pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lakers_dict = {'first_name': ['Lebron', 'Kyle', 'Lonzo', 'Brandon', 'Josh'], \n",
    "        'last_name': ['James', 'Kuzma', 'Ball', 'Ingram', 'Hart'], \n",
    "        'ppg': [27.4, 18.7, 9.9, 18.3, 7.8], \n",
    "        'apg': [8.3, 2.5, 5.4, 3.0, 1.4],\n",
    "        'rpg': [8.5, 5.5, 5.3, 5.1, 3.7]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(lakers_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(lakers_dict, index=[1,2,3,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(lakers_dict, index=['playmaker', 'pf', 'pg', 'sf', 'sg'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats = [['Lebron', 4, 6], ['Kuzma', 2, 3], ['Ball', 4, 4]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats_df = pd.DataFrame(stats, columns = ['player', 'oreb', 'dreb'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(stats_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rebs = [6, 9, 11, 7, 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "series = pd.DataFrame(rebs, columns=['lebron_reb'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Attributes & Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../nba-stats-csv/player_general_traditional_per_game_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    ".method()\n",
    ".attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Head, Tail & Sample Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_5_row = df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(df_5_row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.tail(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sample(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading CSV Files in Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../nba-stats-csv/player_info.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../nba-stats-csv/player_info_no_header.csv', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../nba-stats-csv/player_info.csv', index_col='player_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../nba-stats-csv/player_general_traditional_per_game_data.csv', usecols=['player_id', 'season_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataframe Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../nba-stats-csv/player_stats_total.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim = df.sample(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "list(df_slim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim.player_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim['pts']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(df_slim['pts'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim[['player_name', 'pts']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim[['pts', 'player_name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_of_columns = ['player_name', 'ftm', 'fta']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim[list_of_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "list(df_slim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim.iloc[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim.iloc[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim.iloc[:, 0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim.iloc[:, [1,4,6]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding & Deleting Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../nba-stats-csv/player_stats_total.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim = df.sample(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim['ppg'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim['ppg'] = round((df_slim['pts'] / df_slim['gp']), 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim.insert(3, column = 'league', value = 'NBA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df_slim['ppg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_slim.drop('league', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# new_df = old_df[['col1', 'col2', 'col3']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reb_df = df_slim[['player_name', 'oreb', 'dreb']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reb_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Renaming Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../nba-stats-csv/player_stats_total.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['player_name', 'gp', 'min']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim = df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim.columns = ['player_name', 'games_played', 'minutes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_column_names = ['player', 'games_p', 'minutos']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim.columns = new_column_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = df_slim.rename(columns={\n",
    "    'games_p': 'games_played',\n",
    "    'minutos': 'minutes'\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting Rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../nba-stats-csv/player_stats_total.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim = df.sample(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim.iloc[8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_print = df_slim.iloc[1:11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_print"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lakers_dict = {'first_name': ['Lebron', 'Kyle', 'Lonzo', 'Brandon', 'Josh'], \n",
    "        'last_name': ['James', 'Kuzma', 'Ball', 'Ingram', 'Hart'], \n",
    "        'ppg': [27.4, 18.7, 9.9, 18.3, 7.8], \n",
    "        'apg': [8.3, 2.5, 5.4, 3.0, 1.4],\n",
    "        'rpg': [8.5, 5.5, 5.3, 5.1, 3.7]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(lakers_dict, index=['playmaker', 'pf', 'pg', 'sf', 'sg'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc['sf']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[['pf', 'pg']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.ix[2, 'last_name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding and Dropping Rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data = {'player_name': ['Lebron James', 'Lonzo Ball', 'Kyle Kuzma', np.nan, 'Josh Hart'], \n",
    "            'team': ['LAL', 'LAL', 'LAL', np.nan, 'LAL'], \n",
    "            'gp': [55, 47, 70, np.nan, np.nan],\n",
    "            'blocks': [33, 19, np.nan, np.nan, 40]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "laker_df = pd.DataFrame(raw_data, columns = ['player_name', 'team', 'gp', 'blocks'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "laker_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_df1 = laker_df.dropna(axis=0, how='any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_df2 = laker_df.dropna(axis=0, how='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_df3 = laker_df.dropna(subset = ['gp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "updated_df1 = laker_df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "updated_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame([['Lebron James', '2014-15', 25.3], ['Lebron James', '2015-16', 25.3]]\n",
    "                  , columns = ['player','season','ppg'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame([['Lebron James', '2016-17', 26.4]], columns = ['player','season','ppg'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = df1.append(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inplace Parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../nba-stats-csv/player_stats_total.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim = df.sample(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim.sort_values('gp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim.sort_values('gp', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim.sort_values('pts', inplace = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sorted_pts = df_slim.sort_values('pts', inplace = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sorted_pts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sorting Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "laker_stats = {'first_name': ['Lebron', 'Kyle', 'Lonzo', 'Brandon', 'Lance', 'Josh', 'Michael', 'Tyson'], \n",
    "        'last_name': ['James', 'Kuzma', 'Ball', 'Ingram','Stephenson', 'Hart', 'Beasley', 'Chander'], \n",
    "        'ppg': [27.4, 18.7, 9.9, 18.3, 7.2, 7.8, 7.0, 3.1], \n",
    "        'apg': [8.3, 2.5, 5.4, 3.0, 2.1, 1.4, 1.0, 0.6],\n",
    "        'rpg': [8.5, 5.5, 5.3, 5.1, 3.2, 3.7, 2.3, 5.6]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "unsorted_df = pd.DataFrame(laker_stats, index=[1,4,6,2,3,5,0,7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "unsorted_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_df = unsorted_df.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_points_df = sorted_df.sort_values('ppg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_points_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_points_df = sorted_df.sort_values('ppg', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_points_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "laker_stats = {'first_name': ['Lebron', 'Kyle', 'Lonzo', 'Brandon', 'Lance', 'Josh', 'Michael', 'Tyson'], \n",
    "        'last_name': ['James', 'Kuzma', 'Ball', 'Ingram','Stephenson', 'Hart', 'Beasley', 'Chander'], \n",
    "        'ppg': [27.4, 18.7, np.nan, 18.3, np.nan, 7.8, 7.0, 3.1], \n",
    "        'apg': [8.3, 2.5, 5.4, 3.0, 2.1, 1.4, 1.0, 0.6],\n",
    "        'rpg': [8.5, 5.5, 5.3, 5.1, 3.2, 3.7, 2.3, 5.6]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_missing = pd.DataFrame(laker_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sort_by_points = df_missing.sort_values('ppg', ascending = False, na_position='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sort_by_points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "laker_stats = {'first_name': ['Lebron', 'Kyle', 'Lonzo', 'Brandon', 'Lance', 'Josh', 'Michael', 'Tyson'], \n",
    "        'last_name': ['James', 'Kuzma', 'Ball', 'Ingram','Stephenson', 'Hart', 'Beasley', 'Chander'], \n",
    "        'ppg': [27.4, 18.7, 9.9, 18.7, 7.2, 7.8, 7.0, 3.1], \n",
    "        'apg': [8.3, 2.5, 5.4, 3.0, 2.1, 1.4, 1.0, 0.6],\n",
    "        'rpg': [8.5, 5.5, 5.3, 5.1, 3.2, 3.7, 2.3, 5.6]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(laker_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sort_by_two_cols = df.sort_values(['ppg', 'apg'], ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sort_by_two_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtering Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../nba-stats-csv/nba_season_stats.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Tm'] == 'OKC'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "okc_df = df[df['Tm'] == 'OKC']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "okc_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "okc = (df['Tm'] == 'OKC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[okc]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dual_df = df[(df['Tm'] == 'OKC') | (df['Tm'] == 'LAL')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dual_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "okc = (df['Tm'] == 'OKC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lal = (df['Tm'] == 'LAL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[okc | lal]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[(df['Tm'] == 'OKC') & (df['GS'] > 10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "okc = (df['Tm'] == 'OKC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gs = df['GS'] > 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "and_df = df[okc & gs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "and_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fg_50 = (df['FG%'] > .50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "three_point_40 = (df['3P%'] > .40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ft_90 = (df['FT%'] > .9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "shooters_df = df[fg_50 & three_point_40 & ft_90]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "shooters_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "okc = (df['Tm'] == 'OKC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lal = (df['Tm'] == 'LAL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gs = df['GS'] > 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[(okc | lal) & gs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    ".isin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[df['Tm'].isin(['OKC', 'LAL', 'CHI'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "python_list = ['OKC', 'LAL', 'CHI']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[df['Tm'].isin(python_list)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Groupby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../nba-stats-csv/player_general_traditional_per_game_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(df.groupby('season_id'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_csv('../nba-stats-csv/nba_season_stats.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped = df2.groupby('Tm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped['Age'].mean().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lakers = grouped.get_group('LAL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lakers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "seasons = df.groupby('season_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "seasons['pts'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "seasons['pts', 'ast'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(seasons)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split\n",
    "seasons = df.groupby('season_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply\n",
    "seasons['fg3m'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine\n",
    "df_3p_by_season = seasons['fg3m'].sum()\n",
    "df_3p_by_season.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "seasons.agg({\n",
    "    'pts': 'sum',\n",
    "    'ast': 'mean'\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../nba-stats-csv/player_general_traditional_per_game_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "list(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\n",
    "    'player_id',\n",
    "    'season_id',\n",
    "    'gp',\n",
    "    'age',\n",
    "    'fg_pct',\n",
    "    'pts'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = df[columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Concatenate & Append"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_A = df_new.sample(10)\n",
    "df_B = df_new.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.concat([df_A, df_B])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data = {\n",
    "        'player_id': ['1', '2', '3', '4', '5'],\n",
    "        'ppg': [24.3, 28.3, 21.2, 17.2, 11.4],\n",
    "        'fg%': [.57, .43, .38, .39, .54]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1 = pd.DataFrame(raw_data, columns = ['player_id', 'ppg', 'fg%'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data = {\n",
    "        'player_id': ['6', '7', '8', '9', '10'],\n",
    "        'ppg': [14.3, 22.3, 11.6, 10.9, 11.7],\n",
    "        'fg%': [.52, .53, .48, .39, .49]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2 = pd.DataFrame(raw_data, columns = ['player_id', 'ppg', 'fg%'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = pd.concat([df_1, df_2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = pd.concat([df_1, df_2], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1.append(df_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2.append(df_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = df_2.append(df_1)\n",
    "df_new.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging & Joining"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('../nba-stats-csv/player_general_traditional_per_game_data.csv')\n",
    "df2 = pd.read_csv('../nba-stats-csv/player_id_player_name.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df = pd.merge(df1, df2, on='player_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df2 = pd.merge(df2, df1[['player_id', 'season_id', 'ast', 'stl', 'pts']], on='player_id')\n",
    "merged_df2.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df3 = pd.merge(df2, df1[['player_id', 'season_id', 'ast', 'stl', 'pts']], on='player_id', how='inner')\n",
    "merged_df3.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data = {\n",
    "        'player_id': ['1', '2', '3', '4', '5', '6', '7'],\n",
    "        'first_name': ['Lebron', 'Dirk', 'Vince', 'Enes', 'Fred', 'Derrick', 'Paul'], \n",
    "        'last_name': ['James', 'Nowitzki', 'Carter', 'Kaner', 'Van Fleet', 'Rose', 'George']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_A = pd.DataFrame(raw_data, columns = ['player_id', 'first_name', 'last_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data = {\n",
    "        'player_id': ['1', '2', '3', '4', '5'],\n",
    "        'career_ppg': [27.2, 20.7, 17.2, 11.9, 18.8]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_B = pd.DataFrame(raw_data, columns = ['player_id', 'career_ppg'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "left_merge = pd.merge(df_A, df_B, on='player_id', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "left_merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "right_merge = pd.merge(df_A, df_B, on='player_id', how='right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "right_merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "outer_merge = pd.merge(df_A, df_B, on='player_id', how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "outer_merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data = {\n",
    "        'id': ['1', '2', '3', '4', '5', '6', '7'],\n",
    "        'first_name': ['Lebron', 'Dirk', 'Vince', 'Enes', 'Fred', 'Derrick', 'Paul'], \n",
    "        'last_name': ['James', 'Nowitzki', 'Carter', 'Kaner', 'Van Fleet', 'Rose', 'George']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1 = pd.DataFrame(raw_data, columns = ['id', 'first_name', 'last_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data = {\n",
    "        'player_id': ['1', '2', '3', '4', '5'],\n",
    "        'career_ppg': [27.2, 20.7, 17.2, 11.9, 18.8]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2 = pd.DataFrame(raw_data, columns = ['player_id','career_ppg'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = pd.merge(df_1, df_2, how = 'right', left_on = 'id', right_on = 'player_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4 = pd.merge(df_1, df_2, how = 'right', left_on = 'id', right_on = 'player_id').drop('id', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5 = df_1.merge(df_2, how = 'inner', left_on = 'id', right_on = 'player_id').drop('player_id', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Iterating Over a Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "nba_stats = {'first_name': ['Lebron', 'Kyle', 'Lonzo', 'Brandon', 'Josh', 'Lance', 'Michael', 'Tyson', 'Ivica'], \n",
    "        'last_name': ['James', 'Kuzma', 'Ball', 'Ingram', 'Hart', 'Stephenson', 'Beasley', 'Chander', 'Zubac'], \n",
    "        'ppg': [28.2, 21.1, 14.3, 17.2, 18.2, 11.2, 14.2, 4.5, 6.7], \n",
    "        'apg': [12.3, 2.1, 8.9, 2.5, 3.6, 0.2, 1.8, 1.9, 1.5],\n",
    "        'rpg': [7.1, 5.4, 5.5, 8.1, 3.2, 5.5, 7.7, 2.3, 8.1]}\n",
    "laker_df = pd.DataFrame(nba_stats, columns = ['first_name', 'last_name', 'ppg', 'apg', 'rpg'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, first_name    Lebron\n",
      "last_name      James\n",
      "ppg             28.2\n",
      "apg             12.3\n",
      "rpg              7.1\n",
      "Name: 0, dtype: object)\n",
      "(1, first_name     Kyle\n",
      "last_name     Kuzma\n",
      "ppg            21.1\n",
      "apg             2.1\n",
      "rpg             5.4\n",
      "Name: 1, dtype: object)\n",
      "(2, first_name    Lonzo\n",
      "last_name      Ball\n",
      "ppg            14.3\n",
      "apg             8.9\n",
      "rpg             5.5\n",
      "Name: 2, dtype: object)\n",
      "(3, first_name    Brandon\n",
      "last_name      Ingram\n",
      "ppg              17.2\n",
      "apg               2.5\n",
      "rpg               8.1\n",
      "Name: 3, dtype: object)\n",
      "(4, first_name    Josh\n",
      "last_name     Hart\n",
      "ppg           18.2\n",
      "apg            3.6\n",
      "rpg            3.2\n",
      "Name: 4, dtype: object)\n",
      "(5, first_name         Lance\n",
      "last_name     Stephenson\n",
      "ppg                 11.2\n",
      "apg                  0.2\n",
      "rpg                  5.5\n",
      "Name: 5, dtype: object)\n",
      "(6, first_name    Michael\n",
      "last_name     Beasley\n",
      "ppg              14.2\n",
      "apg               1.8\n",
      "rpg               7.7\n",
      "Name: 6, dtype: object)\n",
      "(7, first_name      Tyson\n",
      "last_name     Chander\n",
      "ppg               4.5\n",
      "apg               1.9\n",
      "rpg               2.3\n",
      "Name: 7, dtype: object)\n",
      "(8, first_name    Ivica\n",
      "last_name     Zubac\n",
      "ppg             6.7\n",
      "apg             1.5\n",
      "rpg             8.1\n",
      "Name: 8, dtype: object)\n"
     ]
    }
   ],
   "source": [
    "for row in laker_df.iterrows():\n",
    "    print(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lebron James 28.2\n",
      "Kyle Kuzma 21.1\n",
      "Lonzo Ball 14.3\n",
      "Brandon Ingram 17.2\n",
      "Josh Hart 18.2\n",
      "Lance Stephenson 11.2\n",
      "Michael Beasley 14.2\n",
      "Tyson Chander 4.5\n",
      "Ivica Zubac 6.7\n"
     ]
    }
   ],
   "source": [
    "for index, row in laker_df.iterrows():\n",
    "    print(row['first_name'], row['last_name'], row['ppg'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lebron James 28.2\n",
      "Kyle Kuzma 21.1\n",
      "Lonzo Ball 14.3\n",
      "Brandon Ingram 17.2\n",
      "Josh Hart 18.2\n",
      "Lance Stephenson 11.2\n",
      "Michael Beasley 14.2\n",
      "Tyson Chander 4.5\n",
      "Ivica Zubac 6.7\n"
     ]
    }
   ],
   "source": [
    "for row in laker_df.itertuples():\n",
    "    print(row.first_name, row.last_name, row.ppg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ppg_data = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "for row in laker_df.itertuples():\n",
    "    ppg_data.append(row.ppg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[28.2, 21.1, 14.3, 17.2, 18.2, 11.2, 14.2, 4.5, 6.7]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppg_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying Functions to Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../nba-stats-csv/player_stats_total.csv')"
   ]
  },
  {
   "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>player_name</th>\n",
       "      <th>player_id</th>\n",
       "      <th>season_id</th>\n",
       "      <th>gp</th>\n",
       "      <th>age</th>\n",
       "      <th>min</th>\n",
       "      <th>fgm</th>\n",
       "      <th>fga</th>\n",
       "      <th>fg3m</th>\n",
       "      <th>ftm</th>\n",
       "      <th>fta</th>\n",
       "      <th>oreb</th>\n",
       "      <th>dreb</th>\n",
       "      <th>ast</th>\n",
       "      <th>tov</th>\n",
       "      <th>stl</th>\n",
       "      <th>blk</th>\n",
       "      <th>pts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Chris Andersen</td>\n",
       "      <td>2365</td>\n",
       "      <td>2001-02</td>\n",
       "      <td>24.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>260.658</td>\n",
       "      <td>25.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2204</th>\n",
       "      <td>Mirza Teletovic</td>\n",
       "      <td>203141</td>\n",
       "      <td>2014-15</td>\n",
       "      <td>40.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>892.335</td>\n",
       "      <td>122.0</td>\n",
       "      <td>319.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>339.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1124</th>\n",
       "      <td>Luis Scola</td>\n",
       "      <td>2449</td>\n",
       "      <td>2011-12</td>\n",
       "      <td>66.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2067.070</td>\n",
       "      <td>436.0</td>\n",
       "      <td>888.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1025.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1821</th>\n",
       "      <td>Larry Sanders</td>\n",
       "      <td>202336</td>\n",
       "      <td>2014-15</td>\n",
       "      <td>27.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>585.528</td>\n",
       "      <td>86.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>196.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>Deron Williams</td>\n",
       "      <td>101114</td>\n",
       "      <td>2009-10</td>\n",
       "      <td>76.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2801.880</td>\n",
       "      <td>494.0</td>\n",
       "      <td>1053.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>335.0</td>\n",
       "      <td>418.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>798.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1419.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          player_name  player_id season_id    gp   age       min    fgm  \\\n",
       "126    Chris Andersen       2365   2001-02  24.0  23.0   260.658   25.0   \n",
       "2204  Mirza Teletovic     203141   2014-15  40.0  29.0   892.335  122.0   \n",
       "1124       Luis Scola       2449   2011-12  66.0  32.0  2067.070  436.0   \n",
       "1821    Larry Sanders     202336   2014-15  27.0  26.0   585.528   86.0   \n",
       "735    Deron Williams     101114   2009-10  76.0  26.0  2801.880  494.0   \n",
       "\n",
       "         fga  fg3m    ftm    fta   oreb   dreb    ast    tov   stl   blk  \\\n",
       "126     74.0   0.0   22.0   28.0   38.0   38.0    7.0   13.0   7.0  28.0   \n",
       "2204   319.0  62.0   33.0   46.0   27.0  167.0   46.0   53.0  14.0  16.0   \n",
       "1124   888.0   0.0  153.0  198.0  122.0  306.0  140.0  153.0  35.0  24.0   \n",
       "1821   172.0   0.0   24.0   48.0   67.0   99.0   23.0   28.0  26.0  39.0   \n",
       "735   1053.0  96.0  335.0  418.0   50.0  253.0  798.0  252.0  96.0  16.0   \n",
       "\n",
       "         pts  \n",
       "126     72.0  \n",
       "2204   339.0  \n",
       "1124  1025.0  \n",
       "1821   196.0  \n",
       "735   1419.0  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_year(current_age):\n",
    "    new_age = current_age + 1\n",
    "    return new_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2741 entries, 0 to 2740\n",
      "Data columns (total 18 columns):\n",
      "player_name    2741 non-null object\n",
      "player_id      2741 non-null int64\n",
      "season_id      2741 non-null object\n",
      "gp             2741 non-null float64\n",
      "age            2741 non-null float64\n",
      "min            2741 non-null float64\n",
      "fgm            2741 non-null float64\n",
      "fga            2741 non-null float64\n",
      "fg3m           2741 non-null float64\n",
      "ftm            2741 non-null float64\n",
      "fta            2741 non-null float64\n",
      "oreb           2741 non-null float64\n",
      "dreb           2741 non-null float64\n",
      "ast            2741 non-null float64\n",
      "tov            2741 non-null float64\n",
      "stl            2741 non-null float64\n",
      "blk            2741 non-null float64\n",
      "pts            2741 non-null float64\n",
      "dtypes: float64(15), int64(1), object(2)\n",
      "memory usage: 385.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       22.0\n",
       "1       23.0\n",
       "2       24.0\n",
       "3       25.0\n",
       "4       26.0\n",
       "5       27.0\n",
       "6       28.0\n",
       "7       29.0\n",
       "8       30.0\n",
       "9       31.0\n",
       "10      32.0\n",
       "11      33.0\n",
       "12      34.0\n",
       "13      35.0\n",
       "14      36.0\n",
       "15      37.0\n",
       "16      38.0\n",
       "17      39.0\n",
       "18      39.0\n",
       "19      22.0\n",
       "20      23.0\n",
       "21      24.0\n",
       "22      25.0\n",
       "23      26.0\n",
       "24      27.0\n",
       "25      28.0\n",
       "26      29.0\n",
       "27      30.0\n",
       "28      31.0\n",
       "29      32.0\n",
       "        ... \n",
       "2711    23.0\n",
       "2712    28.0\n",
       "2713    25.0\n",
       "2714    28.0\n",
       "2715    25.0\n",
       "2716    24.0\n",
       "2717    24.0\n",
       "2718    22.0\n",
       "2719    24.0\n",
       "2720    26.0\n",
       "2721    25.0\n",
       "2722    26.0\n",
       "2723    25.0\n",
       "2724    25.0\n",
       "2725    25.0\n",
       "2726    25.0\n",
       "2727    22.0\n",
       "2728    21.0\n",
       "2729    24.0\n",
       "2730    21.0\n",
       "2731    23.0\n",
       "2732    22.0\n",
       "2733    26.0\n",
       "2734    24.0\n",
       "2735    23.0\n",
       "2736    22.0\n",
       "2737    22.0\n",
       "2738    23.0\n",
       "2739    23.0\n",
       "2740    24.0\n",
       "Name: age, Length: 2741, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age'].apply(add_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "player_stats = {'player_name': ['Lebron', 'Dirk', 'Wade', 'Curry', 'George'], \n",
    "        'season_id': ['2017-18', '2017-18', '2017-18', '2017-18', '2017-18'], \n",
    "        'total_points': [2251, 927, 765, 1346, 1734],\n",
    "        'games_played': [82, 77, 81, 51, 79]}\n",
    "df = pd.DataFrame(player_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_name</th>\n",
       "      <th>season_id</th>\n",
       "      <th>total_points</th>\n",
       "      <th>games_played</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lebron</td>\n",
       "      <td>2017-18</td>\n",
       "      <td>2251</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dirk</td>\n",
       "      <td>2017-18</td>\n",
       "      <td>927</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Wade</td>\n",
       "      <td>2017-18</td>\n",
       "      <td>765</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Curry</td>\n",
       "      <td>2017-18</td>\n",
       "      <td>1346</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>George</td>\n",
       "      <td>2017-18</td>\n",
       "      <td>1734</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  player_name season_id  total_points  games_played\n",
       "0      Lebron   2017-18          2251            82\n",
       "1        Dirk   2017-18           927            77\n",
       "2        Wade   2017-18           765            81\n",
       "3       Curry   2017-18          1346            51\n",
       "4      George   2017-18          1734            79"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ppg_calculator(row):\n",
    "    gp = row[3]\n",
    "    pts = row[2]\n",
    "    ppg = pts / gp\n",
    "    return ppg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    27.451220\n",
       "1    12.038961\n",
       "2     9.444444\n",
       "3    26.392157\n",
       "4    21.949367\n",
       "dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(ppg_calculator, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ppg'] = df.apply(ppg_calculator, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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",
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       "    }\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_name</th>\n",
       "      <th>season_id</th>\n",
       "      <th>total_points</th>\n",
       "      <th>games_played</th>\n",
       "      <th>ppg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lebron</td>\n",
       "      <td>2017-18</td>\n",
       "      <td>2251</td>\n",
       "      <td>82</td>\n",
       "      <td>27.451220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dirk</td>\n",
       "      <td>2017-18</td>\n",
       "      <td>927</td>\n",
       "      <td>77</td>\n",
       "      <td>12.038961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Wade</td>\n",
       "      <td>2017-18</td>\n",
       "      <td>765</td>\n",
       "      <td>81</td>\n",
       "      <td>9.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Curry</td>\n",
       "      <td>2017-18</td>\n",
       "      <td>1346</td>\n",
       "      <td>51</td>\n",
       "      <td>26.392157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>George</td>\n",
       "      <td>2017-18</td>\n",
       "      <td>1734</td>\n",
       "      <td>79</td>\n",
       "      <td>21.949367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  player_name season_id  total_points  games_played        ppg\n",
       "0      Lebron   2017-18          2251            82  27.451220\n",
       "1        Dirk   2017-18           927            77  12.038961\n",
       "2        Wade   2017-18           765            81   9.444444\n",
       "3       Curry   2017-18          1346            51  26.392157\n",
       "4      George   2017-18          1734            79  21.949367"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../nba-stats-csv/player_stats_total.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ft_calculator(row):\n",
    "    ft_perc = (row['ftm'] / (row['fta'] + .00001))\n",
    "    return round(ft_perc,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ft_perc'] = df.apply(ft_calculator, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_name</th>\n",
       "      <th>player_id</th>\n",
       "      <th>season_id</th>\n",
       "      <th>gp</th>\n",
       "      <th>age</th>\n",
       "      <th>min</th>\n",
       "      <th>fgm</th>\n",
       "      <th>fga</th>\n",
       "      <th>fg3m</th>\n",
       "      <th>ftm</th>\n",
       "      <th>fta</th>\n",
       "      <th>oreb</th>\n",
       "      <th>dreb</th>\n",
       "      <th>ast</th>\n",
       "      <th>tov</th>\n",
       "      <th>stl</th>\n",
       "      <th>blk</th>\n",
       "      <th>pts</th>\n",
       "      <th>ft_perc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1574</th>\n",
       "      <td>Jordan Hill</td>\n",
       "      <td>201941</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>72.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1499.790</td>\n",
       "      <td>286.0</td>\n",
       "      <td>521.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>341.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>696.0</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2618</th>\n",
       "      <td>Nikola Jokic</td>\n",
       "      <td>203999</td>\n",
       "      <td>2016-17</td>\n",
       "      <td>73.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2037.880</td>\n",
       "      <td>494.0</td>\n",
       "      <td>854.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1221.0</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2626</th>\n",
       "      <td>Rakeem Christmas</td>\n",
       "      <td>1626176</td>\n",
       "      <td>2016-17</td>\n",
       "      <td>29.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>219.388</td>\n",
       "      <td>19.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2561</th>\n",
       "      <td>Delon Wright</td>\n",
       "      <td>1626153</td>\n",
       "      <td>2015-16</td>\n",
       "      <td>27.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>229.242</td>\n",
       "      <td>36.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2178</th>\n",
       "      <td>Maurice Harkless</td>\n",
       "      <td>203090</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>80.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1949.560</td>\n",
       "      <td>224.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>592.0</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           player_name  player_id season_id    gp   age       min    fgm  \\\n",
       "1574       Jordan Hill     201941   2013-14  72.0  26.0  1499.790  286.0   \n",
       "2618      Nikola Jokic     203999   2016-17  73.0  22.0  2037.880  494.0   \n",
       "2626  Rakeem Christmas    1626176   2016-17  29.0  25.0   219.388   19.0   \n",
       "2561      Delon Wright    1626153   2015-16  27.0  24.0   229.242   36.0   \n",
       "2178  Maurice Harkless     203090   2013-14  80.0  21.0  1949.560  224.0   \n",
       "\n",
       "        fga  fg3m    ftm    fta   oreb   dreb    ast    tov   stl   blk  \\\n",
       "1574  521.0   0.0  124.0  181.0  194.0  341.0   54.0   75.0  29.0  64.0   \n",
       "2618  854.0  45.0  188.0  228.0  212.0  506.0  359.0  171.0  61.0  55.0   \n",
       "2626   43.0   0.0   21.0   29.0   26.0   30.0    4.0    8.0   3.0   6.0   \n",
       "2561   80.0   5.0   26.0   35.0    8.0   29.0   31.0   16.0   8.0   3.0   \n",
       "2178  483.0  59.0   85.0  143.0   69.0  195.0   80.0   86.0  97.0  50.0   \n",
       "\n",
       "         pts  ft_perc  \n",
       "1574   696.0     0.69  \n",
       "2618  1221.0     0.82  \n",
       "2626    59.0     0.72  \n",
       "2561   103.0     0.74  \n",
       "2178   592.0     0.59  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "final = df.groupby('player_name').sum().apply(ft_calculator, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "player_name\n",
       "Nicolas Laprovittola    1.00\n",
       "Georges Niang           1.00\n",
       "Michael Gbinije         1.00\n",
       "Diamond Stone           1.00\n",
       "Chinanu Onuaku          1.00\n",
       "Chasson Randle          0.95\n",
       "Jarell Eddie            0.94\n",
       "Brian Roberts           0.91\n",
       "Okaro White             0.91\n",
       "Alex Abrines            0.90\n",
       "Stephen Curry           0.90\n",
       "Marcus Georges-Hunt     0.90\n",
       "JJ Redick               0.89\n",
       "Isaiah Thomas           0.88\n",
       "Jamal Murray            0.88\n",
       "Damian Lillard          0.88\n",
       "Steve Novak             0.88\n",
       "Jose Calderon           0.88\n",
       "Kyle Korver             0.88\n",
       "Dirk Nowitzki           0.88\n",
       "Anthony Morrow          0.88\n",
       "Kevin Durant            0.88\n",
       "Jodie Meeks             0.88\n",
       "Malcolm Brogdon         0.87\n",
       "Chris Paul              0.87\n",
       "Kyrie Irving            0.87\n",
       "Khris Middleton         0.87\n",
       "Georgios Papagiannis    0.86\n",
       "Bojan Bogdanovic        0.86\n",
       "Jarrett Jack            0.86\n",
       "                        ... \n",
       "Thomas Robinson         0.51\n",
       "Demetrius Jackson       0.50\n",
       "Joel Bolomboy           0.50\n",
       "Kyle Wiltjer            0.50\n",
       "Henry Ellenson          0.50\n",
       "Chris McCullough        0.50\n",
       "Isaiah Taylor           0.50\n",
       "Andre Roberson          0.49\n",
       "AJ Hammons              0.45\n",
       "Aaron Harrison          0.43\n",
       "DeAndre Jordan          0.43\n",
       "Clint Capela            0.43\n",
       "Marshall Plumlee        0.42\n",
       "Arinze Onuaku           0.40\n",
       "Quinn Cook              0.40\n",
       "Andre Drummond          0.38\n",
       "DeAndre' Bembry         0.37\n",
       "Dragan Bender           0.36\n",
       "Justin Harper           0.33\n",
       "Damian Jones            0.30\n",
       "Edy Tavares             0.27\n",
       "Patricio Garino         0.00\n",
       "Daniel Ochefu           0.00\n",
       "Josh Huestis            0.00\n",
       "Bruno Caboclo           0.00\n",
       "Mike Tobey              0.00\n",
       "Brice Johnson           0.00\n",
       "Ben Bentil              0.00\n",
       "Tim Quarterman          0.00\n",
       "Danuel House            0.00\n",
       "Length: 486, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "kawhi_steals = [4, 5, 2, 1, 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 5, 2, 1, 3]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kawhi_steals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "klaw = np.array(kawhi_steals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 5, 2, 1, 3])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "klaw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "klaw.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "klaw_2 = klaw + 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 7, 4, 3, 5])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "klaw_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "klaw_2.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "player_stats = np.array([[1, 2, 3, 4],\n",
    "                   [5, 6, 7, 8],\n",
    "                   [9, 10, 11, 12]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 4)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player_stats.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "some_other_player_stats = np.array([[4, 3, 2, 4],\n",
    "                   [56, 46, 27, 18],\n",
    "                   [4, 4, 4, 5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 4)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "some_other_player_stats.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "final = player_stats + some_other_player_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5,  5,  5,  8],\n",
       "       [61, 52, 34, 26],\n",
       "       [13, 14, 15, 17]])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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