2019 College Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Name</th>\n",
       "      <th>School</th>\n",
       "      <th>G</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>2P</th>\n",
       "      <th>2PA</th>\n",
       "      <th>2P%</th>\n",
       "      <th>...</th>\n",
       "      <th>FTA</th>\n",
       "      <th>FT%</th>\n",
       "      <th>TRB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "      <th>SOS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>Zion Williamson</td>\n",
       "      <td>Duke</td>\n",
       "      <td>33.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.2</td>\n",
       "      <td>0.680</td>\n",
       "      <td>8.2</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.747</td>\n",
       "      <td>...</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.640</td>\n",
       "      <td>8.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>22.6</td>\n",
       "      <td>11.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>Jaxson Hayes</td>\n",
       "      <td>Texas</td>\n",
       "      <td>32.0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>3.8</td>\n",
       "      <td>5.3</td>\n",
       "      <td>0.728</td>\n",
       "      <td>3.8</td>\n",
       "      <td>5.3</td>\n",
       "      <td>0.728</td>\n",
       "      <td>...</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.740</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>3.3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>Nickeil Alexander-Walker</td>\n",
       "      <td>Virginia Tech</td>\n",
       "      <td>67.0</td>\n",
       "      <td>29.9</td>\n",
       "      <td>4.7</td>\n",
       "      <td>10.1</td>\n",
       "      <td>0.464</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.529</td>\n",
       "      <td>...</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.763</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1.9</td>\n",
       "      <td>13.5</td>\n",
       "      <td>7.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>RJ Barrett</td>\n",
       "      <td>Duke</td>\n",
       "      <td>38.0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>8.4</td>\n",
       "      <td>18.5</td>\n",
       "      <td>0.454</td>\n",
       "      <td>6.5</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0.529</td>\n",
       "      <td>...</td>\n",
       "      <td>5.9</td>\n",
       "      <td>0.665</td>\n",
       "      <td>7.6</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.8</td>\n",
       "      <td>22.6</td>\n",
       "      <td>11.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>Ignas Brazdeikis</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>37.0</td>\n",
       "      <td>29.6</td>\n",
       "      <td>5.1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.462</td>\n",
       "      <td>3.6</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.500</td>\n",
       "      <td>...</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.773</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.1</td>\n",
       "      <td>14.8</td>\n",
       "      <td>10.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Name         School     G    MP   FG   FGA    FG%  \\\n",
       "559           Zion Williamson           Duke  33.0  30.0  9.0  13.2  0.680   \n",
       "560              Jaxson Hayes          Texas  32.0  23.3  3.8   5.3  0.728   \n",
       "561  Nickeil Alexander-Walker  Virginia Tech  67.0  29.9  4.7  10.1  0.464   \n",
       "562                RJ Barrett           Duke  38.0  35.3  8.4  18.5  0.454   \n",
       "563          Ignas Brazdeikis       Michigan  37.0  29.6  5.1  11.0  0.462   \n",
       "\n",
       "      2P   2PA    2P%  ...  FTA    FT%  TRB  AST  STL  BLK  TOV   PF   PTS  \\\n",
       "559  8.2  11.0  0.747  ...  6.2  0.640  8.9  2.1  2.1  1.8  2.4  2.1  22.6   \n",
       "560  3.8   5.3  0.728  ...  3.1  0.740  5.0  0.3  0.6  2.2  0.9  3.3  10.0   \n",
       "561  3.0   5.6  0.529  ...  3.1  0.763  4.0  2.7  1.4  0.5  2.2  1.9  13.5   \n",
       "562  6.5  12.2  0.529  ...  5.9  0.665  7.6  4.3  0.9  0.4  3.2  1.8  22.6   \n",
       "563  3.6   7.1  0.500  ...  4.1  0.773  5.4  0.8  0.7  0.5  1.2  2.1  14.8   \n",
       "\n",
       "       SOS  \n",
       "559  11.97  \n",
       "560  11.46  \n",
       "561   7.25  \n",
       "562  11.97  \n",
       "563  10.55  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "from sklearn.metrics import r2_score\n",
    "import matplotlib.pyplot as plt \n",
    "df = pd.read_csv('CollegeRookieStatLog5.csv')\n",
    "df = df[df['Year'] == 2019]\n",
    "df = df.drop(['NBATRB', 'NBAAST', 'NBASTL', 'NBABLK','NBAPTS', 'Pk', 'Year', 'Pos', '#', 'Wins', 'Losses', 'Pts', 'Rbds','Unnamed: 0','Offense', 'Defense', 'Diff'], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>G</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>2P</th>\n",
       "      <th>2PA</th>\n",
       "      <th>2P%</th>\n",
       "      <th>3P</th>\n",
       "      <th>3PA</th>\n",
       "      <th>...</th>\n",
       "      <th>FTA</th>\n",
       "      <th>FT%</th>\n",
       "      <th>TRB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "      <th>SOS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</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>Zion Williamson</th>\n",
       "      <td>33.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.2</td>\n",
       "      <td>0.680</td>\n",
       "      <td>8.2</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.747</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>...</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.640</td>\n",
       "      <td>8.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>22.6</td>\n",
       "      <td>11.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jaxson Hayes</th>\n",
       "      <td>32.0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>3.8</td>\n",
       "      <td>5.3</td>\n",
       "      <td>0.728</td>\n",
       "      <td>3.8</td>\n",
       "      <td>5.3</td>\n",
       "      <td>0.728</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.740</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>3.3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nickeil Alexander-Walker</th>\n",
       "      <td>67.0</td>\n",
       "      <td>29.9</td>\n",
       "      <td>4.7</td>\n",
       "      <td>10.1</td>\n",
       "      <td>0.464</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.529</td>\n",
       "      <td>1.7</td>\n",
       "      <td>4.5</td>\n",
       "      <td>...</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.763</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1.9</td>\n",
       "      <td>13.5</td>\n",
       "      <td>7.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RJ Barrett</th>\n",
       "      <td>38.0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>8.4</td>\n",
       "      <td>18.5</td>\n",
       "      <td>0.454</td>\n",
       "      <td>6.5</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0.529</td>\n",
       "      <td>1.9</td>\n",
       "      <td>6.2</td>\n",
       "      <td>...</td>\n",
       "      <td>5.9</td>\n",
       "      <td>0.665</td>\n",
       "      <td>7.6</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.8</td>\n",
       "      <td>22.6</td>\n",
       "      <td>11.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ignas Brazdeikis</th>\n",
       "      <td>37.0</td>\n",
       "      <td>29.6</td>\n",
       "      <td>5.1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.462</td>\n",
       "      <td>3.6</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.500</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>...</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.773</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.1</td>\n",
       "      <td>14.8</td>\n",
       "      <td>10.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             G    MP   FG   FGA    FG%   2P   2PA    2P%   3P  \\\n",
       "Name                                                                            \n",
       "Zion Williamson           33.0  30.0  9.0  13.2  0.680  8.2  11.0  0.747  0.7   \n",
       "Jaxson Hayes              32.0  23.3  3.8   5.3  0.728  3.8   5.3  0.728  0.0   \n",
       "Nickeil Alexander-Walker  67.0  29.9  4.7  10.1  0.464  3.0   5.6  0.529  1.7   \n",
       "RJ Barrett                38.0  35.3  8.4  18.5  0.454  6.5  12.2  0.529  1.9   \n",
       "Ignas Brazdeikis          37.0  29.6  5.1  11.0  0.462  3.6   7.1  0.500  1.5   \n",
       "\n",
       "                          3PA  ...  FTA    FT%  TRB  AST  STL  BLK  TOV   PF  \\\n",
       "Name                           ...                                             \n",
       "Zion Williamson           2.2  ...  6.2  0.640  8.9  2.1  2.1  1.8  2.4  2.1   \n",
       "Jaxson Hayes              0.0  ...  3.1  0.740  5.0  0.3  0.6  2.2  0.9  3.3   \n",
       "Nickeil Alexander-Walker  4.5  ...  3.1  0.763  4.0  2.7  1.4  0.5  2.2  1.9   \n",
       "RJ Barrett                6.2  ...  5.9  0.665  7.6  4.3  0.9  0.4  3.2  1.8   \n",
       "Ignas Brazdeikis          3.9  ...  4.1  0.773  5.4  0.8  0.7  0.5  1.2  2.1   \n",
       "\n",
       "                           PTS    SOS  \n",
       "Name                                   \n",
       "Zion Williamson           22.6  11.97  \n",
       "Jaxson Hayes              10.0  11.46  \n",
       "Nickeil Alexander-Walker  13.5   7.25  \n",
       "RJ Barrett                22.6  11.97  \n",
       "Ignas Brazdeikis          14.8  10.55  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.set_index('Name')\n",
    "df2 = df1.copy(deep=True)\n",
    "df2 = df2.drop(['School'], axis=1)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "df2.dropna()\n",
    "cols = df2.columns\n",
    "sc2 = StandardScaler()\n",
    "for col in cols:\n",
    "    df2[col] = pd.to_numeric(df2[col], errors='coerce')\n",
    "    df2[[col]] = sc2.fit_transform(df2[[col]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.127273,0.125;0.617727x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d.axes3d import Axes3D \n",
    "\n",
    "sns.set(style=\"white\")\n",
    "\n",
    "d = df2\n",
    "\n",
    "corr = d.corr()\n",
    "\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "\n",
    "print(sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0, square=True, linewidths=.5, cbar_kws={\"shrink\": .5}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_embedding(X, y, title=None, num=10):\n",
    "    x_min = np.min(X, 0)\n",
    "    x_max = np.max(X, 0)\n",
    "    X = (X-x_min)/(x_max-x_min)\n",
    "    \n",
    "    plt.figure(figsize=(18, 9))\n",
    "    plt.scatter(X[:,0], X[:,1], color=plt.cm.Set1(y/num))\n",
    "    \n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    if title is not None:\n",
    "        plt.title(title)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_embedding3d(X, y, title=None):\n",
    "    x_min = np.min(X, 0)\n",
    "    x_max = np.max(X, 0)\n",
    "    X = (X-x_min)/(x_max-x_min)\n",
    "    \n",
    "    plt.figure()\n",
    "    \n",
    "    ax = Axes3D(plt.figure(), rect=[0, 0, .95, 1], elev=48, azim=134)\n",
    "    ax.scatter(X[:,0], X[:,1], color=plt.cm.Set1(y/10.))\n",
    "    \n",
    "    ax.w_xaxis.set_ticklabels([])\n",
    "    ax.w_yaxis.set_ticklabels([])\n",
    "    ax.w_zaxis.set_ticklabels([])\n",
    "    if title is not None:\n",
    "        plt.title(title)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "subSpace = TSNE().fit_transform(df2.values)\n",
    "#subSpace = PCA().fit_transform(df3.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "cluster = KMeans(n_clusters=4, max_iter= 10000, n_init=100).fit(df2.values)\n",
    "plot_embedding(subSpace, cluster.labels_, 'K-Means projection',4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>G</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>2P</th>\n",
       "      <th>2PA</th>\n",
       "      <th>2P%</th>\n",
       "      <th>3P</th>\n",
       "      <th>3PA</th>\n",
       "      <th>...</th>\n",
       "      <th>FT%</th>\n",
       "      <th>TRB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "      <th>SOS</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</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>Zion Williamson</th>\n",
       "      <td>-1.108943</td>\n",
       "      <td>0.492341</td>\n",
       "      <td>2.961429</td>\n",
       "      <td>1.203738</td>\n",
       "      <td>2.574998</td>\n",
       "      <td>3.247239</td>\n",
       "      <td>2.042969</td>\n",
       "      <td>2.992583</td>\n",
       "      <td>-0.659698</td>\n",
       "      <td>-0.536341</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.323586</td>\n",
       "      <td>2.028241</td>\n",
       "      <td>-0.078765</td>\n",
       "      <td>2.426820</td>\n",
       "      <td>1.438938</td>\n",
       "      <td>0.739072</td>\n",
       "      <td>-0.129021</td>\n",
       "      <td>2.445555</td>\n",
       "      <td>1.005805</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jaxson Hayes</th>\n",
       "      <td>-1.136891</td>\n",
       "      <td>-0.969842</td>\n",
       "      <td>-0.738578</td>\n",
       "      <td>-1.731302</td>\n",
       "      <td>3.222593</td>\n",
       "      <td>0.101521</td>\n",
       "      <td>-0.684179</td>\n",
       "      <td>2.721954</td>\n",
       "      <td>-1.629841</td>\n",
       "      <td>-1.675289</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.011286</td>\n",
       "      <td>-0.144874</td>\n",
       "      <td>-1.300982</td>\n",
       "      <td>-0.950017</td>\n",
       "      <td>2.005448</td>\n",
       "      <td>-1.629748</td>\n",
       "      <td>2.147826</td>\n",
       "      <td>-0.936139</td>\n",
       "      <td>0.899567</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nickeil Alexander-Walker</th>\n",
       "      <td>-0.158740</td>\n",
       "      <td>0.470517</td>\n",
       "      <td>-0.098193</td>\n",
       "      <td>0.052013</td>\n",
       "      <td>-0.339178</td>\n",
       "      <td>-0.470428</td>\n",
       "      <td>-0.540645</td>\n",
       "      <td>-0.112525</td>\n",
       "      <td>0.726222</td>\n",
       "      <td>0.654378</td>\n",
       "      <td>...</td>\n",
       "      <td>0.290543</td>\n",
       "      <td>-0.702083</td>\n",
       "      <td>0.328641</td>\n",
       "      <td>0.850963</td>\n",
       "      <td>-0.402223</td>\n",
       "      <td>0.423229</td>\n",
       "      <td>-0.508496</td>\n",
       "      <td>0.003221</td>\n",
       "      <td>0.022581</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RJ Barrett</th>\n",
       "      <td>-0.969208</td>\n",
       "      <td>1.648993</td>\n",
       "      <td>2.534505</td>\n",
       "      <td>3.172814</td>\n",
       "      <td>-0.474093</td>\n",
       "      <td>2.031848</td>\n",
       "      <td>2.617105</td>\n",
       "      <td>-0.112525</td>\n",
       "      <td>1.003405</td>\n",
       "      <td>1.534474</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.995511</td>\n",
       "      <td>1.303869</td>\n",
       "      <td>1.415056</td>\n",
       "      <td>-0.274649</td>\n",
       "      <td>-0.543850</td>\n",
       "      <td>2.002443</td>\n",
       "      <td>-0.698233</td>\n",
       "      <td>2.445555</td>\n",
       "      <td>1.005805</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ignas Brazdeikis</th>\n",
       "      <td>-0.997155</td>\n",
       "      <td>0.405047</td>\n",
       "      <td>0.186423</td>\n",
       "      <td>0.386385</td>\n",
       "      <td>-0.366161</td>\n",
       "      <td>-0.041466</td>\n",
       "      <td>0.177025</td>\n",
       "      <td>-0.525589</td>\n",
       "      <td>0.449038</td>\n",
       "      <td>0.343755</td>\n",
       "      <td>...</td>\n",
       "      <td>0.421773</td>\n",
       "      <td>0.078009</td>\n",
       "      <td>-0.961477</td>\n",
       "      <td>-0.724894</td>\n",
       "      <td>-0.402223</td>\n",
       "      <td>-1.155984</td>\n",
       "      <td>-0.129021</td>\n",
       "      <td>0.352126</td>\n",
       "      <td>0.710005</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 G        MP        FG       FGA       FG%  \\\n",
       "Name                                                                         \n",
       "Zion Williamson          -1.108943  0.492341  2.961429  1.203738  2.574998   \n",
       "Jaxson Hayes             -1.136891 -0.969842 -0.738578 -1.731302  3.222593   \n",
       "Nickeil Alexander-Walker -0.158740  0.470517 -0.098193  0.052013 -0.339178   \n",
       "RJ Barrett               -0.969208  1.648993  2.534505  3.172814 -0.474093   \n",
       "Ignas Brazdeikis         -0.997155  0.405047  0.186423  0.386385 -0.366161   \n",
       "\n",
       "                                2P       2PA       2P%        3P       3PA  \\\n",
       "Name                                                                         \n",
       "Zion Williamson           3.247239  2.042969  2.992583 -0.659698 -0.536341   \n",
       "Jaxson Hayes              0.101521 -0.684179  2.721954 -1.629841 -1.675289   \n",
       "Nickeil Alexander-Walker -0.470428 -0.540645 -0.112525  0.726222  0.654378   \n",
       "RJ Barrett                2.031848  2.617105 -0.112525  1.003405  1.534474   \n",
       "Ignas Brazdeikis         -0.041466  0.177025 -0.525589  0.449038  0.343755   \n",
       "\n",
       "                          ...       FT%       TRB       AST       STL  \\\n",
       "Name                      ...                                           \n",
       "Zion Williamson           ... -1.323586  2.028241 -0.078765  2.426820   \n",
       "Jaxson Hayes              ... -0.011286 -0.144874 -1.300982 -0.950017   \n",
       "Nickeil Alexander-Walker  ...  0.290543 -0.702083  0.328641  0.850963   \n",
       "RJ Barrett                ... -0.995511  1.303869  1.415056 -0.274649   \n",
       "Ignas Brazdeikis          ...  0.421773  0.078009 -0.961477 -0.724894   \n",
       "\n",
       "                               BLK       TOV        PF       PTS       SOS  \\\n",
       "Name                                                                         \n",
       "Zion Williamson           1.438938  0.739072 -0.129021  2.445555  1.005805   \n",
       "Jaxson Hayes              2.005448 -1.629748  2.147826 -0.936139  0.899567   \n",
       "Nickeil Alexander-Walker -0.402223  0.423229 -0.508496  0.003221  0.022581   \n",
       "RJ Barrett               -0.543850  2.002443 -0.698233  2.445555  1.005805   \n",
       "Ignas Brazdeikis         -0.402223 -1.155984 -0.129021  0.352126  0.710005   \n",
       "\n",
       "                          label  \n",
       "Name                             \n",
       "Zion Williamson               2  \n",
       "Jaxson Hayes                  3  \n",
       "Nickeil Alexander-Walker      1  \n",
       "RJ Barrett                    2  \n",
       "Ignas Brazdeikis              1  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df2.copy(deep=True)\n",
    "df4['label'] = cluster.labels_ \n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>G</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>2P</th>\n",
       "      <th>2PA</th>\n",
       "      <th>2P%</th>\n",
       "      <th>3P</th>\n",
       "      <th>3PA</th>\n",
       "      <th>...</th>\n",
       "      <th>FT%</th>\n",
       "      <th>TRB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "      <th>SOS</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</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>Zion Williamson</th>\n",
       "      <td>-1.108943</td>\n",
       "      <td>0.492341</td>\n",
       "      <td>2.961429</td>\n",
       "      <td>1.203738</td>\n",
       "      <td>2.574998</td>\n",
       "      <td>3.247239</td>\n",
       "      <td>2.042969</td>\n",
       "      <td>2.992583</td>\n",
       "      <td>-0.659698</td>\n",
       "      <td>-0.536341</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.323586</td>\n",
       "      <td>2.028241</td>\n",
       "      <td>-0.078765</td>\n",
       "      <td>2.426820</td>\n",
       "      <td>1.438938</td>\n",
       "      <td>0.739072</td>\n",
       "      <td>-0.129021</td>\n",
       "      <td>2.445555</td>\n",
       "      <td>1.005805</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RJ Barrett</th>\n",
       "      <td>-0.969208</td>\n",
       "      <td>1.648993</td>\n",
       "      <td>2.534505</td>\n",
       "      <td>3.172814</td>\n",
       "      <td>-0.474093</td>\n",
       "      <td>2.031848</td>\n",
       "      <td>2.617105</td>\n",
       "      <td>-0.112525</td>\n",
       "      <td>1.003405</td>\n",
       "      <td>1.534474</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.995511</td>\n",
       "      <td>1.303869</td>\n",
       "      <td>1.415056</td>\n",
       "      <td>-0.274649</td>\n",
       "      <td>-0.543850</td>\n",
       "      <td>2.002443</td>\n",
       "      <td>-0.698233</td>\n",
       "      <td>2.445555</td>\n",
       "      <td>1.005805</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brandon Clarke</th>\n",
       "      <td>-0.997155</td>\n",
       "      <td>0.077692</td>\n",
       "      <td>1.467195</td>\n",
       "      <td>0.052013</td>\n",
       "      <td>2.669439</td>\n",
       "      <td>2.246329</td>\n",
       "      <td>1.420988</td>\n",
       "      <td>2.394351</td>\n",
       "      <td>-1.491249</td>\n",
       "      <td>-1.468208</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.614944</td>\n",
       "      <td>1.861078</td>\n",
       "      <td>-0.214567</td>\n",
       "      <td>0.400718</td>\n",
       "      <td>3.421726</td>\n",
       "      <td>-0.682220</td>\n",
       "      <td>-0.129021</td>\n",
       "      <td>0.915741</td>\n",
       "      <td>-0.444034</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bol Bol</th>\n",
       "      <td>-1.779675</td>\n",
       "      <td>0.448694</td>\n",
       "      <td>2.392197</td>\n",
       "      <td>1.761023</td>\n",
       "      <td>0.969503</td>\n",
       "      <td>2.246329</td>\n",
       "      <td>2.473571</td>\n",
       "      <td>0.471464</td>\n",
       "      <td>0.310446</td>\n",
       "      <td>-0.225719</td>\n",
       "      <td>...</td>\n",
       "      <td>0.211805</td>\n",
       "      <td>2.418287</td>\n",
       "      <td>-0.825676</td>\n",
       "      <td>-0.499772</td>\n",
       "      <td>2.713587</td>\n",
       "      <td>0.107387</td>\n",
       "      <td>-0.887970</td>\n",
       "      <td>2.016134</td>\n",
       "      <td>-0.210727</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        G        MP        FG       FGA       FG%        2P  \\\n",
       "Name                                                                          \n",
       "Zion Williamson -1.108943  0.492341  2.961429  1.203738  2.574998  3.247239   \n",
       "RJ Barrett      -0.969208  1.648993  2.534505  3.172814 -0.474093  2.031848   \n",
       "Brandon Clarke  -0.997155  0.077692  1.467195  0.052013  2.669439  2.246329   \n",
       "Bol Bol         -1.779675  0.448694  2.392197  1.761023  0.969503  2.246329   \n",
       "\n",
       "                      2PA       2P%        3P       3PA  ...       FT%  \\\n",
       "Name                                                     ...             \n",
       "Zion Williamson  2.042969  2.992583 -0.659698 -0.536341  ... -1.323586   \n",
       "RJ Barrett       2.617105 -0.112525  1.003405  1.534474  ... -0.995511   \n",
       "Brandon Clarke   1.420988  2.394351 -1.491249 -1.468208  ... -0.614944   \n",
       "Bol Bol          2.473571  0.471464  0.310446 -0.225719  ...  0.211805   \n",
       "\n",
       "                      TRB       AST       STL       BLK       TOV        PF  \\\n",
       "Name                                                                          \n",
       "Zion Williamson  2.028241 -0.078765  2.426820  1.438938  0.739072 -0.129021   \n",
       "RJ Barrett       1.303869  1.415056 -0.274649 -0.543850  2.002443 -0.698233   \n",
       "Brandon Clarke   1.861078 -0.214567  0.400718  3.421726 -0.682220 -0.129021   \n",
       "Bol Bol          2.418287 -0.825676 -0.499772  2.713587  0.107387 -0.887970   \n",
       "\n",
       "                      PTS       SOS  label  \n",
       "Name                                        \n",
       "Zion Williamson  2.445555  1.005805      2  \n",
       "RJ Barrett       2.445555  1.005805      2  \n",
       "Brandon Clarke   0.915741 -0.444034      2  \n",
       "Bol Bol          2.016134 -0.210727      2  \n",
       "\n",
       "[4 rows x 23 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name = 'Zion Williamson'\n",
    "labnum = df4[df4.index == name].iloc[0].label\n",
    "df4[df4['label'] == labnum]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Agglomeative Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "cluster = AgglomerativeClustering(n_clusters=4).fit(df2.values)\n",
    "plot_embedding(subSpace, cluster.labels_, 'Agglomerative projection',4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>G</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>2P</th>\n",
       "      <th>2PA</th>\n",
       "      <th>2P%</th>\n",
       "      <th>3P</th>\n",
       "      <th>3PA</th>\n",
       "      <th>...</th>\n",
       "      <th>FT%</th>\n",
       "      <th>TRB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "      <th>SOS</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</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>Zion Williamson</th>\n",
       "      <td>-1.108943</td>\n",
       "      <td>0.492341</td>\n",
       "      <td>2.961429</td>\n",
       "      <td>1.203738</td>\n",
       "      <td>2.574998</td>\n",
       "      <td>3.247239</td>\n",
       "      <td>2.042969</td>\n",
       "      <td>2.992583</td>\n",
       "      <td>-0.659698</td>\n",
       "      <td>-0.536341</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.323586</td>\n",
       "      <td>2.028241</td>\n",
       "      <td>-0.078765</td>\n",
       "      <td>2.426820</td>\n",
       "      <td>1.438938</td>\n",
       "      <td>0.739072</td>\n",
       "      <td>-0.129021</td>\n",
       "      <td>2.445555</td>\n",
       "      <td>1.005805</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jaxson Hayes</th>\n",
       "      <td>-1.136891</td>\n",
       "      <td>-0.969842</td>\n",
       "      <td>-0.738578</td>\n",
       "      <td>-1.731302</td>\n",
       "      <td>3.222593</td>\n",
       "      <td>0.101521</td>\n",
       "      <td>-0.684179</td>\n",
       "      <td>2.721954</td>\n",
       "      <td>-1.629841</td>\n",
       "      <td>-1.675289</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.011286</td>\n",
       "      <td>-0.144874</td>\n",
       "      <td>-1.300982</td>\n",
       "      <td>-0.950017</td>\n",
       "      <td>2.005448</td>\n",
       "      <td>-1.629748</td>\n",
       "      <td>2.147826</td>\n",
       "      <td>-0.936139</td>\n",
       "      <td>0.899567</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nickeil Alexander-Walker</th>\n",
       "      <td>-0.158740</td>\n",
       "      <td>0.470517</td>\n",
       "      <td>-0.098193</td>\n",
       "      <td>0.052013</td>\n",
       "      <td>-0.339178</td>\n",
       "      <td>-0.470428</td>\n",
       "      <td>-0.540645</td>\n",
       "      <td>-0.112525</td>\n",
       "      <td>0.726222</td>\n",
       "      <td>0.654378</td>\n",
       "      <td>...</td>\n",
       "      <td>0.290543</td>\n",
       "      <td>-0.702083</td>\n",
       "      <td>0.328641</td>\n",
       "      <td>0.850963</td>\n",
       "      <td>-0.402223</td>\n",
       "      <td>0.423229</td>\n",
       "      <td>-0.508496</td>\n",
       "      <td>0.003221</td>\n",
       "      <td>0.022581</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RJ Barrett</th>\n",
       "      <td>-0.969208</td>\n",
       "      <td>1.648993</td>\n",
       "      <td>2.534505</td>\n",
       "      <td>3.172814</td>\n",
       "      <td>-0.474093</td>\n",
       "      <td>2.031848</td>\n",
       "      <td>2.617105</td>\n",
       "      <td>-0.112525</td>\n",
       "      <td>1.003405</td>\n",
       "      <td>1.534474</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.995511</td>\n",
       "      <td>1.303869</td>\n",
       "      <td>1.415056</td>\n",
       "      <td>-0.274649</td>\n",
       "      <td>-0.543850</td>\n",
       "      <td>2.002443</td>\n",
       "      <td>-0.698233</td>\n",
       "      <td>2.445555</td>\n",
       "      <td>1.005805</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ignas Brazdeikis</th>\n",
       "      <td>-0.997155</td>\n",
       "      <td>0.405047</td>\n",
       "      <td>0.186423</td>\n",
       "      <td>0.386385</td>\n",
       "      <td>-0.366161</td>\n",
       "      <td>-0.041466</td>\n",
       "      <td>0.177025</td>\n",
       "      <td>-0.525589</td>\n",
       "      <td>0.449038</td>\n",
       "      <td>0.343755</td>\n",
       "      <td>...</td>\n",
       "      <td>0.421773</td>\n",
       "      <td>0.078009</td>\n",
       "      <td>-0.961477</td>\n",
       "      <td>-0.724894</td>\n",
       "      <td>-0.402223</td>\n",
       "      <td>-1.155984</td>\n",
       "      <td>-0.129021</td>\n",
       "      <td>0.352126</td>\n",
       "      <td>0.710005</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 G        MP        FG       FGA       FG%  \\\n",
       "Name                                                                         \n",
       "Zion Williamson          -1.108943  0.492341  2.961429  1.203738  2.574998   \n",
       "Jaxson Hayes             -1.136891 -0.969842 -0.738578 -1.731302  3.222593   \n",
       "Nickeil Alexander-Walker -0.158740  0.470517 -0.098193  0.052013 -0.339178   \n",
       "RJ Barrett               -0.969208  1.648993  2.534505  3.172814 -0.474093   \n",
       "Ignas Brazdeikis         -0.997155  0.405047  0.186423  0.386385 -0.366161   \n",
       "\n",
       "                                2P       2PA       2P%        3P       3PA  \\\n",
       "Name                                                                         \n",
       "Zion Williamson           3.247239  2.042969  2.992583 -0.659698 -0.536341   \n",
       "Jaxson Hayes              0.101521 -0.684179  2.721954 -1.629841 -1.675289   \n",
       "Nickeil Alexander-Walker -0.470428 -0.540645 -0.112525  0.726222  0.654378   \n",
       "RJ Barrett                2.031848  2.617105 -0.112525  1.003405  1.534474   \n",
       "Ignas Brazdeikis         -0.041466  0.177025 -0.525589  0.449038  0.343755   \n",
       "\n",
       "                          ...       FT%       TRB       AST       STL  \\\n",
       "Name                      ...                                           \n",
       "Zion Williamson           ... -1.323586  2.028241 -0.078765  2.426820   \n",
       "Jaxson Hayes              ... -0.011286 -0.144874 -1.300982 -0.950017   \n",
       "Nickeil Alexander-Walker  ...  0.290543 -0.702083  0.328641  0.850963   \n",
       "RJ Barrett                ... -0.995511  1.303869  1.415056 -0.274649   \n",
       "Ignas Brazdeikis          ...  0.421773  0.078009 -0.961477 -0.724894   \n",
       "\n",
       "                               BLK       TOV        PF       PTS       SOS  \\\n",
       "Name                                                                         \n",
       "Zion Williamson           1.438938  0.739072 -0.129021  2.445555  1.005805   \n",
       "Jaxson Hayes              2.005448 -1.629748  2.147826 -0.936139  0.899567   \n",
       "Nickeil Alexander-Walker -0.402223  0.423229 -0.508496  0.003221  0.022581   \n",
       "RJ Barrett               -0.543850  2.002443 -0.698233  2.445555  1.005805   \n",
       "Ignas Brazdeikis         -0.402223 -1.155984 -0.129021  0.352126  0.710005   \n",
       "\n",
       "                          label  \n",
       "Name                             \n",
       "Zion Williamson               3  \n",
       "Jaxson Hayes                  2  \n",
       "Nickeil Alexander-Walker      0  \n",
       "RJ Barrett                    0  \n",
       "Ignas Brazdeikis              0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df2.copy(deep=True)\n",
    "df4['label'] = cluster.labels_ \n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>G</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>2P</th>\n",
       "      <th>2PA</th>\n",
       "      <th>2P%</th>\n",
       "      <th>3P</th>\n",
       "      <th>3PA</th>\n",
       "      <th>...</th>\n",
       "      <th>FT%</th>\n",
       "      <th>TRB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "      <th>SOS</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</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>Zion Williamson</th>\n",
       "      <td>-1.108943</td>\n",
       "      <td>0.492341</td>\n",
       "      <td>2.961429</td>\n",
       "      <td>1.203738</td>\n",
       "      <td>2.574998</td>\n",
       "      <td>3.247239</td>\n",
       "      <td>2.042969</td>\n",
       "      <td>2.992583</td>\n",
       "      <td>-0.659698</td>\n",
       "      <td>-0.536341</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.323586</td>\n",
       "      <td>2.028241</td>\n",
       "      <td>-0.078765</td>\n",
       "      <td>2.426820</td>\n",
       "      <td>1.438938</td>\n",
       "      <td>0.739072</td>\n",
       "      <td>-0.129021</td>\n",
       "      <td>2.445555</td>\n",
       "      <td>1.005805</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brandon Clarke</th>\n",
       "      <td>-0.997155</td>\n",
       "      <td>0.077692</td>\n",
       "      <td>1.467195</td>\n",
       "      <td>0.052013</td>\n",
       "      <td>2.669439</td>\n",
       "      <td>2.246329</td>\n",
       "      <td>1.420988</td>\n",
       "      <td>2.394351</td>\n",
       "      <td>-1.491249</td>\n",
       "      <td>-1.468208</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.614944</td>\n",
       "      <td>1.861078</td>\n",
       "      <td>-0.214567</td>\n",
       "      <td>0.400718</td>\n",
       "      <td>3.421726</td>\n",
       "      <td>-0.682220</td>\n",
       "      <td>-0.129021</td>\n",
       "      <td>0.915741</td>\n",
       "      <td>-0.444034</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bol Bol</th>\n",
       "      <td>-1.779675</td>\n",
       "      <td>0.448694</td>\n",
       "      <td>2.392197</td>\n",
       "      <td>1.761023</td>\n",
       "      <td>0.969503</td>\n",
       "      <td>2.246329</td>\n",
       "      <td>2.473571</td>\n",
       "      <td>0.471464</td>\n",
       "      <td>0.310446</td>\n",
       "      <td>-0.225719</td>\n",
       "      <td>...</td>\n",
       "      <td>0.211805</td>\n",
       "      <td>2.418287</td>\n",
       "      <td>-0.825676</td>\n",
       "      <td>-0.499772</td>\n",
       "      <td>2.713587</td>\n",
       "      <td>0.107387</td>\n",
       "      <td>-0.887970</td>\n",
       "      <td>2.016134</td>\n",
       "      <td>-0.210727</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        G        MP        FG       FGA       FG%        2P  \\\n",
       "Name                                                                          \n",
       "Zion Williamson -1.108943  0.492341  2.961429  1.203738  2.574998  3.247239   \n",
       "Brandon Clarke  -0.997155  0.077692  1.467195  0.052013  2.669439  2.246329   \n",
       "Bol Bol         -1.779675  0.448694  2.392197  1.761023  0.969503  2.246329   \n",
       "\n",
       "                      2PA       2P%        3P       3PA  ...       FT%  \\\n",
       "Name                                                     ...             \n",
       "Zion Williamson  2.042969  2.992583 -0.659698 -0.536341  ... -1.323586   \n",
       "Brandon Clarke   1.420988  2.394351 -1.491249 -1.468208  ... -0.614944   \n",
       "Bol Bol          2.473571  0.471464  0.310446 -0.225719  ...  0.211805   \n",
       "\n",
       "                      TRB       AST       STL       BLK       TOV        PF  \\\n",
       "Name                                                                          \n",
       "Zion Williamson  2.028241 -0.078765  2.426820  1.438938  0.739072 -0.129021   \n",
       "Brandon Clarke   1.861078 -0.214567  0.400718  3.421726 -0.682220 -0.129021   \n",
       "Bol Bol          2.418287 -0.825676 -0.499772  2.713587  0.107387 -0.887970   \n",
       "\n",
       "                      PTS       SOS  label  \n",
       "Name                                        \n",
       "Zion Williamson  2.445555  1.005805      3  \n",
       "Brandon Clarke   0.915741 -0.444034      3  \n",
       "Bol Bol          2.016134 -0.210727      3  \n",
       "\n",
       "[3 rows x 23 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name = 'Zion Williamson'\n",
    "labnum = df4[df4.index == name].iloc[0].label\n",
    "df4[df4['label'] == labnum]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Affinity Propagation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import AffinityPropagation\n",
    "cluster = AffinityPropagation(convergence_iter=15).fit(df2.values)\n",
    "plot_embedding(subSpace, cluster.labels_, 'AffinityPropagation projection',10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>G</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>2P</th>\n",
       "      <th>2PA</th>\n",
       "      <th>2P%</th>\n",
       "      <th>3P</th>\n",
       "      <th>3PA</th>\n",
       "      <th>...</th>\n",
       "      <th>FT%</th>\n",
       "      <th>TRB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "      <th>SOS</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</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>Zion Williamson</th>\n",
       "      <td>-1.108943</td>\n",
       "      <td>0.492341</td>\n",
       "      <td>2.961429</td>\n",
       "      <td>1.203738</td>\n",
       "      <td>2.574998</td>\n",
       "      <td>3.247239</td>\n",
       "      <td>2.042969</td>\n",
       "      <td>2.992583</td>\n",
       "      <td>-0.659698</td>\n",
       "      <td>-0.536341</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.323586</td>\n",
       "      <td>2.028241</td>\n",
       "      <td>-0.078765</td>\n",
       "      <td>2.426820</td>\n",
       "      <td>1.438938</td>\n",
       "      <td>0.739072</td>\n",
       "      <td>-0.129021</td>\n",
       "      <td>2.445555</td>\n",
       "      <td>1.005805</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jaxson Hayes</th>\n",
       "      <td>-1.136891</td>\n",
       "      <td>-0.969842</td>\n",
       "      <td>-0.738578</td>\n",
       "      <td>-1.731302</td>\n",
       "      <td>3.222593</td>\n",
       "      <td>0.101521</td>\n",
       "      <td>-0.684179</td>\n",
       "      <td>2.721954</td>\n",
       "      <td>-1.629841</td>\n",
       "      <td>-1.675289</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.011286</td>\n",
       "      <td>-0.144874</td>\n",
       "      <td>-1.300982</td>\n",
       "      <td>-0.950017</td>\n",
       "      <td>2.005448</td>\n",
       "      <td>-1.629748</td>\n",
       "      <td>2.147826</td>\n",
       "      <td>-0.936139</td>\n",
       "      <td>0.899567</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nickeil Alexander-Walker</th>\n",
       "      <td>-0.158740</td>\n",
       "      <td>0.470517</td>\n",
       "      <td>-0.098193</td>\n",
       "      <td>0.052013</td>\n",
       "      <td>-0.339178</td>\n",
       "      <td>-0.470428</td>\n",
       "      <td>-0.540645</td>\n",
       "      <td>-0.112525</td>\n",
       "      <td>0.726222</td>\n",
       "      <td>0.654378</td>\n",
       "      <td>...</td>\n",
       "      <td>0.290543</td>\n",
       "      <td>-0.702083</td>\n",
       "      <td>0.328641</td>\n",
       "      <td>0.850963</td>\n",
       "      <td>-0.402223</td>\n",
       "      <td>0.423229</td>\n",
       "      <td>-0.508496</td>\n",
       "      <td>0.003221</td>\n",
       "      <td>0.022581</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RJ Barrett</th>\n",
       "      <td>-0.969208</td>\n",
       "      <td>1.648993</td>\n",
       "      <td>2.534505</td>\n",
       "      <td>3.172814</td>\n",
       "      <td>-0.474093</td>\n",
       "      <td>2.031848</td>\n",
       "      <td>2.617105</td>\n",
       "      <td>-0.112525</td>\n",
       "      <td>1.003405</td>\n",
       "      <td>1.534474</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.995511</td>\n",
       "      <td>1.303869</td>\n",
       "      <td>1.415056</td>\n",
       "      <td>-0.274649</td>\n",
       "      <td>-0.543850</td>\n",
       "      <td>2.002443</td>\n",
       "      <td>-0.698233</td>\n",
       "      <td>2.445555</td>\n",
       "      <td>1.005805</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ignas Brazdeikis</th>\n",
       "      <td>-0.997155</td>\n",
       "      <td>0.405047</td>\n",
       "      <td>0.186423</td>\n",
       "      <td>0.386385</td>\n",
       "      <td>-0.366161</td>\n",
       "      <td>-0.041466</td>\n",
       "      <td>0.177025</td>\n",
       "      <td>-0.525589</td>\n",
       "      <td>0.449038</td>\n",
       "      <td>0.343755</td>\n",
       "      <td>...</td>\n",
       "      <td>0.421773</td>\n",
       "      <td>0.078009</td>\n",
       "      <td>-0.961477</td>\n",
       "      <td>-0.724894</td>\n",
       "      <td>-0.402223</td>\n",
       "      <td>-1.155984</td>\n",
       "      <td>-0.129021</td>\n",
       "      <td>0.352126</td>\n",
       "      <td>0.710005</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 G        MP        FG       FGA       FG%  \\\n",
       "Name                                                                         \n",
       "Zion Williamson          -1.108943  0.492341  2.961429  1.203738  2.574998   \n",
       "Jaxson Hayes             -1.136891 -0.969842 -0.738578 -1.731302  3.222593   \n",
       "Nickeil Alexander-Walker -0.158740  0.470517 -0.098193  0.052013 -0.339178   \n",
       "RJ Barrett               -0.969208  1.648993  2.534505  3.172814 -0.474093   \n",
       "Ignas Brazdeikis         -0.997155  0.405047  0.186423  0.386385 -0.366161   \n",
       "\n",
       "                                2P       2PA       2P%        3P       3PA  \\\n",
       "Name                                                                         \n",
       "Zion Williamson           3.247239  2.042969  2.992583 -0.659698 -0.536341   \n",
       "Jaxson Hayes              0.101521 -0.684179  2.721954 -1.629841 -1.675289   \n",
       "Nickeil Alexander-Walker -0.470428 -0.540645 -0.112525  0.726222  0.654378   \n",
       "RJ Barrett                2.031848  2.617105 -0.112525  1.003405  1.534474   \n",
       "Ignas Brazdeikis         -0.041466  0.177025 -0.525589  0.449038  0.343755   \n",
       "\n",
       "                          ...       FT%       TRB       AST       STL  \\\n",
       "Name                      ...                                           \n",
       "Zion Williamson           ... -1.323586  2.028241 -0.078765  2.426820   \n",
       "Jaxson Hayes              ... -0.011286 -0.144874 -1.300982 -0.950017   \n",
       "Nickeil Alexander-Walker  ...  0.290543 -0.702083  0.328641  0.850963   \n",
       "RJ Barrett                ... -0.995511  1.303869  1.415056 -0.274649   \n",
       "Ignas Brazdeikis          ...  0.421773  0.078009 -0.961477 -0.724894   \n",
       "\n",
       "                               BLK       TOV        PF       PTS       SOS  \\\n",
       "Name                                                                         \n",
       "Zion Williamson           1.438938  0.739072 -0.129021  2.445555  1.005805   \n",
       "Jaxson Hayes              2.005448 -1.629748  2.147826 -0.936139  0.899567   \n",
       "Nickeil Alexander-Walker -0.402223  0.423229 -0.508496  0.003221  0.022581   \n",
       "RJ Barrett               -0.543850  2.002443 -0.698233  2.445555  1.005805   \n",
       "Ignas Brazdeikis         -0.402223 -1.155984 -0.129021  0.352126  0.710005   \n",
       "\n",
       "                          label  \n",
       "Name                             \n",
       "Zion Williamson               6  \n",
       "Jaxson Hayes                  0  \n",
       "Nickeil Alexander-Walker      2  \n",
       "RJ Barrett                    1  \n",
       "Ignas Brazdeikis              2  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df2.copy(deep=True)\n",
    "df4['label'] = cluster.labels_ \n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>G</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>2P</th>\n",
       "      <th>2PA</th>\n",
       "      <th>2P%</th>\n",
       "      <th>3P</th>\n",
       "      <th>3PA</th>\n",
       "      <th>...</th>\n",
       "      <th>FT%</th>\n",
       "      <th>TRB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "      <th>SOS</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</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>Zion Williamson</th>\n",
       "      <td>-1.108943</td>\n",
       "      <td>0.492341</td>\n",
       "      <td>2.961429</td>\n",
       "      <td>1.203738</td>\n",
       "      <td>2.574998</td>\n",
       "      <td>3.247239</td>\n",
       "      <td>2.042969</td>\n",
       "      <td>2.992583</td>\n",
       "      <td>-0.659698</td>\n",
       "      <td>-0.536341</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.323586</td>\n",
       "      <td>2.028241</td>\n",
       "      <td>-0.078765</td>\n",
       "      <td>2.426820</td>\n",
       "      <td>1.438938</td>\n",
       "      <td>0.739072</td>\n",
       "      <td>-0.129021</td>\n",
       "      <td>2.445555</td>\n",
       "      <td>1.005805</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brandon Clarke</th>\n",
       "      <td>-0.997155</td>\n",
       "      <td>0.077692</td>\n",
       "      <td>1.467195</td>\n",
       "      <td>0.052013</td>\n",
       "      <td>2.669439</td>\n",
       "      <td>2.246329</td>\n",
       "      <td>1.420988</td>\n",
       "      <td>2.394351</td>\n",
       "      <td>-1.491249</td>\n",
       "      <td>-1.468208</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.614944</td>\n",
       "      <td>1.861078</td>\n",
       "      <td>-0.214567</td>\n",
       "      <td>0.400718</td>\n",
       "      <td>3.421726</td>\n",
       "      <td>-0.682220</td>\n",
       "      <td>-0.129021</td>\n",
       "      <td>0.915741</td>\n",
       "      <td>-0.444034</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bol Bol</th>\n",
       "      <td>-1.779675</td>\n",
       "      <td>0.448694</td>\n",
       "      <td>2.392197</td>\n",
       "      <td>1.761023</td>\n",
       "      <td>0.969503</td>\n",
       "      <td>2.246329</td>\n",
       "      <td>2.473571</td>\n",
       "      <td>0.471464</td>\n",
       "      <td>0.310446</td>\n",
       "      <td>-0.225719</td>\n",
       "      <td>...</td>\n",
       "      <td>0.211805</td>\n",
       "      <td>2.418287</td>\n",
       "      <td>-0.825676</td>\n",
       "      <td>-0.499772</td>\n",
       "      <td>2.713587</td>\n",
       "      <td>0.107387</td>\n",
       "      <td>-0.887970</td>\n",
       "      <td>2.016134</td>\n",
       "      <td>-0.210727</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        G        MP        FG       FGA       FG%        2P  \\\n",
       "Name                                                                          \n",
       "Zion Williamson -1.108943  0.492341  2.961429  1.203738  2.574998  3.247239   \n",
       "Brandon Clarke  -0.997155  0.077692  1.467195  0.052013  2.669439  2.246329   \n",
       "Bol Bol         -1.779675  0.448694  2.392197  1.761023  0.969503  2.246329   \n",
       "\n",
       "                      2PA       2P%        3P       3PA  ...       FT%  \\\n",
       "Name                                                     ...             \n",
       "Zion Williamson  2.042969  2.992583 -0.659698 -0.536341  ... -1.323586   \n",
       "Brandon Clarke   1.420988  2.394351 -1.491249 -1.468208  ... -0.614944   \n",
       "Bol Bol          2.473571  0.471464  0.310446 -0.225719  ...  0.211805   \n",
       "\n",
       "                      TRB       AST       STL       BLK       TOV        PF  \\\n",
       "Name                                                                          \n",
       "Zion Williamson  2.028241 -0.078765  2.426820  1.438938  0.739072 -0.129021   \n",
       "Brandon Clarke   1.861078 -0.214567  0.400718  3.421726 -0.682220 -0.129021   \n",
       "Bol Bol          2.418287 -0.825676 -0.499772  2.713587  0.107387 -0.887970   \n",
       "\n",
       "                      PTS       SOS  label  \n",
       "Name                                        \n",
       "Zion Williamson  2.445555  1.005805      6  \n",
       "Brandon Clarke   0.915741 -0.444034      6  \n",
       "Bol Bol          2.016134 -0.210727      6  \n",
       "\n",
       "[3 rows x 23 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name = 'Zion Williamson'\n",
    "labnum = df4[df4.index == name].iloc[0].label\n",
    "df4[df4['label'] == labnum]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}