NBA_PlayerStats_ML.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn.model_selection as model_selection\n",
    "import numpy as np\n",
    "import seaborn as sb\n",
    "from math import sqrt\n",
    "import sklearn.metrics  as metrics\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats = pd.read_csv('NBA_player_info_and_stats_2020_2021.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats.drop(columns =[\"id\",\"height\",\"weight\",\"age\",\"experience\"], inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats.rename(columns={'Unnamed: 0': 'Name',},inplace= True)\n",
    "stats.set_index('Name', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "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>position</th>\n",
       "      <th>jersey</th>\n",
       "      <th>salary</th>\n",
       "      <th>FLName</th>\n",
       "      <th>Team</th>\n",
       "      <th>hand</th>\n",
       "      <th>GP</th>\n",
       "      <th>GS</th>\n",
       "      <th>MIN</th>\n",
       "      <th>FGM</th>\n",
       "      <th>...</th>\n",
       "      <th>FT%</th>\n",
       "      <th>OR</th>\n",
       "      <th>DR</th>\n",
       "      <th>REB</th>\n",
       "      <th>AST</th>\n",
       "      <th>BLK</th>\n",
       "      <th>STL</th>\n",
       "      <th>PF</th>\n",
       "      <th>TO</th>\n",
       "      <th>PTS</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>Jaylen Brown</th>\n",
       "      <td>SG</td>\n",
       "      <td>7</td>\n",
       "      <td>23735118.0</td>\n",
       "      <td>Jaylen Brown</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>286.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>67.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carsen Edwards</th>\n",
       "      <td>PG</td>\n",
       "      <td>4</td>\n",
       "      <td>1517981.0</td>\n",
       "      <td>Carsen Edwards</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>1.2</td>\n",
       "      <td>...</td>\n",
       "      <td>68.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tacko Fall</th>\n",
       "      <td>C</td>\n",
       "      <td>99</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Tacko Fall</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>...</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Javonte Green</th>\n",
       "      <td>SG</td>\n",
       "      <td>43</td>\n",
       "      <td>1517981.0</td>\n",
       "      <td>Javonte Green</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>...</td>\n",
       "      <td>66.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romeo Langford</th>\n",
       "      <td>SG</td>\n",
       "      <td>45</td>\n",
       "      <td>3631200.0</td>\n",
       "      <td>Romeo Langford</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>...</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jakob Poeltl</th>\n",
       "      <td>C</td>\n",
       "      <td>25</td>\n",
       "      <td>8101852.0</td>\n",
       "      <td>Jakob Poeltl</td>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>285.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>16.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>...</td>\n",
       "      <td>52.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luka Samanic</th>\n",
       "      <td>PF</td>\n",
       "      <td>19</td>\n",
       "      <td>2892000.0</td>\n",
       "      <td>Luka Samanic</td>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>...</td>\n",
       "      <td>66.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Devin Vassell</th>\n",
       "      <td>SG</td>\n",
       "      <td>24</td>\n",
       "      <td>4033440.0</td>\n",
       "      <td>Devin Vassell</td>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quinndary Weatherspoon</th>\n",
       "      <td>SG</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Quinndary Weatherspoon</td>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derrick White</th>\n",
       "      <td>PG</td>\n",
       "      <td>4</td>\n",
       "      <td>3516284.0</td>\n",
       "      <td>Derrick White</td>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>153.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>...</td>\n",
       "      <td>81.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>438 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       position  jersey      salary                  FLName  \\\n",
       "Name                                                                          \n",
       "Jaylen Brown                 SG       7  23735118.0            Jaylen Brown   \n",
       "Carsen Edwards               PG       4   1517981.0          Carsen Edwards   \n",
       "Tacko Fall                    C      99         0.0              Tacko Fall   \n",
       "Javonte Green                SG      43   1517981.0           Javonte Green   \n",
       "Romeo Langford               SG      45   3631200.0          Romeo Langford   \n",
       "...                         ...     ...         ...                     ...   \n",
       "Jakob Poeltl                  C      25   8101852.0            Jakob Poeltl   \n",
       "Luka Samanic                 PF      19   2892000.0            Luka Samanic   \n",
       "Devin Vassell                SG      24   4033440.0           Devin Vassell   \n",
       "Quinndary Weatherspoon       SG      15         0.0  Quinndary Weatherspoon   \n",
       "Derrick White                PG       4   3516284.0           Derrick White   \n",
       "\n",
       "                                     Team hand     GP     GS   MIN  FGM  ...  \\\n",
       "Name                                                                     ...   \n",
       "Jaylen Brown               Boston Celtics  NaN  286.0  179.0  26.5  5.0  ...   \n",
       "Carsen Edwards             Boston Celtics  NaN   40.0    0.0   9.3  1.2  ...   \n",
       "Tacko Fall                 Boston Celtics  NaN    8.0    0.0   4.9  1.4  ...   \n",
       "Javonte Green              Boston Celtics  NaN   50.0    2.0   9.9  1.3  ...   \n",
       "Romeo Langford             Boston Celtics  NaN   32.0    2.0  11.6  0.9  ...   \n",
       "...                                   ...  ...    ...    ...   ...  ...  ...   \n",
       "Jakob Poeltl            San Antonio Spurs  NaN  285.0   49.0  16.6  2.4  ...   \n",
       "Luka Samanic            San Antonio Spurs  NaN    5.0    1.0  11.0  1.2  ...   \n",
       "Devin Vassell           San Antonio Spurs  NaN    5.0    0.0  16.0  1.4  ...   \n",
       "Quinndary Weatherspoon  San Antonio Spurs  NaN   11.0    0.0   7.1  0.5  ...   \n",
       "Derrick White           San Antonio Spurs  NaN  153.0   75.0  23.3  3.5  ...   \n",
       "\n",
       "                         FT%   OR   DR  REB  AST  BLK  STL   PF   TO   PTS  \n",
       "Name                                                                        \n",
       "Jaylen Brown            67.8  0.9  3.6  4.5  1.5  0.4  0.9  2.4  1.5  13.5  \n",
       "Carsen Edwards          68.4  0.2  1.1  1.3  0.7  0.1  0.3  0.9  0.4   3.3  \n",
       "Tacko Fall              60.0  0.3  1.8  2.0  0.1  0.8  0.1  0.9  0.4   3.1  \n",
       "Javonte Green           66.7  0.5  1.4  2.0  0.5  0.2  0.5  0.9  0.5   3.4  \n",
       "Romeo Langford          72.0  0.5  0.8  1.3  0.4  0.3  0.3  1.0  0.3   2.5  \n",
       "...                      ...  ...  ...  ...  ...  ...  ...  ...  ...   ...  \n",
       "Jakob Poeltl            52.6  2.0  2.9  4.9  1.0  1.0  0.4  2.1  0.8   5.5  \n",
       "Luka Samanic            66.7  0.2  1.8  2.0  1.2  0.4  0.0  1.2  0.4   4.0  \n",
       "Devin Vassell            0.0  0.2  2.8  3.0  1.0  0.4  1.0  1.0  0.6   3.4  \n",
       "Quinndary Weatherspoon  50.0  0.1  0.5  0.6  1.0  0.1  0.3  0.7  0.5   1.1  \n",
       "Derrick White           81.7  0.5  2.8  3.2  3.4  0.7  0.8  2.0  1.3   9.7  \n",
       "\n",
       "[438 rows x 27 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Predicting the correlation between Average Points scored throughout career and the salary\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "stats.plot(kind ='scatter', x=\"PTS\",y=\"salary\", color='blue')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "stats.plot(kind ='scatter', x=\"REB\",y=\"salary\", color='red')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "stats.plot(kind ='scatter', x=\"AST\",y=\"salary\", color='green')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "pts =stats[\"PTS\"].values.reshape(-1,1)\n",
    "sal = stats[\"salary\"].values.reshape(-1,1)\n",
    "\n",
    "stats[\"PTS\"] = stats[\"PTS\"].fillna(0)\n",
    "stats[\"salary\"] = stats[\"salary\"].fillna(0)\n",
    "stats[\"AST\"] = stats[\"AST\"].fillna(0)\n",
    "stats[\"REB\"] = stats[\"REB\"].fillna(0)\n",
    "stats[\"MIN\"] = stats[\"REB\"].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "g = sb.pairplot(stats, vars=[\"PTS\", \"salary\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confidence Test Set Score: 0.52\n",
      "Confidence train Set Score: 0.59\n",
      "Root Mean Squared Error: 6897034.992024086\n"
     ]
    }
   ],
   "source": [
    "x_train,x_test, y_train,y_test = model_selection.train_test_split(stats[[\"PTS\", \"AST\",\"REB\", \"MIN\"]],stats[[\"salary\"]],test_size=0.2, random_state =0)\n",
    "lr = LinearRegression()\n",
    "lr.fit(x_train, y_train) #train the data\n",
    "pred = lr.predict(x_test)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "print('Confidence Test Set Score: %.2f'% lr.score(x_test, y_test))\n",
    "print('Confidence train Set Score: %.2f'% lr.score(x_train, y_train))\n",
    "\n",
    "\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, pred)))\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KNN Clustering NBA Players "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>position</th>\n",
       "      <th>jersey</th>\n",
       "      <th>salary</th>\n",
       "      <th>FLName</th>\n",
       "      <th>Team</th>\n",
       "      <th>hand</th>\n",
       "      <th>GP</th>\n",
       "      <th>GS</th>\n",
       "      <th>MIN</th>\n",
       "      <th>FGM</th>\n",
       "      <th>...</th>\n",
       "      <th>FT%</th>\n",
       "      <th>OR</th>\n",
       "      <th>DR</th>\n",
       "      <th>REB</th>\n",
       "      <th>AST</th>\n",
       "      <th>BLK</th>\n",
       "      <th>STL</th>\n",
       "      <th>PF</th>\n",
       "      <th>TO</th>\n",
       "      <th>PTS</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>Jaylen Brown</th>\n",
       "      <td>SG</td>\n",
       "      <td>7</td>\n",
       "      <td>23735118.0</td>\n",
       "      <td>Jaylen Brown</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>286.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>67.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carsen Edwards</th>\n",
       "      <td>PG</td>\n",
       "      <td>4</td>\n",
       "      <td>1517981.0</td>\n",
       "      <td>Carsen Edwards</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>1.2</td>\n",
       "      <td>...</td>\n",
       "      <td>68.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tacko Fall</th>\n",
       "      <td>C</td>\n",
       "      <td>99</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Tacko Fall</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>...</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Javonte Green</th>\n",
       "      <td>SG</td>\n",
       "      <td>43</td>\n",
       "      <td>1517981.0</td>\n",
       "      <td>Javonte Green</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>...</td>\n",
       "      <td>66.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romeo Langford</th>\n",
       "      <td>SG</td>\n",
       "      <td>45</td>\n",
       "      <td>3631200.0</td>\n",
       "      <td>Romeo Langford</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>...</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jakob Poeltl</th>\n",
       "      <td>C</td>\n",
       "      <td>25</td>\n",
       "      <td>8101852.0</td>\n",
       "      <td>Jakob Poeltl</td>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>285.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>16.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>...</td>\n",
       "      <td>52.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luka Samanic</th>\n",
       "      <td>PF</td>\n",
       "      <td>19</td>\n",
       "      <td>2892000.0</td>\n",
       "      <td>Luka Samanic</td>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>...</td>\n",
       "      <td>66.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Devin Vassell</th>\n",
       "      <td>SG</td>\n",
       "      <td>24</td>\n",
       "      <td>4033440.0</td>\n",
       "      <td>Devin Vassell</td>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quinndary Weatherspoon</th>\n",
       "      <td>SG</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Quinndary Weatherspoon</td>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derrick White</th>\n",
       "      <td>PG</td>\n",
       "      <td>4</td>\n",
       "      <td>3516284.0</td>\n",
       "      <td>Derrick White</td>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>153.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>...</td>\n",
       "      <td>81.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>438 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       position  jersey      salary                  FLName  \\\n",
       "Name                                                                          \n",
       "Jaylen Brown                 SG       7  23735118.0            Jaylen Brown   \n",
       "Carsen Edwards               PG       4   1517981.0          Carsen Edwards   \n",
       "Tacko Fall                    C      99         0.0              Tacko Fall   \n",
       "Javonte Green                SG      43   1517981.0           Javonte Green   \n",
       "Romeo Langford               SG      45   3631200.0          Romeo Langford   \n",
       "...                         ...     ...         ...                     ...   \n",
       "Jakob Poeltl                  C      25   8101852.0            Jakob Poeltl   \n",
       "Luka Samanic                 PF      19   2892000.0            Luka Samanic   \n",
       "Devin Vassell                SG      24   4033440.0           Devin Vassell   \n",
       "Quinndary Weatherspoon       SG      15         0.0  Quinndary Weatherspoon   \n",
       "Derrick White                PG       4   3516284.0           Derrick White   \n",
       "\n",
       "                                     Team hand     GP     GS   MIN  FGM  ...  \\\n",
       "Name                                                                     ...   \n",
       "Jaylen Brown               Boston Celtics  NaN  286.0  179.0  26.5  5.0  ...   \n",
       "Carsen Edwards             Boston Celtics  NaN   40.0    0.0   9.3  1.2  ...   \n",
       "Tacko Fall                 Boston Celtics  NaN    8.0    0.0   4.9  1.4  ...   \n",
       "Javonte Green              Boston Celtics  NaN   50.0    2.0   9.9  1.3  ...   \n",
       "Romeo Langford             Boston Celtics  NaN   32.0    2.0  11.6  0.9  ...   \n",
       "...                                   ...  ...    ...    ...   ...  ...  ...   \n",
       "Jakob Poeltl            San Antonio Spurs  NaN  285.0   49.0  16.6  2.4  ...   \n",
       "Luka Samanic            San Antonio Spurs  NaN    5.0    1.0  11.0  1.2  ...   \n",
       "Devin Vassell           San Antonio Spurs  NaN    5.0    0.0  16.0  1.4  ...   \n",
       "Quinndary Weatherspoon  San Antonio Spurs  NaN   11.0    0.0   7.1  0.5  ...   \n",
       "Derrick White           San Antonio Spurs  NaN  153.0   75.0  23.3  3.5  ...   \n",
       "\n",
       "                         FT%   OR   DR  REB  AST  BLK  STL   PF   TO   PTS  \n",
       "Name                                                                        \n",
       "Jaylen Brown            67.8  0.9  3.6  4.5  1.5  0.4  0.9  2.4  1.5  13.5  \n",
       "Carsen Edwards          68.4  0.2  1.1  1.3  0.7  0.1  0.3  0.9  0.4   3.3  \n",
       "Tacko Fall              60.0  0.3  1.8  2.0  0.1  0.8  0.1  0.9  0.4   3.1  \n",
       "Javonte Green           66.7  0.5  1.4  2.0  0.5  0.2  0.5  0.9  0.5   3.4  \n",
       "Romeo Langford          72.0  0.5  0.8  1.3  0.4  0.3  0.3  1.0  0.3   2.5  \n",
       "...                      ...  ...  ...  ...  ...  ...  ...  ...  ...   ...  \n",
       "Jakob Poeltl            52.6  2.0  2.9  4.9  1.0  1.0  0.4  2.1  0.8   5.5  \n",
       "Luka Samanic            66.7  0.2  1.8  2.0  1.2  0.4  0.0  1.2  0.4   4.0  \n",
       "Devin Vassell            0.0  0.2  2.8  3.0  1.0  0.4  1.0  1.0  0.6   3.4  \n",
       "Quinndary Weatherspoon  50.0  0.1  0.5  0.6  1.0  0.1  0.3  0.7  0.5   1.1  \n",
       "Derrick White           81.7  0.5  2.8  3.2  3.4  0.7  0.8  2.0  1.3   9.7  \n",
       "\n",
       "[438 rows x 27 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba_player = pd.read_csv(\"NBA_player_info_and_stats_2020_2021.csv\")\n",
    "nba_player.drop(columns =[\"id\",\"height\",\"weight\",\"age\",\"experience\"], inplace =True)\n",
    "nba_player.rename(columns={'Unnamed: 0': 'Name',},inplace= True)\n",
    "nba_player.set_index('Name', inplace=True)\n",
    "\n",
    "\n",
    "nba_player.dropna(how=\"all\", axis =0, thresh=4, inplace=True)\n",
    "nba_player"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "nba_player[\"PTS\"] = nba_player[\"PTS\"].fillna(0)\n",
    "point_guards = nba_player.loc[nba_player[\"position\"] == \"PG\"]\n",
    "point_guards =point_guards[[\"PTS\",\"AST\",\"STL\",\"REB\", \"TO\", \"MIN\", \"FGA\"]]\n",
    "point_guards.dropna(inplace= True, axis = 0 , thresh=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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": [
    "point_guards.plot(kind =\"scatter\", x =\"PTS\",y =\"MIN\", color='red')\n",
    "plt.title(\"Point Guard Efficiency\")\n",
    "plt.show()\n",
    "p =point_guards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  PTS  AST  STL  REB   TO   MIN   FGA\n",
      "Name                                                 \n",
      "Dejounte Murray   8.3  3.1  1.2  4.8  1.6  20.6   7.6\n",
      "Markelle Fultz   11.0  4.6  1.1  3.3  1.8  25.7  10.2\n",
      "Jeff Teague      12.5  5.7  1.2  2.5  2.2  27.5  10.0\n",
      "Monte Morris      9.7  3.5  0.8  2.1  0.7  23.2   8.1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Minutes per Game')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusters = 4 \n",
    "points = np.random.choice(point_guards.index, size = clusters)\n",
    "centroids = point_guards.loc[points]\n",
    "print(centroids)\n",
    "fig = plt.figure(num =None, figsize=(6,6), edgecolor='k')\n",
    "ax = fig.add_subplot(1,1,1, facecolor =\"1.0\")\n",
    "ax.scatter(point_guards['PTS'], point_guards['MIN'], alpha =0.5, edgecolor ='g', s=150)\n",
    "plt.title(\"Point Guard Efficiency Centroids\")\n",
    "plt.xlabel(\"Points Per Game\")\n",
    "plt.ylabel(\"Minutes per Game\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "def centroids_dict(centroids):\n",
    "    centroid_dict = dict()\n",
    "    counter = 0\n",
    "    for index, row in centroids.iterrows():\n",
    "        coordinate = [row[\"PTS\"],row[\"MIN\"]]\n",
    "        centroid_dict[counter] = coordinate\n",
    "        counter+=1\n",
    "    return centroid_dict\n",
    "\n",
    "\n",
    "centroids_dict = centroids_dict(centroids)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.247220505424423"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Eculidean Distance\n",
    "def eculidean_distance_calculate(centroids, player_stats):\n",
    "    root_distance = 0\n",
    "    for x in range(0,len(centroids)):\n",
    "        difference = centroids[x] - player_stats[x]\n",
    "        squared_diff = difference **2\n",
    "        root_distance += squared_diff\n",
    "    \n",
    "    euclidean_dist = math.sqrt(root_distance)\n",
    "    return euclidean_dist\n",
    "\n",
    "eculidean_distance_calculate([3.5, 10.3], [5.4, 11.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>PTS</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>REB</th>\n",
       "      <th>TO</th>\n",
       "      <th>MIN</th>\n",
       "      <th>FGA</th>\n",
       "      <th>Cluster</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Carsen Edwards</th>\n",
       "      <td>3.3</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>9.3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Payton Pritchard</th>\n",
       "      <td>6.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>21.9</td>\n",
       "      <td>5.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marcus Smart</th>\n",
       "      <td>9.9</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>29.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jeff Teague</th>\n",
       "      <td>12.5</td>\n",
       "      <td>5.7</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.2</td>\n",
       "      <td>27.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kemba Walker</th>\n",
       "      <td>19.9</td>\n",
       "      <td>5.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>33.8</td>\n",
       "      <td>16.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eric Bledsoe</th>\n",
       "      <td>14.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>27.8</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tre Jones</th>\n",
       "      <td>5.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patty Mills</th>\n",
       "      <td>8.8</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dejounte Murray</th>\n",
       "      <td>8.3</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>20.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derrick White</th>\n",
       "      <td>9.7</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>23.3</td>\n",
       "      <td>7.4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   PTS  AST  STL  REB   TO   MIN   FGA  Cluster\n",
       "Name                                                           \n",
       "Carsen Edwards     3.3  0.7  0.3  1.3  0.4   9.3   3.5        0\n",
       "Payton Pritchard   6.6  2.4  1.6  2.0  1.9  21.9   5.3        0\n",
       "Marcus Smart       9.9  4.1  1.6  3.5  1.7  29.0   8.8        2\n",
       "Jeff Teague       12.5  5.7  1.2  2.5  2.2  27.5  10.0        2\n",
       "Kemba Walker      19.9  5.4  1.3  3.8  2.2  33.8  16.4        2\n",
       "...                ...  ...  ...  ...  ...   ...   ...      ...\n",
       "Eric Bledsoe      14.2  4.8  1.4  4.0  2.6  27.8  11.0        2\n",
       "Tre Jones          5.5  0.5  0.0  1.5  0.0   5.0   3.5        0\n",
       "Patty Mills        8.8  2.3  0.7  1.7  1.0  19.4   7.3        0\n",
       "Dejounte Murray    8.3  3.1  1.2  4.8  1.6  20.6   7.6        0\n",
       "Derrick White      9.7  3.4  0.8  3.2  1.3  23.3   7.4        3\n",
       "\n",
       "[90 rows x 8 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Assign stats to clusters\n",
    "\n",
    "def clusters(player):\n",
    "    player_val = [player[\"PTS\"], player[\"MIN\"]]\n",
    "    dist_prev = -1\n",
    "    cluster_id = None\n",
    "    \n",
    "    for centroid_id, centroid_value in centroids_dict.items():\n",
    "        dist = eculidean_distance_calculate(centroid_value, player_val)\n",
    "        if dist_prev == -1 or dist <dist_prev:\n",
    "            cluster_id = centroid_id\n",
    "            dist_prev = dist\n",
    "    return cluster_id\n",
    "\n",
    "point_guards[\"Cluster\"] = point_guards.apply(lambda row: clusters(row), axis = 1)\n",
    "\n",
    "point_guards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>PTS</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>REB</th>\n",
       "      <th>TO</th>\n",
       "      <th>MIN</th>\n",
       "      <th>FGA</th>\n",
       "      <th>Cluster</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Marcus Smart</th>\n",
       "      <td>9.9</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>29.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jeff Teague</th>\n",
       "      <td>12.5</td>\n",
       "      <td>5.7</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.2</td>\n",
       "      <td>27.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kemba Walker</th>\n",
       "      <td>19.9</td>\n",
       "      <td>5.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>33.8</td>\n",
       "      <td>16.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spencer Dinwiddie</th>\n",
       "      <td>12.9</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>25.7</td>\n",
       "      <td>10.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kyrie Irving</th>\n",
       "      <td>22.5</td>\n",
       "      <td>5.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>3.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>33.8</td>\n",
       "      <td>17.7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elfrid Payton</th>\n",
       "      <td>11.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>29.3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ben Simmons</th>\n",
       "      <td>16.3</td>\n",
       "      <td>7.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>8.3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>34.3</td>\n",
       "      <td>11.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kyle Lowry</th>\n",
       "      <td>14.8</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>4.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>31.6</td>\n",
       "      <td>11.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coby White</th>\n",
       "      <td>13.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.8</td>\n",
       "      <td>26.4</td>\n",
       "      <td>12.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Darius Garland</th>\n",
       "      <td>12.8</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>31.4</td>\n",
       "      <td>12.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Collin Sexton</th>\n",
       "      <td>18.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>32.5</td>\n",
       "      <td>15.7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derrick Rose</th>\n",
       "      <td>18.7</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2.7</td>\n",
       "      <td>32.5</td>\n",
       "      <td>16.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malcolm Brogdon</th>\n",
       "      <td>13.8</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>28.9</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jamal Murray</th>\n",
       "      <td>15.7</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.9</td>\n",
       "      <td>29.3</td>\n",
       "      <td>13.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ricky Rubio</th>\n",
       "      <td>11.3</td>\n",
       "      <td>7.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>4.2</td>\n",
       "      <td>2.7</td>\n",
       "      <td>30.8</td>\n",
       "      <td>9.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>George Hill</th>\n",
       "      <td>11.1</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>27.7</td>\n",
       "      <td>8.6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Damian Lillard</th>\n",
       "      <td>24.2</td>\n",
       "      <td>6.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>36.4</td>\n",
       "      <td>18.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jordan Clarkson</th>\n",
       "      <td>14.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.7</td>\n",
       "      <td>26.9</td>\n",
       "      <td>12.7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mike Conley</th>\n",
       "      <td>14.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>32.4</td>\n",
       "      <td>12.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stephen Curry</th>\n",
       "      <td>23.6</td>\n",
       "      <td>6.6</td>\n",
       "      <td>1.7</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.1</td>\n",
       "      <td>34.3</td>\n",
       "      <td>17.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dennis Schroder</th>\n",
       "      <td>14.1</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.9</td>\n",
       "      <td>2.4</td>\n",
       "      <td>25.6</td>\n",
       "      <td>12.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chris Paul</th>\n",
       "      <td>18.4</td>\n",
       "      <td>9.5</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>34.8</td>\n",
       "      <td>13.7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rajon Rondo</th>\n",
       "      <td>10.2</td>\n",
       "      <td>8.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>4.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>31.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trae Young</th>\n",
       "      <td>23.8</td>\n",
       "      <td>8.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>32.8</td>\n",
       "      <td>17.7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avery Bradley</th>\n",
       "      <td>11.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>28.2</td>\n",
       "      <td>10.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goran Dragic</th>\n",
       "      <td>13.9</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>28.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russell Westbrook</th>\n",
       "      <td>23.2</td>\n",
       "      <td>8.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>7.1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>34.6</td>\n",
       "      <td>18.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luka Doncic</th>\n",
       "      <td>24.7</td>\n",
       "      <td>7.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>3.8</td>\n",
       "      <td>32.8</td>\n",
       "      <td>18.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>John Wall</th>\n",
       "      <td>19.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>1.7</td>\n",
       "      <td>4.3</td>\n",
       "      <td>3.8</td>\n",
       "      <td>35.9</td>\n",
       "      <td>15.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ja Morant</th>\n",
       "      <td>18.2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>30.8</td>\n",
       "      <td>14.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lonzo Ball</th>\n",
       "      <td>10.8</td>\n",
       "      <td>6.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.7</td>\n",
       "      <td>32.3</td>\n",
       "      <td>10.6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eric Bledsoe</th>\n",
       "      <td>14.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>27.8</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    PTS  AST  STL  REB   TO   MIN   FGA  Cluster\n",
       "Name                                                            \n",
       "Marcus Smart        9.9  4.1  1.6  3.5  1.7  29.0   8.8        2\n",
       "Jeff Teague        12.5  5.7  1.2  2.5  2.2  27.5  10.0        2\n",
       "Kemba Walker       19.9  5.4  1.3  3.8  2.2  33.8  16.4        2\n",
       "Spencer Dinwiddie  12.9  5.0  0.7  2.8  1.8  25.7  10.1        2\n",
       "Kyrie Irving       22.5  5.7  1.3  3.7  2.6  33.8  17.7        2\n",
       "Elfrid Payton      11.0  6.6  1.3  4.4  2.4  29.3  10.0        2\n",
       "Ben Simmons        16.3  7.9  1.7  8.3  3.5  34.3  11.9        2\n",
       "Kyle Lowry         14.8  6.2  1.4  4.3  2.3  31.6  11.2        2\n",
       "Coby White         13.5  3.0  0.7  3.7  1.8  26.4  12.3        2\n",
       "Darius Garland     12.8  4.1  0.8  2.0  2.6  31.4  12.1        2\n",
       "Collin Sexton      18.8  3.0  0.8  3.0  2.3  32.5  15.7        2\n",
       "Derrick Rose       18.7  5.6  0.8  3.4  2.7  32.5  16.1        2\n",
       "Malcolm Brogdon    13.8  4.4  0.9  3.8  1.7  28.9  11.0        2\n",
       "Jamal Murray       15.7  3.7  0.9  3.6  1.9  29.3  13.1        2\n",
       "Ricky Rubio        11.3  7.8  1.9  4.2  2.7  30.8   9.3        2\n",
       "George Hill        11.1  3.2  0.9  3.1  1.3  27.7   8.6        2\n",
       "Damian Lillard     24.2  6.5  1.0  4.2  2.8  36.4  18.2        2\n",
       "Jordan Clarkson    14.8  2.6  0.9  3.2  1.7  26.9  12.7        2\n",
       "Mike Conley        14.9  5.6  1.4  3.0  2.0  32.4  12.1        2\n",
       "Stephen Curry      23.6  6.6  1.7  4.5  3.1  34.3  17.1        2\n",
       "Dennis Schroder    14.1  4.6  0.8  2.9  2.4  25.6  12.2        2\n",
       "Chris Paul         18.4  9.5  2.2  4.5  2.4  34.8  13.7        2\n",
       "Rajon Rondo        10.2  8.3  1.7  4.7  2.8  31.0   9.3        2\n",
       "Trae Young         23.8  8.6  0.9  4.0  4.2  32.8  17.7        2\n",
       "Avery Bradley      11.8  1.8  1.0  2.9  1.4  28.2  10.8        2\n",
       "Goran Dragic       13.9  4.8  1.0  3.1  2.3  28.0  11.0        2\n",
       "Russell Westbrook  23.2  8.3  1.7  7.1  4.1  34.6  18.8        2\n",
       "Luka Doncic        24.7  7.2  1.0  8.5  3.8  32.8  18.4        2\n",
       "John Wall          19.0  9.2  1.7  4.3  3.8  35.9  15.9        2\n",
       "Ja Morant          18.2  7.2  0.9  3.8  3.2  30.8  14.2        2\n",
       "Lonzo Ball         10.8  6.5  1.5  6.1  2.7  32.3  10.6        2\n",
       "Eric Bledsoe       14.2  4.8  1.4  4.0  2.6  27.8  11.0        2"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "point_guards[point_guards[\"Cluster\"] ==2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def visualize_cluster(df, num , iteration):\n",
    "    colors=['b','y','r','c']\n",
    "    fig = plt.figure(num=None,figsize=(6,6), edgecolor='k')\n",
    "    ax = fig.add_subplot(1, 1, 1, facecolor=\"1.0\")\n",
    "    for i in range(num):\n",
    "        cluster_df = df[df['Cluster'] == i]\n",
    "        ax.scatter(cluster_df['PTS'], cluster_df['MIN'],c=colors[i-1], edgecolors='g', alpha=0.5, s=150)\n",
    "        plt.xlabel(\"Points Per Game\")\n",
    "        plt.ylabel(\"Minutes Per Game\")\n",
    "        plt.title('Iteration Number %s'%(iteration))\n",
    "    plt.show()\n",
    "iteration =0\n",
    "visualize_cluster(point_guards, 4, iteration)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sklearn kMeans\n",
    "point_guards = point_guards[point_guards['PTS'].notna()]\n",
    "point_guards = point_guards[point_guards['MIN'].notna()]\n",
    "from sklearn.cluster import KMeans\n",
    "kmeans = KMeans(n_clusters= 4, max_iter=100)\n",
    "kmeans.fit(point_guards[[\"PTS\",\"MIN\"]])\n",
    "\n",
    "point_guards['Cluster'] = kmeans.labels_\n",
    "\n",
    "visualize_cluster(point_guards, 3, 100)"
   ]
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}