NBA Player Clustering - PCA x1, KMeans Clustering x2, LDA x1 (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.0 Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from urllib.request import urlopen\n",
    "from bs4 import BeautifulSoup\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.0 Use BeautifulSoup and Urlopen to Retrieve Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "list_of_years = [2018, 2019]\n",
    "\n",
    "list_data = []\n",
    "\n",
    "for year in list_of_years:\n",
    "    \n",
    "    url = f'https://www.basketball-reference.com/leagues/NBA_{year}_advanced.html'\n",
    "\n",
    "    html = urlopen(url)\n",
    "\n",
    "    soup = BeautifulSoup(html)\n",
    "    \n",
    "    headers = [th.getText() for th in soup.findAll('tr')[0].findAll('th')]\n",
    "\n",
    "    headers = headers[1:] # columns\n",
    "    \n",
    "    rows = soup.findAll('tr')\n",
    "\n",
    "    player_stats_1 = [[td.getText() for td in rows[i].findAll('td')] for i in range(len(rows))] # rows\n",
    "    \n",
    "    tmp_df = pd.DataFrame(player_stats_1, columns=headers)\n",
    "    \n",
    "    tmp_mat = tmp_df.values\n",
    "    \n",
    "    list_data.append(tmp_mat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Build Full DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1426, 28)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_data = np.array(list_data[0])\n",
    "\n",
    "for list_i in list_data[1:]:\n",
    "    \n",
    "    tmp_data = np.array(list_i)\n",
    "    \n",
    "    full_data = np.vstack((full_data, tmp_data))\n",
    "    \n",
    "    \n",
    "full_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Player</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tm</th>\n",
       "      <th>G</th>\n",
       "      <th>MP</th>\n",
       "      <th>PER</th>\n",
       "      <th>TS%</th>\n",
       "      <th>3PAr</th>\n",
       "      <th>FTr</th>\n",
       "      <th>...</th>\n",
       "      <th></th>\n",
       "      <th>OWS</th>\n",
       "      <th>DWS</th>\n",
       "      <th>WS</th>\n",
       "      <th>WS/48</th>\n",
       "      <th></th>\n",
       "      <th>OBPM</th>\n",
       "      <th>DBPM</th>\n",
       "      <th>BPM</th>\n",
       "      <th>VORP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Álex Abrines</td>\n",
       "      <td>SG</td>\n",
       "      <td>24</td>\n",
       "      <td>OKC</td>\n",
       "      <td>75</td>\n",
       "      <td>1134</td>\n",
       "      <td>9.0</td>\n",
       "      <td>.567</td>\n",
       "      <td>.759</td>\n",
       "      <td>.158</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>.094</td>\n",
       "      <td></td>\n",
       "      <td>-1.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Quincy Acy</td>\n",
       "      <td>PF</td>\n",
       "      <td>27</td>\n",
       "      <td>BRK</td>\n",
       "      <td>70</td>\n",
       "      <td>1359</td>\n",
       "      <td>8.2</td>\n",
       "      <td>.525</td>\n",
       "      <td>.800</td>\n",
       "      <td>.164</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>-0.1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>.036</td>\n",
       "      <td></td>\n",
       "      <td>-2.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>-0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Steven Adams</td>\n",
       "      <td>C</td>\n",
       "      <td>24</td>\n",
       "      <td>OKC</td>\n",
       "      <td>76</td>\n",
       "      <td>2487</td>\n",
       "      <td>20.6</td>\n",
       "      <td>.630</td>\n",
       "      <td>.003</td>\n",
       "      <td>.402</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>.187</td>\n",
       "      <td></td>\n",
       "      <td>1.7</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bam Adebayo</td>\n",
       "      <td>C</td>\n",
       "      <td>20</td>\n",
       "      <td>MIA</td>\n",
       "      <td>69</td>\n",
       "      <td>1368</td>\n",
       "      <td>15.7</td>\n",
       "      <td>.570</td>\n",
       "      <td>.021</td>\n",
       "      <td>.526</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>4.2</td>\n",
       "      <td>.148</td>\n",
       "      <td></td>\n",
       "      <td>-1.1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arron Afflalo</td>\n",
       "      <td>SG</td>\n",
       "      <td>32</td>\n",
       "      <td>ORL</td>\n",
       "      <td>53</td>\n",
       "      <td>682</td>\n",
       "      <td>5.8</td>\n",
       "      <td>.516</td>\n",
       "      <td>.432</td>\n",
       "      <td>.160</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>.009</td>\n",
       "      <td></td>\n",
       "      <td>-3.8</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>-5.4</td>\n",
       "      <td>-0.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Player Pos Age   Tm   G    MP   PER   TS%  3PAr   FTr  ...     OWS  \\\n",
       "0   Álex Abrines  SG  24  OKC  75  1134   9.0  .567  .759  .158  ...     1.3   \n",
       "1     Quincy Acy  PF  27  BRK  70  1359   8.2  .525  .800  .164  ...    -0.1   \n",
       "2   Steven Adams   C  24  OKC  76  2487  20.6  .630  .003  .402  ...     6.7   \n",
       "3    Bam Adebayo   C  20  MIA  69  1368  15.7  .570  .021  .526  ...     2.3   \n",
       "4  Arron Afflalo  SG  32  ORL  53   682   5.8  .516  .432  .160  ...    -0.1   \n",
       "\n",
       "   DWS   WS WS/48    OBPM  DBPM   BPM  VORP  \n",
       "0  1.0  2.2  .094    -1.9   0.4  -1.5   0.1  \n",
       "1  1.1  1.0  .036    -2.6   0.1  -2.5  -0.2  \n",
       "2  3.0  9.7  .187     1.7  -0.6   1.1   2.0  \n",
       "3  1.9  4.2  .148    -1.1   0.7  -0.4   0.6  \n",
       "4  0.2  0.1  .009    -3.8  -1.5  -5.4  -0.6  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "og_df = pd.DataFrame(full_data[1:], columns=headers)\n",
    "\n",
    "og_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Create DataFrame and Group By Player Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "og_df = og_df.apply(pd.to_numeric, errors='ignore')\n",
    "\n",
    "# Converts ordinal variable (Pos) into numeric\n",
    "og_df['Pos'] = og_df['Pos'].map({'PG': 1, 'SG': 2, 'SF': 3, 'PF': 4, 'C': 5})\n",
    "\n",
    "new_df = og_df.groupby(by='Player').mean()\n",
    "\n",
    "new_df = new_df.round(3) # round to 3 decimal places"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Control Size of Player Sample by Minimum Minutes Played"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Player</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Age</th>\n",
       "      <th>G</th>\n",
       "      <th>MP</th>\n",
       "      <th>PER</th>\n",
       "      <th>TS%</th>\n",
       "      <th>3PAr</th>\n",
       "      <th>FTr</th>\n",
       "      <th>ORB%</th>\n",
       "      <th>...</th>\n",
       "      <th></th>\n",
       "      <th>OWS</th>\n",
       "      <th>DWS</th>\n",
       "      <th>WS</th>\n",
       "      <th>WS/48</th>\n",
       "      <th></th>\n",
       "      <th>OBPM</th>\n",
       "      <th>DBPM</th>\n",
       "      <th>BPM</th>\n",
       "      <th>VORP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aaron Gordon</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.5</td>\n",
       "      <td>68.0</td>\n",
       "      <td>2271.0</td>\n",
       "      <td>15.8</td>\n",
       "      <td>0.534</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.251</td>\n",
       "      <td>5.10</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2.65</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.083</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.60</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Al Horford</td>\n",
       "      <td>5.0</td>\n",
       "      <td>31.5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2125.0</td>\n",
       "      <td>18.9</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.290</td>\n",
       "      <td>0.145</td>\n",
       "      <td>5.75</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.25</td>\n",
       "      <td>3.35</td>\n",
       "      <td>7.65</td>\n",
       "      <td>0.173</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.85</td>\n",
       "      <td>1.95</td>\n",
       "      <td>4.75</td>\n",
       "      <td>3.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Al-Farouq Aminu</td>\n",
       "      <td>4.0</td>\n",
       "      <td>27.5</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2182.0</td>\n",
       "      <td>12.6</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.527</td>\n",
       "      <td>0.214</td>\n",
       "      <td>5.25</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.95</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.109</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.55</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alex Len</td>\n",
       "      <td>5.0</td>\n",
       "      <td>24.5</td>\n",
       "      <td>73.0</td>\n",
       "      <td>1469.5</td>\n",
       "      <td>18.3</td>\n",
       "      <td>0.593</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.472</td>\n",
       "      <td>11.85</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1.15</td>\n",
       "      <td>3.85</td>\n",
       "      <td>0.127</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.30</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allen Crabbe</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.5</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1665.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>0.669</td>\n",
       "      <td>0.156</td>\n",
       "      <td>1.40</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.20</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.053</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.20</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-1.75</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Player  Pos   Age     G      MP   PER    TS%   3PAr    FTr   ORB%  \\\n",
       "0     Aaron Gordon  4.0  22.5  68.0  2271.0  15.8  0.534  0.364  0.251   5.10   \n",
       "1       Al Horford  5.0  31.5  70.0  2125.0  18.9  0.590  0.290  0.145   5.75   \n",
       "2  Al-Farouq Aminu  4.0  27.5  75.0  2182.0  12.6  0.545  0.527  0.214   5.25   \n",
       "3         Alex Len  5.0  24.5  73.0  1469.5  18.3  0.593  0.162  0.472  11.85   \n",
       "4     Allen Crabbe  2.0  25.5  59.0  1665.0  10.0  0.538  0.669  0.156   1.40   \n",
       "\n",
       "   ...       OWS   DWS    WS  WS/48      OBPM  DBPM   BPM  VORP  \n",
       "0  ... NaN  1.35  2.65  4.00  0.083 NaN  0.60 -0.15  0.45  1.40  \n",
       "1  ... NaN  4.25  3.35  7.65  0.173 NaN  2.85  1.95  4.75  3.65  \n",
       "2  ... NaN  1.95  3.00  5.00  0.109 NaN -0.30  0.85  0.55  1.40  \n",
       "3  ... NaN  2.70  1.15  3.85  0.127 NaN  0.30 -0.80 -0.45  0.55  \n",
       "4  ... NaN  0.80  1.20  2.00  0.053 NaN -1.20 -0.55 -1.75  0.25  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_mins_played = 1000\n",
    "\n",
    "new_df = new_df[new_df['MP'] > min_mins_played]\n",
    "\n",
    "new_df = new_df.reset_index(drop=False)\n",
    "\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = new_df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5 Convert to Numbers and Drop Unnecessary Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Keep specific columns\n",
    "columns_to_keep = ['Player', 'Pos', 'MP', 'Age', 'TS%', '3PAr', 'FTr', 'ORB%', 'DRB%',\n",
    "                   'AST%', 'TOV%', 'USG%', 'STL%', 'BLK%', 'DBPM', 'WS']\n",
    "\n",
    "df = pd.DataFrame(new_df[columns_to_keep], columns=columns_to_keep)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.6 Convert to Base English ASCII Letters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import unidecode\n",
    "\n",
    "df['Player'] = df['Player'].apply(lambda x: unidecode.unidecode(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.0 Import 'DRAYMOND' data from FiveThirtyEight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "sys.path.append('/Users/seanmcalevey/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "directory = '/Users/seanmcalevey/documents/github/nbaplayerclustering/fivethirtyeight_data/nba-draymond/'\n",
    "\n",
    "df1 = pd.read_csv(directory + 'draymond.csv', sep=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Limit DRAYMOND to Players with >500 Poss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_possessions = 500\n",
    "\n",
    "df1 = df1[df1['season'] >= min(list_of_years)]\n",
    "\n",
    "df1 = df1[df1['possessions'] > min_possessions]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Build Total DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_df = pd.merge(df, df1, left_on='Player', right_on='player')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_df = total_df.drop(['player', 'season', 'possessions'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "total_df = total_df[['Player', 'Pos', 'MP', 'Age', 'TS%', '3PAr', 'FTr', 'ORB%', 'DRB%', 'AST%', 'TOV%',\n",
    "       'USG%', 'STL%', 'BLK%', 'DBPM', 'DRAYMOND', 'WS']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_df = total_df.groupby('Player').mean()\n",
    "\n",
    "total_df = total_df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 273 unique players who qualify for this clustering.\n",
      "There are 273 total player names in this dataframe.\n"
     ]
    }
   ],
   "source": [
    "print('There are ' + str(len(total_df['Player'].unique())) + ' unique players who qualify for this clustering.')\n",
    "\n",
    "print('There are ' + str(len(total_df)) + ' total player names in this dataframe.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Convert Pandas DataFrame into Matrix for Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_as_matrix = total_df.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.0 Import and Scale Vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "standard_scaler = StandardScaler()\n",
    "\n",
    "scaled_vectors = standard_scaler.fit_transform(df_as_matrix[:,1:-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Import PCA and Reduce Dimensionality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "search_space = np.arange(3,13)\n",
    "\n",
    "list_of_ev = []\n",
    "\n",
    "for n_components in search_space:\n",
    "    \n",
    "    pca = PCA(n_components)\n",
    "\n",
    "    dim_reduction = pca.fit_transform(scaled_vectors)\n",
    "\n",
    "    explained_variance_1 = round(100*sum(pca.explained_variance_ratio_), 3)\n",
    "    \n",
    "    list_of_ev.append(explained_variance_1)\n",
    "\n",
    "plt.plot(search_space, list_of_ev)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_components = 8\n",
    "\n",
    "pca = PCA(n_components)\n",
    "\n",
    "dim_reduction = pca.fit_transform(scaled_vectors)\n",
    "\n",
    "reduced_df = total_df.copy()\n",
    "\n",
    "for i in range(n_components):\n",
    "\n",
    "    reduced_df[f'PCA{i+1}'] = [dim_reduction[x][i] for x in range(len(dim_reduction))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Scale and Cluster with KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale before clustering:\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "dim_reduction = scaler.fit_transform(dim_reduction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Graph Silhouette_Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "num_clusters_list = np.arange(2,15)\n",
    "\n",
    "sil_score = []\n",
    "\n",
    "for num_clusters in num_clusters_list:\n",
    "\n",
    "    kmeans = KMeans(num_clusters, random_state=42)\n",
    "\n",
    "    clusters = kmeans.fit_transform(dim_reduction)\n",
    "\n",
    "    sil_score.append(silhouette_score(clusters, kmeans.labels_))\n",
    "    \n",
    "\n",
    "plt.plot(num_clusters_list, sil_score)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4 Set Num_Clusters According to Silhouette_Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_clusters = 6\n",
    "\n",
    "kmeans = KMeans(num_clusters, random_state=42)\n",
    "\n",
    "clusters = kmeans.fit_predict(dim_reduction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced_df['Cluster'] = clusters\n",
    "\n",
    "reduced_df = reduced_df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 Build Averages Table to See Clusters Grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "averages_df = reduced_df.groupby('Cluster').mean()\n",
    "\n",
    "averages_df = averages_df.reset_index(drop=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "averages_df = averages_df.apply(lambda x: round(x, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "averages_df = averages_df[['Cluster', 'Pos', 'MP', 'Age', 'TS%', '3PAr', 'FTr', 'ORB%', 'DRB%', 'AST%', 'TOV%', 'USG%',\n",
    "       'STL%', 'BLK%', 'DBPM', 'DRAYMOND']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "averages_df['Cluster'] = averages_df['Cluster'].apply(lambda x: int(x))\n",
    "\n",
    "averages_df = averages_df.sort_values(by='Pos', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Cluster</th>\n",
       "      <th>Pos</th>\n",
       "      <th>MP</th>\n",
       "      <th>Age</th>\n",
       "      <th>TS%</th>\n",
       "      <th>3PAr</th>\n",
       "      <th>FTr</th>\n",
       "      <th>ORB%</th>\n",
       "      <th>DRB%</th>\n",
       "      <th>AST%</th>\n",
       "      <th>TOV%</th>\n",
       "      <th>USG%</th>\n",
       "      <th>STL%</th>\n",
       "      <th>BLK%</th>\n",
       "      <th>DBPM</th>\n",
       "      <th>DRAYMOND</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.817</td>\n",
       "      <td>1514.868</td>\n",
       "      <td>26.100</td>\n",
       "      <td>0.521</td>\n",
       "      <td>0.315</td>\n",
       "      <td>0.248</td>\n",
       "      <td>2.667</td>\n",
       "      <td>12.253</td>\n",
       "      <td>22.734</td>\n",
       "      <td>13.724</td>\n",
       "      <td>23.578</td>\n",
       "      <td>1.432</td>\n",
       "      <td>0.847</td>\n",
       "      <td>-1.127</td>\n",
       "      <td>-0.635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2.163</td>\n",
       "      <td>1479.833</td>\n",
       "      <td>25.317</td>\n",
       "      <td>0.543</td>\n",
       "      <td>0.351</td>\n",
       "      <td>0.255</td>\n",
       "      <td>3.557</td>\n",
       "      <td>13.978</td>\n",
       "      <td>16.678</td>\n",
       "      <td>13.433</td>\n",
       "      <td>18.902</td>\n",
       "      <td>2.309</td>\n",
       "      <td>1.682</td>\n",
       "      <td>0.898</td>\n",
       "      <td>-0.089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2.707</td>\n",
       "      <td>1775.059</td>\n",
       "      <td>26.257</td>\n",
       "      <td>0.568</td>\n",
       "      <td>0.492</td>\n",
       "      <td>0.193</td>\n",
       "      <td>2.847</td>\n",
       "      <td>12.653</td>\n",
       "      <td>10.709</td>\n",
       "      <td>10.085</td>\n",
       "      <td>18.011</td>\n",
       "      <td>1.340</td>\n",
       "      <td>1.001</td>\n",
       "      <td>-0.469</td>\n",
       "      <td>-0.434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2.839</td>\n",
       "      <td>2487.742</td>\n",
       "      <td>25.694</td>\n",
       "      <td>0.574</td>\n",
       "      <td>0.303</td>\n",
       "      <td>0.312</td>\n",
       "      <td>4.644</td>\n",
       "      <td>18.377</td>\n",
       "      <td>23.682</td>\n",
       "      <td>12.219</td>\n",
       "      <td>27.781</td>\n",
       "      <td>1.727</td>\n",
       "      <td>2.129</td>\n",
       "      <td>0.552</td>\n",
       "      <td>0.516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>3.725</td>\n",
       "      <td>1474.198</td>\n",
       "      <td>28.849</td>\n",
       "      <td>0.543</td>\n",
       "      <td>0.384</td>\n",
       "      <td>0.197</td>\n",
       "      <td>5.139</td>\n",
       "      <td>17.592</td>\n",
       "      <td>11.156</td>\n",
       "      <td>12.443</td>\n",
       "      <td>16.906</td>\n",
       "      <td>1.345</td>\n",
       "      <td>2.506</td>\n",
       "      <td>0.416</td>\n",
       "      <td>0.869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4.852</td>\n",
       "      <td>1686.074</td>\n",
       "      <td>24.000</td>\n",
       "      <td>0.611</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.418</td>\n",
       "      <td>11.742</td>\n",
       "      <td>23.425</td>\n",
       "      <td>9.118</td>\n",
       "      <td>13.289</td>\n",
       "      <td>18.230</td>\n",
       "      <td>1.286</td>\n",
       "      <td>3.548</td>\n",
       "      <td>0.321</td>\n",
       "      <td>0.978</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cluster    Pos        MP     Age    TS%   3PAr    FTr    ORB%    DRB%  \\\n",
       "2        2  1.817  1514.868  26.100  0.521  0.315  0.248   2.667  12.253   \n",
       "3        3  2.163  1479.833  25.317  0.543  0.351  0.255   3.557  13.978   \n",
       "0        0  2.707  1775.059  26.257  0.568  0.492  0.193   2.847  12.653   \n",
       "1        1  2.839  2487.742  25.694  0.574  0.303  0.312   4.644  18.377   \n",
       "5        5  3.725  1474.198  28.849  0.543  0.384  0.197   5.139  17.592   \n",
       "4        4  4.852  1686.074  24.000  0.611  0.048  0.418  11.742  23.425   \n",
       "\n",
       "     AST%    TOV%    USG%   STL%   BLK%   DBPM  DRAYMOND  \n",
       "2  22.734  13.724  23.578  1.432  0.847 -1.127    -0.635  \n",
       "3  16.678  13.433  18.902  2.309  1.682  0.898    -0.089  \n",
       "0  10.709  10.085  18.011  1.340  1.001 -0.469    -0.434  \n",
       "1  23.682  12.219  27.781  1.727  2.129  0.552     0.516  \n",
       "5  11.156  12.443  16.906  1.345  2.506  0.416     0.869  \n",
       "4   9.118  13.289  18.230  1.286  3.548  0.321     0.978  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "averages_df.sort_values(by='Pos', ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.0 Linear Discriminant Analysis w/ Clusters as Targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71.429"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "\n",
    "num_components = 3\n",
    "\n",
    "scale = StandardScaler() # scale before LDA\n",
    "\n",
    "dim_reduction = scale.fit_transform(dim_reduction)\n",
    "\n",
    "lda = LinearDiscriminantAnalysis(n_components=num_components)\n",
    "\n",
    "X = dim_reduction\n",
    "\n",
    "y = clusters\n",
    "\n",
    "discriminated = lda.fit_transform(X, y)\n",
    "\n",
    "explained_variance = round(100*sum(lda.explained_variance_ratio_), 3)\n",
    "\n",
    "explained_variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced_df['LDA1'] = [discriminated[x][0] for x in range(len(dim_reduction))]\n",
    "\n",
    "reduced_df['LDA2'] = [discriminated[x][1] for x in range(len(dim_reduction))]\n",
    "\n",
    "reduced_df['LDA3'] = [discriminated[x][2] for x in range(len(dim_reduction))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 Graph Silhouette_Score for KMeans "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "num_clusters_list = np.arange(3,14)\n",
    "\n",
    "sil_score = []\n",
    "\n",
    "for num_clusters in num_clusters_list:\n",
    "    \n",
    "    scaled = scaler.fit_transform(discriminated)\n",
    "\n",
    "    kmeans = KMeans(num_clusters, random_state=42)\n",
    "\n",
    "    clusters = kmeans.fit_transform(scaled)\n",
    "\n",
    "    sil_score.append(silhouette_score(clusters, kmeans.labels_))\n",
    "    \n",
    "plt.plot(num_clusters_list, sil_score)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 KMeans Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_clusters_2 = 7\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "reduced = reduced_df[['LDA1', 'LDA2', 'LDA3']]\n",
    "\n",
    "red_scaled = scaler.fit_transform(reduced)\n",
    "\n",
    "k_cluster = KMeans(num_clusters_2, random_state=42)\n",
    "    \n",
    "clusters = k_cluster.fit_predict(red_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced_df['Cluster_2'] = clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 2nd Cluster Averages Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "averages_dict_2 = {}\n",
    "\n",
    "total_per_cluster_2 = {}\n",
    "\n",
    "for cluster in reduced_df['Cluster_2'].unique():\n",
    "    \n",
    "    cluster_group = reduced_df[reduced_df['Cluster_2'] == cluster]\n",
    "    \n",
    "    averages_dict_2[cluster] = np.mean(cluster_group)\n",
    "    \n",
    "    total_per_cluster_2[cluster] = len(cluster_group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Cluster_2</th>\n",
       "      <th>Pos</th>\n",
       "      <th>MP</th>\n",
       "      <th>Age</th>\n",
       "      <th>TS%</th>\n",
       "      <th>3PAr</th>\n",
       "      <th>FTr</th>\n",
       "      <th>ORB%</th>\n",
       "      <th>DRB%</th>\n",
       "      <th>AST%</th>\n",
       "      <th>TOV%</th>\n",
       "      <th>USG%</th>\n",
       "      <th>STL%</th>\n",
       "      <th>BLK%</th>\n",
       "      <th>DBPM</th>\n",
       "      <th>DRAYMOND</th>\n",
       "      <th>Total Players</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1.452</td>\n",
       "      <td>1522.081</td>\n",
       "      <td>27.218</td>\n",
       "      <td>0.521</td>\n",
       "      <td>0.311</td>\n",
       "      <td>0.244</td>\n",
       "      <td>2.317</td>\n",
       "      <td>11.660</td>\n",
       "      <td>26.979</td>\n",
       "      <td>14.078</td>\n",
       "      <td>24.549</td>\n",
       "      <td>1.768</td>\n",
       "      <td>0.903</td>\n",
       "      <td>-0.748</td>\n",
       "      <td>-0.579</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>1.724</td>\n",
       "      <td>1312.310</td>\n",
       "      <td>25.614</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0.372</td>\n",
       "      <td>0.217</td>\n",
       "      <td>2.778</td>\n",
       "      <td>12.047</td>\n",
       "      <td>16.725</td>\n",
       "      <td>14.414</td>\n",
       "      <td>17.866</td>\n",
       "      <td>2.045</td>\n",
       "      <td>1.235</td>\n",
       "      <td>0.440</td>\n",
       "      <td>-0.168</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2.224</td>\n",
       "      <td>2387.052</td>\n",
       "      <td>24.819</td>\n",
       "      <td>0.564</td>\n",
       "      <td>0.313</td>\n",
       "      <td>0.330</td>\n",
       "      <td>3.404</td>\n",
       "      <td>15.453</td>\n",
       "      <td>26.216</td>\n",
       "      <td>12.741</td>\n",
       "      <td>28.664</td>\n",
       "      <td>1.572</td>\n",
       "      <td>1.645</td>\n",
       "      <td>-0.204</td>\n",
       "      <td>0.025</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2.896</td>\n",
       "      <td>1504.718</td>\n",
       "      <td>27.553</td>\n",
       "      <td>0.549</td>\n",
       "      <td>0.519</td>\n",
       "      <td>0.166</td>\n",
       "      <td>3.077</td>\n",
       "      <td>13.740</td>\n",
       "      <td>10.173</td>\n",
       "      <td>10.632</td>\n",
       "      <td>15.920</td>\n",
       "      <td>1.441</td>\n",
       "      <td>1.382</td>\n",
       "      <td>0.030</td>\n",
       "      <td>-0.014</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3.188</td>\n",
       "      <td>2159.181</td>\n",
       "      <td>26.315</td>\n",
       "      <td>0.573</td>\n",
       "      <td>0.395</td>\n",
       "      <td>0.226</td>\n",
       "      <td>3.797</td>\n",
       "      <td>15.116</td>\n",
       "      <td>13.559</td>\n",
       "      <td>11.017</td>\n",
       "      <td>21.205</td>\n",
       "      <td>1.341</td>\n",
       "      <td>1.620</td>\n",
       "      <td>-0.169</td>\n",
       "      <td>0.082</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3.667</td>\n",
       "      <td>1449.312</td>\n",
       "      <td>25.266</td>\n",
       "      <td>0.560</td>\n",
       "      <td>0.243</td>\n",
       "      <td>0.301</td>\n",
       "      <td>6.203</td>\n",
       "      <td>18.832</td>\n",
       "      <td>12.105</td>\n",
       "      <td>12.322</td>\n",
       "      <td>19.925</td>\n",
       "      <td>1.592</td>\n",
       "      <td>2.225</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.206</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4.875</td>\n",
       "      <td>1705.982</td>\n",
       "      <td>24.857</td>\n",
       "      <td>0.615</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.411</td>\n",
       "      <td>12.104</td>\n",
       "      <td>23.963</td>\n",
       "      <td>8.847</td>\n",
       "      <td>13.214</td>\n",
       "      <td>18.446</td>\n",
       "      <td>1.301</td>\n",
       "      <td>3.750</td>\n",
       "      <td>0.354</td>\n",
       "      <td>1.008</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cluster_2    Pos        MP     Age    TS%   3PAr    FTr    ORB%    DRB%  \\\n",
       "6          6  1.452  1522.081  27.218  0.521  0.311  0.244   2.317  11.660   \n",
       "5          5  1.724  1312.310  25.614  0.520  0.372  0.217   2.778  12.047   \n",
       "3          3  2.224  2387.052  24.819  0.564  0.313  0.330   3.404  15.453   \n",
       "2          2  2.896  1504.718  27.553  0.549  0.519  0.166   3.077  13.740   \n",
       "0          0  3.188  2159.181  26.315  0.573  0.395  0.226   3.797  15.116   \n",
       "1          1  3.667  1449.312  25.266  0.560  0.243  0.301   6.203  18.832   \n",
       "4          4  4.875  1705.982  24.857  0.615  0.044  0.411  12.104  23.963   \n",
       "\n",
       "     AST%    TOV%    USG%   STL%   BLK%   DBPM  DRAYMOND  Total Players  \n",
       "6  26.979  14.078  24.549  1.768  0.903 -0.748    -0.579             31  \n",
       "5  16.725  14.414  17.866  2.045  1.235  0.440    -0.168             35  \n",
       "3  26.216  12.741  28.664  1.572  1.645 -0.204     0.025             29  \n",
       "2  10.173  10.632  15.920  1.441  1.382  0.030    -0.014             72  \n",
       "0  13.559  11.017  21.205  1.341  1.620 -0.169     0.082             46  \n",
       "1  12.105  12.322  19.925  1.592  2.225  0.116     0.206             32  \n",
       "4   8.847  13.214  18.446  1.301  3.750  0.354     1.008             28  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "averages_df_2 = pd.DataFrame.from_dict(averages_dict_2, orient='index', \n",
    "                                       columns=reduced_df.drop('Player',axis=1).columns)\n",
    "\n",
    "averages_df_2['Total Players'] = total_per_cluster_2.values()\n",
    "\n",
    "averages_df_2 = averages_df_2.apply(lambda x: round(x, 3))\n",
    "\n",
    "averages_df_2 = averages_df_2[['Cluster_2', 'Pos', 'MP', 'Age', 'TS%', '3PAr', 'FTr', 'ORB%', 'DRB%', 'AST%',\n",
    "                               'TOV%', 'USG%', 'STL%', 'BLK%', 'DBPM', 'DRAYMOND', 'Total Players']]\n",
    "\n",
    "averages_df_2['Cluster_2'] = averages_df_2['Cluster_2'].apply(lambda x: int(x))\n",
    "\n",
    "averages_df_2 = averages_df_2.sort_values(by='Pos', ascending=True)\n",
    "\n",
    "averages_df_2.sort_values(by='Pos', ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.4 Find Cluster Centers and Calculate the Closest Player to Each"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_centers = k_cluster.cluster_centers_.tolist()\n",
    "\n",
    "example_players = []\n",
    "\n",
    "for x, y, z in cluster_centers:\n",
    "    \n",
    "    min_dist = 1000\n",
    "    \n",
    "    player = str()\n",
    "    \n",
    "    tmp_df = reduced_df.copy()\n",
    "    \n",
    "    tmp_df['L2_dist'] = np.sqrt( ( (tmp_df['LDA1'] - x) ** 2) + ( (tmp_df['LDA2'] - y) ** 2) + \n",
    "                               ( (tmp_df['LDA3'] - z) ** 2) ) # euclidian dist (l2)\n",
    "    \n",
    "    for i in range(len(tmp_df)):\n",
    "        \n",
    "        if tmp_df.loc[i,'L2_dist'] < min_dist:\n",
    "            \n",
    "            min_dist = tmp_df.loc[i, 'L2_dist']\n",
    "            \n",
    "            player = tmp_df.loc[i, 'Player']\n",
    "    \n",
    "    example_players.append(player)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "player_clusters = {}\n",
    "\n",
    "for player in example_players:\n",
    "    \n",
    "    index = reduced_df[reduced_df.loc[:,'Player'] == player].index\n",
    "    \n",
    "    cluster = reduced_df.loc[index, 'Cluster_2'].values\n",
    "    \n",
    "    player_clusters[player] = cluster\n",
    "\n",
    "averages_df_2 = averages_df_2.sort_values('Cluster_2', ascending=True)\n",
    "\n",
    "averages_df_2['Example'] = list(player_clusters.keys())\n",
    "\n",
    "averages_df_2 = averages_df_2.sort_values('Pos', ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.5 Averages DF with Example Players Added"
   ]
  },
  {
   "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>Cluster_2</th>\n",
       "      <th>Pos</th>\n",
       "      <th>MP</th>\n",
       "      <th>Age</th>\n",
       "      <th>TS%</th>\n",
       "      <th>3PAr</th>\n",
       "      <th>FTr</th>\n",
       "      <th>ORB%</th>\n",
       "      <th>DRB%</th>\n",
       "      <th>AST%</th>\n",
       "      <th>TOV%</th>\n",
       "      <th>USG%</th>\n",
       "      <th>STL%</th>\n",
       "      <th>BLK%</th>\n",
       "      <th>DBPM</th>\n",
       "      <th>DRAYMOND</th>\n",
       "      <th>Total Players</th>\n",
       "      <th>Example</th>\n",
       "      <th>Cluster Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1.452</td>\n",
       "      <td>1522.081</td>\n",
       "      <td>27.218</td>\n",
       "      <td>0.521</td>\n",
       "      <td>0.311</td>\n",
       "      <td>0.244</td>\n",
       "      <td>2.317</td>\n",
       "      <td>11.660</td>\n",
       "      <td>26.979</td>\n",
       "      <td>14.078</td>\n",
       "      <td>24.549</td>\n",
       "      <td>1.768</td>\n",
       "      <td>0.903</td>\n",
       "      <td>-0.748</td>\n",
       "      <td>-0.579</td>\n",
       "      <td>31</td>\n",
       "      <td>Victor Oladipo</td>\n",
       "      <td>Point guards</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>1.724</td>\n",
       "      <td>1312.310</td>\n",
       "      <td>25.614</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0.372</td>\n",
       "      <td>0.217</td>\n",
       "      <td>2.778</td>\n",
       "      <td>12.047</td>\n",
       "      <td>16.725</td>\n",
       "      <td>14.414</td>\n",
       "      <td>17.866</td>\n",
       "      <td>2.045</td>\n",
       "      <td>1.235</td>\n",
       "      <td>0.440</td>\n",
       "      <td>-0.168</td>\n",
       "      <td>35</td>\n",
       "      <td>Mario Hezonja</td>\n",
       "      <td>Shooting guards</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2.224</td>\n",
       "      <td>2387.052</td>\n",
       "      <td>24.819</td>\n",
       "      <td>0.564</td>\n",
       "      <td>0.313</td>\n",
       "      <td>0.330</td>\n",
       "      <td>3.404</td>\n",
       "      <td>15.453</td>\n",
       "      <td>26.216</td>\n",
       "      <td>12.741</td>\n",
       "      <td>28.664</td>\n",
       "      <td>1.572</td>\n",
       "      <td>1.645</td>\n",
       "      <td>-0.204</td>\n",
       "      <td>0.025</td>\n",
       "      <td>29</td>\n",
       "      <td>Kristaps Porzingis</td>\n",
       "      <td>Star wings</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2.896</td>\n",
       "      <td>1504.718</td>\n",
       "      <td>27.553</td>\n",
       "      <td>0.549</td>\n",
       "      <td>0.519</td>\n",
       "      <td>0.166</td>\n",
       "      <td>3.077</td>\n",
       "      <td>13.740</td>\n",
       "      <td>10.173</td>\n",
       "      <td>10.632</td>\n",
       "      <td>15.920</td>\n",
       "      <td>1.441</td>\n",
       "      <td>1.382</td>\n",
       "      <td>0.030</td>\n",
       "      <td>-0.014</td>\n",
       "      <td>72</td>\n",
       "      <td>Justin Jackson</td>\n",
       "      <td>Three-and-d wings</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3.188</td>\n",
       "      <td>2159.181</td>\n",
       "      <td>26.315</td>\n",
       "      <td>0.573</td>\n",
       "      <td>0.395</td>\n",
       "      <td>0.226</td>\n",
       "      <td>3.797</td>\n",
       "      <td>15.116</td>\n",
       "      <td>13.559</td>\n",
       "      <td>11.017</td>\n",
       "      <td>21.205</td>\n",
       "      <td>1.341</td>\n",
       "      <td>1.620</td>\n",
       "      <td>-0.169</td>\n",
       "      <td>0.082</td>\n",
       "      <td>46</td>\n",
       "      <td>Taurean Prince</td>\n",
       "      <td>Skilled forwards</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3.667</td>\n",
       "      <td>1449.312</td>\n",
       "      <td>25.266</td>\n",
       "      <td>0.560</td>\n",
       "      <td>0.243</td>\n",
       "      <td>0.301</td>\n",
       "      <td>6.203</td>\n",
       "      <td>18.832</td>\n",
       "      <td>12.105</td>\n",
       "      <td>12.322</td>\n",
       "      <td>19.925</td>\n",
       "      <td>1.592</td>\n",
       "      <td>2.225</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.206</td>\n",
       "      <td>32</td>\n",
       "      <td>Paul Millsap</td>\n",
       "      <td>Power forwards</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4.875</td>\n",
       "      <td>1705.982</td>\n",
       "      <td>24.857</td>\n",
       "      <td>0.615</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.411</td>\n",
       "      <td>12.104</td>\n",
       "      <td>23.963</td>\n",
       "      <td>8.847</td>\n",
       "      <td>13.214</td>\n",
       "      <td>18.446</td>\n",
       "      <td>1.301</td>\n",
       "      <td>3.750</td>\n",
       "      <td>0.354</td>\n",
       "      <td>1.008</td>\n",
       "      <td>28</td>\n",
       "      <td>Hassan Whiteside</td>\n",
       "      <td>Centers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cluster_2    Pos        MP     Age    TS%   3PAr    FTr    ORB%    DRB%  \\\n",
       "6          6  1.452  1522.081  27.218  0.521  0.311  0.244   2.317  11.660   \n",
       "5          5  1.724  1312.310  25.614  0.520  0.372  0.217   2.778  12.047   \n",
       "3          3  2.224  2387.052  24.819  0.564  0.313  0.330   3.404  15.453   \n",
       "2          2  2.896  1504.718  27.553  0.549  0.519  0.166   3.077  13.740   \n",
       "0          0  3.188  2159.181  26.315  0.573  0.395  0.226   3.797  15.116   \n",
       "1          1  3.667  1449.312  25.266  0.560  0.243  0.301   6.203  18.832   \n",
       "4          4  4.875  1705.982  24.857  0.615  0.044  0.411  12.104  23.963   \n",
       "\n",
       "     AST%    TOV%    USG%   STL%   BLK%   DBPM  DRAYMOND  Total Players  \\\n",
       "6  26.979  14.078  24.549  1.768  0.903 -0.748    -0.579             31   \n",
       "5  16.725  14.414  17.866  2.045  1.235  0.440    -0.168             35   \n",
       "3  26.216  12.741  28.664  1.572  1.645 -0.204     0.025             29   \n",
       "2  10.173  10.632  15.920  1.441  1.382  0.030    -0.014             72   \n",
       "0  13.559  11.017  21.205  1.341  1.620 -0.169     0.082             46   \n",
       "1  12.105  12.322  19.925  1.592  2.225  0.116     0.206             32   \n",
       "4   8.847  13.214  18.446  1.301  3.750  0.354     1.008             28   \n",
       "\n",
       "              Example       Cluster Name  \n",
       "6      Victor Oladipo       Point guards  \n",
       "5       Mario Hezonja    Shooting guards  \n",
       "3  Kristaps Porzingis         Star wings  \n",
       "2      Justin Jackson  Three-and-d wings  \n",
       "0      Taurean Prince   Skilled forwards  \n",
       "1        Paul Millsap     Power forwards  \n",
       "4    Hassan Whiteside            Centers  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "averages_df_2['Cluster Name'] = ['Point guards', 'Shooting guards', 'Star wings', 'Three-and-d wings',\n",
    "                                'Skilled forwards', 'Power forwards', 'Centers']\n",
    "\n",
    "averages_df_2.sort_values('Pos', ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.0 Graph Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# averages_df_2 = averages_df_2.sort_values('Cluster_2', ascending=True)\n",
    "\n",
    "cluster_and_player = []\n",
    "\n",
    "for i in range(len(averages_df_2)):\n",
    "    \n",
    "    cluster = averages_df_2.loc[i, 'Cluster Name']\n",
    "    \n",
    "    player = averages_df_2.loc[i, 'Example']\n",
    "    \n",
    "    cluster_and_player.append(str(cluster) + ': ' + str(player))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1 Graph in 3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "\n",
    "fig = plt.figure(figsize=(12,8))\n",
    "\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "ax.set_title('NBA Player Style (Reduced to Three Dims)', size=20)\n",
    "\n",
    "ax.set_xlabel('LDA1: Size, Position, and Defense', size=15)\n",
    "\n",
    "ax.set_ylabel('LDA2: Outside shooting', size=15)\n",
    "\n",
    "ax.set_zlabel('LDA3: Usage and Playmaking', size=15)\n",
    "\n",
    "handles = []\n",
    "\n",
    "for cluster in range(num_clusters_2):\n",
    "\n",
    "    handles.append(cluster)\n",
    "\n",
    "    tmp_df = reduced_df[reduced_df['Cluster_2'] == cluster]\n",
    "\n",
    "    ax.scatter(tmp_df['LDA1'], tmp_df['LDA2'], tmp_df['LDA3'], label=cluster, s=100, alpha=0.8)\n",
    "\n",
    "ax.legend(cluster_and_player)\n",
    "\n",
    "ax.view_init(30,60)\n",
    "\n",
    "ax.grid()\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the axes are given descriptions only to aid in viewing. The axes are some low-dimensional mixture of these higher order skills/talents. The descriptive names are simple what the axes *appear* to be showing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2 Graph Clusters in 2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# cmap = plt.cm.get_cmap('jet', reduced_df['Cluster_2'].nunique())\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,7))\n",
    "\n",
    "ax.set_title('NBA Player Style (Reduced to Two Dims)', size=20)\n",
    "\n",
    "ax.set_xlabel('LDA1: Size, Position, and Defense', size=15)\n",
    "\n",
    "ax.set_ylabel('LDA2: Outside shooting', size=15)\n",
    "\n",
    "handles = []\n",
    "\n",
    "for cluster in range(num_clusters_2):\n",
    "    \n",
    "    handles.append(cluster)\n",
    "    \n",
    "    tmp_df = reduced_df[reduced_df['Cluster_2'] == cluster]\n",
    "    \n",
    "    ax.scatter(tmp_df['LDA1'], tmp_df['LDA2'], label=cluster, s=100, alpha=0.8)\n",
    "\n",
    "ax.legend(cluster_and_player)\n",
    "\n",
    "ax.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.0 Input Player to Get Comps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Player name: Mario Hezonja\n"
     ]
    }
   ],
   "source": [
    "# Input player name:\n",
    "\n",
    "player_name = input('Player name: ')\n",
    "\n",
    "# Retrieve index of player name:\n",
    "\n",
    "index = reduced_df['Player'].tolist().index(f'{player_name}')\n",
    "\n",
    "# Pull player's stats:\n",
    "\n",
    "player_stats = reduced_df.iloc[index,:]\n",
    "\n",
    "player_stats = np.asarray(player_stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.1 Input Player You Would Like to Compare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Comp player name: Kristaps Porzingis\n"
     ]
    }
   ],
   "source": [
    "comp_name = input('Comp player name: ')\n",
    "\n",
    "comp_index = reduced_df['Player'].tolist().index(f'{comp_name}')\n",
    "\n",
    "comp_stats = reduced_df.iloc[comp_index,:]\n",
    "\n",
    "comp_stats = np.asarray(comp_stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.2 Calculate Dissimilarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_player_diff = []\n",
    "\n",
    "for player in reduced_df['Player']:\n",
    "    \n",
    "    new_index = reduced_df['Player'].tolist().index(player)\n",
    "    \n",
    "    player_stats_tmp = np.asarray(reduced_df.iloc[new_index,:])\n",
    "    \n",
    "    sub_player_diff = []\n",
    "    \n",
    "    for i in range(len(reduced_df)):\n",
    "        \n",
    "        lda1, lda2, lda3 = player_stats_tmp[-4], player_stats_tmp[-3], player_stats_tmp[-2]\n",
    "        ilda1, ilda2, ilda3 = reduced_df['LDA1'][i], reduced_df['LDA2'][i], reduced_df['LDA3'][i]\n",
    "        total_1 = lda1 - ilda1\n",
    "        total_2 = lda2 - ilda2\n",
    "        total_3 = lda3 - ilda3\n",
    "        l2_norm = np.sqrt((total_1 ** 2) + (total_2 ** 2) + (total_3 ** 2))\n",
    "        \n",
    "        sub_player_diff.append(l2_norm)\n",
    "    \n",
    "    sub_mean = np.mean(sub_player_diff)\n",
    "    \n",
    "    total_player_diff.append(sub_mean)\n",
    "\n",
    "total_mean, total_std = np.mean(total_player_diff), np.std(total_player_diff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.3 Calculate L2-Norms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loop through and create a list of L2-Norms:\n",
    "\n",
    "player_diff = []\n",
    "\n",
    "for i in range(len(reduced_df)):\n",
    "    \n",
    "    lda1, lda2, lda3 = player_stats[-4], player_stats[-3], player_stats[-2]\n",
    "    ilda1, ilda2, ilda3 = reduced_df['LDA1'][i], reduced_df['LDA2'][i], reduced_df['LDA3'][i]\n",
    "    total_1 = lda1 - ilda1\n",
    "    total_2 = lda2 - ilda2\n",
    "    total_3 = lda3  - ilda3\n",
    "    l2_norm = np.sqrt((total_1 ** 2) + (total_2 ** 2) + (total_3 ** 2))\n",
    "    player_diff += [(i,l2_norm)]\n",
    "\n",
    "# And for just Target Comp:\n",
    "\n",
    "lda1, lda2, lda3 = player_stats[-4], player_stats[-3], player_stats[-2]\n",
    "ilda1, ilda2, ilda3 = comp_stats[-4], comp_stats[-3], comp_stats[-2]\n",
    "total_1 = lda1 - ilda1\n",
    "total_2 = lda2 - ilda2\n",
    "total_3 = lda3 - ilda3\n",
    "l2_norm = np.sqrt((total_1 ** 2) + (total_2 ** 2) + (total_3 ** 2))\n",
    "target_comp = l2_norm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.4 Calculate Comps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize:\n",
    "\n",
    "comp_1, it_1, comp_2, it_2, comp_3, it_3 = [x + 3 for x in np.zeros(6)]\n",
    "\n",
    "# 1. Best Comp:\n",
    "\n",
    "for i in range(len(player_diff)):\n",
    "    if (player_diff[i][1] < comp_1) and (player_diff[i][1] > 0):\n",
    "        comp_1 = player_diff[i][1]\n",
    "        it_1 = i\n",
    "\n",
    "# 2. Second Comp:\n",
    "\n",
    "for i in range(len(player_diff)):\n",
    "    if (player_diff[i][1] < comp_2) and (player_diff[i][1] > comp_1):\n",
    "        comp_2 = player_diff[i][1]\n",
    "        it_2 = i\n",
    "\n",
    "# 3. Third Comp:\n",
    "\n",
    "for i in range(len(player_diff)):\n",
    "    if (player_diff[i][1] < comp_3) and (player_diff[i][1] > comp_1) and (player_diff[i][1] > comp_2):\n",
    "        comp_3 = player_diff[i][1]\n",
    "        it_3 = i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dissimilarity (dissim_factor) calculation to add to final comp_df:\n",
    "\n",
    "dissimilarity = 0\n",
    "\n",
    "for i in range(len(player_diff)):\n",
    "    \n",
    "    dissimilarity += player_diff[i][1]\n",
    "\n",
    "dissimilarity = dissimilarity / len(player_diff)\n",
    "\n",
    "dissim_factor = (dissimilarity - total_mean) / total_std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.0 Player Comp Df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "player_stat_array = np.array(reduced_df.iloc[index,1:]).reshape(1,-1)\n",
    "\n",
    "player_stat_array = np.vstack((player_stat_array, np.array(reduced_df.iloc[it_1, 1:]).reshape(1,-1)))\n",
    "\n",
    "player_stat_array = np.vstack((player_stat_array, np.array(reduced_df.iloc[it_2, 1:]).reshape(1,-1)))\n",
    "\n",
    "player_stat_array = np.vstack((player_stat_array, np.array(reduced_df.iloc[it_3, 1:]).reshape(1,-1)))\n",
    "\n",
    "player_stat_array = np.vstack((player_stat_array, np.array(reduced_df.iloc[comp_index, 1:]).reshape(1,-1)))\n",
    "\n",
    "# player_comp_df['Top Comp'] = reduced_df.iloc[it_1,1:]\n",
    "# player_comp_df['Second-Best Comp'] = reduced_df.iloc[it_2,1:]\n",
    "# player_comp_df['Third-Best Comp'] = reduced_df.iloc[it_3,1:]\n",
    "# player_comp_df['Target Comp'] = reduced_df.iloc[comp_index,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 29)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player_stat_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "comp_df = pd.DataFrame(player_stat_array)\n",
    "\n",
    "list_of_columns = list(total_df.columns.drop('Player')) + [f'PCA{i}' for i in range(n_components)] \\\n",
    "+ ['Cluster', 'LDA1', 'LDA2', 'LDA3','Cluster_2']\n",
    "\n",
    "comp_df.columns = list_of_columns\n",
    "\n",
    "comp_df['Player'] = [reduced_df.iloc[index,0], reduced_df.iloc[it_1,0], reduced_df.iloc[it_2,0],\n",
    "                     reduced_df.iloc[it_3,0], reduced_df.iloc[comp_index,0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "player_subject = pd.Series(scaled_vectors[index, :].reshape(-1))\n",
    "\n",
    "comp_a = pd.Series(scaled_vectors[it_1, :])\n",
    "comp_b = pd.Series(scaled_vectors[it_2, :])\n",
    "comp_c = pd.Series(scaled_vectors[it_3, :])\n",
    "comp_target = pd.Series(scaled_vectors[comp_index, :])\n",
    "\n",
    "scaled_array = np.array([player_subject, comp_a, comp_b, comp_c, comp_target])\n",
    "\n",
    "new_columns = ['sPos', 'sAge', 'sTS%', 's3PAr', 'sFTr', 'sORB%', 'sDRB%', 'sAST%', 'sTOV%', \n",
    "               'sUSG%', 'sSTL%', 'sBLK%', 'sDBPM', 'sDRAY']\n",
    "\n",
    "for i, column in enumerate(new_columns):\n",
    "    comp_df[column] = [round(scaled_array[x][i], 2) for x in range(5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "comp_df['Similarity Score'] = [1, round(1 - comp_1/dissimilarity, 3), round(1 - comp_2/dissimilarity, 3), \n",
    "                               round(1 - comp_3/dissimilarity, 3), round(1 - target_comp/dissimilarity, 3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "keep_columns = comp_df.columns\n",
    "\n",
    "keep_columns = ['Player', 'Pos', 'Age', 'sPos', 'sAge', 'sTS%', 's3PAr',\n",
    "       'sFTr', 'sORB%', 'sDRB%', 'sAST%', 'sTOV%', 'sUSG%', 'sSTL%', 'sBLK%', 'sDBPM', 'sDRAY',\n",
    "       'Similarity Score', 'Cluster_2']\n",
    "\n",
    "comp_df = comp_df[keep_columns]\n",
    "\n",
    "comp_df.columns = ['Player', 'Pos', 'Age', 'sPos', 'sAge', 'sTS%', 's3PAr',\n",
    "       'sFTr', 'sORB%', 'sDRB%', 'sAST%', 'sTOV%', 'sUSG%', 'sSTL%', 'sBLK%', 'sDBPM', 'sDRAY',\n",
    "       'Similarity', 'Cluster_2']\n",
    "\n",
    "comp_df['sDRAY'] = comp_df['sDRAY'].apply(lambda x: round(x,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.1 DataFrame with Comps and Selected Player"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": false
   },
   "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>Player</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Age</th>\n",
       "      <th>sPos</th>\n",
       "      <th>sAge</th>\n",
       "      <th>sTS%</th>\n",
       "      <th>s3PAr</th>\n",
       "      <th>sFTr</th>\n",
       "      <th>sORB%</th>\n",
       "      <th>sDRB%</th>\n",
       "      <th>sAST%</th>\n",
       "      <th>sTOV%</th>\n",
       "      <th>sUSG%</th>\n",
       "      <th>sSTL%</th>\n",
       "      <th>sBLK%</th>\n",
       "      <th>sDBPM</th>\n",
       "      <th>sDRAY</th>\n",
       "      <th>Similarity</th>\n",
       "      <th>Cluster_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mario Hezonja</td>\n",
       "      <td>3</td>\n",
       "      <td>22.5</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>0.15</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>0.33</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.09</td>\n",
       "      <td>1.48</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>0.06</td>\n",
       "      <td>1.000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tyler Johnson</td>\n",
       "      <td>1</td>\n",
       "      <td>25.75</td>\n",
       "      <td>-1.33</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>0.67</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.881</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kent Bazemore</td>\n",
       "      <td>2</td>\n",
       "      <td>28.5</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.53</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.24</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1.95</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.818</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Josh Okogie</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>0.12</td>\n",
       "      <td>-1.44</td>\n",
       "      <td>-1.58</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.66</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>-0.92</td>\n",
       "      <td>-0.78</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>1.58</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.812</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kristaps Porzingis</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>0.82</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.44</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.23</td>\n",
       "      <td>2.07</td>\n",
       "      <td>-0.85</td>\n",
       "      <td>3.26</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>0.087</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Player Pos    Age  sPos  sAge  sTS%  s3PAr  sFTr  sORB%  sDRB%  \\\n",
       "0       Mario Hezonja   3   22.5  0.11 -0.55 -0.86  -0.86  0.15  -0.40  -0.53   \n",
       "1       Tyler Johnson   1  25.75 -1.33 -0.87 -0.11  -0.59  0.67  -0.11  -0.54   \n",
       "2       Kent Bazemore   2   28.5 -0.61  0.03  0.53  -0.71  0.36   0.24  -0.71   \n",
       "3         Josh Okogie   2     20 -0.61  0.12 -1.44  -1.58  0.38   0.66  -0.55   \n",
       "4  Kristaps Porzingis   4     22  0.82 -0.30 -0.98  -0.42 -0.52   0.56  -0.00   \n",
       "\n",
       "   sAST%  sTOV%  sUSG%  sSTL%  sBLK%  sDBPM  sDRAY  Similarity Cluster_2  \n",
       "0   0.33  -0.50   0.25   0.09   1.48  -0.47   0.06       1.000         5  \n",
       "1  -0.80   0.02  -0.50  -0.37   0.13  -0.17   0.13       0.881         5  \n",
       "2  -0.18   0.17   0.97   0.40   1.95   0.33   0.56       0.818         5  \n",
       "3  -0.71  -0.92  -0.78  -0.90   1.58  -0.08   0.38       0.812         5  \n",
       "4   0.44  -1.00  -1.23   2.07  -0.85   3.26  -0.07       0.087         3  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp_df"
   ]
  },
  {
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}