>game_id</th>\n",
       "      <th>lg_id</th>\n",
       "      <th>_iscopy</th>\n",
       "      <th>year_id</th>\n",
       "      <th>date_game</th>\n",
       "      <th>seasongame</th>\n",
       "      <th>is_playoffs</th>\n",
       "      <th>team_id</th>\n",
       "      <th>fran_id</th>\n",
       "      <th>pts</th>\n",
       "      <th>elo_i</th>\n",
       "      <th>elo_n</th>\n",
       "      <th>win_equiv</th>\n",
       "      <th>opp_id</th>\n",
       "      <th>opp_fran</th>\n",
       "      <th>opp_pts</th>\n",
       "      <th>opp_elo_i</th>\n",
       "      <th>opp_elo_n</th>\n",
       "      <th>game_location</th>\n",
       "      <th>game_result</th>\n",
       "      <th>forecast</th>\n",
       "      <th>notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1726</th>\n",
       "      <td>864</td>\n",
       "      <td>194902260BLB</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1949</td>\n",
       "      <td>2/26/1949</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>BLB</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>114</td>\n",
       "      <td>1421.94</td>\n",
       "      <td>1419.43</td>\n",
       "      <td>38.56</td>\n",
       "      <td>MNL</td>\n",
       "      <td>Lakers</td>\n",
       "      <td>115</td>\n",
       "      <td>1637.99</td>\n",
       "      <td>1640.49</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.34</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4890</th>\n",
       "      <td>2446</td>\n",
       "      <td>195301100BLB</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1953</td>\n",
       "      <td>1/10/1953</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>BLB</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>126</td>\n",
       "      <td>1328.67</td>\n",
       "      <td>1356.65</td>\n",
       "      <td>25.80</td>\n",
       "      <td>BOS</td>\n",
       "      <td>Celtics</td>\n",
       "      <td>105</td>\n",
       "      <td>1591.14</td>\n",
       "      <td>1563.17</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.28</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4909</th>\n",
       "      <td>2455</td>\n",
       "      <td>195301140BLB</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1953</td>\n",
       "      <td>1/14/1953</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>BLB</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>104</td>\n",
       "      <td>1349.83</td>\n",
       "      <td>1346.36</td>\n",
       "      <td>24.88</td>\n",
       "      <td>MNL</td>\n",
       "      <td>Lakers</td>\n",
       "      <td>112</td>\n",
       "      <td>1665.44</td>\n",
       "      <td>1668.91</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.22</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5208</th>\n",
       "      <td>2605</td>\n",
       "      <td>195303110BLB</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1953</td>\n",
       "      <td>3/11/1953</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>BLB</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>107</td>\n",
       "      <td>1284.52</td>\n",
       "      <td>1282.24</td>\n",
       "      <td>19.58</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>113</td>\n",
       "      <td>1649.15</td>\n",
       "      <td>1651.44</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.18</td>\n",
       "      <td>at Boston MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5825</th>\n",
       "      <td>2913</td>\n",
       "      <td>195402220BLB</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1954</td>\n",
       "      <td>2/22/1954</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>BLB</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>110</td>\n",
       "      <td>1303.75</td>\n",
       "      <td>1301.97</td>\n",
       "      <td>20.74</td>\n",
       "      <td>BOS</td>\n",
       "      <td>Celtics</td>\n",
       "      <td>111</td>\n",
       "      <td>1591.49</td>\n",
       "      <td>1593.27</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.25</td>\n",
       "      <td>at Worcester MA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      gameorder       game_id lg_id  _iscopy  year_id  date_game  seasongame  \\\n",
       "1726        864  194902260BLB   NBA        0     1949  2/26/1949          53   \n",
       "4890       2446  195301100BLB   NBA        0     1953  1/10/1953          32   \n",
       "4909       2455  195301140BLB   NBA        0     1953  1/14/1953          34   \n",
       "5208       2605  195303110BLB   NBA        0     1953  3/11/1953          66   \n",
       "5825       2913  195402220BLB   NBA        0     1954  2/22/1954          60   \n",
       "\n",
       "      is_playoffs team_id    fran_id  pts    elo_i    elo_n  win_equiv opp_id  \\\n",
       "1726            0     BLB  Baltimore  114  1421.94  1419.43      38.56    MNL   \n",
       "4890            0     BLB  Baltimore  126  1328.67  1356.65      25.80    BOS   \n",
       "4909            0     BLB  Baltimore  104  1349.83  1346.36      24.88    MNL   \n",
       "5208            0     BLB  Baltimore  107  1284.52  1282.24      19.58    NYK   \n",
       "5825            0     BLB  Baltimore  110  1303.75  1301.97      20.74    BOS   \n",
       "\n",
       "     opp_fran  opp_pts  opp_elo_i  opp_elo_n game_location game_result  \\\n",
       "1726   Lakers      115    1637.99    1640.49             H           L   \n",
       "4890  Celtics      105    1591.14    1563.17             H           W   \n",
       "4909   Lakers      112    1665.44    1668.91             H           L   \n",
       "5208   Knicks      113    1649.15    1651.44             H           L   \n",
       "5825  Celtics      111    1591.49    1593.27             H           L   \n",
       "\n",
       "      forecast            notes  \n",
       "1726      0.34              NaN  \n",
       "4890      0.28              NaN  \n",
       "4909      0.22              NaN  \n",
       "5208      0.18     at Boston MA  \n",
       "5825      0.25  at Worcester MA  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\n",
    "    (df[\"_iscopy\"] == 0) &\n",
    "    (df[\"pts\"] > 100) &\n",
    "    (df[\"opp_pts\"] > 100) &\n",
    "    (df[\"team_id\"] == \"BLB\")\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "e61356f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>opp_id</th>\n",
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       "      <th>opp_pts</th>\n",
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       "      <th>opp_elo_n</th>\n",
       "      <th>game_location</th>\n",
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       "      <th>notes</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>72344</th>\n",
       "      <td>36173</td>\n",
       "      <td>199401210LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1994</td>\n",
       "      <td>1/21/1994</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>90</td>\n",
       "      <td>1387.72</td>\n",
       "      <td>1377.64</td>\n",
       "      <td>28.91</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>106</td>\n",
       "      <td>1571.10</td>\n",
       "      <td>1581.18</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.38</td>\n",
       "      <td>at Inglewood, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72426</th>\n",
       "      <td>36214</td>\n",
       "      <td>199401270LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1994</td>\n",
       "      <td>1/27/1994</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>101</td>\n",
       "      <td>1401.38</td>\n",
       "      <td>1398.68</td>\n",
       "      <td>30.96</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>103</td>\n",
       "      <td>1640.02</td>\n",
       "      <td>1642.72</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.31</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73767</th>\n",
       "      <td>36884</td>\n",
       "      <td>199411050POR</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1995</td>\n",
       "      <td>11/5/1994</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>95</td>\n",
       "      <td>1373.86</td>\n",
       "      <td>1367.04</td>\n",
       "      <td>27.70</td>\n",
       "      <td>POR</td>\n",
       "      <td>Trailblazers</td>\n",
       "      <td>112</td>\n",
       "      <td>1550.25</td>\n",
       "      <td>1557.08</td>\n",
       "      <td>N</td>\n",
       "      <td>L</td>\n",
       "      <td>0.27</td>\n",
       "      <td>at Yokohama, Japan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73841</th>\n",
       "      <td>36921</td>\n",
       "      <td>199411120LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1995</td>\n",
       "      <td>11/12/1994</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>101</td>\n",
       "      <td>1356.21</td>\n",
       "      <td>1351.31</td>\n",
       "      <td>26.25</td>\n",
       "      <td>PHO</td>\n",
       "      <td>Suns</td>\n",
       "      <td>108</td>\n",
       "      <td>1585.83</td>\n",
       "      <td>1590.73</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.32</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74254</th>\n",
       "      <td>37128</td>\n",
       "      <td>199412150LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1995</td>\n",
       "      <td>12/15/1994</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>89</td>\n",
       "      <td>1316.25</td>\n",
       "      <td>1326.85</td>\n",
       "      <td>24.07</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>82</td>\n",
       "      <td>1483.84</td>\n",
       "      <td>1473.23</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.40</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74283</th>\n",
       "      <td>37142</td>\n",
       "      <td>199412170LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1995</td>\n",
       "      <td>12/17/1994</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>87</td>\n",
       "      <td>1326.85</td>\n",
       "      <td>1307.69</td>\n",
       "      <td>22.43</td>\n",
       "      <td>DAL</td>\n",
       "      <td>Mavericks</td>\n",
       "      <td>106</td>\n",
       "      <td>1371.42</td>\n",
       "      <td>1390.58</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.58</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74722</th>\n",
       "      <td>37362</td>\n",
       "      <td>199501210LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1995</td>\n",
       "      <td>1/21/1995</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>86</td>\n",
       "      <td>1294.04</td>\n",
       "      <td>1289.08</td>\n",
       "      <td>20.91</td>\n",
       "      <td>SAC</td>\n",
       "      <td>Kings</td>\n",
       "      <td>92</td>\n",
       "      <td>1502.60</td>\n",
       "      <td>1507.56</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.35</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75216</th>\n",
       "      <td>37609</td>\n",
       "      <td>199503020LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1995</td>\n",
       "      <td>3/2/1995</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>88</td>\n",
       "      <td>1325.14</td>\n",
       "      <td>1318.65</td>\n",
       "      <td>23.36</td>\n",
       "      <td>SEA</td>\n",
       "      <td>Thunder</td>\n",
       "      <td>116</td>\n",
       "      <td>1679.72</td>\n",
       "      <td>1686.20</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.19</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75634</th>\n",
       "      <td>37818</td>\n",
       "      <td>199504010LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1995</td>\n",
       "      <td>4/1/1995</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>116</td>\n",
       "      <td>1293.60</td>\n",
       "      <td>1327.38</td>\n",
       "      <td>24.11</td>\n",
       "      <td>UTA</td>\n",
       "      <td>Jazz</td>\n",
       "      <td>98</td>\n",
       "      <td>1689.87</td>\n",
       "      <td>1656.08</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.15</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76282</th>\n",
       "      <td>38142</td>\n",
       "      <td>199511160LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1996</td>\n",
       "      <td>11/16/1995</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>103</td>\n",
       "      <td>1396.59</td>\n",
       "      <td>1399.63</td>\n",
       "      <td>32.05</td>\n",
       "      <td>VAN</td>\n",
       "      <td>Grizzlies</td>\n",
       "      <td>98</td>\n",
       "      <td>1304.50</td>\n",
       "      <td>1301.47</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.75</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76506</th>\n",
       "      <td>38254</td>\n",
       "      <td>199512020LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1996</td>\n",
       "      <td>12/2/1995</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>98</td>\n",
       "      <td>1400.21</td>\n",
       "      <td>1396.17</td>\n",
       "      <td>31.70</td>\n",
       "      <td>CHI</td>\n",
       "      <td>Bulls</td>\n",
       "      <td>104</td>\n",
       "      <td>1653.13</td>\n",
       "      <td>1657.17</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.29</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77162</th>\n",
       "      <td>38582</td>\n",
       "      <td>199601200LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1996</td>\n",
       "      <td>1/20/1996</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>82</td>\n",
       "      <td>1401.71</td>\n",
       "      <td>1390.10</td>\n",
       "      <td>31.10</td>\n",
       "      <td>UTA</td>\n",
       "      <td>Jazz</td>\n",
       "      <td>106</td>\n",
       "      <td>1616.50</td>\n",
       "      <td>1628.12</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.34</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77266</th>\n",
       "      <td>38634</td>\n",
       "      <td>199601270LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1996</td>\n",
       "      <td>1/27/1996</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>87</td>\n",
       "      <td>1393.54</td>\n",
       "      <td>1388.76</td>\n",
       "      <td>30.97</td>\n",
       "      <td>SEA</td>\n",
       "      <td>Thunder</td>\n",
       "      <td>96</td>\n",
       "      <td>1659.33</td>\n",
       "      <td>1664.11</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.28</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77511</th>\n",
       "      <td>38756</td>\n",
       "      <td>199602170LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1996</td>\n",
       "      <td>2/17/1996</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>100</td>\n",
       "      <td>1332.11</td>\n",
       "      <td>1340.06</td>\n",
       "      <td>26.33</td>\n",
       "      <td>POR</td>\n",
       "      <td>Trailblazers</td>\n",
       "      <td>96</td>\n",
       "      <td>1498.94</td>\n",
       "      <td>1490.99</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.40</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77657</th>\n",
       "      <td>38829</td>\n",
       "      <td>199602270LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1996</td>\n",
       "      <td>2/27/1996</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>95</td>\n",
       "      <td>1328.99</td>\n",
       "      <td>1342.11</td>\n",
       "      <td>26.52</td>\n",
       "      <td>SAS</td>\n",
       "      <td>Spurs</td>\n",
       "      <td>90</td>\n",
       "      <td>1642.09</td>\n",
       "      <td>1628.97</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.23</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77887</th>\n",
       "      <td>38944</td>\n",
       "      <td>199603140LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1996</td>\n",
       "      <td>3/14/1996</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>110</td>\n",
       "      <td>1381.56</td>\n",
       "      <td>1385.52</td>\n",
       "      <td>30.65</td>\n",
       "      <td>DAL</td>\n",
       "      <td>Mavericks</td>\n",
       "      <td>106</td>\n",
       "      <td>1367.47</td>\n",
       "      <td>1363.51</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.66</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78297</th>\n",
       "      <td>39149</td>\n",
       "      <td>199604110LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1996</td>\n",
       "      <td>4/11/1996</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>101</td>\n",
       "      <td>1395.23</td>\n",
       "      <td>1381.94</td>\n",
       "      <td>30.30</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>111</td>\n",
       "      <td>1422.58</td>\n",
       "      <td>1435.87</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.60</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79014</th>\n",
       "      <td>39508</td>\n",
       "      <td>199611300LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1997</td>\n",
       "      <td>11/30/1996</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>95</td>\n",
       "      <td>1416.72</td>\n",
       "      <td>1398.00</td>\n",
       "      <td>31.42</td>\n",
       "      <td>NJN</td>\n",
       "      <td>Nets</td>\n",
       "      <td>106</td>\n",
       "      <td>1342.60</td>\n",
       "      <td>1361.32</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.73</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79240</th>\n",
       "      <td>39621</td>\n",
       "      <td>199612160LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1997</td>\n",
       "      <td>12/16/1996</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>122</td>\n",
       "      <td>1400.03</td>\n",
       "      <td>1403.83</td>\n",
       "      <td>32.00</td>\n",
       "      <td>PHO</td>\n",
       "      <td>Suns</td>\n",
       "      <td>121</td>\n",
       "      <td>1483.82</td>\n",
       "      <td>1480.01</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.52</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79752</th>\n",
       "      <td>39877</td>\n",
       "      <td>199701230LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1997</td>\n",
       "      <td>1/23/1997</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>102</td>\n",
       "      <td>1403.31</td>\n",
       "      <td>1411.17</td>\n",
       "      <td>32.74</td>\n",
       "      <td>SEA</td>\n",
       "      <td>Thunder</td>\n",
       "      <td>100</td>\n",
       "      <td>1660.14</td>\n",
       "      <td>1652.28</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.29</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79919</th>\n",
       "      <td>39960</td>\n",
       "      <td>199702040LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1997</td>\n",
       "      <td>2/4/1997</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>108</td>\n",
       "      <td>1415.27</td>\n",
       "      <td>1441.67</td>\n",
       "      <td>35.85</td>\n",
       "      <td>LAL</td>\n",
       "      <td>Lakers</td>\n",
       "      <td>86</td>\n",
       "      <td>1643.52</td>\n",
       "      <td>1617.11</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.32</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80308</th>\n",
       "      <td>40155</td>\n",
       "      <td>199703060LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1997</td>\n",
       "      <td>3/6/1997</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>93</td>\n",
       "      <td>1430.59</td>\n",
       "      <td>1427.22</td>\n",
       "      <td>34.37</td>\n",
       "      <td>ORL</td>\n",
       "      <td>Magic</td>\n",
       "      <td>94</td>\n",
       "      <td>1577.69</td>\n",
       "      <td>1581.06</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.43</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80849</th>\n",
       "      <td>40425</td>\n",
       "      <td>199704120LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1997</td>\n",
       "      <td>4/12/1997</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>116</td>\n",
       "      <td>1443.53</td>\n",
       "      <td>1449.17</td>\n",
       "      <td>36.63</td>\n",
       "      <td>DEN</td>\n",
       "      <td>Nuggets</td>\n",
       "      <td>94</td>\n",
       "      <td>1294.84</td>\n",
       "      <td>1289.21</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.81</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81259</th>\n",
       "      <td>40630</td>\n",
       "      <td>199711080LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1998</td>\n",
       "      <td>11/8/1997</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>105</td>\n",
       "      <td>1437.93</td>\n",
       "      <td>1421.46</td>\n",
       "      <td>33.43</td>\n",
       "      <td>PHO</td>\n",
       "      <td>Suns</td>\n",
       "      <td>123</td>\n",
       "      <td>1516.18</td>\n",
       "      <td>1532.65</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.53</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81517</th>\n",
       "      <td>40759</td>\n",
       "      <td>199711280LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1998</td>\n",
       "      <td>11/28/1997</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>92</td>\n",
       "      <td>1382.55</td>\n",
       "      <td>1370.31</td>\n",
       "      <td>28.39</td>\n",
       "      <td>NJN</td>\n",
       "      <td>Nets</td>\n",
       "      <td>104</td>\n",
       "      <td>1468.49</td>\n",
       "      <td>1480.74</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.52</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81794</th>\n",
       "      <td>40898</td>\n",
       "      <td>199712180LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1998</td>\n",
       "      <td>12/18/1997</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>82</td>\n",
       "      <td>1348.13</td>\n",
       "      <td>1352.13</td>\n",
       "      <td>26.69</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>78</td>\n",
       "      <td>1336.38</td>\n",
       "      <td>1332.38</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.66</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82273</th>\n",
       "      <td>41137</td>\n",
       "      <td>199801220LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1998</td>\n",
       "      <td>1/22/1998</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>76</td>\n",
       "      <td>1344.57</td>\n",
       "      <td>1333.45</td>\n",
       "      <td>25.00</td>\n",
       "      <td>DET</td>\n",
       "      <td>Pistons</td>\n",
       "      <td>94</td>\n",
       "      <td>1523.62</td>\n",
       "      <td>1534.74</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.39</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82440</th>\n",
       "      <td>41221</td>\n",
       "      <td>199802030LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2/3/1998</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>111</td>\n",
       "      <td>1289.86</td>\n",
       "      <td>1310.38</td>\n",
       "      <td>23.00</td>\n",
       "      <td>UTA</td>\n",
       "      <td>Jazz</td>\n",
       "      <td>102</td>\n",
       "      <td>1660.32</td>\n",
       "      <td>1639.80</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.17</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82628</th>\n",
       "      <td>41315</td>\n",
       "      <td>199802190LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2/19/1998</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>80</td>\n",
       "      <td>1289.71</td>\n",
       "      <td>1286.67</td>\n",
       "      <td>21.04</td>\n",
       "      <td>MIA</td>\n",
       "      <td>Heat</td>\n",
       "      <td>89</td>\n",
       "      <td>1643.84</td>\n",
       "      <td>1646.88</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.19</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82920</th>\n",
       "      <td>41461</td>\n",
       "      <td>199803120LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1998</td>\n",
       "      <td>3/12/1998</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>85</td>\n",
       "      <td>1286.15</td>\n",
       "      <td>1280.67</td>\n",
       "      <td>20.57</td>\n",
       "      <td>LAL</td>\n",
       "      <td>Lakers</td>\n",
       "      <td>108</td>\n",
       "      <td>1645.59</td>\n",
       "      <td>1651.07</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.18</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83474</th>\n",
       "      <td>41738</td>\n",
       "      <td>199804180LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1998</td>\n",
       "      <td>4/18/1998</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>83</td>\n",
       "      <td>1233.51</td>\n",
       "      <td>1240.79</td>\n",
       "      <td>17.59</td>\n",
       "      <td>SAC</td>\n",
       "      <td>Kings</td>\n",
       "      <td>77</td>\n",
       "      <td>1317.31</td>\n",
       "      <td>1310.03</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.52</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83806</th>\n",
       "      <td>41904</td>\n",
       "      <td>199902160LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2/16/1999</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>89</td>\n",
       "      <td>1285.07</td>\n",
       "      <td>1276.79</td>\n",
       "      <td>20.06</td>\n",
       "      <td>VAN</td>\n",
       "      <td>Grizzlies</td>\n",
       "      <td>93</td>\n",
       "      <td>1305.31</td>\n",
       "      <td>1313.59</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.61</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83955</th>\n",
       "      <td>41978</td>\n",
       "      <td>199902250LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2/25/1999</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>100</td>\n",
       "      <td>1253.53</td>\n",
       "      <td>1249.40</td>\n",
       "      <td>18.01</td>\n",
       "      <td>LAL</td>\n",
       "      <td>Lakers</td>\n",
       "      <td>115</td>\n",
       "      <td>1611.34</td>\n",
       "      <td>1615.46</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.18</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83986</th>\n",
       "      <td>41994</td>\n",
       "      <td>199902270LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2/27/1999</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>74</td>\n",
       "      <td>1248.09</td>\n",
       "      <td>1241.29</td>\n",
       "      <td>17.44</td>\n",
       "      <td>ATL</td>\n",
       "      <td>Hawks</td>\n",
       "      <td>103</td>\n",
       "      <td>1598.35</td>\n",
       "      <td>1605.16</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.19</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84444</th>\n",
       "      <td>42223</td>\n",
       "      <td>199903270LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1999</td>\n",
       "      <td>3/27/1999</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>103</td>\n",
       "      <td>1254.16</td>\n",
       "      <td>1302.97</td>\n",
       "      <td>22.16</td>\n",
       "      <td>UTA</td>\n",
       "      <td>Jazz</td>\n",
       "      <td>77</td>\n",
       "      <td>1709.66</td>\n",
       "      <td>1660.86</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.11</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84670</th>\n",
       "      <td>42336</td>\n",
       "      <td>199904100LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1999</td>\n",
       "      <td>4/10/1999</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>70</td>\n",
       "      <td>1289.79</td>\n",
       "      <td>1282.41</td>\n",
       "      <td>20.50</td>\n",
       "      <td>POR</td>\n",
       "      <td>Trailblazers</td>\n",
       "      <td>97</td>\n",
       "      <td>1614.94</td>\n",
       "      <td>1622.32</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.21</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84817</th>\n",
       "      <td>42409</td>\n",
       "      <td>199904190LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1999</td>\n",
       "      <td>4/19/1999</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>98</td>\n",
       "      <td>1313.54</td>\n",
       "      <td>1307.33</td>\n",
       "      <td>22.52</td>\n",
       "      <td>SAC</td>\n",
       "      <td>Kings</td>\n",
       "      <td>102</td>\n",
       "      <td>1418.43</td>\n",
       "      <td>1424.64</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.49</td>\n",
       "      <td>at Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95379</th>\n",
       "      <td>47690</td>\n",
       "      <td>200310300SEA</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>2004</td>\n",
       "      <td>10/30/2003</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>100</td>\n",
       "      <td>1428.09</td>\n",
       "      <td>1421.46</td>\n",
       "      <td>32.56</td>\n",
       "      <td>SEA</td>\n",
       "      <td>Thunder</td>\n",
       "      <td>109</td>\n",
       "      <td>1523.13</td>\n",
       "      <td>1529.76</td>\n",
       "      <td>N</td>\n",
       "      <td>L</td>\n",
       "      <td>0.37</td>\n",
       "      <td>at Saitama, Japan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95384</th>\n",
       "      <td>47693</td>\n",
       "      <td>200310310LAC</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>2004</td>\n",
       "      <td>10/31/2003</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>LAC</td>\n",
       "      <td>Clippers</td>\n",
       "      <td>105</td>\n",
       "      <td>1421.46</td>\n",
       "      <td>1411.31</td>\n",
       "      <td>31.54</td>\n",
       "      <td>SEA</td>\n",
       "      <td>Thunder</td>\n",
       "      <td>124</td>\n",
       "      <td>1529.76</td>\n",
       "      <td>1539.91</td>\n",
       "      <td>N</td>\n",
       "      <td>L</td>\n",
       "      <td>0.35</td>\n",
       "      <td>at Saitama, Japan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       gameorder       game_id lg_id  _iscopy  year_id   date_game  \\\n",
       "72344      36173  199401210LAC   NBA        0     1994   1/21/1994   \n",
       "72426      36214  199401270LAC   NBA        0     1994   1/27/1994   \n",
       "73767      36884  199411050POR   NBA        0     1995   11/5/1994   \n",
       "73841      36921  199411120LAC   NBA        0     1995  11/12/1994   \n",
       "74254      37128  199412150LAC   NBA        0     1995  12/15/1994   \n",
       "74283      37142  199412170LAC   NBA        0     1995  12/17/1994   \n",
       "74722      37362  199501210LAC   NBA        0     1995   1/21/1995   \n",
       "75216      37609  199503020LAC   NBA        0     1995    3/2/1995   \n",
       "75634      37818  199504010LAC   NBA        0     1995    4/1/1995   \n",
       "76282      38142  199511160LAC   NBA        0     1996  11/16/1995   \n",
       "76506      38254  199512020LAC   NBA        0     1996   12/2/1995   \n",
       "77162      38582  199601200LAC   NBA        0     1996   1/20/1996   \n",
       "77266      38634  199601270LAC   NBA        0     1996   1/27/1996   \n",
       "77511      38756  199602170LAC   NBA        0     1996   2/17/1996   \n",
       "77657      38829  199602270LAC   NBA        0     1996   2/27/1996   \n",
       "77887      38944  199603140LAC   NBA        0     1996   3/14/1996   \n",
       "78297      39149  199604110LAC   NBA        0     1996   4/11/1996   \n",
       "79014      39508  199611300LAC   NBA        0     1997  11/30/1996   \n",
       "79240      39621  199612160LAC   NBA        0     1997  12/16/1996   \n",
       "79752      39877  199701230LAC   NBA        0     1997   1/23/1997   \n",
       "79919      39960  199702040LAC   NBA        0     1997    2/4/1997   \n",
       "80308      40155  199703060LAC   NBA        0     1997    3/6/1997   \n",
       "80849      40425  199704120LAC   NBA        0     1997   4/12/1997   \n",
       "81259      40630  199711080LAC   NBA        0     1998   11/8/1997   \n",
       "81517      40759  199711280LAC   NBA        0     1998  11/28/1997   \n",
       "81794      40898  199712180LAC   NBA        0     1998  12/18/1997   \n",
       "82273      41137  199801220LAC   NBA        0     1998   1/22/1998   \n",
       "82440      41221  199802030LAC   NBA        0     1998    2/3/1998   \n",
       "82628      41315  199802190LAC   NBA        0     1998   2/19/1998   \n",
       "82920      41461  199803120LAC   NBA        0     1998   3/12/1998   \n",
       "83474      41738  199804180LAC   NBA        0     1998   4/18/1998   \n",
       "83806      41904  199902160LAC   NBA        0     1999   2/16/1999   \n",
       "83955      41978  199902250LAC   NBA        0     1999   2/25/1999   \n",
       "83986      41994  199902270LAC   NBA        0     1999   2/27/1999   \n",
       "84444      42223  199903270LAC   NBA        0     1999   3/27/1999   \n",
       "84670      42336  199904100LAC   NBA        0     1999   4/10/1999   \n",
       "84817      42409  199904190LAC   NBA        0     1999   4/19/1999   \n",
       "95379      47690  200310300SEA   NBA        0     2004  10/30/2003   \n",
       "95384      47693  200310310LAC   NBA        0     2004  10/31/2003   \n",
       "\n",
       "       seasongame  is_playoffs team_id   fran_id  pts    elo_i    elo_n  \\\n",
       "72344          37            0     LAC  Clippers   90  1387.72  1377.64   \n",
       "72426          39            0     LAC  Clippers  101  1401.38  1398.68   \n",
       "73767           2            0     LAC  Clippers   95  1373.86  1367.04   \n",
       "73841           4            0     LAC  Clippers  101  1356.21  1351.31   \n",
       "74254          21            0     LAC  Clippers   89  1316.25  1326.85   \n",
       "74283          22            0     LAC  Clippers   87  1326.85  1307.69   \n",
       "74722          39            0     LAC  Clippers   86  1294.04  1289.08   \n",
       "75216          58            0     LAC  Clippers   88  1325.14  1318.65   \n",
       "75634          73            0     LAC  Clippers  116  1293.60  1327.38   \n",
       "76282           8            0     LAC  Clippers  103  1396.59  1399.63   \n",
       "76506          16            0     LAC  Clippers   98  1400.21  1396.17   \n",
       "77162          39            0     LAC  Clippers   82  1401.71  1390.10   \n",
       "77266          41            0     LAC  Clippers   87  1393.54  1388.76   \n",
       "77511          50            0     LAC  Clippers  100  1332.11  1340.06   \n",
       "77657          55            0     LAC  Clippers   95  1328.99  1342.11   \n",
       "77887          63            0     LAC  Clippers  110  1381.56  1385.52   \n",
       "78297          77            0     LAC  Clippers  101  1395.23  1381.94   \n",
       "79014          16            0     LAC  Clippers   95  1416.72  1398.00   \n",
       "79240          24            0     LAC  Clippers  122  1400.03  1403.83   \n",
       "79752          39            0     LAC  Clippers  102  1403.31  1411.17   \n",
       "79919          44            0     LAC  Clippers  108  1415.27  1441.67   \n",
       "80308          57            0     LAC  Clippers   93  1430.59  1427.22   \n",
       "80849          78            0     LAC  Clippers  116  1443.53  1449.17   \n",
       "81259           5            0     LAC  Clippers  105  1437.93  1421.46   \n",
       "81517          15            0     LAC  Clippers   92  1382.55  1370.31   \n",
       "81794          25            0     LAC  Clippers   82  1348.13  1352.13   \n",
       "82273          42            0     LAC  Clippers   76  1344.57  1333.45   \n",
       "82440          48            0     LAC  Clippers  111  1289.86  1310.38   \n",
       "82628          53            0     LAC  Clippers   80  1289.71  1286.67   \n",
       "82920          62            0     LAC  Clippers   85  1286.15  1280.67   \n",
       "83474          82            0     LAC  Clippers   83  1233.51  1240.79   \n",
       "83806           6            0     LAC  Clippers   89  1285.07  1276.79   \n",
       "83955          10            0     LAC  Clippers  100  1253.53  1249.40   \n",
       "83986          12            0     LAC  Clippers   74  1248.09  1241.29   \n",
       "84444          27            0     LAC  Clippers  103  1254.16  1302.97   \n",
       "84670          35            0     LAC  Clippers   70  1289.79  1282.41   \n",
       "84817          41            0     LAC  Clippers   98  1313.54  1307.33   \n",
       "95379           1            0     LAC  Clippers  100  1428.09  1421.46   \n",
       "95384           2            0     LAC  Clippers  105  1421.46  1411.31   \n",
       "\n",
       "       win_equiv opp_id      opp_fran  opp_pts  opp_elo_i  opp_elo_n  \\\n",
       "72344      28.91    CLE     Cavaliers      106    1571.10    1581.18   \n",
       "72426      30.96    NYK        Knicks      103    1640.02    1642.72   \n",
       "73767      27.70    POR  Trailblazers      112    1550.25    1557.08   \n",
       "73841      26.25    PHO          Suns      108    1585.83    1590.73   \n",
       "74254      24.07    GSW      Warriors       82    1483.84    1473.23   \n",
       "74283      22.43    DAL     Mavericks      106    1371.42    1390.58   \n",
       "74722      20.91    SAC         Kings       92    1502.60    1507.56   \n",
       "75216      23.36    SEA       Thunder      116    1679.72    1686.20   \n",
       "75634      24.11    UTA          Jazz       98    1689.87    1656.08   \n",
       "76282      32.05    VAN     Grizzlies       98    1304.50    1301.47   \n",
       "76506      31.70    CHI         Bulls      104    1653.13    1657.17   \n",
       "77162      31.10    UTA          Jazz      106    1616.50    1628.12   \n",
       "77266      30.97    SEA       Thunder       96    1659.33    1664.11   \n",
       "77511      26.33    POR  Trailblazers       96    1498.94    1490.99   \n",
       "77657      26.52    SAS         Spurs       90    1642.09    1628.97   \n",
       "77887      30.65    DAL     Mavericks      106    1367.47    1363.51   \n",
       "78297      30.30    GSW      Warriors      111    1422.58    1435.87   \n",
       "79014      31.42    NJN          Nets      106    1342.60    1361.32   \n",
       "79240      32.00    PHO          Suns      121    1483.82    1480.01   \n",
       "79752      32.74    SEA       Thunder      100    1660.14    1652.28   \n",
       "79919      35.85    LAL        Lakers       86    1643.52    1617.11   \n",
       "80308      34.37    ORL         Magic       94    1577.69    1581.06   \n",
       "80849      36.63    DEN       Nuggets       94    1294.84    1289.21   \n",
       "81259      33.43    PHO          Suns      123    1516.18    1532.65   \n",
       "81517      28.39    NJN          Nets      104    1468.49    1480.74   \n",
       "81794      26.69    GSW      Warriors       78    1336.38    1332.38   \n",
       "82273      25.00    DET       Pistons       94    1523.62    1534.74   \n",
       "82440      23.00    UTA          Jazz      102    1660.32    1639.80   \n",
       "82628      21.04    MIA          Heat       89    1643.84    1646.88   \n",
       "82920      20.57    LAL        Lakers      108    1645.59    1651.07   \n",
       "83474      17.59    SAC         Kings       77    1317.31    1310.03   \n",
       "83806      20.06    VAN     Grizzlies       93    1305.31    1313.59   \n",
       "83955      18.01    LAL        Lakers      115    1611.34    1615.46   \n",
       "83986      17.44    ATL         Hawks      103    1598.35    1605.16   \n",
       "84444      22.16    UTA          Jazz       77    1709.66    1660.86   \n",
       "84670      20.50    POR  Trailblazers       97    1614.94    1622.32   \n",
       "84817      22.52    SAC         Kings      102    1418.43    1424.64   \n",
       "95379      32.56    SEA       Thunder      109    1523.13    1529.76   \n",
       "95384      31.54    SEA       Thunder      124    1529.76    1539.91   \n",
       "\n",
       "      game_location game_result  forecast               notes  \n",
       "72344             H           L      0.38    at Inglewood, CA  \n",
       "72426             H           L      0.31      at Anaheim, CA  \n",
       "73767             N           L      0.27  at Yokohama, Japan  \n",
       "73841             H           L      0.32      at Anaheim, CA  \n",
       "74254             H           W      0.40      at Anaheim, CA  \n",
       "74283             H           L      0.58      at Anaheim, CA  \n",
       "74722             H           L      0.35      at Anaheim, CA  \n",
       "75216             H           L      0.19      at Anaheim, CA  \n",
       "75634             H           W      0.15      at Anaheim, CA  \n",
       "76282             H           W      0.75      at Anaheim, CA  \n",
       "76506             H           L      0.29      at Anaheim, CA  \n",
       "77162             H           L      0.34      at Anaheim, CA  \n",
       "77266             H           L      0.28      at Anaheim, CA  \n",
       "77511             H           W      0.40      at Anaheim, CA  \n",
       "77657             H           W      0.23      at Anaheim, CA  \n",
       "77887             H           W      0.66      at Anaheim, CA  \n",
       "78297             H           L      0.60      at Anaheim, CA  \n",
       "79014             H           L      0.73      at Anaheim, CA  \n",
       "79240             H           W      0.52      at Anaheim, CA  \n",
       "79752             H           W      0.29      at Anaheim, CA  \n",
       "79919             H           W      0.32      at Anaheim, CA  \n",
       "80308             H           L      0.43      at Anaheim, CA  \n",
       "80849             H           W      0.81      at Anaheim, CA  \n",
       "81259             H           L      0.53      at Anaheim, CA  \n",
       "81517             H           L      0.52      at Anaheim, CA  \n",
       "81794             H           W      0.66      at Anaheim, CA  \n",
       "82273             H           L      0.39      at Anaheim, CA  \n",
       "82440             H           W      0.17      at Anaheim, CA  \n",
       "82628             H           L      0.19      at Anaheim, CA  \n",
       "82920             H           L      0.18      at Anaheim, CA  \n",
       "83474             H           W      0.52      at Anaheim, CA  \n",
       "83806             H           L      0.61      at Anaheim, CA  \n",
       "83955             H           L      0.18      at Anaheim, CA  \n",
       "83986             H           L      0.19      at Anaheim, CA  \n",
       "84444             H           W      0.11      at Anaheim, CA  \n",
       "84670             H           L      0.21      at Anaheim, CA  \n",
       "84817             H           L      0.49      at Anaheim, CA  \n",
       "95379             N           L      0.37   at Saitama, Japan  \n",
       "95384             N           L      0.35   at Saitama, Japan  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\n",
    "    (df[\"_iscopy\"] == 0) &\n",
    "    (df[\"team_id\"].str.startswith(\"LA\")) &\n",
    "    (df[\"year_id\"] > 1992) &\n",
    "    (df[\"notes\"].notnull())\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f18c6d2",
   "metadata": {},
   "source": [
    "# Grouping and Aggregating Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "415667ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18700"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_revenues.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "35b6b12c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8000"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_revenues.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "58e05e4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12976235"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"pts\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "43bd4068",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fran_id\n",
       "Huskies           3995\n",
       "Knicks          582497\n",
       "Stags            20398\n",
       "Falcons           3797\n",
       "Capitols         22387\n",
       "Celtics         626484\n",
       "Steamrollers     12372\n",
       "Ironmen           3674\n",
       "Bombers          17793\n",
       "Rebels            4474\n",
       "Warriors        591224\n",
       "Baltimore        37219\n",
       "Jets              4482\n",
       "Pistons         572758\n",
       "Lakers          637444\n",
       "Kings           569245\n",
       "Hawks           567261\n",
       "Denver            4818\n",
       "Olympians        22864\n",
       "Redskins          5372\n",
       "Waterloo          4921\n",
       "Packers           6193\n",
       "Sixers          585891\n",
       "Wizards         474809\n",
       "Bulls           437269\n",
       "Thunder         437735\n",
       "Squires          91127\n",
       "Stars            84940\n",
       "Rockets         432504\n",
       "Colonels         94435\n",
       "Pacers          438288\n",
       "Nuggets         445780\n",
       "Spurs           453822\n",
       "Spirits          85874\n",
       "Sounds           75582\n",
       "Floridians       49568\n",
       "Nets            417809\n",
       "Condors          49642\n",
       "Bucks           418326\n",
       "Suns            437486\n",
       "Clippers        380523\n",
       "Cavaliers       380416\n",
       "Trailblazers    402695\n",
       "Sails            30080\n",
       "Jazz            363155\n",
       "Mavericks       309239\n",
       "Pelicans        220794\n",
       "Heat            229103\n",
       "Timberwolves    207693\n",
       "Magic           219436\n",
       "Grizzlies       157683\n",
       "Raptors         158370\n",
       "Hornets          84489\n",
       "Name: pts, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"fran_id\",sort = False)[\"pts\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "8210a14a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year_id  game_result\n",
       "2011     L              25\n",
       "         W              63\n",
       "2012     L              20\n",
       "         W              60\n",
       "2013     L              30\n",
       "         W              73\n",
       "2014     L              27\n",
       "         W              78\n",
       "2015     L              31\n",
       "         W              58\n",
       "Name: game_id, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\n",
    "    (df[\"fran_id\"] == \"Spurs\") &\n",
    "    (df[\"year_id\"] > 2010)\n",
    "].groupby([\"year_id\",\"game_result\"])[\"game_id\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "d56ec578",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "is_playoffs  game_result\n",
       "0            L              15\n",
       "             W              67\n",
       "1            L               5\n",
       "             W              16\n",
       "Name: game_id, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\n",
    "    (df[\"fran_id\"] == \"Warriors\") &\n",
    "    (df[\"year_id\"] == 2015)\n",
    "].groupby([\"is_playoffs\",\"game_result\"])[\"game_id\"].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee509718",
   "metadata": {},
   "source": [
    "# Manipulating Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "0114aec6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(126314, 23)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba = df.copy()\n",
    "nba.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "a11b0ae0",
   "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>gameorder</th>\n",
       "      <th>game_id</th>\n",
       "      <th>lg_id</th>\n",
       "      <th>_iscopy</th>\n",
       "      <th>year_id</th>\n",
       "      <th>date_game</th>\n",
       "      <th>seasongame</th>\n",
       "      <th>is_playoffs</th>\n",
       "      <th>team_id</th>\n",
       "      <th>fran_id</th>\n",
       "      <th>pts</th>\n",
       "      <th>elo_i</th>\n",
       "      <th>elo_n</th>\n",
       "      <th>win_equiv</th>\n",
       "      <th>opp_id</th>\n",
       "      <th>opp_fran</th>\n",
       "      <th>opp_pts</th>\n",
       "      <th>opp_elo_i</th>\n",
       "      <th>opp_elo_n</th>\n",
       "      <th>game_location</th>\n",
       "      <th>game_result</th>\n",
       "      <th>forecast</th>\n",
       "      <th>notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>194611010TRH</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/1/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>TRH</td>\n",
       "      <td>Huskies</td>\n",
       "      <td>66</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1293.28</td>\n",
       "      <td>40.29</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>68</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1306.72</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.64</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>194611010TRH</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/1/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>68</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1306.72</td>\n",
       "      <td>41.71</td>\n",
       "      <td>TRH</td>\n",
       "      <td>Huskies</td>\n",
       "      <td>66</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1293.28</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>0.36</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>194611020CHS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/2/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>CHS</td>\n",
       "      <td>Stags</td>\n",
       "      <td>63</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1309.65</td>\n",
       "      <td>42.01</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>47</td>\n",
       "      <td>1306.72</td>\n",
       "      <td>1297.07</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.63</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>194611020CHS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/2/1946</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>47</td>\n",
       "      <td>1306.72</td>\n",
       "      <td>1297.07</td>\n",
       "      <td>40.69</td>\n",
       "      <td>CHS</td>\n",
       "      <td>Stags</td>\n",
       "      <td>63</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1309.65</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>0.37</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>194611020DTF</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/2/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>DTF</td>\n",
       "      <td>Falcons</td>\n",
       "      <td>33</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1279.62</td>\n",
       "      <td>38.86</td>\n",
       "      <td>WSC</td>\n",
       "      <td>Capitols</td>\n",
       "      <td>50</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1320.38</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.64</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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",
       "      <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>126309</th>\n",
       "      <td>63155</td>\n",
       "      <td>201506110CLE</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>6/11/2015</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>82</td>\n",
       "      <td>1723.41</td>\n",
       "      <td>1704.39</td>\n",
       "      <td>60.31</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>103</td>\n",
       "      <td>1790.96</td>\n",
       "      <td>1809.98</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.55</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126310</th>\n",
       "      <td>63156</td>\n",
       "      <td>201506140GSW</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>6/14/2015</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>104</td>\n",
       "      <td>1809.98</td>\n",
       "      <td>1813.63</td>\n",
       "      <td>68.01</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>91</td>\n",
       "      <td>1704.39</td>\n",
       "      <td>1700.74</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.77</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126311</th>\n",
       "      <td>63156</td>\n",
       "      <td>201506140GSW</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>6/14/2015</td>\n",
       "      <td>101</td>\n",
       "      <td>1</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>91</td>\n",
       "      <td>1704.39</td>\n",
       "      <td>1700.74</td>\n",
       "      <td>60.01</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>104</td>\n",
       "      <td>1809.98</td>\n",
       "      <td>1813.63</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>0.23</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126312</th>\n",
       "      <td>63157</td>\n",
       "      <td>201506170CLE</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>6/16/2015</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>97</td>\n",
       "      <td>1700.74</td>\n",
       "      <td>1692.09</td>\n",
       "      <td>59.29</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>105</td>\n",
       "      <td>1813.63</td>\n",
       "      <td>1822.29</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.48</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126313</th>\n",
       "      <td>63157</td>\n",
       "      <td>201506170CLE</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>6/16/2015</td>\n",
       "      <td>103</td>\n",
       "      <td>1</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>105</td>\n",
       "      <td>1813.63</td>\n",
       "      <td>1822.29</td>\n",
       "      <td>68.52</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>97</td>\n",
       "      <td>1700.74</td>\n",
       "      <td>1692.09</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>0.52</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>126314 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        gameorder       game_id lg_id  _iscopy  year_id  date_game  \\\n",
       "0               1  194611010TRH   NBA        0     1947  11/1/1946   \n",
       "1               1  194611010TRH   NBA        1     1947  11/1/1946   \n",
       "2               2  194611020CHS   NBA        0     1947  11/2/1946   \n",
       "3               2  194611020CHS   NBA        1     1947  11/2/1946   \n",
       "4               3  194611020DTF   NBA        0     1947  11/2/1946   \n",
       "...           ...           ...   ...      ...      ...        ...   \n",
       "126309      63155  201506110CLE   NBA        0     2015  6/11/2015   \n",
       "126310      63156  201506140GSW   NBA        0     2015  6/14/2015   \n",
       "126311      63156  201506140GSW   NBA        1     2015  6/14/2015   \n",
       "126312      63157  201506170CLE   NBA        0     2015  6/16/2015   \n",
       "126313      63157  201506170CLE   NBA        1     2015  6/16/2015   \n",
       "\n",
       "        seasongame  is_playoffs team_id    fran_id  pts    elo_i    elo_n  \\\n",
       "0                1            0     TRH    Huskies   66  1300.00  1293.28   \n",
       "1                1            0     NYK     Knicks   68  1300.00  1306.72   \n",
       "2                1            0     CHS      Stags   63  1300.00  1309.65   \n",
       "3                2            0     NYK     Knicks   47  1306.72  1297.07   \n",
       "4                1            0     DTF    Falcons   33  1300.00  1279.62   \n",
       "...            ...          ...     ...        ...  ...      ...      ...   \n",
       "126309         100            1     CLE  Cavaliers   82  1723.41  1704.39   \n",
       "126310         102            1     GSW   Warriors  104  1809.98  1813.63   \n",
       "126311         101            1     CLE  Cavaliers   91  1704.39  1700.74   \n",
       "126312         102            1     CLE  Cavaliers   97  1700.74  1692.09   \n",
       "126313         103            1     GSW   Warriors  105  1813.63  1822.29   \n",
       "\n",
       "        win_equiv opp_id   opp_fran  opp_pts  opp_elo_i  opp_elo_n  \\\n",
       "0           40.29    NYK     Knicks       68    1300.00    1306.72   \n",
       "1           41.71    TRH    Huskies       66    1300.00    1293.28   \n",
       "2           42.01    NYK     Knicks       47    1306.72    1297.07   \n",
       "3           40.69    CHS      Stags       63    1300.00    1309.65   \n",
       "4           38.86    WSC   Capitols       50    1300.00    1320.38   \n",
       "...           ...    ...        ...      ...        ...        ...   \n",
       "126309      60.31    GSW   Warriors      103    1790.96    1809.98   \n",
       "126310      68.01    CLE  Cavaliers       91    1704.39    1700.74   \n",
       "126311      60.01    GSW   Warriors      104    1809.98    1813.63   \n",
       "126312      59.29    GSW   Warriors      105    1813.63    1822.29   \n",
       "126313      68.52    CLE  Cavaliers       97    1700.74    1692.09   \n",
       "\n",
       "       game_location game_result  forecast notes  \n",
       "0                  H           L      0.64   NaN  \n",
       "1                  A           W      0.36   NaN  \n",
       "2                  H           W      0.63   NaN  \n",
       "3                  A           L      0.37   NaN  \n",
       "4                  H           L      0.64   NaN  \n",
       "...              ...         ...       ...   ...  \n",
       "126309             H           L      0.55   NaN  \n",
       "126310             H           W      0.77   NaN  \n",
       "126311             A           L      0.23   NaN  \n",
       "126312             H           L      0.48   NaN  \n",
       "126313             A           W      0.52   NaN  \n",
       "\n",
       "[126314 rows x 23 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "44fbae74",
   "metadata": {},
   "outputs": [],
   "source": [
    "nba[\"difference\"] = df.pts - df.opp_pts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "8d638c6b",
   "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>gameorder</th>\n",
       "      <th>game_id</th>\n",
       "      <th>lg_id</th>\n",
       "      <th>_iscopy</th>\n",
       "      <th>year_id</th>\n",
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       "      <th>team_id</th>\n",
       "      <th>fran_id</th>\n",
       "      <th>pts</th>\n",
       "      <th>elo_i</th>\n",
       "      <th>elo_n</th>\n",
       "      <th>win_equiv</th>\n",
       "      <th>opp_id</th>\n",
       "      <th>opp_fran</th>\n",
       "      <th>opp_pts</th>\n",
       "      <th>opp_elo_i</th>\n",
       "      <th>opp_elo_n</th>\n",
       "      <th>game_location</th>\n",
       "      <th>game_result</th>\n",
       "      <th>forecast</th>\n",
       "      <th>notes</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>194611010TRH</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/1/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>TRH</td>\n",
       "      <td>Huskies</td>\n",
       "      <td>66</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1293.28</td>\n",
       "      <td>40.29</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>68</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1306.72</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>194611010TRH</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/1/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>68</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1306.72</td>\n",
       "      <td>41.71</td>\n",
       "      <td>TRH</td>\n",
       "      <td>Huskies</td>\n",
       "      <td>66</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1293.28</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>0.36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>194611020CHS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/2/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>CHS</td>\n",
       "      <td>Stags</td>\n",
       "      <td>63</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1309.65</td>\n",
       "      <td>42.01</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>47</td>\n",
       "      <td>1306.72</td>\n",
       "      <td>1297.07</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>194611020CHS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/2/1946</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>47</td>\n",
       "      <td>1306.72</td>\n",
       "      <td>1297.07</td>\n",
       "      <td>40.69</td>\n",
       "      <td>CHS</td>\n",
       "      <td>Stags</td>\n",
       "      <td>63</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1309.65</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>0.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>194611020DTF</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/2/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>DTF</td>\n",
       "      <td>Falcons</td>\n",
       "      <td>33</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1279.62</td>\n",
       "      <td>38.86</td>\n",
       "      <td>WSC</td>\n",
       "      <td>Capitols</td>\n",
       "      <td>50</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1320.38</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gameorder       game_id lg_id  _iscopy  year_id  date_game  seasongame  \\\n",
       "0          1  194611010TRH   NBA        0     1947  11/1/1946           1   \n",
       "1          1  194611010TRH   NBA        1     1947  11/1/1946           1   \n",
       "2          2  194611020CHS   NBA        0     1947  11/2/1946           1   \n",
       "3          2  194611020CHS   NBA        1     1947  11/2/1946           2   \n",
       "4          3  194611020DTF   NBA        0     1947  11/2/1946           1   \n",
       "\n",
       "   is_playoffs team_id  fran_id  pts    elo_i    elo_n  win_equiv opp_id  \\\n",
       "0            0     TRH  Huskies   66  1300.00  1293.28      40.29    NYK   \n",
       "1            0     NYK   Knicks   68  1300.00  1306.72      41.71    TRH   \n",
       "2            0     CHS    Stags   63  1300.00  1309.65      42.01    NYK   \n",
       "3            0     NYK   Knicks   47  1306.72  1297.07      40.69    CHS   \n",
       "4            0     DTF  Falcons   33  1300.00  1279.62      38.86    WSC   \n",
       "\n",
       "   opp_fran  opp_pts  opp_elo_i  opp_elo_n game_location game_result  \\\n",
       "0    Knicks       68    1300.00    1306.72             H           L   \n",
       "1   Huskies       66    1300.00    1293.28             A           W   \n",
       "2    Knicks       47    1306.72    1297.07             H           W   \n",
       "3     Stags       63    1300.00    1309.65             A           L   \n",
       "4  Capitols       50    1300.00    1320.38             H           L   \n",
       "\n",
       "   forecast notes  difference  \n",
       "0      0.64   NaN          -2  \n",
       "1      0.36   NaN           2  \n",
       "2      0.63   NaN          16  \n",
       "3      0.37   NaN         -16  \n",
       "4      0.64   NaN         -17  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "9b609f8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(126314, 24)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "e0f879cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba[\"difference\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "77c31fd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-68"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba[\"difference\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "5e4bcb6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba[\"difference\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "ec871f70",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>opp_elo_n</th>\n",
       "      <th>location</th>\n",
       "      <th>result</th>\n",
       "      <th>forecast</th>\n",
       "      <th>notes</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>194611010TRH</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/1/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>TRH</td>\n",
       "      <td>Huskies</td>\n",
       "      <td>66</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1293.28</td>\n",
       "      <td>40.29</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>68</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1306.72</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>194611010TRH</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/1/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>68</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1306.72</td>\n",
       "      <td>41.71</td>\n",
       "      <td>TRH</td>\n",
       "      <td>Huskies</td>\n",
       "      <td>66</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1293.28</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>0.36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>194611020CHS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/2/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>CHS</td>\n",
       "      <td>Stags</td>\n",
       "      <td>63</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1309.65</td>\n",
       "      <td>42.01</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>47</td>\n",
       "      <td>1306.72</td>\n",
       "      <td>1297.07</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>194611020CHS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/2/1946</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>47</td>\n",
       "      <td>1306.72</td>\n",
       "      <td>1297.07</td>\n",
       "      <td>40.69</td>\n",
       "      <td>CHS</td>\n",
       "      <td>Stags</td>\n",
       "      <td>63</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1309.65</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>0.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>194611020DTF</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/2/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>DTF</td>\n",
       "      <td>Falcons</td>\n",
       "      <td>33</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1279.62</td>\n",
       "      <td>38.86</td>\n",
       "      <td>WSC</td>\n",
       "      <td>Capitols</td>\n",
       "      <td>50</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>1320.38</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-17</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",
       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126309</th>\n",
       "      <td>63155</td>\n",
       "      <td>201506110CLE</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>6/11/2015</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>82</td>\n",
       "      <td>1723.41</td>\n",
       "      <td>1704.39</td>\n",
       "      <td>60.31</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>103</td>\n",
       "      <td>1790.96</td>\n",
       "      <td>1809.98</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126310</th>\n",
       "      <td>63156</td>\n",
       "      <td>201506140GSW</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>6/14/2015</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>104</td>\n",
       "      <td>1809.98</td>\n",
       "      <td>1813.63</td>\n",
       "      <td>68.01</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>91</td>\n",
       "      <td>1704.39</td>\n",
       "      <td>1700.74</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126311</th>\n",
       "      <td>63156</td>\n",
       "      <td>201506140GSW</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>6/14/2015</td>\n",
       "      <td>101</td>\n",
       "      <td>1</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>91</td>\n",
       "      <td>1704.39</td>\n",
       "      <td>1700.74</td>\n",
       "      <td>60.01</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>104</td>\n",
       "      <td>1809.98</td>\n",
       "      <td>1813.63</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>0.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126312</th>\n",
       "      <td>63157</td>\n",
       "      <td>201506170CLE</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>6/16/2015</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>97</td>\n",
       "      <td>1700.74</td>\n",
       "      <td>1692.09</td>\n",
       "      <td>59.29</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>105</td>\n",
       "      <td>1813.63</td>\n",
       "      <td>1822.29</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126313</th>\n",
       "      <td>63157</td>\n",
       "      <td>201506170CLE</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>6/16/2015</td>\n",
       "      <td>103</td>\n",
       "      <td>1</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>105</td>\n",
       "      <td>1813.63</td>\n",
       "      <td>1822.29</td>\n",
       "      <td>68.52</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>97</td>\n",
       "      <td>1700.74</td>\n",
       "      <td>1692.09</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>0.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>126314 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        gameorder       game_id lg_id  _iscopy  year_id  date_game  \\\n",
       "0               1  194611010TRH   NBA        0     1947  11/1/1946   \n",
       "1               1  194611010TRH   NBA        1     1947  11/1/1946   \n",
       "2               2  194611020CHS   NBA        0     1947  11/2/1946   \n",
       "3               2  194611020CHS   NBA        1     1947  11/2/1946   \n",
       "4               3  194611020DTF   NBA        0     1947  11/2/1946   \n",
       "...           ...           ...   ...      ...      ...        ...   \n",
       "126309      63155  201506110CLE   NBA        0     2015  6/11/2015   \n",
       "126310      63156  201506140GSW   NBA        0     2015  6/14/2015   \n",
       "126311      63156  201506140GSW   NBA        1     2015  6/14/2015   \n",
       "126312      63157  201506170CLE   NBA        0     2015  6/16/2015   \n",
       "126313      63157  201506170CLE   NBA        1     2015  6/16/2015   \n",
       "\n",
       "        seasongame  is_playoffs team_id    fran_id  pts    elo_i    elo_n  \\\n",
       "0                1            0     TRH    Huskies   66  1300.00  1293.28   \n",
       "1                1            0     NYK     Knicks   68  1300.00  1306.72   \n",
       "2                1            0     CHS      Stags   63  1300.00  1309.65   \n",
       "3                2            0     NYK     Knicks   47  1306.72  1297.07   \n",
       "4                1            0     DTF    Falcons   33  1300.00  1279.62   \n",
       "...            ...          ...     ...        ...  ...      ...      ...   \n",
       "126309         100            1     CLE  Cavaliers   82  1723.41  1704.39   \n",
       "126310         102            1     GSW   Warriors  104  1809.98  1813.63   \n",
       "126311         101            1     CLE  Cavaliers   91  1704.39  1700.74   \n",
       "126312         102            1     CLE  Cavaliers   97  1700.74  1692.09   \n",
       "126313         103            1     GSW   Warriors  105  1813.63  1822.29   \n",
       "\n",
       "        win_equiv opp_id   opp_fran  opp_pts  opp_elo_i  opp_elo_n location  \\\n",
       "0           40.29    NYK     Knicks       68    1300.00    1306.72        H   \n",
       "1           41.71    TRH    Huskies       66    1300.00    1293.28        A   \n",
       "2           42.01    NYK     Knicks       47    1306.72    1297.07        H   \n",
       "3           40.69    CHS      Stags       63    1300.00    1309.65        A   \n",
       "4           38.86    WSC   Capitols       50    1300.00    1320.38        H   \n",
       "...           ...    ...        ...      ...        ...        ...      ...   \n",
       "126309      60.31    GSW   Warriors      103    1790.96    1809.98        H   \n",
       "126310      68.01    CLE  Cavaliers       91    1704.39    1700.74        H   \n",
       "126311      60.01    GSW   Warriors      104    1809.98    1813.63        A   \n",
       "126312      59.29    GSW   Warriors      105    1813.63    1822.29        H   \n",
       "126313      68.52    CLE  Cavaliers       97    1700.74    1692.09        A   \n",
       "\n",
       "       result  forecast notes  difference  \n",
       "0           L      0.64   NaN          -2  \n",
       "1           W      0.36   NaN           2  \n",
       "2           W      0.63   NaN          16  \n",
       "3           L      0.37   NaN         -16  \n",
       "4           L      0.64   NaN         -17  \n",
       "...       ...       ...   ...         ...  \n",
       "126309      L      0.55   NaN         -21  \n",
       "126310      W      0.77   NaN          13  \n",
       "126311      L      0.23   NaN         -13  \n",
       "126312      L      0.48   NaN          -8  \n",
       "126313      W      0.52   NaN           8  \n",
       "\n",
       "[126314 rows x 24 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "renamed_nba = nba.rename(columns = {\"game_result\":\"result\",\"game_location\":\"location\"})\n",
    "renamed_nba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "0d9f0c65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['elo_i', 'elo_n', 'opp_elo_i', 'opp_elo_n']"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elo_columns = [\"elo_i\",\"elo_n\",\"opp_elo_i\",\"opp_elo_n\"]\n",
    "elo_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "8dbdcb23",
   "metadata": {},
   "outputs": [],
   "source": [
    "nba.drop(elo_columns,inplace = True,axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "52d65f9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['gameorder', 'game_id', 'lg_id', '_iscopy', 'year_id', 'date_game',\n",
       "       'seasongame', 'is_playoffs', 'team_id', 'fran_id', 'pts', 'win_equiv',\n",
       "       'opp_id', 'opp_fran', 'opp_pts', 'game_location', 'game_result',\n",
       "       'forecast', 'notes', 'difference'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69312693",
   "metadata": {},
   "source": [
    "# Specifying Data Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "47c35ace",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 126314 entries, 0 to 126313\n",
      "Data columns (total 20 columns):\n",
      " #   Column         Non-Null Count   Dtype  \n",
      "---  ------         --------------   -----  \n",
      " 0   gameorder      126314 non-null  int64  \n",
      " 1   game_id        126314 non-null  object \n",
      " 2   lg_id          126314 non-null  object \n",
      " 3   _iscopy        126314 non-null  int64  \n",
      " 4   year_id        126314 non-null  int64  \n",
      " 5   date_game      126314 non-null  object \n",
      " 6   seasongame     126314 non-null  int64  \n",
      " 7   is_playoffs    126314 non-null  int64  \n",
      " 8   team_id        126314 non-null  object \n",
      " 9   fran_id        126314 non-null  object \n",
      " 10  pts            126314 non-null  int64  \n",
      " 11  win_equiv      126314 non-null  float64\n",
      " 12  opp_id         126314 non-null  object \n",
      " 13  opp_fran       126314 non-null  object \n",
      " 14  opp_pts        126314 non-null  int64  \n",
      " 15  game_location  126314 non-null  object \n",
      " 16  game_result    126314 non-null  object \n",
      " 17  forecast       126314 non-null  float64\n",
      " 18  notes          5424 non-null    object \n",
      " 19  difference     126314 non-null  int64  \n",
      "dtypes: float64(2), int64(8), object(10)\n",
      "memory usage: 19.3+ MB\n"
     ]
    }
   ],
   "source": [
    "nba.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "7f491ee4",
   "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>gameorder</th>\n",
       "      <th>game_id</th>\n",
       "      <th>lg_id</th>\n",
       "      <th>_iscopy</th>\n",
       "      <th>year_id</th>\n",
       "      <th>date_game</th>\n",
       "      <th>seasongame</th>\n",
       "      <th>is_playoffs</th>\n",
       "      <th>team_id</th>\n",
       "      <th>fran_id</th>\n",
       "      <th>pts</th>\n",
       "      <th>win_equiv</th>\n",
       "      <th>opp_id</th>\n",
       "      <th>opp_fran</th>\n",
       "      <th>opp_pts</th>\n",
       "      <th>game_location</th>\n",
       "      <th>game_result</th>\n",
       "      <th>forecast</th>\n",
       "      <th>notes</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>194611010TRH</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/1/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>TRH</td>\n",
       "      <td>Huskies</td>\n",
       "      <td>66</td>\n",
       "      <td>40.29</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>68</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>194611010TRH</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/1/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>68</td>\n",
       "      <td>41.71</td>\n",
       "      <td>TRH</td>\n",
       "      <td>Huskies</td>\n",
       "      <td>66</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>0.36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>194611020CHS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/2/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>CHS</td>\n",
       "      <td>Stags</td>\n",
       "      <td>63</td>\n",
       "      <td>42.01</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>47</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>194611020CHS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/2/1946</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>47</td>\n",
       "      <td>40.69</td>\n",
       "      <td>CHS</td>\n",
       "      <td>Stags</td>\n",
       "      <td>63</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>0.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>194611020DTF</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/2/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>DTF</td>\n",
       "      <td>Falcons</td>\n",
       "      <td>33</td>\n",
       "      <td>38.86</td>\n",
       "      <td>WSC</td>\n",
       "      <td>Capitols</td>\n",
       "      <td>50</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gameorder       game_id lg_id  _iscopy  year_id  date_game  seasongame  \\\n",
       "0          1  194611010TRH   NBA        0     1947  11/1/1946           1   \n",
       "1          1  194611010TRH   NBA        1     1947  11/1/1946           1   \n",
       "2          2  194611020CHS   NBA        0     1947  11/2/1946           1   \n",
       "3          2  194611020CHS   NBA        1     1947  11/2/1946           2   \n",
       "4          3  194611020DTF   NBA        0     1947  11/2/1946           1   \n",
       "\n",
       "   is_playoffs team_id  fran_id  pts  win_equiv opp_id  opp_fran  opp_pts  \\\n",
       "0            0     TRH  Huskies   66      40.29    NYK    Knicks       68   \n",
       "1            0     NYK   Knicks   68      41.71    TRH   Huskies       66   \n",
       "2            0     CHS    Stags   63      42.01    NYK    Knicks       47   \n",
       "3            0     NYK   Knicks   47      40.69    CHS     Stags       63   \n",
       "4            0     DTF  Falcons   33      38.86    WSC  Capitols       50   \n",
       "\n",
       "  game_location game_result  forecast notes  difference  \n",
       "0             H           L      0.64   NaN          -2  \n",
       "1             A           W      0.36   NaN           2  \n",
       "2             H           W      0.63   NaN          16  \n",
       "3             A           L      0.37   NaN         -16  \n",
       "4             H           L      0.64   NaN         -17  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "1fa9917d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1946-11-01\n",
       "1        1946-11-01\n",
       "2        1946-11-02\n",
       "3        1946-11-02\n",
       "4        1946-11-02\n",
       "            ...    \n",
       "126309   2015-06-11\n",
       "126310   2015-06-14\n",
       "126311   2015-06-14\n",
       "126312   2015-06-16\n",
       "126313   2015-06-16\n",
       "Name: date_game, Length: 126314, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba[\"date_game\"] = pd.to_datetime(nba[\"date_game\"])\n",
    "nba[\"date_game\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "ad016db3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>gameorder</th>\n",
       "      <th>game_id</th>\n",
       "      <th>lg_id</th>\n",
       "      <th>_iscopy</th>\n",
       "      <th>year_id</th>\n",
       "      <th>date_game</th>\n",
       "      <th>seasongame</th>\n",
       "      <th>is_playoffs</th>\n",
       "      <th>team_id</th>\n",
       "      <th>fran_id</th>\n",
       "      <th>pts</th>\n",
       "      <th>win_equiv</th>\n",
       "      <th>opp_id</th>\n",
       "      <th>opp_fran</th>\n",
       "      <th>opp_pts</th>\n",
       "      <th>game_location</th>\n",
       "      <th>game_result</th>\n",
       "      <th>forecast</th>\n",
       "      <th>notes</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>194611010TRH</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>1946-11-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>TRH</td>\n",
       "      <td>Huskies</td>\n",
       "      <td>66</td>\n",
       "      <td>40.29</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>68</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>194611010TRH</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>1947</td>\n",
       "      <td>1946-11-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>68</td>\n",
       "      <td>41.71</td>\n",
       "      <td>TRH</td>\n",
       "      <td>Huskies</td>\n",
       "      <td>66</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>0.36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>194611020CHS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>1946-11-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>CHS</td>\n",
       "      <td>Stags</td>\n",
       "      <td>63</td>\n",
       "      <td>42.01</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>47</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>194611020CHS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>1947</td>\n",
       "      <td>1946-11-02</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>47</td>\n",
       "      <td>40.69</td>\n",
       "      <td>CHS</td>\n",
       "      <td>Stags</td>\n",
       "      <td>63</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>0.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>194611020DTF</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>1946-11-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>DTF</td>\n",
       "      <td>Falcons</td>\n",
       "      <td>33</td>\n",
       "      <td>38.86</td>\n",
       "      <td>WSC</td>\n",
       "      <td>Capitols</td>\n",
       "      <td>50</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gameorder       game_id lg_id  _iscopy  year_id  date_game  seasongame  \\\n",
       "0          1  194611010TRH   NBA        0     1947 1946-11-01           1   \n",
       "1          1  194611010TRH   NBA        1     1947 1946-11-01           1   \n",
       "2          2  194611020CHS   NBA        0     1947 1946-11-02           1   \n",
       "3          2  194611020CHS   NBA        1     1947 1946-11-02           2   \n",
       "4          3  194611020DTF   NBA        0     1947 1946-11-02           1   \n",
       "\n",
       "   is_playoffs team_id  fran_id  pts  win_equiv opp_id  opp_fran  opp_pts  \\\n",
       "0            0     TRH  Huskies   66      40.29    NYK    Knicks       68   \n",
       "1            0     NYK   Knicks   68      41.71    TRH   Huskies       66   \n",
       "2            0     CHS    Stags   63      42.01    NYK    Knicks       47   \n",
       "3            0     NYK   Knicks   47      40.69    CHS     Stags       63   \n",
       "4            0     DTF  Falcons   33      38.86    WSC  Capitols       50   \n",
       "\n",
       "  game_location game_result  forecast notes  difference  \n",
       "0             H           L      0.64   NaN          -2  \n",
       "1             A           W      0.36   NaN           2  \n",
       "2             H           W      0.63   NaN          16  \n",
       "3             A           L      0.37   NaN         -16  \n",
       "4             H           L      0.64   NaN         -17  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "50d1628b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 126314 entries, 0 to 126313\n",
      "Data columns (total 20 columns):\n",
      " #   Column         Non-Null Count   Dtype         \n",
      "---  ------         --------------   -----         \n",
      " 0   gameorder      126314 non-null  int64         \n",
      " 1   game_id        126314 non-null  object        \n",
      " 2   lg_id          126314 non-null  object        \n",
      " 3   _iscopy        126314 non-null  int64         \n",
      " 4   year_id        126314 non-null  int64         \n",
      " 5   date_game      126314 non-null  datetime64[ns]\n",
      " 6   seasongame     126314 non-null  int64         \n",
      " 7   is_playoffs    126314 non-null  int64         \n",
      " 8   team_id        126314 non-null  object        \n",
      " 9   fran_id        126314 non-null  object        \n",
      " 10  pts            126314 non-null  int64         \n",
      " 11  win_equiv      126314 non-null  float64       \n",
      " 12  opp_id         126314 non-null  object        \n",
      " 13  opp_fran       126314 non-null  object        \n",
      " 14  opp_pts        126314 non-null  int64         \n",
      " 15  game_location  126314 non-null  object        \n",
      " 16  game_result    126314 non-null  object        \n",
      " 17  forecast       126314 non-null  float64       \n",
      " 18  notes          5424 non-null    object        \n",
      " 19  difference     126314 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(2), int64(8), object(9)\n",
      "memory usage: 19.3+ MB\n"
     ]
    }
   ],
   "source": [
    "nba.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "04aca06a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba[\"game_location\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "02b4f0f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "H    63138\n",
       "A    63138\n",
       "N       38\n",
       "Name: game_location, dtype: int64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba[\"game_location\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "f5c14011",
   "metadata": {},
   "outputs": [],
   "source": [
    "nba[\"game_location\"] = pd.Categorical(nba[\"game_location\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "b0206000",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CategoricalDtype(categories=['A', 'H', 'N'], ordered=False)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba[\"game_location\"].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "11ce3a94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 126314 entries, 0 to 126313\n",
      "Data columns (total 20 columns):\n",
      " #   Column         Non-Null Count   Dtype         \n",
      "---  ------         --------------   -----         \n",
      " 0   gameorder      126314 non-null  int64         \n",
      " 1   game_id        126314 non-null  object        \n",
      " 2   lg_id          126314 non-null  object        \n",
      " 3   _iscopy        126314 non-null  int64         \n",
      " 4   year_id        126314 non-null  int64         \n",
      " 5   date_game      126314 non-null  datetime64[ns]\n",
      " 6   seasongame     126314 non-null  int64         \n",
      " 7   is_playoffs    126314 non-null  int64         \n",
      " 8   team_id        126314 non-null  object        \n",
      " 9   fran_id        126314 non-null  object        \n",
      " 10  pts            126314 non-null  int64         \n",
      " 11  win_equiv      126314 non-null  float64       \n",
      " 12  opp_id         126314 non-null  object        \n",
      " 13  opp_fran       126314 non-null  object        \n",
      " 14  opp_pts        126314 non-null  int64         \n",
      " 15  game_location  126314 non-null  category      \n",
      " 16  game_result    126314 non-null  object        \n",
      " 17  forecast       126314 non-null  float64       \n",
      " 18  notes          5424 non-null    object        \n",
      " 19  difference     126314 non-null  int64         \n",
      "dtypes: category(1), datetime64[ns](1), float64(2), int64(8), object(8)\n",
      "memory usage: 18.4+ MB\n"
     ]
    }
   ],
   "source": [
    "nba.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "26eda550",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5424, 20)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows_without_missing_data = nba.dropna()\n",
    "rows_without_missing_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "247f48e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(126314, 19)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_without_missing_columns = nba.dropna(axis=1)\n",
    "data_without_missing_columns.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "0f6e80b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gameorder</th>\n",
       "      <th>game_id</th>\n",
       "      <th>lg_id</th>\n",
       "      <th>_iscopy</th>\n",
       "      <th>year_id</th>\n",
       "      <th>date_game</th>\n",
       "      <th>seasongame</th>\n",
       "      <th>is_playoffs</th>\n",
       "      <th>team_id</th>\n",
       "      <th>fran_id</th>\n",
       "      <th>pts</th>\n",
       "      <th>win_equiv</th>\n",
       "      <th>opp_id</th>\n",
       "      <th>opp_fran</th>\n",
       "      <th>opp_pts</th>\n",
       "      <th>game_location</th>\n",
       "      <th>game_result</th>\n",
       "      <th>forecast</th>\n",
       "      <th>notes</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>194611010TRH</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>1946-11-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>TRH</td>\n",
       "      <td>Huskies</td>\n",
       "      <td>66</td>\n",
       "      <td>40.29</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>68</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>194611010TRH</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>1947</td>\n",
       "      <td>1946-11-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>68</td>\n",
       "      <td>41.71</td>\n",
       "      <td>TRH</td>\n",
       "      <td>Huskies</td>\n",
       "      <td>66</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>0.36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>194611020CHS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>1946-11-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>CHS</td>\n",
       "      <td>Stags</td>\n",
       "      <td>63</td>\n",
       "      <td>42.01</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>47</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>194611020CHS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>1947</td>\n",
       "      <td>1946-11-02</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>47</td>\n",
       "      <td>40.69</td>\n",
       "      <td>CHS</td>\n",
       "      <td>Stags</td>\n",
       "      <td>63</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>0.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>194611020DTF</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>1946-11-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>DTF</td>\n",
       "      <td>Falcons</td>\n",
       "      <td>33</td>\n",
       "      <td>38.86</td>\n",
       "      <td>WSC</td>\n",
       "      <td>Capitols</td>\n",
       "      <td>50</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-17</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",
       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126309</th>\n",
       "      <td>63155</td>\n",
       "      <td>201506110CLE</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015-06-11</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>82</td>\n",
       "      <td>60.31</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>103</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126310</th>\n",
       "      <td>63156</td>\n",
       "      <td>201506140GSW</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015-06-14</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>104</td>\n",
       "      <td>68.01</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>91</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126311</th>\n",
       "      <td>63156</td>\n",
       "      <td>201506140GSW</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015-06-14</td>\n",
       "      <td>101</td>\n",
       "      <td>1</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>91</td>\n",
       "      <td>60.01</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>104</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>0.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126312</th>\n",
       "      <td>63157</td>\n",
       "      <td>201506170CLE</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015-06-16</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>97</td>\n",
       "      <td>59.29</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>105</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126313</th>\n",
       "      <td>63157</td>\n",
       "      <td>201506170CLE</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015-06-16</td>\n",
       "      <td>103</td>\n",
       "      <td>1</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>105</td>\n",
       "      <td>68.52</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>97</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>0.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>126314 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        gameorder       game_id lg_id  _iscopy  year_id  date_game  \\\n",
       "0               1  194611010TRH   NBA        0     1947 1946-11-01   \n",
       "1               1  194611010TRH   NBA        1     1947 1946-11-01   \n",
       "2               2  194611020CHS   NBA        0     1947 1946-11-02   \n",
       "3               2  194611020CHS   NBA        1     1947 1946-11-02   \n",
       "4               3  194611020DTF   NBA        0     1947 1946-11-02   \n",
       "...           ...           ...   ...      ...      ...        ...   \n",
       "126309      63155  201506110CLE   NBA        0     2015 2015-06-11   \n",
       "126310      63156  201506140GSW   NBA        0     2015 2015-06-14   \n",
       "126311      63156  201506140GSW   NBA        1     2015 2015-06-14   \n",
       "126312      63157  201506170CLE   NBA        0     2015 2015-06-16   \n",
       "126313      63157  201506170CLE   NBA        1     2015 2015-06-16   \n",
       "\n",
       "        seasongame  is_playoffs team_id    fran_id  pts  win_equiv opp_id  \\\n",
       "0                1            0     TRH    Huskies   66      40.29    NYK   \n",
       "1                1            0     NYK     Knicks   68      41.71    TRH   \n",
       "2                1            0     CHS      Stags   63      42.01    NYK   \n",
       "3                2            0     NYK     Knicks   47      40.69    CHS   \n",
       "4                1            0     DTF    Falcons   33      38.86    WSC   \n",
       "...            ...          ...     ...        ...  ...        ...    ...   \n",
       "126309         100            1     CLE  Cavaliers   82      60.31    GSW   \n",
       "126310         102            1     GSW   Warriors  104      68.01    CLE   \n",
       "126311         101            1     CLE  Cavaliers   91      60.01    GSW   \n",
       "126312         102            1     CLE  Cavaliers   97      59.29    GSW   \n",
       "126313         103            1     GSW   Warriors  105      68.52    CLE   \n",
       "\n",
       "         opp_fran  opp_pts game_location game_result  forecast notes  \\\n",
       "0          Knicks       68             H           L      0.64   NaN   \n",
       "1         Huskies       66             A           W      0.36   NaN   \n",
       "2          Knicks       47             H           W      0.63   NaN   \n",
       "3           Stags       63             A           L      0.37   NaN   \n",
       "4        Capitols       50             H           L      0.64   NaN   \n",
       "...           ...      ...           ...         ...       ...   ...   \n",
       "126309   Warriors      103             H           L      0.55   NaN   \n",
       "126310  Cavaliers       91             H           W      0.77   NaN   \n",
       "126311   Warriors      104             A           L      0.23   NaN   \n",
       "126312   Warriors      105             H           L      0.48   NaN   \n",
       "126313  Cavaliers       97             A           W      0.52   NaN   \n",
       "\n",
       "        difference  \n",
       "0               -2  \n",
       "1                2  \n",
       "2               16  \n",
       "3              -16  \n",
       "4              -17  \n",
       "...            ...  \n",
       "126309         -21  \n",
       "126310          13  \n",
       "126311         -13  \n",
       "126312          -8  \n",
       "126313           8  \n",
       "\n",
       "[126314 rows x 20 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_with_default_notes = nba.copy()\n",
    "data_with_default_notes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "72b4c4c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_with_default_notes[\"notes\"].fillna(\n",
    "    value = \"no notes at all\",\n",
    "    inplace = True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "c0e7796b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count              126314\n",
       "unique                232\n",
       "top       no notes at all\n",
       "freq               120890\n",
       "Name: notes, dtype: object"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_with_default_notes[\"notes\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3017259",
   "metadata": {},
   "source": [
    "# Invalid Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "c89e7104",
   "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>gameorder</th>\n",
       "      <th>_iscopy</th>\n",
       "      <th>year_id</th>\n",
       "      <th>seasongame</th>\n",
       "      <th>is_playoffs</th>\n",
       "      <th>pts</th>\n",
       "      <th>win_equiv</th>\n",
       "      <th>opp_pts</th>\n",
       "      <th>forecast</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>126314.00</td>\n",
       "      <td>126314.0</td>\n",
       "      <td>126314.00</td>\n",
       "      <td>126314.00</td>\n",
       "      <td>126314.00</td>\n",
       "      <td>126314.00</td>\n",
       "      <td>126314.00</td>\n",
       "      <td>126314.00</td>\n",
       "      <td>126314.00</td>\n",
       "      <td>126314.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>31579.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1988.20</td>\n",
       "      <td>43.53</td>\n",
       "      <td>0.06</td>\n",
       "      <td>102.73</td>\n",
       "      <td>41.71</td>\n",
       "      <td>102.73</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>18231.93</td>\n",
       "      <td>0.5</td>\n",
       "      <td>17.58</td>\n",
       "      <td>25.38</td>\n",
       "      <td>0.24</td>\n",
       "      <td>14.81</td>\n",
       "      <td>10.63</td>\n",
       "      <td>14.81</td>\n",
       "      <td>0.22</td>\n",
       "      <td>13.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1947.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-68.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>15790.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1975.00</td>\n",
       "      <td>22.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>93.00</td>\n",
       "      <td>34.10</td>\n",
       "      <td>93.00</td>\n",
       "      <td>0.33</td>\n",
       "      <td>-9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>31579.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1990.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>103.00</td>\n",
       "      <td>42.11</td>\n",
       "      <td>103.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>47368.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2003.00</td>\n",
       "      <td>65.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>112.00</td>\n",
       "      <td>49.64</td>\n",
       "      <td>112.00</td>\n",
       "      <td>0.67</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>63157.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2015.00</td>\n",
       "      <td>108.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>186.00</td>\n",
       "      <td>71.11</td>\n",
       "      <td>186.00</td>\n",
       "      <td>0.98</td>\n",
       "      <td>68.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       gameorder   _iscopy    year_id  seasongame  is_playoffs        pts  \\\n",
       "count  126314.00  126314.0  126314.00   126314.00    126314.00  126314.00   \n",
       "mean    31579.00       0.5    1988.20       43.53         0.06     102.73   \n",
       "std     18231.93       0.5      17.58       25.38         0.24      14.81   \n",
       "min         1.00       0.0    1947.00        1.00         0.00       0.00   \n",
       "25%     15790.00       0.0    1975.00       22.00         0.00      93.00   \n",
       "50%     31579.00       0.5    1990.00       43.00         0.00     103.00   \n",
       "75%     47368.00       1.0    2003.00       65.00         0.00     112.00   \n",
       "max     63157.00       1.0    2015.00      108.00         1.00     186.00   \n",
       "\n",
       "       win_equiv    opp_pts   forecast  difference  \n",
       "count  126314.00  126314.00  126314.00   126314.00  \n",
       "mean       41.71     102.73       0.50        0.00  \n",
       "std        10.63      14.81       0.22       13.37  \n",
       "min        10.15       0.00       0.02      -68.00  \n",
       "25%        34.10      93.00       0.33       -9.00  \n",
       "50%        42.11     103.00       0.50        0.00  \n",
       "75%        49.64     112.00       0.67        9.00  \n",
       "max        71.11     186.00       0.98       68.00  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "e6f86bb4",
   "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>gameorder</th>\n",
       "      <th>game_id</th>\n",
       "      <th>lg_id</th>\n",
       "      <th>_iscopy</th>\n",
       "      <th>year_id</th>\n",
       "      <th>date_game</th>\n",
       "      <th>seasongame</th>\n",
       "      <th>is_playoffs</th>\n",
       "      <th>team_id</th>\n",
       "      <th>fran_id</th>\n",
       "      <th>pts</th>\n",
       "      <th>win_equiv</th>\n",
       "      <th>opp_id</th>\n",
       "      <th>opp_fran</th>\n",
       "      <th>opp_pts</th>\n",
       "      <th>game_location</th>\n",
       "      <th>game_result</th>\n",
       "      <th>forecast</th>\n",
       "      <th>notes</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26684</th>\n",
       "      <td>13343</td>\n",
       "      <td>197210260VIR</td>\n",
       "      <td>ABA</td>\n",
       "      <td>1</td>\n",
       "      <td>1973</td>\n",
       "      <td>1972-10-26</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>DNR</td>\n",
       "      <td>Nuggets</td>\n",
       "      <td>0</td>\n",
       "      <td>40.41</td>\n",
       "      <td>VIR</td>\n",
       "      <td>Squires</td>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>0.33</td>\n",
       "      <td>at Richmond VA; forfeit to VIR</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       gameorder       game_id lg_id  _iscopy  year_id  date_game  seasongame  \\\n",
       "26684      13343  197210260VIR   ABA        1     1973 1972-10-26           7   \n",
       "\n",
       "       is_playoffs team_id  fran_id  pts  win_equiv opp_id opp_fran  opp_pts  \\\n",
       "26684            0     DNR  Nuggets    0      40.41    VIR  Squires        2   \n",
       "\n",
       "      game_location game_result  forecast                           notes  \\\n",
       "26684             A           L      0.33  at Richmond VA; forfeit to VIR   \n",
       "\n",
       "       difference  \n",
       "26684          -2  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba[nba[\"pts\"] == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b4c6dc2",
   "metadata": {},
   "source": [
    "# Inconsistent Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "6370d08a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba[(nba[\"pts\"] > nba[\"opp_pts\"]) & (nba[\"game_result\"] != \"W\")].empty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "8541ae9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba[(nba[\"pts\"] < nba[\"opp_pts\"]) & (nba[\"game_result\"] != \"L\")].empty"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85673593",
   "metadata": {},
   "source": [
    "# Combining Multiple DataSets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "ca2c94e9",
   "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>revenue</th>\n",
       "      <th>employee_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>7000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barcelona</th>\n",
       "      <td>3000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           revenue  employee_count\n",
       "New York      7000               2\n",
       "Barcelona     3000               2"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "further_city_data = pd.DataFrame(\n",
    "    {\"revenue\":[7000,3000],\"employee_count\":[2,2]},\n",
    "    index = [\"New York\",\"Barcelona\"]\n",
    ")\n",
    "further_city_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "3bc2dac0",
   "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>revenue</th>\n",
       "      <th>employee_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Amsterdam</th>\n",
       "      <td>4200</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokyo</th>\n",
       "      <td>6500</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toronto</th>\n",
       "      <td>8000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           revenue  employee_count\n",
       "Amsterdam     4200             5.0\n",
       "Tokyo         6500             8.0\n",
       "Toronto       8000             NaN"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "f82dc034",
   "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>revenue</th>\n",
       "      <th>employee_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Amsterdam</th>\n",
       "      <td>4200</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokyo</th>\n",
       "      <td>6500</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toronto</th>\n",
       "      <td>8000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>7000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barcelona</th>\n",
       "      <td>3000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           revenue  employee_count\n",
       "Amsterdam     4200             5.0\n",
       "Tokyo         6500             8.0\n",
       "Toronto       8000             NaN\n",
       "New York      7000             2.0\n",
       "Barcelona     3000             2.0"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_city_data = pd.concat([city_data,further_city_data],sort = False)\n",
    "all_city_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "6be406cf",
   "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>country</th>\n",
       "      <th>capital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Amsterdam</th>\n",
       "      <td>Holland</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokyo</th>\n",
       "      <td>Japan</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rotterdam</th>\n",
       "      <td>Holland</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toronto</th>\n",
       "      <td>Canada</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barcelona</th>\n",
       "      <td>Spain</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           country  capital\n",
       "Amsterdam  Holland        1\n",
       "Tokyo        Japan        1\n",
       "Rotterdam  Holland        0\n",
       "Toronto     Canada        0\n",
       "Barcelona    Spain        0"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_countries = pd.DataFrame({\n",
    "    \"country\":[\"Holland\",\"Japan\",\"Holland\",\"Canada\",\"Spain\"],\n",
    "    \"capital\":[1,1,0,0,0]},\n",
    "    index = [\"Amsterdam\",\"Tokyo\",\"Rotterdam\",\"Toronto\",\"Barcelona\"]\n",
    ")\n",
    "city_countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "9d055325",
   "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",
       "    .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>revenue</th>\n",
       "      <th>employee_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Amsterdam</th>\n",
       "      <td>4200</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokyo</th>\n",
       "      <td>6500</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toronto</th>\n",
       "      <td>8000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>7000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barcelona</th>\n",
       "      <td>3000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           revenue  employee_count\n",
       "Amsterdam     4200             5.0\n",
       "Tokyo         6500             8.0\n",
       "Toronto       8000             NaN\n",
       "New York      7000             2.0\n",
       "Barcelona     3000             2.0"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_city_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "7198e7f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>revenue</th>\n",
       "      <th>employee_count</th>\n",
       "      <th>country</th>\n",
       "      <th>capital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Amsterdam</th>\n",
       "      <td>4200.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Holland</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokyo</th>\n",
       "      <td>6500.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toronto</th>\n",
       "      <td>8000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>7000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barcelona</th>\n",
       "      <td>3000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Spain</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rotterdam</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Holland</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           revenue  employee_count  country  capital\n",
       "Amsterdam   4200.0             5.0  Holland      1.0\n",
       "Tokyo       6500.0             8.0    Japan      1.0\n",
       "Toronto     8000.0             NaN   Canada      0.0\n",
       "New York    7000.0             2.0      NaN      NaN\n",
       "Barcelona   3000.0             2.0    Spain      0.0\n",
       "Rotterdam      NaN             NaN  Holland      0.0"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities = pd.concat([all_city_data,city_countries],axis = 1,sort = False)\n",
    "cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "f35c5e00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>revenue</th>\n",
       "      <th>employee_count</th>\n",
       "      <th>country</th>\n",
       "      <th>capital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Amsterdam</th>\n",
       "      <td>4200</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Holland</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokyo</th>\n",
       "      <td>6500</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toronto</th>\n",
       "      <td>8000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barcelona</th>\n",
       "      <td>3000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Spain</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           revenue  employee_count  country  capital\n",
       "Amsterdam     4200             5.0  Holland        1\n",
       "Tokyo         6500             8.0    Japan        1\n",
       "Toronto       8000             NaN   Canada        0\n",
       "Barcelona     3000             2.0    Spain        0"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([all_city_data,city_countries],axis = 1,sort = False, join = \"inner\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "6ebaab3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population_millions</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Holland</th>\n",
       "      <td>17</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>127</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>37</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         population_millions      continent\n",
       "Holland                   17         Europe\n",
       "Japan                    127           Asia\n",
       "Canada                    37  North America"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries = pd.DataFrame({\n",
    "    \"population_millions\":[17,127,37],\n",
    "    \"continent\":[\"Europe\",\"Asia\",\"North America\"]},\n",
    "    index = [\"Holland\",\"Japan\",\"Canada\"]\n",
    ")\n",
    "countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "fa8c936e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>revenue</th>\n",
       "      <th>employee_count</th>\n",
       "      <th>country</th>\n",
       "      <th>capital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Amsterdam</th>\n",
       "      <td>4200.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Holland</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokyo</th>\n",
       "      <td>6500.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toronto</th>\n",
       "      <td>8000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>7000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barcelona</th>\n",
       "      <td>3000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Spain</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rotterdam</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Holland</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           revenue  employee_count  country  capital\n",
       "Amsterdam   4200.0             5.0  Holland      1.0\n",
       "Tokyo       6500.0             8.0    Japan      1.0\n",
       "Toronto     8000.0             NaN   Canada      0.0\n",
       "New York    7000.0             2.0      NaN      NaN\n",
       "Barcelona   3000.0             2.0    Spain      0.0\n",
       "Rotterdam      NaN             NaN  Holland      0.0"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "69d67fa5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>revenue</th>\n",
       "      <th>employee_count</th>\n",
       "      <th>country</th>\n",
       "      <th>capital</th>\n",
       "      <th>population_millions</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Amsterdam</th>\n",
       "      <td>4200.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Holland</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rotterdam</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Holland</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokyo</th>\n",
       "      <td>6500.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1.0</td>\n",
       "      <td>127</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toronto</th>\n",
       "      <td>8000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           revenue  employee_count  country  capital  population_millions  \\\n",
       "Amsterdam   4200.0             5.0  Holland      1.0                   17   \n",
       "Rotterdam      NaN             NaN  Holland      0.0                   17   \n",
       "Tokyo       6500.0             8.0    Japan      1.0                  127   \n",
       "Toronto     8000.0             NaN   Canada      0.0                   37   \n",
       "\n",
       "               continent  \n",
       "Amsterdam         Europe  \n",
       "Rotterdam         Europe  \n",
       "Tokyo               Asia  \n",
       "Toronto    North America  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(cities,countries,left_on = \"country\",right_index = True,how = \"inner\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "13abee44",
   "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>revenue</th>\n",
       "      <th>employee_count</th>\n",
       "      <th>country</th>\n",
       "      <th>capital</th>\n",
       "      <th>population_millions</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Amsterdam</th>\n",
       "      <td>4200.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Holland</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokyo</th>\n",
       "      <td>6500.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toronto</th>\n",
       "      <td>8000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>7000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barcelona</th>\n",
       "      <td>3000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Spain</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rotterdam</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Holland</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           revenue  employee_count  country  capital  population_millions  \\\n",
       "Amsterdam   4200.0             5.0  Holland      1.0                 17.0   \n",
       "Tokyo       6500.0             8.0    Japan      1.0                127.0   \n",
       "Toronto     8000.0             NaN   Canada      0.0                 37.0   \n",
       "New York    7000.0             2.0      NaN      NaN                  NaN   \n",
       "Barcelona   3000.0             2.0    Spain      0.0                  NaN   \n",
       "Rotterdam      NaN             NaN  Holland      0.0                 17.0   \n",
       "\n",
       "               continent  \n",
       "Amsterdam         Europe  \n",
       "Tokyo               Asia  \n",
       "Toronto    North America  \n",
       "New York             NaN  \n",
       "Barcelona            NaN  \n",
       "Rotterdam         Europe  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(cities,countries,left_on = \"country\",right_index = True,how = \"left\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b50e489",
   "metadata": {},
   "source": [
    "# Visualizing Pandas DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "e5b94505",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "41881828",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gameorder</th>\n",
       "      <th>game_id</th>\n",
       "      <th>lg_id</th>\n",
       "      <th>_iscopy</th>\n",
       "      <th>year_id</th>\n",
       "      <th>date_game</th>\n",
       "      <th>seasongame</th>\n",
       "      <th>is_playoffs</th>\n",
       "      <th>team_id</th>\n",
       "      <th>fran_id</th>\n",
       "      <th>pts</th>\n",
       "      <th>win_equiv</th>\n",
       "      <th>opp_id</th>\n",
       "      <th>opp_fran</th>\n",
       "      <th>opp_pts</th>\n",
       "      <th>game_location</th>\n",
       "      <th>game_result</th>\n",
       "      <th>forecast</th>\n",
       "      <th>notes</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>194611010TRH</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>TRH</td>\n",
       "      <td>Huskies</td>\n",
       "      <td>66</td>\n",
       "      <td>40.29</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>68</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>194611010TRH</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>1947</td>\n",
       "      <td>1946-11-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>68</td>\n",
       "      <td>41.71</td>\n",
       "      <td>TRH</td>\n",
       "      <td>Huskies</td>\n",
       "      <td>66</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>0.36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>194611020CHS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>1946-11-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>CHS</td>\n",
       "      <td>Stags</td>\n",
       "      <td>63</td>\n",
       "      <td>42.01</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>47</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>194611020CHS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>1947</td>\n",
       "      <td>1946-11-02</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>47</td>\n",
       "      <td>40.69</td>\n",
       "      <td>CHS</td>\n",
       "      <td>Stags</td>\n",
       "      <td>63</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>0.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>194611020DTF</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>1946-11-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>DTF</td>\n",
       "      <td>Falcons</td>\n",
       "      <td>33</td>\n",
       "      <td>38.86</td>\n",
       "      <td>WSC</td>\n",
       "      <td>Capitols</td>\n",
       "      <td>50</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-17</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",
       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>126309</th>\n",
       "      <td>63155</td>\n",
       "      <td>201506110CLE</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015-06-11</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>82</td>\n",
       "      <td>60.31</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>103</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126310</th>\n",
       "      <td>63156</td>\n",
       "      <td>201506140GSW</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015-06-14</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>104</td>\n",
       "      <td>68.01</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>91</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126311</th>\n",
       "      <td>63156</td>\n",
       "      <td>201506140GSW</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015-06-14</td>\n",
       "      <td>101</td>\n",
       "      <td>1</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>91</td>\n",
       "      <td>60.01</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>104</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>0.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126312</th>\n",
       "      <td>63157</td>\n",
       "      <td>201506170CLE</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015-06-16</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>97</td>\n",
       "      <td>59.29</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>105</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126313</th>\n",
       "      <td>63157</td>\n",
       "      <td>201506170CLE</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015-06-16</td>\n",
       "      <td>103</td>\n",
       "      <td>1</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>105</td>\n",
       "      <td>68.52</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>97</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>0.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>126314 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        gameorder       game_id lg_id  _iscopy  year_id  date_game  \\\n",
       "0               1  194611010TRH   NBA        0     1947 1946-11-01   \n",
       "1               1  194611010TRH   NBA        1     1947 1946-11-01   \n",
       "2               2  194611020CHS   NBA        0     1947 1946-11-02   \n",
       "3               2  194611020CHS   NBA        1     1947 1946-11-02   \n",
       "4               3  194611020DTF   NBA        0     1947 1946-11-02   \n",
       "...           ...           ...   ...      ...      ...        ...   \n",
       "126309      63155  201506110CLE   NBA        0     2015 2015-06-11   \n",
       "126310      63156  201506140GSW   NBA        0     2015 2015-06-14   \n",
       "126311      63156  201506140GSW   NBA        1     2015 2015-06-14   \n",
       "126312      63157  201506170CLE   NBA        0     2015 2015-06-16   \n",
       "126313      63157  201506170CLE   NBA        1     2015 2015-06-16   \n",
       "\n",
       "        seasongame  is_playoffs team_id    fran_id  pts  win_equiv opp_id  \\\n",
       "0                1            0     TRH    Huskies   66      40.29    NYK   \n",
       "1                1            0     NYK     Knicks   68      41.71    TRH   \n",
       "2                1            0     CHS      Stags   63      42.01    NYK   \n",
       "3                2            0     NYK     Knicks   47      40.69    CHS   \n",
       "4                1            0     DTF    Falcons   33      38.86    WSC   \n",
       "...            ...          ...     ...        ...  ...        ...    ...   \n",
       "126309         100            1     CLE  Cavaliers   82      60.31    GSW   \n",
       "126310         102            1     GSW   Warriors  104      68.01    CLE   \n",
       "126311         101            1     CLE  Cavaliers   91      60.01    GSW   \n",
       "126312         102            1     CLE  Cavaliers   97      59.29    GSW   \n",
       "126313         103            1     GSW   Warriors  105      68.52    CLE   \n",
       "\n",
       "         opp_fran  opp_pts game_location game_result  forecast notes  \\\n",
       "0          Knicks       68             H           L      0.64   NaN   \n",
       "1         Huskies       66             A           W      0.36   NaN   \n",
       "2          Knicks       47             H           W      0.63   NaN   \n",
       "3           Stags       63             A           L      0.37   NaN   \n",
       "4        Capitols       50             H           L      0.64   NaN   \n",
       "...           ...      ...           ...         ...       ...   ...   \n",
       "126309   Warriors      103             H           L      0.55   NaN   \n",
       "126310  Cavaliers       91             H           W      0.77   NaN   \n",
       "126311   Warriors      104             A           L      0.23   NaN   \n",
       "126312   Warriors      105             H           L      0.48   NaN   \n",
       "126313  Cavaliers       97             A           W      0.52   NaN   \n",
       "\n",
       "        difference  \n",
       "0               -2  \n",
       "1                2  \n",
       "2               16  \n",
       "3              -16  \n",
       "4              -17  \n",
       "...            ...  \n",
       "126309         -21  \n",
       "126310          13  \n",
       "126311         -13  \n",
       "126312          -8  \n",
       "126313           8  \n",
       "\n",
       "[126314 rows x 20 columns]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "a6ece6f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year_id\n",
       "1947    4234\n",
       "1948    3815\n",
       "1949    5253\n",
       "1950    5924\n",
       "1951    6814\n",
       "        ... \n",
       "2011    9097\n",
       "2012    6872\n",
       "2013    9259\n",
       "2014    8084\n",
       "2015    7535\n",
       "Name: pts, Length: 69, dtype: int64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba[nba[\"fran_id\"] == \"Knicks\"].groupby(\"year_id\")[\"pts\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "84c2ec18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year_id'>"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nba[nba[\"fran_id\"] == \"Knicks\"].groupby(\"year_id\")[\"pts\"].sum().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "2e03d44a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lakers      6024\n",
       "Celtics     5997\n",
       "Knicks      5769\n",
       "Warriors    5657\n",
       "Pistons     5650\n",
       "Sixers      5644\n",
       "Hawks       5572\n",
       "Kings       5475\n",
       "Wizards     4582\n",
       "Spurs       4309\n",
       "Name: fran_id, dtype: int64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba[\"fran_id\"].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "a94468e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nba[\"fran_id\"].value_counts().head(10).plot(kind = \"bar\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "toc": {
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