nba/scrape_all_rookie_stats.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d865cff6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1950\n",
      "1951\n",
      "1952\n",
      "1953\n",
      "1954\n",
      "1955\n",
      "1956\n",
      "1957\n",
      "1958\n",
      "1959\n",
      "1960\n",
      "1961\n",
      "1962\n",
      "1963\n",
      "1964\n",
      "1965\n",
      "1966\n",
      "1967\n",
      "1968\n",
      "1969\n",
      "1970\n",
      "1971\n",
      "1972\n",
      "1973\n",
      "1974\n",
      "1975\n",
      "1976\n",
      "1977\n",
      "1978\n",
      "1979\n",
      "1980\n",
      "1981\n",
      "1982\n",
      "1983\n",
      "1984\n",
      "1985\n",
      "1986\n",
      "1987\n",
      "1988\n",
      "1989\n",
      "1990\n",
      "1991\n",
      "1992\n",
      "1993\n",
      "1994\n",
      "1995\n",
      "1996\n",
      "1997\n",
      "1998\n",
      "1999\n",
      "2000\n",
      "2001\n",
      "2002\n",
      "2003\n",
      "2004\n",
      "2005\n",
      "2006\n",
      "2007\n",
      "2008\n",
      "2009\n",
      "2010\n",
      "2011\n",
      "2012\n",
      "2013\n",
      "2014\n",
      "2015\n",
      "2016\n",
      "2017\n",
      "2018\n",
      "2019\n",
      "2020\n",
      "2021\n",
      "2022\n",
      "2023\n",
      "2024\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import time\n",
    "import numpy as np\n",
    "\n",
    "all_rookie_dfs = []\n",
    "\n",
    "for year in np.arange(1950,2025,1):\n",
    "    print(year)\n",
    "    URL_rookie_page = 'https://www.basketball-reference.com/leagues/NBA_%i_rookies-season-stats.html' % (year)\n",
    "    df = pd.read_html(URL_rookie_page)[0]\n",
    "    all_rookie_dfs.append( df )\n",
    "    # avoid bball jail:\n",
    "    time.sleep(4)\n",
    "    \n",
    "    \n",
    "import pickle\n",
    "with open('all_rookie_dfs.dat', 'wb') as f:\n",
    "    pickle.dump(all_rookie_dfs, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c43f12ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 4942 entries, 0 to 63\n",
      "Data columns (total 29 columns):\n",
      " #   Column                        Non-Null Count  Dtype \n",
      "---  ------                        --------------  ----- \n",
      " 0   (Unnamed: 0_level_0, Rk)      4752 non-null   object\n",
      " 1   (Unnamed: 1_level_0, Player)  4752 non-null   object\n",
      " 2   (Unnamed: 2_level_0, Debut)   4751 non-null   object\n",
      " 3   (Unnamed: 3_level_0, Age)     4752 non-null   object\n",
      " 4   (Unnamed: 4_level_0, Yrs)     4752 non-null   object\n",
      " 5   (Totals, G)                   4942 non-null   object\n",
      " 6   (Totals, MP)                  4792 non-null   object\n",
      " 7   (Totals, FG)                  4942 non-null   object\n",
      " 8   (Totals, FGA)                 4942 non-null   object\n",
      " 9   (Totals, 3P)                  3705 non-null   object\n",
      " 10  (Totals, 3PA)                 3705 non-null   object\n",
      " 11  (Totals, FT)                  4942 non-null   object\n",
      " 12  (Totals, FTA)                 4942 non-null   object\n",
      " 13  (Totals, ORB)                 4068 non-null   object\n",
      " 14  (Totals, TRB)                 4822 non-null   object\n",
      " 15  (Totals, AST)                 4942 non-null   object\n",
      " 16  (Totals, STL)                 4068 non-null   object\n",
      " 17  (Totals, BLK)                 4068 non-null   object\n",
      " 18  (Totals, TOV)                 3815 non-null   object\n",
      " 19  (Totals, PF)                  4942 non-null   object\n",
      " 20  (Totals, PTS)                 4942 non-null   object\n",
      " 21  (Shooting, FG%)               4900 non-null   object\n",
      " 22  (Shooting, 3P%)               3011 non-null   object\n",
      " 23  (Shooting, FT%)               4602 non-null   object\n",
      " 24  (Per Game, MP)                4792 non-null   object\n",
      " 25  (Per Game, PTS)               4942 non-null   object\n",
      " 26  (Per Game, TRB)               4822 non-null   object\n",
      " 27  (Per Game, AST)               4942 non-null   object\n",
      " 28  (SEASON, )                    4942 non-null   int64 \n",
      "dtypes: int64(1), object(28)\n",
      "memory usage: 1.1+ MB\n",
      "None\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0_level_0</th>\n",
       "      <th>Unnamed: 1_level_0</th>\n",
       "      <th>Unnamed: 2_level_0</th>\n",
       "      <th>Unnamed: 3_level_0</th>\n",
       "      <th>Unnamed: 4_level_0</th>\n",
       "      <th colspan=\"8\" halign=\"left\">Totals</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Shooting</th>\n",
       "      <th colspan=\"4\" halign=\"left\">Per Game</th>\n",
       "      <th>SEASON</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Rk</th>\n",
       "      <th>Player</th>\n",
       "      <th>Debut</th>\n",
       "      <th>Age</th>\n",
       "      <th>Yrs</th>\n",
       "      <th>G</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>3P</th>\n",
       "      <th>...</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3P%</th>\n",
       "      <th>FT%</th>\n",
       "      <th>MP</th>\n",
       "      <th>PTS</th>\n",
       "      <th>TRB</th>\n",
       "      <th>AST</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Cliff Barker</td>\n",
       "      <td>Nov 1, '49, INO vs. DNN</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102</td>\n",
       "      <td>274</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>279</td>\n",
       "      <td>.372</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Leo Barnhorst</td>\n",
       "      <td>Nov 1, '49, CHS vs. NYK</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>174</td>\n",
       "      <td>499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>192</td>\n",
       "      <td>438</td>\n",
       "      <td>.349</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.698</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Ed Bartels</td>\n",
       "      <td>Oct 29, '49, DNN @ TRI</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "      <td>86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>29</td>\n",
       "      <td>63</td>\n",
       "      <td>.256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.559</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Ralph Beard</td>\n",
       "      <td>Nov 1, '49, INO vs. DNN</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>340</td>\n",
       "      <td>936</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>132</td>\n",
       "      <td>895</td>\n",
       "      <td>.363</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.762</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Gene Berce</td>\n",
       "      <td>Oct 29, '49, TRI vs. DNN</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>.313</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1950</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",
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       "      <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>59</th>\n",
       "      <td>56</td>\n",
       "      <td>Jarace Walker</td>\n",
       "      <td>Oct 25, '23, IND vs. WAS</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>.333</td>\n",
       "      <td>.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>57</td>\n",
       "      <td>Cason Wallace</td>\n",
       "      <td>Oct 25, '23, OKC @ CHI</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>184</td>\n",
       "      <td>27</td>\n",
       "      <td>39</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>70</td>\n",
       "      <td>.692</td>\n",
       "      <td>.571</td>\n",
       "      <td>1.000</td>\n",
       "      <td>23.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>58</td>\n",
       "      <td>Victor Wembanyama</td>\n",
       "      <td>Oct 25, '23, SAS vs. DAL</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>233</td>\n",
       "      <td>52</td>\n",
       "      <td>118</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>150</td>\n",
       "      <td>.441</td>\n",
       "      <td>.293</td>\n",
       "      <td>.791</td>\n",
       "      <td>29.1</td>\n",
       "      <td>18.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>59</td>\n",
       "      <td>Cam Whitmore</td>\n",
       "      <td>Oct 25, '23, HOU @ ORL</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>.333</td>\n",
       "      <td>.000</td>\n",
       "      <td>.500</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>60</td>\n",
       "      <td>Jalen Wilson</td>\n",
       "      <td>Nov 1, '23, BRK @ MIA</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4942 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0_level_0 Unnamed: 1_level_0        Unnamed: 2_level_0  \\\n",
       "                   Rk             Player                     Debut   \n",
       "0                   1       Cliff Barker   Nov 1, '49, INO vs. DNN   \n",
       "1                   2      Leo Barnhorst   Nov 1, '49, CHS vs. NYK   \n",
       "2                   3         Ed Bartels    Oct 29, '49, DNN @ TRI   \n",
       "3                   4        Ralph Beard   Nov 1, '49, INO vs. DNN   \n",
       "4                   5         Gene Berce  Oct 29, '49, TRI vs. DNN   \n",
       "..                ...                ...                       ...   \n",
       "59                 56      Jarace Walker  Oct 25, '23, IND vs. WAS   \n",
       "60                 57      Cason Wallace    Oct 25, '23, OKC @ CHI   \n",
       "61                 58  Victor Wembanyama  Oct 25, '23, SAS vs. DAL   \n",
       "62                 59       Cam Whitmore    Oct 25, '23, HOU @ ORL   \n",
       "63                 60       Jalen Wilson     Nov 1, '23, BRK @ MIA   \n",
       "\n",
       "   Unnamed: 3_level_0 Unnamed: 4_level_0 Totals                      ...       \\\n",
       "                  Age                Yrs      G   MP   FG  FGA   3P  ...   PF   \n",
       "0                  29                  1     49  NaN  102  274  NaN  ...   99   \n",
       "1                  25                  1     67  NaN  174  499  NaN  ...  192   \n",
       "2                  24                  1     15  NaN   22   86  NaN  ...   29   \n",
       "3                  22                  1     60  NaN  340  936  NaN  ...  132   \n",
       "4                  23                  1      3  NaN    5   16  NaN  ...    6   \n",
       "..                ...                ...    ...  ...  ...  ...  ...  ...  ...   \n",
       "59                 20                  1      3   27    4   12    0  ...    2   \n",
       "60                 20                  1      8  184   27   39    8  ...   15   \n",
       "61                 20                  1      8  233   52  118   12  ...   18   \n",
       "62                 19                  1      3   23    3    9    0  ...    1   \n",
       "63                 23                  1      2    7    0    1    0  ...    0   \n",
       "\n",
       "        Shooting              Per Game                 SEASON  \n",
       "    PTS      FG%   3P%    FT%       MP   PTS  TRB  AST         \n",
       "0   279     .372   NaN   .708      NaN   5.7  NaN  2.2   1950  \n",
       "1   438     .349   NaN   .698      NaN   6.5  NaN  2.1   1950  \n",
       "2    63     .256   NaN   .559      NaN   4.2  NaN  1.3   1950  \n",
       "3   895     .363   NaN   .762      NaN  14.9  NaN  3.9   1950  \n",
       "4    10     .313   NaN   .000      NaN   3.3  NaN  0.7   1950  \n",
       "..  ...      ...   ...    ...      ...   ...  ...  ...    ...  \n",
       "59   10     .333  .000  1.000      9.0   3.3  2.3  1.7   2024  \n",
       "60   70     .692  .571  1.000     23.0   8.8  2.5  1.5   2024  \n",
       "61  150     .441  .293   .791     29.1  18.8  8.5  1.8   2024  \n",
       "62    7     .333  .000   .500      7.7   2.3  1.0  0.3   2024  \n",
       "63    0     .000   NaN    NaN      3.5   0.0  0.5  0.0   2024  \n",
       "\n",
       "[4942 rows x 29 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add season as a column:\n",
    "for iii,df in enumerate(all_rookie_dfs):\n",
    "    player = df['Unnamed: 1_level_0']['Player']\n",
    "    all_rookie_dfs[iii]['SEASON'] = pd.Series([1950+iii for foo in player])\n",
    "\n",
    "one_rookie_df = pd.concat(all_rookie_dfs)\n",
    "print(one_rookie_df.info())\n",
    "one_rookie_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06d1a279",
   "metadata": {},
   "source": [
    "We are now happy with our huge dataframe, but:\n",
    "\n",
    "* the structure of the df uses nested columns\n",
    "\n",
    "* intermediate rows with strings are still present\n",
    "\n",
    "* all columns are 'object' types, we want numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "bc60c39c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Unnamed: 1_level_0', 'Player') Player\n",
      "('Unnamed: 2_level_0', 'Debut') Debut\n",
      "('Unnamed: 3_level_0', 'Age') Age\n",
      "('Unnamed: 4_level_0', 'Yrs') Yrs\n",
      "('Totals', 'G') G\n",
      "('Totals', 'MP') MP\n",
      "('Totals', 'FG') FG\n",
      "('Totals', 'FGA') FGA\n",
      "('Totals', '3P') 3P\n",
      "('Totals', '3PA') 3PA\n",
      "('Totals', 'FT') FT\n",
      "('Totals', 'FTA') FTA\n",
      "('Totals', 'ORB') ORB\n",
      "('Totals', 'TRB') TRB\n",
      "('Totals', 'AST') AST\n",
      "('Totals', 'STL') STL\n",
      "('Totals', 'BLK') BLK\n",
      "('Totals', 'TOV') TOV\n",
      "('Totals', 'PF') PF\n",
      "('Totals', 'PTS') PTS\n",
      "('Shooting', 'FG%') FG%\n",
      "('Shooting', '3P%') 3P%\n",
      "('Shooting', 'FT%') FT%\n",
      "('Per Game', 'MP') MP_pergame\n",
      "('Per Game', 'PTS') PTS_pergame\n",
      "('Per Game', 'TRB') TRB_pergame\n",
      "('Per Game', 'AST') AST_pergame\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 4562 entries, 0 to 63\n",
      "Data columns (total 28 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   (SEASON, )       4562 non-null   int64  \n",
      " 1   (Player, )       4562 non-null   object \n",
      " 2   (Debut, )        4561 non-null   object \n",
      " 3   (Age, )          4562 non-null   float64\n",
      " 4   (Yrs, )          4562 non-null   float64\n",
      " 5   (G, )            4562 non-null   float64\n",
      " 6   (MP, )           4412 non-null   float64\n",
      " 7   (FG, )           4562 non-null   float64\n",
      " 8   (FGA, )          4562 non-null   float64\n",
      " 9   (3P, )           3325 non-null   float64\n",
      " 10  (3PA, )          3325 non-null   float64\n",
      " 11  (FT, )           4562 non-null   float64\n",
      " 12  (FTA, )          4562 non-null   float64\n",
      " 13  (ORB, )          3688 non-null   float64\n",
      " 14  (TRB, )          4442 non-null   float64\n",
      " 15  (AST, )          4562 non-null   float64\n",
      " 16  (STL, )          3688 non-null   float64\n",
      " 17  (BLK, )          3688 non-null   float64\n",
      " 18  (TOV, )          3435 non-null   float64\n",
      " 19  (PF, )           4562 non-null   float64\n",
      " 20  (PTS, )          4562 non-null   float64\n",
      " 21  (FG%, )          4520 non-null   float64\n",
      " 22  (3P%, )          2631 non-null   float64\n",
      " 23  (FT%, )          4222 non-null   float64\n",
      " 24  (MP_pergame, )   4412 non-null   float64\n",
      " 25  (PTS_pergame, )  4562 non-null   float64\n",
      " 26  (TRB_pergame, )  4442 non-null   float64\n",
      " 27  (AST_pergame, )  4562 non-null   float64\n",
      "dtypes: float64(25), int64(1), object(2)\n",
      "memory usage: 1.0+ MB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  app.launch_new_instance()\n",
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    },
    {
     "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>SEASON</th>\n",
       "      <th>Player</th>\n",
       "      <th>Debut</th>\n",
       "      <th>Age</th>\n",
       "      <th>Yrs</th>\n",
       "      <th>G</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>3P</th>\n",
       "      <th>...</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3P%</th>\n",
       "      <th>FT%</th>\n",
       "      <th>MP_pergame</th>\n",
       "      <th>PTS_pergame</th>\n",
       "      <th>TRB_pergame</th>\n",
       "      <th>AST_pergame</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>...</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1950</td>\n",
       "      <td>Cliff Barker</td>\n",
       "      <td>Nov 1, '49, INO vs. DNN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>0.372</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1950</td>\n",
       "      <td>Leo Barnhorst</td>\n",
       "      <td>Nov 1, '49, CHS vs. NYK</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>174.0</td>\n",
       "      <td>499.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>192.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>0.349</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.698</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1950</td>\n",
       "      <td>Ed Bartels</td>\n",
       "      <td>Oct 29, '49, DNN @ TRI</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.559</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1950</td>\n",
       "      <td>Ralph Beard</td>\n",
       "      <td>Nov 1, '49, INO vs. DNN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>340.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>132.0</td>\n",
       "      <td>895.0</td>\n",
       "      <td>0.363</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.762</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1950</td>\n",
       "      <td>Gene Berce</td>\n",
       "      <td>Oct 29, '49, TRI vs. DNN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.313</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.7</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2024</td>\n",
       "      <td>Jarace Walker</td>\n",
       "      <td>Oct 25, '23, IND vs. WAS</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2024</td>\n",
       "      <td>Cason Wallace</td>\n",
       "      <td>Oct 25, '23, OKC @ CHI</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.692</td>\n",
       "      <td>0.571</td>\n",
       "      <td>1.000</td>\n",
       "      <td>23.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2024</td>\n",
       "      <td>Victor Wembanyama</td>\n",
       "      <td>Oct 25, '23, SAS vs. DAL</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>...</td>\n",
       "      <td>28.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.441</td>\n",
       "      <td>0.293</td>\n",
       "      <td>0.791</td>\n",
       "      <td>29.1</td>\n",
       "      <td>18.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>2024</td>\n",
       "      <td>Cam Whitmore</td>\n",
       "      <td>Oct 25, '23, HOU @ ORL</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2024</td>\n",
       "      <td>Jalen Wilson</td>\n",
       "      <td>Nov 1, '23, BRK @ MIA</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4562 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SEASON             Player                     Debut   Age  Yrs     G  \\\n",
       "                                                                          \n",
       "0    1950       Cliff Barker   Nov 1, '49, INO vs. DNN  29.0  1.0  49.0   \n",
       "1    1950      Leo Barnhorst   Nov 1, '49, CHS vs. NYK  25.0  1.0  67.0   \n",
       "2    1950         Ed Bartels    Oct 29, '49, DNN @ TRI  24.0  1.0  15.0   \n",
       "3    1950        Ralph Beard   Nov 1, '49, INO vs. DNN  22.0  1.0  60.0   \n",
       "4    1950         Gene Berce  Oct 29, '49, TRI vs. DNN  23.0  1.0   3.0   \n",
       "..    ...                ...                       ...   ...  ...   ...   \n",
       "59   2024      Jarace Walker  Oct 25, '23, IND vs. WAS  20.0  1.0   3.0   \n",
       "60   2024      Cason Wallace    Oct 25, '23, OKC @ CHI  20.0  1.0   8.0   \n",
       "61   2024  Victor Wembanyama  Oct 25, '23, SAS vs. DAL  20.0  1.0   8.0   \n",
       "62   2024       Cam Whitmore    Oct 25, '23, HOU @ ORL  19.0  1.0   3.0   \n",
       "63   2024       Jalen Wilson     Nov 1, '23, BRK @ MIA  23.0  1.0   2.0   \n",
       "\n",
       "       MP     FG    FGA    3P  ...   TOV     PF    PTS    FG%    3P%    FT%  \\\n",
       "                               ...                                            \n",
       "0     NaN  102.0  274.0   NaN  ...   NaN   99.0  279.0  0.372    NaN  0.708   \n",
       "1     NaN  174.0  499.0   NaN  ...   NaN  192.0  438.0  0.349    NaN  0.698   \n",
       "2     NaN   22.0   86.0   NaN  ...   NaN   29.0   63.0  0.256    NaN  0.559   \n",
       "3     NaN  340.0  936.0   NaN  ...   NaN  132.0  895.0  0.363    NaN  0.762   \n",
       "4     NaN    5.0   16.0   NaN  ...   NaN    6.0   10.0  0.313    NaN  0.000   \n",
       "..    ...    ...    ...   ...  ...   ...    ...    ...    ...    ...    ...   \n",
       "59   27.0    4.0   12.0   0.0  ...   0.0    2.0   10.0  0.333  0.000  1.000   \n",
       "60  184.0   27.0   39.0   8.0  ...   6.0   15.0   70.0  0.692  0.571  1.000   \n",
       "61  233.0   52.0  118.0  12.0  ...  28.0   18.0  150.0  0.441  0.293  0.791   \n",
       "62   23.0    3.0    9.0   0.0  ...   2.0    1.0    7.0  0.333  0.000  0.500   \n",
       "63    7.0    0.0    1.0   0.0  ...   0.0    0.0    0.0  0.000    NaN    NaN   \n",
       "\n",
       "   MP_pergame PTS_pergame TRB_pergame AST_pergame  \n",
       "                                                   \n",
       "0         NaN         5.7         NaN         2.2  \n",
       "1         NaN         6.5         NaN         2.1  \n",
       "2         NaN         4.2         NaN         1.3  \n",
       "3         NaN        14.9         NaN         3.9  \n",
       "4         NaN         3.3         NaN         0.7  \n",
       "..        ...         ...         ...         ...  \n",
       "59        9.0         3.3         2.3         1.7  \n",
       "60       23.0         8.8         2.5         1.5  \n",
       "61       29.1        18.8         8.5         1.8  \n",
       "62        7.7         2.3         1.0         0.3  \n",
       "63        3.5         0.0         0.5         0.0  \n",
       "\n",
       "[4562 rows x 28 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_not_nested = one_rookie_df[ ['SEASON'] ]\n",
    "\n",
    "\n",
    "for col in one_rookie_df.columns.values.tolist():\n",
    "\n",
    "    if col[0]=='Per Game':\n",
    "        newname = col[1]+'_pergame'\n",
    "    elif col[0]=='SEASON' or col[1]=='Rk':\n",
    "        continue\n",
    "    else:\n",
    "        newname = col[1]\n",
    "            \n",
    "    column_to_parse = one_rookie_df[col[0]][col[1]]\n",
    "    \n",
    "    if newname in ['Player','Debut']:\n",
    "        df_not_nested[ newname ] = column_to_parse\n",
    "    else:\n",
    "        df_not_nested[ newname ] = pd.to_numeric( column_to_parse , errors='coerce' )       \n",
    "        \n",
    "    print(col,newname)\n",
    "\n",
    "df_not_nested = df_not_nested[ df_not_nested['Yrs']>0. ]\n",
    "df_not_nested.info()\n",
    "df_not_nested"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "609dd341",
   "metadata": {},
   "source": [
    "Look up the ROTY for every year:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e9c27640",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all_roty = pd.read_html('https://www.basketball-reference.com/awards/roy.html')[0]\n",
    "roty_names = df_all_roty['Unnamed: 2_level_0']['Player']\n",
    "roty_seasons = df_all_roty['Unnamed: 0_level_0']['Season']\n",
    "\n",
    "roty_names = [rrr.split('\\xa0*')[0] for rrr in roty_names]\n",
    "roty_names = [rrr.split('\\xa0(')[0] for rrr in roty_names]\n",
    "\n",
    "\n",
    "# Creating a dictionary by passing Series objects as values\n",
    "frame = {'Player': roty_names,\n",
    "        'SEASON':roty_seasons}\n",
    "\n",
    "# Creating DataFrame by passing Dictionary\n",
    "df_roty_clean = pd.DataFrame(frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "8fcfd442",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42 Alex Groza 1950\n",
      "120 Paul Arizin 1951\n",
      "158 Mel Hutchins 1952\n",
      "177 Bill Tosheff 1952\n",
      "197 Monk Meineke 1953\n",
      "222 Ray Felix 1954\n",
      "273 Bob Pettit 1955\n",
      "309 Maurice Stokes 1956\n",
      "321 Tom Heinsohn 1957\n",
      "354 Woody Sauldsberry 1958\n",
      "359 Elgin Baylor 1959\n",
      "388 Wilt Chamberlain 1960\n",
      "419 Oscar Robertson 1961\n",
      "424 Walt Bellamy 1962\n",
      "465 Terry Dischinger 1963\n",
      "507 Jerry Lucas 1964\n",
      "538 Willis Reed 1965\n",
      "543 Rick Barry 1966\n",
      "569 Dave Bing 1967\n",
      "627 Earl Monroe 1968\n",
      "680 Wes Unseld 1969\n",
      "685 Kareem Abdul-Jabbar 1970\n",
      "727 Dave Cowens 1971\n",
      "758 Geoff Petrie 1971\n",
      "822 Sidney Wicks 1972\n",
      "851 Bob McAdoo 1973\n",
      "888 Ernie DiGregorio 1974\n",
      "964 Jamaal Wilkes 1975\n",
      "968 Alvan Adams 1976\n",
      "1037 Adrian Dantley 1977\n",
      "1142 Walter Davis 1978\n",
      "1156 Larry Johnson 1978\n",
      "1200 Phil Ford 1979\n",
      "1239 Larry Bird 1980\n",
      "1311 Darrell Griffith 1981\n",
      "1424 Buck Williams 1982\n",
      "1439 Terry Cummings 1983\n",
      "1533 Ralph Sampson 1984\n",
      "1578 Michael Jordan 1985\n",
      "1624 Patrick Ewing 1986\n",
      "1710 Chuck Person 1987\n",
      "1757 Mark Jackson 1988\n",
      "1849 Mitch Richmond 1989\n",
      "1935 David Robinson 1990\n",
      "1968 Derrick Coleman 1991\n",
      "2055 Larry Johnson 1992\n",
      "2129 Shaquille O'Neal 1993\n",
      "2218 Chris Webber 1994\n",
      "2239 Grant Hill 1995\n",
      "2244 Jason Kidd 1995\n",
      "2345 Damon Stoudamire 1996\n",
      "2387 Allen Iverson 1997\n",
      "2444 Tim Duncan 1998\n",
      "2501 Vince Carter 1999\n",
      "2570 Elton Brand 2000\n",
      "2581 Steve Francis 2000\n",
      "2656 Mike Miller 2001\n",
      "2720 Pau Gasol 2002\n",
      "2810 Amar'e Stoudemire 2003\n",
      "2858 LeBron James 2004\n",
      "2932 Emeka Okafor 2005\n",
      "3013 Chris Paul 2006\n",
      "3107 Brandon Roy 2007\n",
      "3139 Kevin Durant 2008\n",
      "3233 Derrick Rose 2009\n",
      "3268 Tyreke Evans 2010\n",
      "3328 Patrick Ewing 2011\n",
      "3334 Blake Griffin 2011\n",
      "3402 Kyrie Irving 2012\n",
      "3495 Damian Lillard 2013\n",
      "3546 Michael Carter-Williams 2014\n",
      "3690 Andrew Wiggins 2015\n",
      "3757 Karl-Anthony Towns 2016\n",
      "3775 Malcolm Brogdon 2017\n",
      "3952 Ben Simmons 2018\n",
      "4009 Luka Dončić 2019\n",
      "4153 Ja Morant 2020\n",
      "4202 LaMelo Ball 2021\n",
      "4296 Scottie Barnes 2022\n",
      "4419 Paolo Banchero 2023\n"
     ]
    }
   ],
   "source": [
    "is_roty = []\n",
    "\n",
    "for iii in range(len(df_not_nested['Player'])):\n",
    "    \n",
    "    playername = df_not_nested['Player'].iloc[iii].replace(' *','').replace('*','')\n",
    "    season = df_not_nested['SEASON'].iloc[iii]\n",
    "    \n",
    "    if playername in df_roty_clean['Player'].tolist():\n",
    "        print(iii, playername, season )\n",
    "        if season==2011 and playername=='Patrick Ewing':\n",
    "            is_roty.append( 0 )\n",
    "        elif season==1978 and playername=='Larry Johnson':\n",
    "            is_roty.append( 0 )\n",
    "        else:\n",
    "            is_roty.append( 1 )\n",
    "    else:\n",
    "        is_roty.append( 0 )    \n",
    "        \n",
    "\n",
    "        \n",
    "df_not_nested['ROTY'] = np.array(is_roty)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "2150b7c0",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>SEASON</th>\n",
       "      <th>Player</th>\n",
       "      <th>Debut</th>\n",
       "      <th>Age</th>\n",
       "      <th>Yrs</th>\n",
       "      <th>G</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>3P</th>\n",
       "      <th>...</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3P%</th>\n",
       "      <th>FT%</th>\n",
       "      <th>MP_pergame</th>\n",
       "      <th>PTS_pergame</th>\n",
       "      <th>TRB_pergame</th>\n",
       "      <th>AST_pergame</th>\n",
       "      <th>ROTY</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>...</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1950</td>\n",
       "      <td>Cliff Barker</td>\n",
       "      <td>Nov 1, '49, INO vs. DNN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>99.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>0.372</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1950</td>\n",
       "      <td>Leo Barnhorst</td>\n",
       "      <td>Nov 1, '49, CHS vs. NYK</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>174.0</td>\n",
       "      <td>499.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>192.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>0.349</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.698</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1950</td>\n",
       "      <td>Ed Bartels</td>\n",
       "      <td>Oct 29, '49, DNN @ TRI</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>29.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.559</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1950</td>\n",
       "      <td>Ralph Beard</td>\n",
       "      <td>Nov 1, '49, INO vs. DNN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>340.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>132.0</td>\n",
       "      <td>895.0</td>\n",
       "      <td>0.363</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.762</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1950</td>\n",
       "      <td>Gene Berce</td>\n",
       "      <td>Oct 29, '49, TRI vs. DNN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.313</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>59</th>\n",
       "      <td>2024</td>\n",
       "      <td>Jarace Walker</td>\n",
       "      <td>Oct 25, '23, IND vs. WAS</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2024</td>\n",
       "      <td>Cason Wallace</td>\n",
       "      <td>Oct 25, '23, OKC @ CHI</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.692</td>\n",
       "      <td>0.571</td>\n",
       "      <td>1.000</td>\n",
       "      <td>23.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2024</td>\n",
       "      <td>Victor Wembanyama</td>\n",
       "      <td>Oct 25, '23, SAS vs. DAL</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>...</td>\n",
       "      <td>18.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.441</td>\n",
       "      <td>0.293</td>\n",
       "      <td>0.791</td>\n",
       "      <td>29.1</td>\n",
       "      <td>18.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>2024</td>\n",
       "      <td>Cam Whitmore</td>\n",
       "      <td>Oct 25, '23, HOU @ ORL</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2024</td>\n",
       "      <td>Jalen Wilson</td>\n",
       "      <td>Nov 1, '23, BRK @ MIA</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4562 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SEASON             Player                     Debut   Age  Yrs     G  \\\n",
       "                                                                          \n",
       "0    1950       Cliff Barker   Nov 1, '49, INO vs. DNN  29.0  1.0  49.0   \n",
       "1    1950      Leo Barnhorst   Nov 1, '49, CHS vs. NYK  25.0  1.0  67.0   \n",
       "2    1950         Ed Bartels    Oct 29, '49, DNN @ TRI  24.0  1.0  15.0   \n",
       "3    1950        Ralph Beard   Nov 1, '49, INO vs. DNN  22.0  1.0  60.0   \n",
       "4    1950         Gene Berce  Oct 29, '49, TRI vs. DNN  23.0  1.0   3.0   \n",
       "..    ...                ...                       ...   ...  ...   ...   \n",
       "59   2024      Jarace Walker  Oct 25, '23, IND vs. WAS  20.0  1.0   3.0   \n",
       "60   2024      Cason Wallace    Oct 25, '23, OKC @ CHI  20.0  1.0   8.0   \n",
       "61   2024  Victor Wembanyama  Oct 25, '23, SAS vs. DAL  20.0  1.0   8.0   \n",
       "62   2024       Cam Whitmore    Oct 25, '23, HOU @ ORL  19.0  1.0   3.0   \n",
       "63   2024       Jalen Wilson     Nov 1, '23, BRK @ MIA  23.0  1.0   2.0   \n",
       "\n",
       "       MP     FG    FGA    3P  ...     PF    PTS    FG%    3P%    FT%  \\\n",
       "                               ...                                      \n",
       "0     NaN  102.0  274.0   NaN  ...   99.0  279.0  0.372    NaN  0.708   \n",
       "1     NaN  174.0  499.0   NaN  ...  192.0  438.0  0.349    NaN  0.698   \n",
       "2     NaN   22.0   86.0   NaN  ...   29.0   63.0  0.256    NaN  0.559   \n",
       "3     NaN  340.0  936.0   NaN  ...  132.0  895.0  0.363    NaN  0.762   \n",
       "4     NaN    5.0   16.0   NaN  ...    6.0   10.0  0.313    NaN  0.000   \n",
       "..    ...    ...    ...   ...  ...    ...    ...    ...    ...    ...   \n",
       "59   27.0    4.0   12.0   0.0  ...    2.0   10.0  0.333  0.000  1.000   \n",
       "60  184.0   27.0   39.0   8.0  ...   15.0   70.0  0.692  0.571  1.000   \n",
       "61  233.0   52.0  118.0  12.0  ...   18.0  150.0  0.441  0.293  0.791   \n",
       "62   23.0    3.0    9.0   0.0  ...    1.0    7.0  0.333  0.000  0.500   \n",
       "63    7.0    0.0    1.0   0.0  ...    0.0    0.0  0.000    NaN    NaN   \n",
       "\n",
       "   MP_pergame PTS_pergame TRB_pergame AST_pergame ROTY  \n",
       "                                                        \n",
       "0         NaN         5.7         NaN         2.2    0  \n",
       "1         NaN         6.5         NaN         2.1    0  \n",
       "2         NaN         4.2         NaN         1.3    0  \n",
       "3         NaN        14.9         NaN         3.9    0  \n",
       "4         NaN         3.3         NaN         0.7    0  \n",
       "..        ...         ...         ...         ...  ...  \n",
       "59        9.0         3.3         2.3         1.7    0  \n",
       "60       23.0         8.8         2.5         1.5    0  \n",
       "61       29.1        18.8         8.5         1.8    0  \n",
       "62        7.7         2.3         1.0         0.3    0  \n",
       "63        3.5         0.0         0.5         0.0    0  \n",
       "\n",
       "[4562 rows x 29 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_not_nested"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "e2158a33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'AST')"
      ]
     },
     "execution_count": 68,
     "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_dfs_together = df_not_nested\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "is_roty = all_dfs_together['ROTY']\n",
    "\n",
    "plt.figure()\n",
    "plt.scatter( all_dfs_together['SEASON'] , all_dfs_together['PTS_pergame'] , s=1)\n",
    "plt.scatter( all_dfs_together['SEASON'][is_roty>0] , all_dfs_together['PTS_pergame'][is_roty>0] , c='r' )\n",
    "#plt.xlim(1975,1980)\n",
    "plt.ylabel('PTS')\n",
    "\n",
    "plt.figure()\n",
    "plt.scatter( all_dfs_together['SEASON'] , all_dfs_together['TRB_pergame'] , s=1)\n",
    "plt.scatter( all_dfs_together['SEASON'][is_roty>0] , all_dfs_together['TRB_pergame'][is_roty>0] , c='r' )\n",
    "plt.ylabel('TRB')\n",
    "\n",
    "plt.figure()\n",
    "plt.scatter( all_dfs_together['SEASON'] , all_dfs_together['AST_pergame'] , s=1)\n",
    "plt.scatter( all_dfs_together['SEASON'][is_roty>0] , all_dfs_together['AST_pergame'][is_roty>0] , c='r' )\n",
    "plt.ylabel('AST')\n",
    "\n",
    "plt.figure()\n",
    "plt.scatter( all_dfs_together['PTS'] , all_dfs_together['AST'] , s=1 , c=all_dfs_together['TRB'],vmin=0,vmax=15)\n",
    "plt.scatter( all_dfs_together['PTS'][is_roty>0] , all_dfs_together['AST'][is_roty>0] , c=all_dfs_together['TRB'][is_roty>0] ,vmin=0,vmax=15)\n",
    "plt.colorbar()\n",
    "plt.xlabel('PTS')\n",
    "plt.ylabel('AST')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "8fb7fbd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_dfs_together.to_csv('all_rookie_seasons_fullstats.csv',index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}