Capstone_create(dont_touch).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libaries\n",
    "import requests\n",
    "import numpy as np\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change max rows shown to 3000\n",
    "pd.options.display.max_rows = 3000\n",
    "# change max columns shown to 999\n",
    "pd.options.display.max_columns = 999"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pulls in csv's from data folder and turns them into dataframes\n",
    "df09 = pd.read_csv('./Data/sportsref_2009.csv')\n",
    "df10 = pd.read_csv('./Data/sportsref_2010.csv')\n",
    "df11 = pd.read_csv('./Data/sportsref_2011.csv')\n",
    "df12 = pd.read_csv('./Data/sportsref_2012.csv')\n",
    "df13 = pd.read_csv('./Data/sportsref_2013.csv')\n",
    "df14 = pd.read_csv('./Data/sportsref_2014.csv')\n",
    "df15 = pd.read_csv('./Data/sportsref_2015.csv')\n",
    "df16 = pd.read_csv('./Data/sportsref_2016.csv')\n",
    "df17 = pd.read_csv('./Data/sportsref_2017.csv')\n",
    "df18 = pd.read_csv('./Data/sportsref_2018.csv')\n",
    "df19 = pd.read_csv('./Data/sportsref_2019.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding the year of the player's recorded stats as 'YEAR' column to each dataframe\n",
    "df09['YEAR'] = 2009\n",
    "df10['YEAR'] = 2010\n",
    "df11['YEAR'] = 2011\n",
    "df12['YEAR'] = 2012\n",
    "df13['YEAR'] = 2013\n",
    "df14['YEAR'] = 2014\n",
    "df15['YEAR'] = 2015\n",
    "df16['YEAR'] = 2016\n",
    "df17['YEAR'] = 2017\n",
    "df18['YEAR'] = 2018\n",
    "df19['YEAR'] = 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Tm</th>\n",
       "      <th>Pos</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>TRB%</th>\n",
       "      <th>AST%</th>\n",
       "      <th>STL%</th>\n",
       "      <th>BLK%</th>\n",
       "      <th>TOV%</th>\n",
       "      <th>USG%</th>\n",
       "      <th>OWS</th>\n",
       "      <th>DWS</th>\n",
       "      <th>WS</th>\n",
       "      <th>WS/40</th>\n",
       "      <th>YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Matee Ajavon</td>\n",
       "      <td>WAS</td>\n",
       "      <td>G</td>\n",
       "      <td>34</td>\n",
       "      <td>587</td>\n",
       "      <td>12.4</td>\n",
       "      <td>0.451</td>\n",
       "      <td>0.324</td>\n",
       "      <td>0.435</td>\n",
       "      <td>5.4</td>\n",
       "      <td>6.5</td>\n",
       "      <td>11.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>14.2</td>\n",
       "      <td>25.9</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.064</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ashley Battle</td>\n",
       "      <td>NYL</td>\n",
       "      <td>F</td>\n",
       "      <td>34</td>\n",
       "      <td>548</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.526</td>\n",
       "      <td>0.311</td>\n",
       "      <td>0.311</td>\n",
       "      <td>3.7</td>\n",
       "      <td>9.4</td>\n",
       "      <td>13.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>23.6</td>\n",
       "      <td>18.7</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.058</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tully Bevilaqua</td>\n",
       "      <td>IND</td>\n",
       "      <td>G</td>\n",
       "      <td>34</td>\n",
       "      <td>861</td>\n",
       "      <td>12.3</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.298</td>\n",
       "      <td>1.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>19.0</td>\n",
       "      <td>13.2</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.118</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DeWanna Bonner</td>\n",
       "      <td>PHO</td>\n",
       "      <td>G-F</td>\n",
       "      <td>34</td>\n",
       "      <td>724</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0.551</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.554</td>\n",
       "      <td>11.6</td>\n",
       "      <td>15.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.178</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kiesha Brown</td>\n",
       "      <td>CON</td>\n",
       "      <td>G</td>\n",
       "      <td>34</td>\n",
       "      <td>393</td>\n",
       "      <td>11.2</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.439</td>\n",
       "      <td>0.179</td>\n",
       "      <td>2.5</td>\n",
       "      <td>7.9</td>\n",
       "      <td>16.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>17.9</td>\n",
       "      <td>18.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.054</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Player   Tm  Pos  G▼   MP   PER    TS%   3PAr    FTr  ORB%  TRB%  \\\n",
       "0     Matee Ajavon  WAS    G  34  587  12.4  0.451  0.324  0.435   5.4   6.5   \n",
       "1    Ashley Battle  NYL    F  34  548  12.0  0.526  0.311  0.311   3.7   9.4   \n",
       "2  Tully Bevilaqua  IND    G  34  861  12.3  0.510  0.591  0.298   1.6   5.6   \n",
       "3   DeWanna Bonner  PHO  G-F  34  724  21.1  0.551  0.047  0.554  11.6  15.1   \n",
       "4     Kiesha Brown  CON    G  34  393  11.2  0.490  0.439  0.179   2.5   7.9   \n",
       "\n",
       "   AST%  STL%  BLK%  TOV%  USG%  OWS  DWS   WS  WS/40  YEAR  \n",
       "0  11.8   3.2   0.5  14.2  25.9  0.1  0.9  0.9  0.064  2009  \n",
       "1  13.6   3.0   0.3  23.6  18.7 -0.2  1.0  0.8  0.058  2009  \n",
       "2  20.0   3.8   0.8  19.0  13.2  0.8  1.8  2.5  0.118  2009  \n",
       "3   2.8   1.5   2.4   9.0  22.0  2.7  0.5  3.2  0.178  2009  \n",
       "4  16.9   1.7   0.6  17.9  18.3  0.0  0.5  0.5  0.054  2009  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#displays first five rows of the df09 dataframe to check that 'YEAR' column was added\n",
    "df09.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1683, 21)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#combines all the dataframes into one big dataframe\n",
    "df = pd.concat([df09,df10,df11,df12,df13,df14,df15,df16,df17,df18,df19])\n",
    "\n",
    "#shows us number of rows/columns in the new combined df\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>Tm</th>\n",
       "      <th>Pos</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>TRB%</th>\n",
       "      <th>AST%</th>\n",
       "      <th>STL%</th>\n",
       "      <th>BLK%</th>\n",
       "      <th>TOV%</th>\n",
       "      <th>USG%</th>\n",
       "      <th>OWS</th>\n",
       "      <th>DWS</th>\n",
       "      <th>WS</th>\n",
       "      <th>WS/40</th>\n",
       "      <th>YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Matee Ajavon</td>\n",
       "      <td>WAS</td>\n",
       "      <td>G</td>\n",
       "      <td>34</td>\n",
       "      <td>587</td>\n",
       "      <td>12.4</td>\n",
       "      <td>0.451</td>\n",
       "      <td>0.324</td>\n",
       "      <td>0.435</td>\n",
       "      <td>5.4</td>\n",
       "      <td>6.5</td>\n",
       "      <td>11.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>14.2</td>\n",
       "      <td>25.9</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.064</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ashley Battle</td>\n",
       "      <td>NYL</td>\n",
       "      <td>F</td>\n",
       "      <td>34</td>\n",
       "      <td>548</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.526</td>\n",
       "      <td>0.311</td>\n",
       "      <td>0.311</td>\n",
       "      <td>3.7</td>\n",
       "      <td>9.4</td>\n",
       "      <td>13.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>23.6</td>\n",
       "      <td>18.7</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.058</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tully Bevilaqua</td>\n",
       "      <td>IND</td>\n",
       "      <td>G</td>\n",
       "      <td>34</td>\n",
       "      <td>861</td>\n",
       "      <td>12.3</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.298</td>\n",
       "      <td>1.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>19.0</td>\n",
       "      <td>13.2</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.118</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DeWanna Bonner</td>\n",
       "      <td>PHO</td>\n",
       "      <td>G-F</td>\n",
       "      <td>34</td>\n",
       "      <td>724</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0.551</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.554</td>\n",
       "      <td>11.6</td>\n",
       "      <td>15.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.178</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kiesha Brown</td>\n",
       "      <td>CON</td>\n",
       "      <td>G</td>\n",
       "      <td>34</td>\n",
       "      <td>393</td>\n",
       "      <td>11.2</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.439</td>\n",
       "      <td>0.179</td>\n",
       "      <td>2.5</td>\n",
       "      <td>7.9</td>\n",
       "      <td>16.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>17.9</td>\n",
       "      <td>18.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.054</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Player   Tm  Pos  G▼   MP   PER    TS%   3PAr    FTr  ORB%  TRB%  \\\n",
       "0     Matee Ajavon  WAS    G  34  587  12.4  0.451  0.324  0.435   5.4   6.5   \n",
       "1    Ashley Battle  NYL    F  34  548  12.0  0.526  0.311  0.311   3.7   9.4   \n",
       "2  Tully Bevilaqua  IND    G  34  861  12.3  0.510  0.591  0.298   1.6   5.6   \n",
       "3   DeWanna Bonner  PHO  G-F  34  724  21.1  0.551  0.047  0.554  11.6  15.1   \n",
       "4     Kiesha Brown  CON    G  34  393  11.2  0.490  0.439  0.179   2.5   7.9   \n",
       "\n",
       "   AST%  STL%  BLK%  TOV%  USG%  OWS  DWS   WS  WS/40  YEAR  \n",
       "0  11.8   3.2   0.5  14.2  25.9  0.1  0.9  0.9  0.064  2009  \n",
       "1  13.6   3.0   0.3  23.6  18.7 -0.2  1.0  0.8  0.058  2009  \n",
       "2  20.0   3.8   0.8  19.0  13.2  0.8  1.8  2.5  0.118  2009  \n",
       "3   2.8   1.5   2.4   9.0  22.0  2.7  0.5  3.2  0.178  2009  \n",
       "4  16.9   1.7   0.6  17.9  18.3  0.0  0.5  0.5  0.054  2009  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1683 entries, 0 to 156\n",
      "Data columns (total 21 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Player  1683 non-null   object \n",
      " 1   Tm      1683 non-null   object \n",
      " 2   Pos     1661 non-null   object \n",
      " 3   G▼      1683 non-null   int64  \n",
      " 4   MP      1683 non-null   int64  \n",
      " 5   PER     1681 non-null   float64\n",
      " 6   TS%     1676 non-null   float64\n",
      " 7   3PAr    1676 non-null   float64\n",
      " 8   FTr     1676 non-null   float64\n",
      " 9   ORB%    1681 non-null   float64\n",
      " 10  TRB%    1681 non-null   float64\n",
      " 11  AST%    1681 non-null   float64\n",
      " 12  STL%    1681 non-null   float64\n",
      " 13  BLK%    1681 non-null   float64\n",
      " 14  TOV%    1676 non-null   float64\n",
      " 15  USG%    1681 non-null   float64\n",
      " 16  OWS     1683 non-null   float64\n",
      " 17  DWS     1683 non-null   float64\n",
      " 18  WS      1683 non-null   float64\n",
      " 19  WS/40   1681 non-null   float64\n",
      " 20  YEAR    1683 non-null   int64  \n",
      "dtypes: float64(15), int64(3), object(3)\n",
      "memory usage: 289.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Player', 'Tm', 'Pos', 'NUM GAMES', 'MP', 'PER', 'TS%', '3PAr', 'FTr',\n",
       "       'ORB%', 'TRB%', 'AST%', 'STL%', 'BLK%', 'TOV%', 'USG%', 'OWS', 'DWS',\n",
       "       'WS', 'WS_40', 'YEAR'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#renames 'G▼' column\n",
    "df = df.rename(columns = {'G▼':'NUM GAMES', 'WS/40': 'WS_40'})\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('.\\\\df.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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