FinalProject_group182.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 Final Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Throughout this project, we tested our data science question by splitting it up into three smaller questions. In our first analysis, we compared the average shot distance for the winning team of each season over the past two decades. In our second analysis, we followed the trend of shot distances for 2-pointers, mid-range, and 3-pointer shots over time. In our last analysis, we looked at the relationships between both the percentages and the raw number of three-pointers when compared to the amount of three-pointers attempted, made, and all shots attempted."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Group Members\n",
    "- Eric Yang: Background Research, Exploratory, Conclusions\n",
    "- David Gallardo: Analysis: Part I, Data Wrangling, Conclusions\n",
    "- Alvin Wong: Question & Hypothesis, Analysis - Part II, Conclusions\n",
    "- Vincent Tran: Ethical Considerations, Analysis - Part III, Conclusions\n",
    "- Eric Trinh: Dataset, Data Wrangling, Descriptive, Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Research Question:\n",
    "How has the average NBA shot distance changed over the last two decades, specifically the percentage of three-point shots attempted and made, and how has this affected average shot distance?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background Research"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the most popular sports in the United States is basketball. Within the game of basketball, the three-point shot has increased in popularity and has greatly impacted the game ever since the introduction of the three-point line. Many basketball players today have the three-point shot as part of their skillset and it is an important skill to have for guards. Even centers today, who traditionally are not known for shooting, are beginning to pick up the three-point shot. Additionally, college athletes today are shooting more three-point shots than ever before (1). However, the three-point line is a relatively new creation as it was added to basketball in 1979 while basketball was first invented in 1891 and the National Basketball Association (NBA) created in 1946 (2). \n",
    "\n",
    "Looking at previous projects, students have looked into the impact of the three-point shot and also the practicality or effectiveness of the three-point shot. But, none have explored and analyzed the change in shot distance because of the three-point line. We believe that the amount of shots taken at longer distances have increased ever since the introduction of the three-point line, especially as teams place more and more weight on three-point shots in their game plans.\n",
    "\n",
    "References:\n",
    "- 1) https://bleacherreport.com/articles/2762158-the-3-point-revolution-has-taken-over-college-basketball-too\n",
    "- 2) https://www.usab.com/youth/news/2011/06/the-history-of-the-3-pointer.aspx\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothesis:\n",
    "The average NBA shot distance will have increased due to the addition of the three-point line. \n",
    "\n",
    "With the introduction of the three-point line, as players become better shooters and more familiar with the three-point line, players will take their shots from a farther distance to have a chance at making three-point shots, as statistically, a three-point shot contributes more to a game victory than a two-point shot. Additionally, the average NBA shot distance is directly proportional to the distance of the three-point line from the hoop, so as more players begin to take more and more three-point shots, and the three-point line gets pushed further out, the average shot distance of the NBA will consequently increase."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Set\n",
    "https://www.basketball-reference.com/ will be our primary resource to gather data from. It has nearly everything one would want regarding basketball statistics from statistics for current and past players, teams, and coaches, meaning we are able to collect concise empirical data for absolutely any and all specific data points we would want to extrapolate from just this one resource. This website also allows us to export data through CSV files, which we can easily import into Python. This also means that data wrangling on the datasets coming from this website should be very simple and straightforward.\n",
    "\n",
    "- Data Set Name: Basketball Reference\n",
    "- Link to the Data Set: https://www.basketball-reference.com/\n",
    "- Number of Observations: All basketball teams participating in the NBA from 2000 to 2018"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Packages\n",
    "\n",
    "First we need to read import packages in order to analyze our data and begin to answer our data science question. Pandas and numpy are used to create and manipulate the dataframes that hold our data from our datasets. Matplotlib.pyplot is used to plot the data we will acquire from the datasets. Finally, scipy.stats is imported to run statistical analysis on our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Imports important libraries\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pylab as pl\n",
    "import os\n",
    "from IPython.display import Markdown\n",
    "from scipy.stats import ttest_ind"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Data\n",
    "This imports all the csv files needed for our data analysis. For each year, we import a csv that contains statistics about the average performance per game for each team, the team's shooting, and each team's stats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#NBA Season 2000-2001\n",
    "df_00_01PG = pd.read_csv('00-01_team_per_game_stats.csv')\n",
    "df_00_01TS = pd.read_csv('00-01_team_shooting.csv')\n",
    "df_00_01S = pd.read_csv('00-01_team_stats.csv')\n",
    "\n",
    "#NBA Season 2001-2002\n",
    "df_01_02PG = pd.read_csv('01-02_team_per_game_stats.csv')\n",
    "df_01_02TS = pd.read_csv('01-02_team_shooting.csv')\n",
    "df_01_02S = pd.read_csv('01-02_team_stats.csv')\n",
    "\n",
    "#NBA Season 2002-2003\n",
    "df_02_03PG = pd.read_csv('02-03_team_per_game_stats.csv')\n",
    "df_02_03TS = pd.read_csv('02-03_team_shooting.csv')\n",
    "df_02_03S = pd.read_csv('02-03_team_stats.csv')\n",
    "\n",
    "#NBA Season 2003-2004\n",
    "df_03_04PG = pd.read_csv('03-04_team_per_game_stats.csv')\n",
    "df_03_04TS = pd.read_csv('03-04_team_shooting.csv')\n",
    "df_03_04S = pd.read_csv('03-04_team_stats.csv')\n",
    "\n",
    "#NBA Season 2004-2005\n",
    "df_04_05PG = pd.read_csv('04-05_team_per_game_stats.csv')\n",
    "df_04_05TS = pd.read_csv('04-05_team_shooting.csv')\n",
    "df_04_05S = pd.read_csv('04-05_team_stats.csv')\n",
    "\n",
    "#NBA Season 2005-2006\n",
    "df_05_06PG = pd.read_csv('05-06_team_per_game_stats.csv')\n",
    "df_05_06TS = pd.read_csv('05-06_team_shooting.csv')\n",
    "df_05_06S = pd.read_csv('05-06_team_stats.csv')\n",
    "\n",
    "#NBA Season 2006-2007\n",
    "df_06_07PG = pd.read_csv('06-07_team_per_game_stats.csv')\n",
    "df_06_07TS = pd.read_csv('06-07_team_shooting.csv')\n",
    "df_06_07S = pd.read_csv('06-07_team_stats.csv')\n",
    "\n",
    "#NBA Season 2007-2008\n",
    "df_07_08PG = pd.read_csv('07-08_team_per_game_stats.csv')\n",
    "df_07_08TS = pd.read_csv('07-08_team_shooting.csv')\n",
    "df_07_08S = pd.read_csv('07-08_team_stats.csv')\n",
    "\n",
    "#NBA Season 2008-2009\n",
    "df_08_09PG = pd.read_csv('08-09_team_per_game_stats.csv')\n",
    "df_08_09TS = pd.read_csv('08-09_team_shooting.csv')\n",
    "df_08_09S = pd.read_csv('08-09_team_stats.csv')\n",
    "\n",
    "#NBA Season 2009-2010\n",
    "df_09_10PG = pd.read_csv('09-10_team_per_game_stats.csv')\n",
    "df_09_10TS = pd.read_csv('09-10_team_shooting.csv')\n",
    "df_09_10S = pd.read_csv('09-10_team_stats.csv')\n",
    "\n",
    "#NBA Season 2010-2011\n",
    "df_10_11PG = pd.read_csv('10-11_team_per_game_stats.csv')\n",
    "df_10_11TS = pd.read_csv('10-11_team_shooting.csv')\n",
    "df_10_11S = pd.read_csv('10-11_team_stats.csv')\n",
    "\n",
    "#NBA Season 2011-2012\n",
    "df_11_12PG = pd.read_csv('11-12_team_per_game_stats.csv')\n",
    "df_11_12TS = pd.read_csv('11-12_team_shooting.csv')\n",
    "df_11_12S = pd.read_csv('11-12_team_stats.csv')\n",
    "\n",
    "#NBA Season 2012-2013\n",
    "df_12_13PG = pd.read_csv('12-13_team_per_game_stats.csv')\n",
    "df_12_13TS = pd.read_csv('12-13_team_shooting.csv')\n",
    "df_12_13S = pd.read_csv('12-13_team_stats.csv')\n",
    "\n",
    "#NBA Season 2013-2014\n",
    "df_13_14PG = pd.read_csv('13-14_team_per_game_stats.csv')\n",
    "df_13_14TS = pd.read_csv('13-14_team_shooting.csv')\n",
    "df_13_14S = pd.read_csv('13-14_team_stats.csv')\n",
    "\n",
    "#NBA Season 2014-2015\n",
    "df_14_15PG = pd.read_csv('14-15_team_per_game_stats.csv')\n",
    "df_14_15TS = pd.read_csv('14-15_team_shooting.csv')\n",
    "df_14_15S = pd.read_csv('14-15_team_stats.csv')\n",
    "\n",
    "#NBA Season 2015-2016\n",
    "df_15_16PG = pd.read_csv('15-16_team_per_game_stats.csv')\n",
    "df_15_16TS = pd.read_csv('15-16_team_shooting.csv')\n",
    "df_15_16S = pd.read_csv('15-16_team_stats.csv')\n",
    "\n",
    "#NBA Season 2016-2017\n",
    "df_16_17PG = pd.read_csv('16-17_team_per_game_stats.csv')\n",
    "df_16_17TS = pd.read_csv('16-17_team_shooting.csv')\n",
    "df_16_17S = pd.read_csv('16-17_team_stats.csv')\n",
    "\n",
    "#NBA Season 2017-2018\n",
    "df_17_18PG = pd.read_csv('17-18_team_per_game_stats.csv')\n",
    "df_17_18TS = pd.read_csv('17-18_team_shooting.csv')\n",
    "df_17_18S = pd.read_csv('17-18_team_stats.csv')\n",
    "\n",
    "#NBA Season 2018-2019\n",
    "df_18_19PG = pd.read_csv('18-19_team_per_game_stats.csv')\n",
    "df_18_19TS = pd.read_csv('18-19_team_shooting.csv')\n",
    "df_18_19S = pd.read_csv('18-19_team_stats.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the CSVs have been read into DataFrames, the data is ready to be used for analysis. However, due to the expansive nature of the data, each DataFrame will be modified in each individual analysis to only keep the columns that are pertinent to the analysis at hand."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Descriptive Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>References:</b><br>\n",
    "https://github.com/COGS108/FinalProjects-Wi18/blob/master/001-FinalProject.ipynb<br>\n",
    "https://github.com/COGS108/Lectures-Sp19/blob/master/08_descriptive/08_descriptive.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First things first, we need to set up some pandas options so we do not get overwhelmed with too much data at one time, as well as set up a proper precision for our decimals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configure libraries\n",
    "# Don't display too many rows/cols of DataFrames\n",
    "pd.options.display.max_rows = 11\n",
    "pd.options.display.max_columns = 26\n",
    "\n",
    "# Round decimals when displaying DataFrames\n",
    "pd.set_option('precision', 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each dataset will be held in dictionaries, as that allows us to reference each file by its name, rather than its index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "folder_path   = './'\n",
    "t_per_game = {}\n",
    "t_shooting = {}\n",
    "t_stats    = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each dataset has columns that bear no relation to our research question, such as assists, steals, blocks, minutes played, etc. We will begin our cleaning of the data by removing these columns. After doing so, we will place the files into their respective dictionaries, one for each data set.\n",
    "\n",
    "One thing to note: since we are analyzing so many seasons and have multiple datasets, for this section, only one season, namely the 2018-2019 season's data will be shown to prevent unnecessarily extending the length of this document to extreme levels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**'Team Per Game' Filtered Table**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**2018-2019 Season**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Team</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3P</th>\n",
       "      <th>3PA</th>\n",
       "      <th>3P%</th>\n",
       "      <th>2P</th>\n",
       "      <th>2PA</th>\n",
       "      <th>2P%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Milwaukee Bucks*</td>\n",
       "      <td>43.4</td>\n",
       "      <td>91.1</td>\n",
       "      <td>0.476</td>\n",
       "      <td>13.5</td>\n",
       "      <td>38.2</td>\n",
       "      <td>0.353</td>\n",
       "      <td>29.9</td>\n",
       "      <td>52.9</td>\n",
       "      <td>0.565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Golden State Warriors*</td>\n",
       "      <td>44.0</td>\n",
       "      <td>89.8</td>\n",
       "      <td>0.491</td>\n",
       "      <td>13.3</td>\n",
       "      <td>34.4</td>\n",
       "      <td>0.385</td>\n",
       "      <td>30.8</td>\n",
       "      <td>55.3</td>\n",
       "      <td>0.557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>43.7</td>\n",
       "      <td>92.2</td>\n",
       "      <td>0.473</td>\n",
       "      <td>10.3</td>\n",
       "      <td>29.9</td>\n",
       "      <td>0.344</td>\n",
       "      <td>33.4</td>\n",
       "      <td>62.4</td>\n",
       "      <td>0.536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Philadelphia 76ers*</td>\n",
       "      <td>41.5</td>\n",
       "      <td>88.2</td>\n",
       "      <td>0.471</td>\n",
       "      <td>10.8</td>\n",
       "      <td>30.2</td>\n",
       "      <td>0.359</td>\n",
       "      <td>30.7</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Los Angeles Clippers*</td>\n",
       "      <td>41.3</td>\n",
       "      <td>87.5</td>\n",
       "      <td>0.471</td>\n",
       "      <td>10.0</td>\n",
       "      <td>25.8</td>\n",
       "      <td>0.388</td>\n",
       "      <td>31.3</td>\n",
       "      <td>61.7</td>\n",
       "      <td>0.507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Miami Heat</td>\n",
       "      <td>39.6</td>\n",
       "      <td>88.0</td>\n",
       "      <td>0.450</td>\n",
       "      <td>11.3</td>\n",
       "      <td>32.4</td>\n",
       "      <td>0.349</td>\n",
       "      <td>28.3</td>\n",
       "      <td>55.6</td>\n",
       "      <td>0.509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>39.8</td>\n",
       "      <td>87.9</td>\n",
       "      <td>0.453</td>\n",
       "      <td>9.1</td>\n",
       "      <td>25.9</td>\n",
       "      <td>0.351</td>\n",
       "      <td>30.7</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>38.2</td>\n",
       "      <td>88.3</td>\n",
       "      <td>0.433</td>\n",
       "      <td>10.0</td>\n",
       "      <td>29.5</td>\n",
       "      <td>0.340</td>\n",
       "      <td>28.2</td>\n",
       "      <td>58.8</td>\n",
       "      <td>0.479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Cleveland Cavaliers</td>\n",
       "      <td>38.9</td>\n",
       "      <td>87.6</td>\n",
       "      <td>0.444</td>\n",
       "      <td>10.3</td>\n",
       "      <td>29.1</td>\n",
       "      <td>0.355</td>\n",
       "      <td>28.6</td>\n",
       "      <td>58.5</td>\n",
       "      <td>0.488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Memphis Grizzlies</td>\n",
       "      <td>38.0</td>\n",
       "      <td>84.4</td>\n",
       "      <td>0.450</td>\n",
       "      <td>9.9</td>\n",
       "      <td>28.9</td>\n",
       "      <td>0.342</td>\n",
       "      <td>28.1</td>\n",
       "      <td>55.6</td>\n",
       "      <td>0.505</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Team    FG   FGA    FG%    3P   3PA    3P%    2P   2PA  \\\n",
       "0         Milwaukee Bucks*  43.4  91.1  0.476  13.5  38.2  0.353  29.9  52.9   \n",
       "1   Golden State Warriors*  44.0  89.8  0.491  13.3  34.4  0.385  30.8  55.3   \n",
       "2     New Orleans Pelicans  43.7  92.2  0.473  10.3  29.9  0.344  33.4  62.4   \n",
       "3      Philadelphia 76ers*  41.5  88.2  0.471  10.8  30.2  0.359  30.7  58.0   \n",
       "4    Los Angeles Clippers*  41.3  87.5  0.471  10.0  25.8  0.388  31.3  61.7   \n",
       "..                     ...   ...   ...    ...   ...   ...    ...   ...   ...   \n",
       "25              Miami Heat  39.6  88.0  0.450  11.3  32.4  0.349  28.3  55.6   \n",
       "26           Chicago Bulls  39.8  87.9  0.453   9.1  25.9  0.351  30.7  62.0   \n",
       "27         New York Knicks  38.2  88.3  0.433  10.0  29.5  0.340  28.2  58.8   \n",
       "28     Cleveland Cavaliers  38.9  87.6  0.444  10.3  29.1  0.355  28.6  58.5   \n",
       "29       Memphis Grizzlies  38.0  84.4  0.450   9.9  28.9  0.342  28.1  55.6   \n",
       "\n",
       "      2P%  \n",
       "0   0.565  \n",
       "1   0.557  \n",
       "2   0.536  \n",
       "3   0.529  \n",
       "4   0.507  \n",
       "..    ...  \n",
       "25  0.509  \n",
       "26  0.496  \n",
       "27  0.479  \n",
       "28  0.488  \n",
       "29  0.505  \n",
       "\n",
       "[30 rows x 10 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Markdown('**\\'Team Per Game\\' Filtered Table**'))\n",
    "for file in sorted(x for x in os.listdir(folder_path) if x.endswith(\"_team_per_game_stats.csv\")):\n",
    "    t_per_game[file] = pd.read_csv(folder_path + file, header=0, skipfooter=1,engine='python').drop(['Rk','G','MP','FT','FTA','FT%','ORB','DRB','TRB'\n",
    "                                                                       ,'AST','STL','BLK','TOV','PF','PTS'], axis=1)\n",
    "display(Markdown('**2018-2019 Season**'))\n",
    "display(t_per_game['18-19_team_per_game_stats.csv'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**'Team Shooting' Filtered Table**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**2018-2019 Season**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Team</th>\n",
       "      <th>FG%</th>\n",
       "      <th>Distance</th>\n",
       "      <th>2P Freq.</th>\n",
       "      <th>0-3ft</th>\n",
       "      <th>3-10ft</th>\n",
       "      <th>10-16ft</th>\n",
       "      <th>16-3pt</th>\n",
       "      <th>3P Freq.</th>\n",
       "      <th>2P FG%</th>\n",
       "      <th>0-3ft FG%</th>\n",
       "      <th>3-10ft FG%</th>\n",
       "      <th>10-16ft FG%</th>\n",
       "      <th>16-3P FG%</th>\n",
       "      <th>3P FG%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>0.431</td>\n",
       "      <td>11.4</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0.258</td>\n",
       "      <td>0.190</td>\n",
       "      <td>0.214</td>\n",
       "      <td>0.198</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.443</td>\n",
       "      <td>0.612</td>\n",
       "      <td>0.320</td>\n",
       "      <td>0.387</td>\n",
       "      <td>0.403</td>\n",
       "      <td>0.357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.428</td>\n",
       "      <td>12.5</td>\n",
       "      <td>0.748</td>\n",
       "      <td>0.287</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0.121</td>\n",
       "      <td>0.180</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.344</td>\n",
       "      <td>0.385</td>\n",
       "      <td>0.393</td>\n",
       "      <td>0.363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlotte Hornets*</td>\n",
       "      <td>0.431</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0.849</td>\n",
       "      <td>0.270</td>\n",
       "      <td>0.134</td>\n",
       "      <td>0.180</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.446</td>\n",
       "      <td>0.595</td>\n",
       "      <td>0.386</td>\n",
       "      <td>0.363</td>\n",
       "      <td>0.381</td>\n",
       "      <td>0.346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>0.424</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.852</td>\n",
       "      <td>0.275</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.251</td>\n",
       "      <td>0.148</td>\n",
       "      <td>0.438</td>\n",
       "      <td>0.569</td>\n",
       "      <td>0.360</td>\n",
       "      <td>0.390</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cleveland Cavaliers</td>\n",
       "      <td>0.442</td>\n",
       "      <td>10.9</td>\n",
       "      <td>0.899</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.201</td>\n",
       "      <td>0.190</td>\n",
       "      <td>0.228</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.622</td>\n",
       "      <td>0.335</td>\n",
       "      <td>0.410</td>\n",
       "      <td>0.392</td>\n",
       "      <td>0.334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Seattle SuperSonics</td>\n",
       "      <td>0.456</td>\n",
       "      <td>10.7</td>\n",
       "      <td>0.824</td>\n",
       "      <td>0.305</td>\n",
       "      <td>0.226</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.164</td>\n",
       "      <td>0.176</td>\n",
       "      <td>0.468</td>\n",
       "      <td>0.643</td>\n",
       "      <td>0.356</td>\n",
       "      <td>0.363</td>\n",
       "      <td>0.377</td>\n",
       "      <td>0.399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Toronto Raptors*</td>\n",
       "      <td>0.437</td>\n",
       "      <td>12.8</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.264</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.451</td>\n",
       "      <td>0.602</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.373</td>\n",
       "      <td>0.397</td>\n",
       "      <td>0.369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Utah Jazz*</td>\n",
       "      <td>0.471</td>\n",
       "      <td>10.9</td>\n",
       "      <td>0.865</td>\n",
       "      <td>0.348</td>\n",
       "      <td>0.134</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.485</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.369</td>\n",
       "      <td>0.382</td>\n",
       "      <td>0.404</td>\n",
       "      <td>0.381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Vancouver Grizzlies</td>\n",
       "      <td>0.439</td>\n",
       "      <td>12.9</td>\n",
       "      <td>0.855</td>\n",
       "      <td>0.217</td>\n",
       "      <td>0.184</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.323</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.630</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.424</td>\n",
       "      <td>0.343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Washington Wizards</td>\n",
       "      <td>0.439</td>\n",
       "      <td>11.3</td>\n",
       "      <td>0.869</td>\n",
       "      <td>0.249</td>\n",
       "      <td>0.219</td>\n",
       "      <td>0.199</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.131</td>\n",
       "      <td>0.456</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.356</td>\n",
       "      <td>0.397</td>\n",
       "      <td>0.415</td>\n",
       "      <td>0.324</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Team    FG%  Distance  2P Freq.  0-3ft  3-10ft  10-16ft  \\\n",
       "0         Atlanta Hawks  0.431      11.4     0.860  0.258   0.190    0.214   \n",
       "1        Boston Celtics  0.428      12.5     0.748  0.287   0.161    0.121   \n",
       "2    Charlotte Hornets*  0.431      12.2     0.849  0.270   0.134    0.180   \n",
       "3         Chicago Bulls  0.424      12.0     0.852  0.275   0.151    0.175   \n",
       "4   Cleveland Cavaliers  0.442      10.9     0.899  0.280   0.201    0.190   \n",
       "..                  ...    ...       ...       ...    ...     ...      ...   \n",
       "24  Seattle SuperSonics  0.456      10.7     0.824  0.305   0.226    0.129   \n",
       "25     Toronto Raptors*  0.437      12.8     0.833  0.264   0.124    0.145   \n",
       "26           Utah Jazz*  0.471      10.9     0.865  0.348   0.134    0.124   \n",
       "27  Vancouver Grizzlies  0.439      12.9     0.855  0.217   0.184    0.130   \n",
       "28   Washington Wizards  0.439      11.3     0.869  0.249   0.219    0.199   \n",
       "\n",
       "    16-3pt  3P Freq.  2P FG%  0-3ft FG%  3-10ft FG%  10-16ft FG%  16-3P FG%  \\\n",
       "0    0.198     0.140   0.443      0.612       0.320        0.387      0.403   \n",
       "1    0.180     0.252   0.450      0.571       0.344        0.385      0.393   \n",
       "2    0.265     0.151   0.446      0.595       0.386        0.363      0.381   \n",
       "3    0.251     0.148   0.438      0.569       0.360        0.390      0.375   \n",
       "4    0.228     0.101   0.455      0.622       0.335        0.410      0.392   \n",
       "..     ...       ...     ...        ...         ...          ...        ...   \n",
       "24   0.164     0.176   0.468      0.643       0.356        0.363      0.377   \n",
       "25   0.300     0.167   0.451      0.602       0.350        0.373      0.397   \n",
       "26   0.259     0.135   0.485      0.625       0.369        0.382      0.404   \n",
       "27   0.323     0.145   0.455      0.630       0.359        0.375      0.424   \n",
       "28   0.202     0.131   0.456      0.625       0.356        0.397      0.415   \n",
       "\n",
       "    3P FG%  \n",
       "0    0.357  \n",
       "1    0.363  \n",
       "2    0.346  \n",
       "3    0.346  \n",
       "4    0.334  \n",
       "..     ...  \n",
       "24   0.399  \n",
       "25   0.369  \n",
       "26   0.381  \n",
       "27   0.343  \n",
       "28   0.324  \n",
       "\n",
       "[29 rows x 15 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Markdown('**\\'Team Shooting\\' Filtered Table**'))\n",
    "for file in sorted(x for x in os.listdir(folder_path) if x.endswith(\"_team_shooting.csv\")):\n",
    "    # Note: removed the heaves attempted and made columns as those are outlier shots\n",
    "    if(file == '00-01_team_shooting.csv'):\n",
    "        t_shooting[file] = pd.read_csv(folder_path + file, header=2, skipfooter=1,engine='python').drop(['Rk','G','MP','%Ast\\'d','%Ast\\'d.1'\n",
    "                                                                                         ,'Att.','Md..1','%FGA','Md.'\n",
    "                                                                                         ,'%3PA','3P%'], axis=1)\n",
    "    else:  \n",
    "        t_shooting[file] = pd.read_csv(folder_path + file, header=2, skipfooter=1,engine='python').drop(['Rk','G','MP','%Ast\\'d','%Ast\\'d.1'\n",
    "                                                                                         ,'Att.','Md..2','%FGA','Md.'\n",
    "                                                                                         ,'%FGA.1','Md..1','%3PA','3P%']\n",
    "                                                                                         , axis=1)\n",
    "    t_shooting[file].columns = ['Team','FG%','Distance','2P Freq.','0-3ft','3-10ft','10-16ft','16-3pt','3P Freq.','2P FG%'\n",
    "                                ,'0-3ft FG%','3-10ft FG%','10-16ft FG%','16-3P FG%','3P FG%']\n",
    "display(Markdown('**2018-2019 Season**'))\n",
    "display(t_shooting['00-01_team_shooting.csv'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**'Team Stats' Filtered Table**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**2018-2019 Season**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Team</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3P</th>\n",
       "      <th>3PA</th>\n",
       "      <th>3P%</th>\n",
       "      <th>2P</th>\n",
       "      <th>2PA</th>\n",
       "      <th>2P%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Milwaukee Bucks*</td>\n",
       "      <td>3555</td>\n",
       "      <td>7471</td>\n",
       "      <td>0.476</td>\n",
       "      <td>1105</td>\n",
       "      <td>3134</td>\n",
       "      <td>0.353</td>\n",
       "      <td>2450</td>\n",
       "      <td>4337</td>\n",
       "      <td>0.565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Golden State Warriors*</td>\n",
       "      <td>3612</td>\n",
       "      <td>7361</td>\n",
       "      <td>0.491</td>\n",
       "      <td>1087</td>\n",
       "      <td>2824</td>\n",
       "      <td>0.385</td>\n",
       "      <td>2525</td>\n",
       "      <td>4537</td>\n",
       "      <td>0.557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>3581</td>\n",
       "      <td>7563</td>\n",
       "      <td>0.473</td>\n",
       "      <td>842</td>\n",
       "      <td>2449</td>\n",
       "      <td>0.344</td>\n",
       "      <td>2739</td>\n",
       "      <td>5114</td>\n",
       "      <td>0.536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Philadelphia 76ers*</td>\n",
       "      <td>3407</td>\n",
       "      <td>7233</td>\n",
       "      <td>0.471</td>\n",
       "      <td>889</td>\n",
       "      <td>2474</td>\n",
       "      <td>0.359</td>\n",
       "      <td>2518</td>\n",
       "      <td>4759</td>\n",
       "      <td>0.529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Los Angeles Clippers*</td>\n",
       "      <td>3384</td>\n",
       "      <td>7178</td>\n",
       "      <td>0.471</td>\n",
       "      <td>821</td>\n",
       "      <td>2118</td>\n",
       "      <td>0.388</td>\n",
       "      <td>2563</td>\n",
       "      <td>5060</td>\n",
       "      <td>0.507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Miami Heat</td>\n",
       "      <td>3251</td>\n",
       "      <td>7218</td>\n",
       "      <td>0.450</td>\n",
       "      <td>928</td>\n",
       "      <td>2658</td>\n",
       "      <td>0.349</td>\n",
       "      <td>2323</td>\n",
       "      <td>4560</td>\n",
       "      <td>0.509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>3266</td>\n",
       "      <td>7205</td>\n",
       "      <td>0.453</td>\n",
       "      <td>745</td>\n",
       "      <td>2123</td>\n",
       "      <td>0.351</td>\n",
       "      <td>2521</td>\n",
       "      <td>5082</td>\n",
       "      <td>0.496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>3134</td>\n",
       "      <td>7241</td>\n",
       "      <td>0.433</td>\n",
       "      <td>823</td>\n",
       "      <td>2421</td>\n",
       "      <td>0.340</td>\n",
       "      <td>2311</td>\n",
       "      <td>4820</td>\n",
       "      <td>0.479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Cleveland Cavaliers</td>\n",
       "      <td>3189</td>\n",
       "      <td>7184</td>\n",
       "      <td>0.444</td>\n",
       "      <td>847</td>\n",
       "      <td>2388</td>\n",
       "      <td>0.355</td>\n",
       "      <td>2342</td>\n",
       "      <td>4796</td>\n",
       "      <td>0.488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Memphis Grizzlies</td>\n",
       "      <td>3113</td>\n",
       "      <td>6924</td>\n",
       "      <td>0.450</td>\n",
       "      <td>811</td>\n",
       "      <td>2368</td>\n",
       "      <td>0.342</td>\n",
       "      <td>2302</td>\n",
       "      <td>4556</td>\n",
       "      <td>0.505</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Team    FG   FGA    FG%    3P   3PA    3P%    2P   2PA  \\\n",
       "0         Milwaukee Bucks*  3555  7471  0.476  1105  3134  0.353  2450  4337   \n",
       "1   Golden State Warriors*  3612  7361  0.491  1087  2824  0.385  2525  4537   \n",
       "2     New Orleans Pelicans  3581  7563  0.473   842  2449  0.344  2739  5114   \n",
       "3      Philadelphia 76ers*  3407  7233  0.471   889  2474  0.359  2518  4759   \n",
       "4    Los Angeles Clippers*  3384  7178  0.471   821  2118  0.388  2563  5060   \n",
       "..                     ...   ...   ...    ...   ...   ...    ...   ...   ...   \n",
       "25              Miami Heat  3251  7218  0.450   928  2658  0.349  2323  4560   \n",
       "26           Chicago Bulls  3266  7205  0.453   745  2123  0.351  2521  5082   \n",
       "27         New York Knicks  3134  7241  0.433   823  2421  0.340  2311  4820   \n",
       "28     Cleveland Cavaliers  3189  7184  0.444   847  2388  0.355  2342  4796   \n",
       "29       Memphis Grizzlies  3113  6924  0.450   811  2368  0.342  2302  4556   \n",
       "\n",
       "      2P%  \n",
       "0   0.565  \n",
       "1   0.557  \n",
       "2   0.536  \n",
       "3   0.529  \n",
       "4   0.507  \n",
       "..    ...  \n",
       "25  0.509  \n",
       "26  0.496  \n",
       "27  0.479  \n",
       "28  0.488  \n",
       "29  0.505  \n",
       "\n",
       "[30 rows x 10 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Markdown('**\\'Team Stats\\' Filtered Table**'))\n",
    "for file in sorted(x for x in os.listdir(folder_path) if x.endswith(\"_team_stats.csv\")):\n",
    "    t_stats[file] = pd.read_csv(folder_path + file, header=0,skipfooter=1, engine='python').drop(['Rk','G','MP','FT','FTA','FT%','ORB','DRB','TRB'\n",
    "                                                                    ,'AST','STL','BLK','TOV','PF','PTS'], axis=1)\n",
    "display(Markdown('**2018-2019 Season**'))\n",
    "display(t_stats['18-19_team_stats.csv'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the 2011-2012 season, there were only 66 games played, whereas every other season that we use in our project contains 82 played games. As a result, this season becomes an outlier and will skew our results when doing raw number analysis. We do not want that so we decided to remove this season for any raw number analysis. However, when doing percentage analysis, we can see that the percentage distribution of shooting stays the same, so it exists in our percentage datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Team</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3P</th>\n",
       "      <th>3PA</th>\n",
       "      <th>3P%</th>\n",
       "      <th>2P</th>\n",
       "      <th>2PA</th>\n",
       "      <th>2P%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Denver Nuggets*</td>\n",
       "      <td>2572</td>\n",
       "      <td>5404</td>\n",
       "      <td>0.476</td>\n",
       "      <td>435</td>\n",
       "      <td>1311</td>\n",
       "      <td>0.332</td>\n",
       "      <td>2137</td>\n",
       "      <td>4093</td>\n",
       "      <td>0.522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>San Antonio Spurs*</td>\n",
       "      <td>2611</td>\n",
       "      <td>5463</td>\n",
       "      <td>0.478</td>\n",
       "      <td>552</td>\n",
       "      <td>1405</td>\n",
       "      <td>0.393</td>\n",
       "      <td>2059</td>\n",
       "      <td>4058</td>\n",
       "      <td>0.507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Oklahoma City Thunder*</td>\n",
       "      <td>2462</td>\n",
       "      <td>5229</td>\n",
       "      <td>0.471</td>\n",
       "      <td>472</td>\n",
       "      <td>1317</td>\n",
       "      <td>0.358</td>\n",
       "      <td>1990</td>\n",
       "      <td>3912</td>\n",
       "      <td>0.509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Utah Jazz*</td>\n",
       "      <td>2523</td>\n",
       "      <td>5531</td>\n",
       "      <td>0.456</td>\n",
       "      <td>273</td>\n",
       "      <td>845</td>\n",
       "      <td>0.323</td>\n",
       "      <td>2250</td>\n",
       "      <td>4686</td>\n",
       "      <td>0.480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Milwaukee Bucks</td>\n",
       "      <td>2502</td>\n",
       "      <td>5652</td>\n",
       "      <td>0.443</td>\n",
       "      <td>436</td>\n",
       "      <td>1265</td>\n",
       "      <td>0.345</td>\n",
       "      <td>2066</td>\n",
       "      <td>4387</td>\n",
       "      <td>0.471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Boston Celtics*</td>\n",
       "      <td>2341</td>\n",
       "      <td>5086</td>\n",
       "      <td>0.460</td>\n",
       "      <td>363</td>\n",
       "      <td>988</td>\n",
       "      <td>0.367</td>\n",
       "      <td>1978</td>\n",
       "      <td>4098</td>\n",
       "      <td>0.483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>2291</td>\n",
       "      <td>5232</td>\n",
       "      <td>0.438</td>\n",
       "      <td>317</td>\n",
       "      <td>916</td>\n",
       "      <td>0.346</td>\n",
       "      <td>1974</td>\n",
       "      <td>4316</td>\n",
       "      <td>0.457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>2268</td>\n",
       "      <td>5154</td>\n",
       "      <td>0.440</td>\n",
       "      <td>365</td>\n",
       "      <td>1075</td>\n",
       "      <td>0.340</td>\n",
       "      <td>1903</td>\n",
       "      <td>4079</td>\n",
       "      <td>0.467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>New Orleans Hornets</td>\n",
       "      <td>2300</td>\n",
       "      <td>5103</td>\n",
       "      <td>0.451</td>\n",
       "      <td>259</td>\n",
       "      <td>777</td>\n",
       "      <td>0.333</td>\n",
       "      <td>2041</td>\n",
       "      <td>4326</td>\n",
       "      <td>0.472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Charlotte Bobcats</td>\n",
       "      <td>2193</td>\n",
       "      <td>5293</td>\n",
       "      <td>0.414</td>\n",
       "      <td>263</td>\n",
       "      <td>892</td>\n",
       "      <td>0.295</td>\n",
       "      <td>1930</td>\n",
       "      <td>4401</td>\n",
       "      <td>0.439</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Team    FG   FGA    FG%   3P   3PA    3P%    2P   2PA  \\\n",
       "0          Denver Nuggets*  2572  5404  0.476  435  1311  0.332  2137  4093   \n",
       "1       San Antonio Spurs*  2611  5463  0.478  552  1405  0.393  2059  4058   \n",
       "2   Oklahoma City Thunder*  2462  5229  0.471  472  1317  0.358  1990  3912   \n",
       "3               Utah Jazz*  2523  5531  0.456  273   845  0.323  2250  4686   \n",
       "4          Milwaukee Bucks  2502  5652  0.443  436  1265  0.345  2066  4387   \n",
       "..                     ...   ...   ...    ...  ...   ...    ...   ...   ...   \n",
       "25         Boston Celtics*  2341  5086  0.460  363   988  0.367  1978  4098   \n",
       "26         Detroit Pistons  2291  5232  0.438  317   916  0.346  1974  4316   \n",
       "27         Toronto Raptors  2268  5154  0.440  365  1075  0.340  1903  4079   \n",
       "28     New Orleans Hornets  2300  5103  0.451  259   777  0.333  2041  4326   \n",
       "29       Charlotte Bobcats  2193  5293  0.414  263   892  0.295  1930  4401   \n",
       "\n",
       "      2P%  \n",
       "0   0.522  \n",
       "1   0.507  \n",
       "2   0.509  \n",
       "3   0.480  \n",
       "4   0.471  \n",
       "..    ...  \n",
       "25  0.483  \n",
       "26  0.457  \n",
       "27  0.467  \n",
       "28  0.472  \n",
       "29  0.439  \n",
       "\n",
       "[30 rows x 10 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_per_game.pop('11-12_team_per_game_stats.csv')\n",
    "t_shooting.pop('11-12_team_shooting.csv')\n",
    "t_stats.pop('11-12_team_stats.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data from 2011-2012 season 'Team Per Game' dataset:\t None\n",
      "Data from 2011-2012 season 'Team Shooting' dataset:\t None\n",
      "Data from 2011-2012 season 'Team Stats' dataset:\t None\n"
     ]
    }
   ],
   "source": [
    "# Checking to see if the 2011-2012 datasets still exist\n",
    "print('Data from 2011-2012 season \\'Team Per Game\\' dataset:\\t',t_per_game.get('11-12_team_per_game_stats.csv'))\n",
    "print('Data from 2011-2012 season \\'Team Shooting\\' dataset:\\t',t_shooting.get('11-12_team_shooting.csv'))\n",
    "print('Data from 2011-2012 season \\'Team Stats\\' dataset:\\t',t_stats.get('11-12_team_stats.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of 'Team Per Game' dataset of one season after cleaning:\t (29, 10)\n",
      "Size of 'Team Shooting' dataset of one season after cleaning:\t (29, 15)\n",
      "Size of 'Team Stats' dataset of one season after cleaning:\t (29, 10)\n"
     ]
    }
   ],
   "source": [
    "print('Size of \\'Team Per Game\\' dataset of one season after cleaning:\\t', t_per_game['00-01_team_per_game_stats.csv']\n",
    "                                                                                                              .shape)\n",
    "print('Size of \\'Team Shooting\\' dataset of one season after cleaning:\\t', t_shooting['00-01_team_shooting.csv'].shape)\n",
    "print('Size of \\'Team Stats\\' dataset of one season after cleaning:\\t', t_stats['00-01_team_stats.csv'].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_rows = 11"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have finished cleaning the data, we can first begin to get some descriptive overview of our datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**'Team Per Game' Descriptive Table**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**2018-2019 Season**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3P</th>\n",
       "      <th>3PA</th>\n",
       "      <th>3P%</th>\n",
       "      <th>2P</th>\n",
       "      <th>2PA</th>\n",
       "      <th>2P%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>41.073</td>\n",
       "      <td>89.213</td>\n",
       "      <td>0.460</td>\n",
       "      <td>11.360</td>\n",
       "      <td>32.007</td>\n",
       "      <td>0.356</td>\n",
       "      <td>29.717</td>\n",
       "      <td>57.203</td>\n",
       "      <td>0.520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.672</td>\n",
       "      <td>2.110</td>\n",
       "      <td>0.013</td>\n",
       "      <td>1.509</td>\n",
       "      <td>4.301</td>\n",
       "      <td>0.015</td>\n",
       "      <td>2.174</td>\n",
       "      <td>4.533</td>\n",
       "      <td>0.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>38.000</td>\n",
       "      <td>84.400</td>\n",
       "      <td>0.433</td>\n",
       "      <td>9.100</td>\n",
       "      <td>25.300</td>\n",
       "      <td>0.329</td>\n",
       "      <td>23.100</td>\n",
       "      <td>42.000</td>\n",
       "      <td>0.479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>39.875</td>\n",
       "      <td>87.675</td>\n",
       "      <td>0.450</td>\n",
       "      <td>10.150</td>\n",
       "      <td>29.350</td>\n",
       "      <td>0.348</td>\n",
       "      <td>28.300</td>\n",
       "      <td>55.300</td>\n",
       "      <td>0.507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>41.350</td>\n",
       "      <td>89.100</td>\n",
       "      <td>0.461</td>\n",
       "      <td>11.300</td>\n",
       "      <td>31.750</td>\n",
       "      <td>0.353</td>\n",
       "      <td>30.200</td>\n",
       "      <td>57.500</td>\n",
       "      <td>0.518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>42.275</td>\n",
       "      <td>90.500</td>\n",
       "      <td>0.471</td>\n",
       "      <td>12.325</td>\n",
       "      <td>34.300</td>\n",
       "      <td>0.359</td>\n",
       "      <td>31.300</td>\n",
       "      <td>60.925</td>\n",
       "      <td>0.534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>44.000</td>\n",
       "      <td>94.000</td>\n",
       "      <td>0.491</td>\n",
       "      <td>16.100</td>\n",
       "      <td>45.400</td>\n",
       "      <td>0.392</td>\n",
       "      <td>33.400</td>\n",
       "      <td>63.200</td>\n",
       "      <td>0.565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           FG     FGA     FG%      3P     3PA     3P%      2P     2PA     2P%\n",
       "count  30.000  30.000  30.000  30.000  30.000  30.000  30.000  30.000  30.000\n",
       "mean   41.073  89.213   0.460  11.360  32.007   0.356  29.717  57.203   0.520\n",
       "std     1.672   2.110   0.013   1.509   4.301   0.015   2.174   4.533   0.020\n",
       "min    38.000  84.400   0.433   9.100  25.300   0.329  23.100  42.000   0.479\n",
       "25%    39.875  87.675   0.450  10.150  29.350   0.348  28.300  55.300   0.507\n",
       "50%    41.350  89.100   0.461  11.300  31.750   0.353  30.200  57.500   0.518\n",
       "75%    42.275  90.500   0.471  12.325  34.300   0.359  31.300  60.925   0.534\n",
       "max    44.000  94.000   0.491  16.100  45.400   0.392  33.400  63.200   0.565"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "des_t_per_game = {}\n",
    "display(Markdown('**\\'Team Per Game\\' Descriptive Table**'))\n",
    "for file in t_per_game:\n",
    "    des_t_per_game[file] = t_per_game[file].describe()\n",
    "display(Markdown('**2018-2019 Season**'))\n",
    "display(des_t_per_game['18-19_team_per_game_stats.csv'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**'Team Shooting' Descriptive Table**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**2018-2019 Season**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FG%</th>\n",
       "      <th>Distance</th>\n",
       "      <th>2P Freq.</th>\n",
       "      <th>0-3ft</th>\n",
       "      <th>3-10ft</th>\n",
       "      <th>10-16ft</th>\n",
       "      <th>16-3pt</th>\n",
       "      <th>3P Freq.</th>\n",
       "      <th>2P FG%</th>\n",
       "      <th>0-3ft FG%</th>\n",
       "      <th>3-10ft FG%</th>\n",
       "      <th>10-16ft FG%</th>\n",
       "      <th>16-3P FG%</th>\n",
       "      <th>3P FG%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.460</td>\n",
       "      <td>13.557</td>\n",
       "      <td>0.641</td>\n",
       "      <td>0.292</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.096</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0.658</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0.411</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0.355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.013</td>\n",
       "      <td>0.756</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.433</td>\n",
       "      <td>12.400</td>\n",
       "      <td>0.481</td>\n",
       "      <td>0.214</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.479</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.345</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.344</td>\n",
       "      <td>0.329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.450</td>\n",
       "      <td>13.100</td>\n",
       "      <td>0.617</td>\n",
       "      <td>0.277</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.084</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0.507</td>\n",
       "      <td>0.638</td>\n",
       "      <td>0.387</td>\n",
       "      <td>0.387</td>\n",
       "      <td>0.386</td>\n",
       "      <td>0.348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.461</td>\n",
       "      <td>13.500</td>\n",
       "      <td>0.653</td>\n",
       "      <td>0.290</td>\n",
       "      <td>0.163</td>\n",
       "      <td>0.098</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.348</td>\n",
       "      <td>0.518</td>\n",
       "      <td>0.659</td>\n",
       "      <td>0.401</td>\n",
       "      <td>0.407</td>\n",
       "      <td>0.394</td>\n",
       "      <td>0.353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.471</td>\n",
       "      <td>13.900</td>\n",
       "      <td>0.667</td>\n",
       "      <td>0.312</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.383</td>\n",
       "      <td>0.534</td>\n",
       "      <td>0.677</td>\n",
       "      <td>0.416</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.413</td>\n",
       "      <td>0.359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.491</td>\n",
       "      <td>15.600</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.345</td>\n",
       "      <td>0.203</td>\n",
       "      <td>0.156</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.519</td>\n",
       "      <td>0.565</td>\n",
       "      <td>0.703</td>\n",
       "      <td>0.457</td>\n",
       "      <td>0.477</td>\n",
       "      <td>0.458</td>\n",
       "      <td>0.392</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          FG%  Distance  2P Freq.   0-3ft  3-10ft  10-16ft  16-3pt  3P Freq.  \\\n",
       "count  30.000    30.000    30.000  30.000  30.000   30.000  30.000    30.000   \n",
       "mean    0.460    13.557     0.641   0.292   0.162    0.096   0.092     0.359   \n",
       "std     0.013     0.756     0.048   0.031   0.025    0.020   0.032     0.048   \n",
       "min     0.433    12.400     0.481   0.214   0.107    0.050   0.025     0.286   \n",
       "25%     0.450    13.100     0.617   0.277   0.145    0.084   0.069     0.333   \n",
       "50%     0.461    13.500     0.653   0.290   0.163    0.098   0.094     0.348   \n",
       "75%     0.471    13.900     0.667   0.312   0.178    0.104   0.108     0.383   \n",
       "max     0.491    15.600     0.714   0.345   0.203    0.156   0.165     0.519   \n",
       "\n",
       "       2P FG%  0-3ft FG%  3-10ft FG%  10-16ft FG%  16-3P FG%  3P FG%  \n",
       "count  30.000     30.000      30.000       30.000     30.000  30.000  \n",
       "mean    0.520      0.658       0.399        0.411      0.399   0.355  \n",
       "std     0.020      0.027       0.027        0.030      0.026   0.015  \n",
       "min     0.479      0.603       0.345        0.365      0.344   0.329  \n",
       "25%     0.507      0.638       0.387        0.387      0.386   0.348  \n",
       "50%     0.518      0.659       0.401        0.407      0.394   0.353  \n",
       "75%     0.534      0.677       0.416        0.435      0.413   0.359  \n",
       "max     0.565      0.703       0.457        0.477      0.458   0.392  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "des_t_shooting = {}\n",
    "display(Markdown('**\\'Team Shooting\\' Descriptive Table**'))\n",
    "for file in t_shooting:\n",
    "    des_t_shooting[file] = t_shooting[file].describe()\n",
    "display(Markdown('**2018-2019 Season**'))\n",
    "display(des_t_shooting['18-19_team_shooting.csv'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**'Team Stats' Descriptive Table**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**2018-2019 Season**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3P</th>\n",
       "      <th>3PA</th>\n",
       "      <th>3P%</th>\n",
       "      <th>2P</th>\n",
       "      <th>2PA</th>\n",
       "      <th>2P%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3368.733</td>\n",
       "      <td>7315.267</td>\n",
       "      <td>0.460</td>\n",
       "      <td>931.833</td>\n",
       "      <td>2624.733</td>\n",
       "      <td>0.356</td>\n",
       "      <td>2436.900</td>\n",
       "      <td>4690.533</td>\n",
       "      <td>0.520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>136.818</td>\n",
       "      <td>173.173</td>\n",
       "      <td>0.013</td>\n",
       "      <td>123.406</td>\n",
       "      <td>352.777</td>\n",
       "      <td>0.015</td>\n",
       "      <td>178.247</td>\n",
       "      <td>372.194</td>\n",
       "      <td>0.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3113.000</td>\n",
       "      <td>6924.000</td>\n",
       "      <td>0.433</td>\n",
       "      <td>745.000</td>\n",
       "      <td>2071.000</td>\n",
       "      <td>0.329</td>\n",
       "      <td>1895.000</td>\n",
       "      <td>3442.000</td>\n",
       "      <td>0.479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3271.750</td>\n",
       "      <td>7189.250</td>\n",
       "      <td>0.450</td>\n",
       "      <td>830.750</td>\n",
       "      <td>2405.250</td>\n",
       "      <td>0.348</td>\n",
       "      <td>2321.500</td>\n",
       "      <td>4534.750</td>\n",
       "      <td>0.507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3391.000</td>\n",
       "      <td>7306.000</td>\n",
       "      <td>0.461</td>\n",
       "      <td>927.500</td>\n",
       "      <td>2602.000</td>\n",
       "      <td>0.353</td>\n",
       "      <td>2474.500</td>\n",
       "      <td>4716.500</td>\n",
       "      <td>0.518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3466.000</td>\n",
       "      <td>7424.500</td>\n",
       "      <td>0.471</td>\n",
       "      <td>1009.500</td>\n",
       "      <td>2815.250</td>\n",
       "      <td>0.359</td>\n",
       "      <td>2564.500</td>\n",
       "      <td>4998.500</td>\n",
       "      <td>0.534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3612.000</td>\n",
       "      <td>7706.000</td>\n",
       "      <td>0.491</td>\n",
       "      <td>1323.000</td>\n",
       "      <td>3721.000</td>\n",
       "      <td>0.392</td>\n",
       "      <td>2739.000</td>\n",
       "      <td>5182.000</td>\n",
       "      <td>0.565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             FG       FGA     FG%        3P       3PA     3P%        2P  \\\n",
       "count    30.000    30.000  30.000    30.000    30.000  30.000    30.000   \n",
       "mean   3368.733  7315.267   0.460   931.833  2624.733   0.356  2436.900   \n",
       "std     136.818   173.173   0.013   123.406   352.777   0.015   178.247   \n",
       "min    3113.000  6924.000   0.433   745.000  2071.000   0.329  1895.000   \n",
       "25%    3271.750  7189.250   0.450   830.750  2405.250   0.348  2321.500   \n",
       "50%    3391.000  7306.000   0.461   927.500  2602.000   0.353  2474.500   \n",
       "75%    3466.000  7424.500   0.471  1009.500  2815.250   0.359  2564.500   \n",
       "max    3612.000  7706.000   0.491  1323.000  3721.000   0.392  2739.000   \n",
       "\n",
       "            2PA     2P%  \n",
       "count    30.000  30.000  \n",
       "mean   4690.533   0.520  \n",
       "std     372.194   0.020  \n",
       "min    3442.000   0.479  \n",
       "25%    4534.750   0.507  \n",
       "50%    4716.500   0.518  \n",
       "75%    4998.500   0.534  \n",
       "max    5182.000   0.565  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "des_t_stats = {}\n",
    "display(Markdown('**\\'Team Stats\\' Descriptive Table**'))\n",
    "\n",
    "for file in t_stats:\n",
    "    des_t_stats[file] = t_stats[file].describe()\n",
    "display(Markdown('**2018-2019 Season**'))\n",
    "display(des_t_stats['18-19_team_stats.csv'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The descriptive tables do not give us any information about variance, so we can calculate that ourselves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**2018-2019 Season 'Team Per Game' Variances**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "FG      2.7958\n",
       "FGA     4.4529\n",
       "FG%     0.0002\n",
       "3P      2.2777\n",
       "3PA    18.4958\n",
       "3P%     0.0002\n",
       "2P      4.7249\n",
       "2PA    20.5445\n",
       "2P%     0.0004\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('precision', 4)\n",
    "display(Markdown('**2018-2019 Season \\'Team Per Game\\' Variances**'))\n",
    "t_per_game['18-19_team_per_game_stats.csv'].var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**2018-2019 Season 'Team Shooting' Variances**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "FG%            0.00018\n",
       "Distance       0.57151\n",
       "2P Freq.       0.00233\n",
       "0-3ft          0.00098\n",
       "3-10ft         0.00061\n",
       "                ...   \n",
       "0-3ft FG%      0.00071\n",
       "3-10ft FG%     0.00076\n",
       "10-16ft FG%    0.00088\n",
       "16-3P FG%      0.00066\n",
       "3P FG%         0.00023\n",
       "Length: 14, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('precision', 5)\n",
    "display(Markdown('**2018-2019 Season \\'Team Shooting\\' Variances**'))\n",
    "t_shooting['18-19_team_shooting.csv'].var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**2018-2019 Season 'Team Stats' Variances**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "FG      18719.0299\n",
       "FGA     29988.9609\n",
       "FG%         0.0002\n",
       "3P      15229.1092\n",
       "3PA    124451.5126\n",
       "3P%         0.0002\n",
       "2P      31771.9552\n",
       "2PA    138528.3264\n",
       "2P%         0.0004\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('precision', 4)\n",
    "display(Markdown('**2018-2019 Season \\'Team Stats\\' Variances**'))\n",
    "t_stats['18-19_team_stats.csv'].var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('precision', 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now begin to check if our dataset contains any missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Total number of missing values by column for entire 'Team Per Game' dataset:**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Team    0\n",
       "FG      0\n",
       "FGA     0\n",
       "FG%     0\n",
       "3P      0\n",
       "3PA     0\n",
       "3P%     0\n",
       "2P      0\n",
       "2PA     0\n",
       "2P%     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display(Markdown('**Total number of missing values by column for entire \\'Team Per Game\\' dataset:**'))\n",
    "tpg_null_total = 0\n",
    "for file in t_per_game:\n",
    "    tpg_null_total += t_per_game[file].isnull().sum()\n",
    "tpg_null_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Total number of missing values by column for entire 'Team Shooting' dataset:**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Team           0\n",
       "FG%            0\n",
       "Distance       0\n",
       "2P Freq.       0\n",
       "0-3ft          0\n",
       "3-10ft         0\n",
       "10-16ft        0\n",
       "16-3pt         0\n",
       "3P Freq.       0\n",
       "2P FG%         0\n",
       "0-3ft FG%      0\n",
       "3-10ft FG%     0\n",
       "10-16ft FG%    0\n",
       "16-3P FG%      0\n",
       "3P FG%         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_rows = 20\n",
    "\n",
    "display(Markdown('**Total number of missing values by column for entire \\'Team Shooting\\' dataset:**'))\n",
    "tshooting_null_total = 0\n",
    "for file in t_shooting:\n",
    "    tshooting_null_total += t_shooting[file].isnull().sum()\n",
    "tshooting_null_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Total number of missing values by column for entire 'Team Stats' dataset:**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Team    0\n",
       "FG      0\n",
       "FGA     0\n",
       "FG%     0\n",
       "3P      0\n",
       "3PA     0\n",
       "3P%     0\n",
       "2P      0\n",
       "2PA     0\n",
       "2P%     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_rows = 11\n",
    "\n",
    "display(Markdown('**Total number of missing values by column for entire \\'Team Stats\\' dataset:**'))\n",
    "tstats_null_total = 0\n",
    "for file in t_stats:\n",
    "    tstats_null_total += t_stats[file].isnull().sum()\n",
    "tstats_null_total"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As one may notice, our datasets do not contain any missing data, and that can be attributed to due diligence from the NBA and its analytical/statistics team. Props to the NBA analytics and statistics team!\n",
    "\n",
    "With that worry out of the way, we can now look at the distributions for each of the columns in each of the datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Raw distributions for 'Team Per Game' for the 2018-2019 season**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, '\\n2018-2019 Season')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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y4aJkZmbZcFEys2xImibpBkmX1B2L1aPs2HdmVoNuj1XWB04gzWy9Zd2BWD18pmRmWZA0G5gPnF13LFYfnymZWS5OA94PbNFqhV7NcN1NVc2W3cls1a2e0+x5ZeKuYsZsFyUzq52kg4H7ImKppOFW6/Vqhutuqmq27E5mq271nGbPKxN3FTNmu/nOzHKwF3CIpJXAYmA/SefVG5LVwUXJzGoXESdFxOyIGAKOAK6OiDfWHJbVoHRRcldNMzOrWjvXlNxV08wqFxEjwEjNYVhNSp0puaummZn1Qtnmu9GumusrjMXMzKa4CZvvynbV7NX3B6rq899Kt/vht4q/iv7+Zmb9psw1pdGumgcBmwJbSjpvbM+YXn1/oKo+/610ux9+q/ir6O9vZtZvJmy+c1dNMzPrFX9PyczMstHWMEPuqmlmZlXymZKZmWXDRcm6TtKOkq6RtELSckkn1B2TmfUHjxJuVVgHLIiI6yVtASyVdFVE3Fx3YGbWnvEmmqyCz5Ss6yLi3oi4vvj9UdLwVDvUG5WZ9QMXJauUpCFgd+C6eiMxs37g5jurjKQZwHeA90TE75o8XussoqOja4w3G2crX1h0UdvPWTC39WOt9r2dmUKb6fUIKGaT5aJklZC0EakgLYqIC5utU/csoqOja4w3G2evtBq5o52ZQpvp9QgoZpPlolSBVhcGVy6c37Vtdbq9XpAk4BxgRUR8ru54zKx/+JqSVWEv4GjSlNY3FreD6g7KzPLnMyXruoi4FlDdcZhZ//GZkpmZZcNFyczMsuGiZGZm2XBRMrPaebxEG+WODmaWA4+XaIDPlMwsAx4v0UZNeKYkaUfgX4E/BdYDZ0bE6VUHZmZT03jjJdY9NFUnygz1tGz1I21vt9WwVeMNgTXeUFdjnzdrs7RsvOe0MpnPpUzznU+rzawnJhovse6hqTpRZqinHIa6GmvB3HWcsqyzKzxlhsBqZcLmO59Wm1kvlBkv0QZfW9eUPA2BmVXB4yXaqNLnZrlMQ9DrofhbTR3QSbvtyMhIy/i7PX3C3B22ant7ZjUaHS9xmaQbi2UfjIhLa4zJalCqKOU0DUGvh+LvZlvvyqOGW8bf7TblybTpmvWax0u0URM23/m02szMeqXMNSVPQ2BmZj0xYfOdT6vNzKxXPMyQZWu8WXe7YcHcddl8P6STfS3znG7sY64zHNtg8jBDZmaWDRclMzPLhouSmZllw0XJzMyy4aJkZmbZcO87MxsYrXok9qoHYbPXH+0B6V6M5fSsKPUqWaruRjwZQyd+r2fdkOv+4zQz64Sb78zMLBsuSmZmlg0XJTMzy4aLkpmZZcNFyczMsuGiZGZm2XBRMjOzbLgomZlZNkoVJUkHSLpV0u2STqw6KOt/zhlrl3PGoERRkjQN+CJwIDAHOFLSnKoDs/7lnLF2OWdsVJkzpZcCt0fEnRHxJLAYOLTasKzPOWesXc4ZA8oVpR2AuxvuryqWmbXinLF2OWcMKDcgq5osi2etJB0PHF/cXSvp1jIB6NNl1nqGmcADbT8rE++uOf4W7/fzuv0yTZZ1LWe6pe7Pohe6sY+DkDMdHGe6ZvQzqDOGTkwmdyaTM2WK0ipgx4b7s4F7xq4UEWcCZ5Z50cmQtCQi5lX9OlXp9/hLyipnWpkKn0Uf7WNf5Ewn+ugzeIa64i7TfPdzYFdJO0naGDgCuLjasKzPOWesXc4ZA0qcKUXEOknvBK4ApgFfjYjllUdmfcs5Y+1yztioUpP8RcSlwKUVx1JWX526N9Hv8ZeSWc60MhU+i77Zxz7JmU70zWcwRi1xK+JZ1xLNzMxq4WGGzMwsG9kWJUk7SrpG0gpJyyWdUCw/WdJqSTcWt4PqjrUZSZtK+pmkXxTx/3OxfCdJ10m6TdK3iou61kUTDVcj6ThJ9zfk0FsaHju2+Gxuk3RsbyMvb5L7+FTDcncm6CJJKyUtK97bJcWybSVdVeTUVZK2qTvOZiRtLekCSbcUx90964g92+Y7SdsD20fE9ZK2AJYCrwVeD6yNiM/WGuAEJAmYHhFrJW0EXAucALwPuDAiFks6A/hFRHy5zlgHSTFczX8BryJ1M/45cGRE3NywznHAvIh455jnbgssAeaRviOzFNgjIh7qTfTlTGYfi8fWRsSMHoU7pUhaSXrfH2hY9hngwYhYWPwDsU1EfKCuGFuRdC7wo4g4u/hneXPgg/Q49mzPlCLi3oi4vvj9UWAFffQN70jWFnc3Km4B7AdcUCw/l1RorXsmM1zNa4CrIuLBohBdBRxQUZyT4SF5+suhpL91yPRvXtKWwD7AOQAR8WREPEwNsWdblBpJGgJ2B64rFr1T0i8lfTXXU2FI/9FKuhG4j3SAuwN4OCLWFat4KJXuKztczeuKHLpA0uiXNvtlqJvJ7CPAppKWSPqppOwOkH0ugCslLS1GnwCYFRH3QvpnG3hObdG1tjNwP/A1STdIOlvSdGqIPfuiJGkG8B3gPRHxO+DLwC7AbsC9wCk1hjeuiHgqInYjfTv9pcALmq3W26gGXpnhav4DGIqIvwS+z9P/CZYa6iYDk9lHgOcW39R/A3CapF2qCXNK2isiXkIa7fwdkvapO6CSNgReAnw5InYHHgNqmT4k66JUXIv5DrAoIi4EiIjfFAf79cBZpIN91orT4BHg5cDWkka/H9Z0KBWblAmHq4mI30bEE8Xds4A9yj43E5PZRyLinuLnnaS83L3KYKeShvf2PuC7pOPTb4pr5KPXyu+rL8KWVgGrImK0NeoCUpHqeezZFqWio8A5wIqI+FzD8u0bVjsMuKnXsZUhaTtJWxe/bwbsT7oudg1weLHascBF9UQ4sCYcrmZMDh1C+lwgjSbwaknbFM3Cry6W5abjfSz2bZPi95nAXsDN2KRJml50yqJo+no16fh0MelvHTL9m4+INcDdkp5fLHolKS96HnupER1qshdwNLCsuC4DqSfIkZJ2IzVXrATeWk94E9oeOLfoKbUBcH5EXCLpZmCxpE8AN1BcWLTuaDVcjaSPAUsi4mLg3ZIOAdYBDwLHFc99UNLHSQd9gI9FxIM934kJTGYfSU3IX5G0npSXCxt77dmkzAK+m/6fZkPgGxFxuaSfA+dLejPwa+DvaoxxPO8CFhX/6NwJvIni2NXL2LPtEm5mZlNPts13ZmY29bgoNSHpPEn3SvqdpP8a/Ta8pGFJ6yWtlfSo0jfq31R3vNZbneSHpK0kXSHpYUmLimbd0e2dJemwuvbHemMyx5XietVaSYM4YO0zuCg19ylSd9otSReJPyFptPfSPcW34bcEPgCcJWlOTXFaPTrJj7eSriHOAoZInXSQtCdp5JLv9nYXrAaTOa4cDjxB6ojT2Ill4LgoNRERyxu600Zx22XMOhER/w48BLgoTSEd5sdOwDXF834E7FycLZ1KGn7KBtwkjyvHAmcAvwSO6kG4tXFRakHSlyT9HriF9CXdS8c8vkHR5LI1sKyGEK1GHeTHTcD+xdcD9gaWA+8GLouIO3oavNWmk+OKpOcCw8Ci4nZML2PuNRelFiLiH4AtSAeQC0mnzgB/Julh4AHgo8DREXFrPVFaXTrIj3OArUhDZf0I+AXpKw+nSfqypB8WXxOwAdbhceUY4JdF1/1vAi+UNLBfeHaX8BKURvO+mXTqfF5EzK45JMtIJ/kh6dukQrUzacistwJXAqdExOUVhmuZKJs3kv4LOCsi/qW4fzWpSL2nZ8H2kM+UytmQMW2/Zg3ayg9JB5D+IbwcmEv6wmuQps34y2pCtAxNmDeSXgHsCpwkaY2kNcDLSIMI5Dz4QcdclMaQ9BxJR0iaoTTK92uAI4Gr647N6jfZ/JC0KbAQeG+x6C5guPgW/V6kb9LbgJlE3hxLmmFgDumMejfgRaS5jg6sMua6uCg9WwBvJw1Q+BDwWdII5dmNV2W1mGx+fJA0wPDo1BNfAWaSpg1YRRrE0wZP23lT/APzeuALEbGm4XYX8G88PSbdQPE1JTMzy4bPlMzMLBsuSmZmlg0XJTMzy4aLkpmZZcNFyczMslHJl69mzpwZQ0NDk9rGY489xvTp07sT0IDp9nuzdOnSByJiu65tsAPdyJluyz0H64xvKuZM7vlQRj/kTCVFaWhoiCVLlkxqGyMjIwwPD3cnoAHT7fdG0q+6trEOdSNnui33HKwzvqmYM7nnQxn9kDNuvjMzs2y4KJmZWTZclMzMLBsDN8rs0Infa7p85cL5PY7E+t2y1Y9wnPPJuqTVsQmcT418pmRmZtlwUTIzs2y4KJmZWTZclMzMLBsuSmZmlg0XJTMzy4aLkpmZZcNFyczMsuGiZGZm2ShVlCRtLekCSbdIWiFpz6oDs/7mnDGzTpQdZuh04PKIOFzSxsDmFcZkg8E5Y2Ztm7AoSdoS2Ac4DiAingSerDYs62fOGTPrVJnmu52B+4GvSbpB0tmS+nv6Rauac8bMOlKm+W5D4CXAuyLiOkmnAycCH25cSdLxwPEAs2bNYmRkZFKBrV27tqNtLJi7runyycaTk07fmx6qJWe6bdZmeedTH+SBWdvKFKVVwKqIuK64fwHpAPMMEXEmcCbAvHnzYrJT7nY6bW/LqQaOmlw8OemDaZlryZlu+8KiizhlWfM/kRzyqQ/ywKxtEzbfRcQa4G5Jzy8WvRK4udKorK85Z6xTkqYVTb6X1B2L1aNs77t3AYuKXlR3Am+qLiQbEM4Z68QJwApgy7oDsXqUKkoRcSMwr+JYbIA4Z6xdkmYD84FPAu+rORyriUd0MLNcnAa8H1hfdyBWn7LNd2ZmlZF0MHBfRCyVNDzOeuP22Fy2+pGmz5u7w1YtX7vVc8aatVnq/DLe9sbb1oK5rbfdq16U/dBj00XJzHKwF3CIpIOATYEtJZ0XEW9sXGmiHpud9L5t9ZyxFsxd99+9MVttr+y2xupVb85+6LHp5jszq11EnBQRsyNiCDgCuHpsQbKpwUXJzMyy4eY7M8tKRIwAIzWHYTXxmZKZmWXDRcnMzLLhomRmZtmo/ZrSUIsulAvmrmO4t6GYmVnNfKZkZmbZcFEyM7NsuCiZmVk2XJTMzCwbLkpmZpYNFyUzM8uGi5KZmWXDRcnMzLLhomRmZtlwUTIzs2yULkqSpkm6QdIlVQZkg8M5Y2btamfsuxOAFcCWFcVig8c5YwOp1ZidvXydlQvn9ySGXit1piRpNjAfOLvacGxQOGfMrBNlm+9OA94PrK8wFhsszhkza9uEzXeSDgbui4ilkobHWe944HiAWbNmMTIyUiqABXPXNV0+azP4wqKLmj42d4et2t5e2XjGWrb6kbZjqNratWs73p9eqDpnemXWZq3zqVVuQu9yI/c8MOtEmWtKewGHSDoI2BTYUtJ5EfHGxpUi4kzgTIB58+bF8PBwqQCOG2c+pVOWNQ9v5VGtt91qe+M9Zzzd3l43jIyMUPb9rUmlOdMrX1h0UcscHE+vcqMP8sCsbRM230XESRExOyKGgCOAq8ceXMwaOWfMrFP+npKZmWWjrbaJiBgBRiqJxAaSc8bM2uEzJTMzy4aLkpmZZcNFyczMsuGiZGZm2XBRMjOzbLgomZlZNlyUzMwsGy5KZmaWDRclM6udpB0lXSNphaTlkk6oOyarR/ujTQ6gXk3YZdVr9VmONyFaq+csmNuVkKycdcCCiLhe0hbAUklXRcTNdQdmveUzJTOrXUTcGxHXF78/SpqxeId6o7I6uCiZWVYkDQG7A9fVG4nVwc13ZpYNSTOA7wDviYjfNXl83IkhO5nks9Vzxhpv0sfJahXfeK/XyQSP/TAxpIuSmWVB0kakgrQoIi5sts5EE0N2Milnq+eMNd7Eo5PVKr7xYutkMsl+mBjSzXdmVjtJAs4BVkTE5+qOx+rjomRmOdgLOBrYT9KNxe2guoOy3nPznZnVLiKuBVR3HFY/nymZmVk2XJTMzCwbLkpmZpaNCYuSx6SydjlnzKxTZTo6eEwqa5dzxsw6MuGZkseksnY5Z8ysU21dU/KYVNYu54yZtaP095QmOybVstWPNN1uq+kBxhtnqpNxrLox9lXZ7bXa17k7bNX26zTTD+NXQXU5M9772Oqz/MKii8Z5TvPlnY51Nt5rtdJJbnQ7D6rOW7MyShWlKsdvgSSkAAAU6klEQVSkamW8caY6GceqG2NfVRlDO/ph/KrcxzEro8qxzsbKYRyzqvPWrIwyve88JpW1xTljZp0qc03JY1JZu5wzZtaRCdsmPCaVtcs5Y2ad8ogOZmaWDRclMzPLhouSmZllw/MpmZnVbKiDrzK0es7KhfMnG06tMfhMyczMsuGiZGZm2XBRMjOzbLgomZlZNlyUzMwsG33Z+66TniqDGMN4cuiZY5M3Xp4tmLuu6SCq433Gueetmc+UzMwsGy5KZmaWDRclMzPLhouSmZllw0XJzMyy4aJkZmbZcFEyM7NsuCiZmVk2XJTMzCwbLkpmZpaNUkVJ0gGSbpV0u6QTqw7K+p9zxtrlnDEoUZQkTQO+CBwIzAGOlDSn6sCsfzlnrF3OGRtV5kzppcDtEXFnRDwJLAYOrTYs63POGWuXc8YAUESMv4J0OHBARLyluH808LKIeOeY9Y4Hji/uPh+4dZKxzQQemOQ2BlW335vnRcR23dpYjTnTbbnnYJ3xTcWcyT0fysg+Z8pMXaEmy55VySLiTODMEtsrRdKSiJjXre0Nkj54b2rJmW7L/X3OPb42ZZ8zg/B+98M+lGm+WwXs2HB/NnBPNeHYgHDOWLucMwaUK0o/B3aVtJOkjYEjgIurDcv6nHPG2uWcMaBE811ErJP0TuAKYBrw1YhYXnlkGTfrZCDr96bGnOm2rN9n8o+vtD7JmUF4v7Pfhwk7OpiZmfWKR3QwM7NsuCiZmVk2sihKkr4q6T5JNzUs21bSVZJuK35uU2eMdWjxvpwsabWkG4vbQXXG2E8mGsZG0tskLSve12tHRxSQNCTp8Yb3/IyG5+xRPOd2SZ+X1Kxrc5WxHdUQ142S1kvarXhspNjm6GPP6SS2qUbSppJ+JukXkpZL+udi+U6SriuOSd8qOmRkTdI0STdIuqS4n/8+RETtN2Af4CXATQ3LPgOcWPx+IvDpuuPM5H05GfjHumPrtxvp4vkdwM7AxsAvgDlj1tmy4fdDgMuL34caP4Mxz/kZsCfpezaXAQf2MrYx68wF7my4PwLMq/u977db8VnOKH7fCLgOeDlwPnBEsfwM4O11x1piX94HfAO4pLif/T5kcaYUET8EHhyz+FDg3OL3c4HX9jSoDLR4X6wzEw5jExG/a7g7nSZf3mwkaXtSsfhJpL/yf6WzPO1WbEcC3+zg9a1BJGuLuxsVtwD2Ay4olmd/TJI0G5gPnF3cF32wD1kUpRZmRcS9AMVPNz087Z2Sflk07025Zs0O7QDc3XB/VbHsGSS9Q9IdpDP1dzc8tFPRDPIDSXs3bHPVRNvsQWyj/p5nF6WvFU13H+60aXEqKpq9bgTuA64inck+HBHrilU6/ax76TTg/cD64v6f0Af7kHNRsua+DOwC7AbcC5xSbzh9o+wwNl+MiF2ADwAfKhbfCzw3InanaA6RtGXZbVYcW9qA9DLg9xFxU8PioyJiLrB3cTu6g9impIh4KiJ2I40s8VLgBc1W621U5Uk6GLgvIpY2Lm6yanb7kHNR+k3RPDLaTHJfzfFkISJ+U/zBrAfOIv3B2MTaHcZmMUXTRkQ8ERG/LX5fSvqv+c+Lbc5uY5tdj63BEYw5S4qI1cXPR0nXFZwrbYqIh0nX5l4ObC1pdMCB3IdB2gs4RNJKUr7sRzpzyn4fci5KFwPHFr8fC1xUYyzZGC3UhcOAm1qta88w4TA2knZtuDsfuK1Yvp3SfD9I2hnYldSh4F7gUUkvL5rGjqGzPO04tuKxDYC/Ix18RpdtKGlm8ftGwME4V0opPu+ti983A/YHVgDXAIcXq2V9TIqIkyJidkQMkfLp6og4ij7YhzKjhFdO0jeBYWCmpFXAR4GFwPmS3gz8mvRHN6W0eF+Giy6/AawE3lpbgH0kWgxjI+ljwJKIuJh0rW5/4I/AQzz9T9E+wMckrQOeAt4WEaMdUN4OfB3YjNT77rIexzYa36qIuLNh2SbAFUVBmgZ8n3RmbRPbHji3+EdkA+D8iLhE0s3AYkmfAG4AzqkzyA59gMz3wcMMmZlZNnJuvquFpJXFFyXXNtz+TNLGkj5SfBnxMaUvsF4m6dV1x2y9MZnckHSapIck/UTSDg3Lj5J0ej17ZL3SjeOK0pehH5K0SR370CsuSs39TUTMaLjdQ+rbfyjpusE2wE7A6aT2fZs62s4NSS8F9gD+FLgWOKlYvhXwj8BHer4XVoeOjyuShkg9KIP05emBlcU1pdwVbfmvAnaNiMbvpVxe3GyKKpkbOwHXRsQTkv6Tp79j9EngXyLikZ4FbNlo87hyDPBT0ugSxwLf7kmQNfCZUjn7A9eNSRwzKJcby4G9i55crwSWS5oHPD8ivtGLIC1L7RxXjgEWFbfXSJpVaWQ18plSc/9e9LSC9B2FB4A1ow9K2ha4k/RltE0iYtOeR2h1aTs3IuImSd8h/ad7C/AOUlfct0h6N6mL7t3AO4rvxdhg6ui4Iul/As8j9QJ8oBjV4w3AqT2MvWd8ptTcayNi6+L2WuC3pG6iAETEgxGxNek6wUBfdLRn6Sg3IuLUiHhxRPw9aTigH5H+/o4nnT2tIA08bIOr0+PKscCVEfFAcf8bPPMrAQPFRamc/wT+SmmAQ7NGbeVG0ezyVuBjwIuAX0bEH0lfoP3LyqK0HE2YO0WT7+uBv5a0RtIa4L3AiyW9uEdx9pSLUgkRcSXpm9D/LullRTfOjUhDj9gU1kFufA74aET8HriLdFCaQfqS9J0tnmMDqGTuvJb0he05pPEudyONw/cj0nWmgeNrSuX9Lakr73mkkXUfBJYBB9QZlGWhVG5I2hfYOiK+CxARP5P0PdL1pFt5evgXmzomyp1jga9FxK8bnyTp/wKfl/SBhlG/B4JHdDAzs2y4+c7MzLLhomRmZtlwUTIzs2y4KJmZWTZclMzMLBuVdAmfOXNmDA0NVbHpjj322GNMnz697jDGVVeMS5cufSAituv5CzfIIWf6IUdG1R1rv+VM3e9XN/XrvpTNmUqK0tDQEEuWLKli0x0bGRlheHi47jDGVVeMkn7V8xcdI4ec6YccGVV3rP2WM3W/X93Ur/tSNmfcfGdmZtlwUTIzs2y4KJmZWTY89l0PDZ34vZaPrVzoWdXrMvq5LJi7juMaPqNefiatcsN5kadefV7NXmc0Twc1N3ymZGZm2XBRMjOzbLgomZlZNlyUzMwsGy5KZmaWDRclMzPLhouSmZllw0XJzMyy4aJkZmbZcFEyM7NsuCiZmVk2XJTMzCwbLkpmZpYNFyUzM8uGi5KZmWXDRcnMzLJRuihJmibpBkmXVBmQDQ7njLXLOWPtnCmdAKyoKhAbSM4Za5dzZoorVZQkzQbmA2dXG44NCueMtcs5YwCKiIlXki4APgVsAfxjRBzcZJ3jgeMBZs2atcfixYu7HOrkrF27lhkzZnRte8tWP9Lysbk7bNXRc7odY1n77rvv0oiY181t9lPOjH4uszaD3zxe7jmtPuMyr9OOVq9TV66M6rec6dXffyd50e7rjOZpt1+ramVzZsOJVpB0MHBfRCyVNNxqvYg4EzgTYN68eTE83HLVWoyMjNDNmI478XstH1t5VPPXmeg53Y6xLv2WM6Ofy4K56zhl2YR/EkDrz7jM67Sj1esMSq6MqjpnevX330letPs6o3na7dfKRZnmu72AQyStBBYD+0k6r9KorN85Z6xdzhkDShSliDgpImZHxBBwBHB1RLyx8sisbzlnrF3OGRvl7ymZmVk2yjWgFyJiBBipJBIbSM4Za5dzZmrzmZKZmWXDRcnMzLLhomRmZtlwUTIzs2y4KJmZWTZclMzMLBsuSmZmlg0XJTMzy4aLkpmZZcNFyczMsuGiZGZm2XBRMjOzbLgomZlZNlyUzMwsG21NXdFrQ62mHF44v+1tLVv9SOspjDvYnlkvtPobWDB3HcO9DcVKaPV5TaSTY1A3j4858ZmSmZllw0XJzMyy4aJkZmbZcFEyM7NsuCiZmVk2XJTMzCwbLkpmZpYNFyUzM8uGi5KZmWXDRcnMzLLhomRmZtlwUTIzs2y4KJmZWTZclMzMLBsuSmZmlg0XJTMzy8aERUnSjpKukbRC0nJJJ/QiMOtfzhlrl3PGRpWZeXYdsCAirpe0BbBU0lURcXPFsVn/cs5Yu5wzBpQ4U4qIeyPi+uL3R4EVwA5VB2b9yzlj7XLO2Ki2rilJGgJ2B66rIhgbPM4Za5dzZmpTRJRbUZoB/AD4ZERc2OTx44HjAWbNmrXH4sWLn/H4stWPNN3u3B22avma3XzOrM3gN4+3fFpLrV6r1etMRrMYO9nX8TTb3r777rs0Iua1vbEJTDZnuqnMe9VOjoz3uUwmhrLGi7WT2NrVbzmzdu1aZsyY0VYsVfyNd0OnxzLoTW60UjZnShUlSRsBlwBXRMTnJlp/3rx5sWTJkmcsGzrxe03XXblwfsvtdPM5C+au45RlZS6hlXutVq8zGc1i7GRfx9Nse5K6foDpRs50U5n3qp0cGe9zmUwMZY0XayextavfcmZkZITh4eG24qnib7wbOj2WQW9yo5WyOVOm952Ac4AVZRLFzDlj7XLO2Kgy15T2Ao4G9pN0Y3E7qOK4rL85Z6xdzhkDSnQJj4hrAfUgFhsQzhlrl3PGRnlEBzMzy4aLkpmZZcNFyczMsuGiZGZm2XBRMjOzbLgomZlZNlyUzMwsGy5KZmaWDRclMzPLhouSmZllw0XJzMyy4aJkZmbZcFEyM7NsuCiZmVk2XJTMzCwbnc2pW7Ncpym27sr5cx4vtjqnnJ6MVvvUT/vTah++fsD0HkeSp179TU0mZ3ymZGZm2XBRMjOzbLgomZlZNlyUzMwsGy5KZmaWDRclMzPLhouSmZllw0XJzMyy4aJkZmbZcFEyM7NsuCiZmVk2XJTMzCwbLkpmZpYNFyUzM8uGi5KZmWWjVFGSdICkWyXdLunEqoOy/uecsXY5ZwxKFCVJ04AvAgcCc4AjJc2pOjDrX84Za5dzxkaVOVN6KXB7RNwZEU8Ci4FDqw3L+pxzxtrlnDGgXFHaAbi74f6qYplZK84Za5dzxgDYsMQ6arIsnrWSdDxwfHF3raRbywSgT5dZa/LeDTOBB9p9Xq/ig+Yxdvv1W2zved19lWpzpiqd5shYvciZ8WJ1zjzbvp/uzmebg27laZUmkzNlitIqYMeG+7OBe8auFBFnAmeWedE6SFoSEfPqjmM8/RBjSX2ZM/30/vdTrCVVmjOD9H4N0r40U6b57ufArpJ2krQxcARwcbVhWZ9zzli7nDMGlDhTioh1kt4JXAFMA74aEcsrj8z6lnPG2uWcsVFlmu+IiEuBSyuOpWrZNBONox9iLKVPc6af3v9+irWUinNmkN6vQdqXZ1HEs64lmpmZ1cLDDJmZWTYGpihJmibpBkmXFPd3knSdpNskfau4eNrseScVw5rcKuk1PY5xUfG6N0n6qqSNWjzvKUk3Fjdf/C2p7LA1kg6XFJLmFfeHJD3e8J6f0bDuHpKWFdv8vKRmXZl7FedRDTHeKGm9pN2Kx0aKbY4+9pzJxtkvJL1X0vLi7+qbkjaV9HVJdzW8H7vVHWcZkk4o9mO5pPcUy7aVdFVxbLtK0jZ1x9lVETEQN+B9wDeAS4r75wNHFL+fAby9yXPmAL8ANgF2Au4ApvUwxoNI388Q8M1mMRbrra37/e23G+li+R3AzsDGxec8p8l6WwA/BH4KzCuWDQE3tdjuz4A9i8/sMuDAuuIc8/hc4M6G+yPN1hv0G+kLt3cBmxX3zweOA74OHF53fG3uy4uAm4DNSdf/vw/sCnwGOLFY50Tg03XH2s3bQJwpSZoNzAfOLu4L2A+4oFjlXOC1TZ56KLA4Ip6IiLuA20nDnVQeI6QLu1EgHexmV/HaU1TZYWs+Tvoj/8NEG5S0PbBlRPyk+Mz+leZ5VUecR5L+sbF0AN9M0oakA/qzvu/UJ14A/DQifh8R64AfAIeR8uPcYp1Wx7a+NRBFCTgNeD+wvrj/J8DDxQcJrYcs6eXQJmNj/G9Fs93RwOUtnruppCWSfippoBKwQhN+tpJ2B3aMiEuaPH+noqn1B5L2btjmqvG2WUOco/6eZxelrxVNVR/uRjNjP4iI1cBngV8D9wKPRMSVxcOflPRLSadK2qS2IMu7CdhH0p9I2pzUsrIjMCsi7gUofg5U02zfFyVJBwP3RcTSxsVNVm3WzbDsepPSIsZGXwJ+GBE/avH4cyN9g/sNwGmSdul2jANo3M9W0gbAqcCCJuvdS3rPd6docpW05UTbrCHO0XVeBvw+Im5qWHxURMwF9i5uR08yzr5QXF85lNQc/2fAdElvBE4C/gL4K2Bb4AO1BVlSRKwAPg1cRfqH9RfAunGfNAD6vigBewGHSFpJavrYj3RWsnVx+g4thiyh5NAmVcQo6TwASR8FtiMd/JqKiHuKn3eSrhXsXkGMg2aiz3YLUpv9SPG5vBy4WNK8ojn3twDFPxJ3AH9ebHP2ONvsaZwN6xzBmLOk4oyBiHiUdB2zkmbpDO0P3BUR90fEH4ELgVdExL1FS/kTwNfok/cjIs6JiJdExD7Ag8BtwG+KpuTRJuX76oyx6+q+qNXNGzDM050Ivs0zOzr8Q5P1X8gzOzrcSYUdHZrE+BbgxxQXZVusvw2wSfH7TFJSPutCuG/Pet82LD7PnXi6A8ELx1l/hKc7Omw3mgekDgirgW2L+z8nFYbRjg4H1RVncX8DUmHbecw2Zxa/b0S6tvq2uj+THn3uLwOWk64liXTN5V3A9sXjIv3TurDuWEvuz3OKn88FbimOB//CMzs6fKbuOLt5KzWiQ5/6ALBY0ieAG4BzACQdQvqj/khELJd0PnAz6bT4HRHxVA9jPAP4FfCTosn/woj4WPFf8Nsi4i2ki51fkbSedABaGBE39zDGvhQthq2R9DFgSUSM17V+H+BjktYBT5E+iweLx95O6sm1GakoXVZjnKOxrop0Fj1qE+CK4lrlNFKvrbMmE2e/iIjrJF0AXE/6m76BNALCZZK2IxWlG4G31RdlW74j6U+AP5KOTw9JWgicL+nNpGtnf1drhF3mER3MzCwbg3BNyczMBoSLkpmZZcNFyczMsuGiZGZm2XBRMjOzbLgomZlZNlyUzMwsGy5KZmaWjf8P5firYz1OIVoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x504 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Markdown('**Raw distributions for \\'Team Per Game\\' for the 2018-2019 season**'))\n",
    "        \n",
    "t_per_game['18-19_team_per_game_stats.csv'].hist(figsize=(7,7), bins=15)\n",
    "pl.suptitle('\\n2018-2019 Season', fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Raw distributions for 'Team Shooting' for the 2018-2019 season**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, '\\n2018-2019 Season')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Markdown('**Raw distributions for \\'Team Shooting\\' for the 2018-2019 season**'))\n",
    "\n",
    "t_shooting['18-19_team_shooting.csv'].hist(figsize=(12,12), bins=15)\n",
    "pl.suptitle('\\n2018-2019 Season', fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Raw distributions for 'Team Stats' for the 2018-2019 season**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, '\\n2018-2019 Season')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Markdown('**Raw distributions for \\'Team Stats\\' for the 2018-2019 season**'))\n",
    "\n",
    "t_stats['18-19_team_stats.csv'].hist(figsize=(7,7), bins=15)\n",
    "pl.suptitle('\\n2018-2019 Season', fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distributions for each category in each dataset run the gamut from normal distributions such as for the 16ft - 3P shooting percentage, bimodal distributions such as for the Field Goal Percentage (FG%), and even Poisson distributions such as for 3PA under the 'Team Per Game' dataset.\n",
    "\n",
    "Now, let's calculate the means for both a single season and over the entirety of each of our datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**2018-2019 Season 'Team Per Game' Means**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "FG     41.073\n",
       "FGA    89.213\n",
       "FG%     0.460\n",
       "3P     11.360\n",
       "3PA    32.007\n",
       "3P%     0.356\n",
       "2P     29.717\n",
       "2PA    57.203\n",
       "2P%     0.520\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**2018-2019 Season 'Team Shooting' Means**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "FG%             0.460\n",
       "Distance       13.557\n",
       "2P Freq.        0.641\n",
       "0-3ft           0.292\n",
       "3-10ft          0.162\n",
       "10-16ft         0.096\n",
       "16-3pt          0.092\n",
       "3P Freq.        0.359\n",
       "2P FG%          0.520\n",
       "0-3ft FG%       0.658\n",
       "3-10ft FG%      0.399\n",
       "10-16ft FG%     0.411\n",
       "16-3P FG%       0.399\n",
       "3P FG%          0.355\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**2018-2019 Season 'Team Stats' Mean**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "FG     3368.733\n",
       "FGA    7315.267\n",
       "FG%       0.460\n",
       "3P      931.833\n",
       "3PA    2624.733\n",
       "3P%       0.356\n",
       "2P     2436.900\n",
       "2PA    4690.533\n",
       "2P%       0.520\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Means of the 2018-2019 Season Datasets\n",
    "pd.options.display.max_rows = 20\n",
    "\n",
    "display(Markdown('**2018-2019 Season \\'Team Per Game\\' Means**'))\n",
    "display(t_per_game['18-19_team_per_game_stats.csv'].mean())\n",
    "display(Markdown('**2018-2019 Season \\'Team Shooting\\' Means**'))\n",
    "display(t_shooting['18-19_team_shooting.csv'].mean())\n",
    "display(Markdown('**2018-2019 Season \\'Team Stats\\' Mean**'))\n",
    "display(t_stats['18-19_team_stats.csv'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Means for entire 'Team Per Game' dataset across 18 seasons**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "FG     37.251\n",
       "FGA    82.261\n",
       "FG%     0.453\n",
       "3P      7.036\n",
       "3PA    19.729\n",
       "3P%     0.355\n",
       "2P     30.221\n",
       "2PA    62.530\n",
       "2P%     0.484\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total team per game dataset mean\n",
    "total_tpg_mean = 0\n",
    "for file in t_per_game:\n",
    "    total_tpg_mean += t_per_game[file].mean()/len(t_per_game)\n",
    "display(Markdown('**Means for entire \\'Team Per Game\\' dataset across 18 seasons**'))\n",
    "total_tpg_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Means for entire 'Team Shooting' dataset across 18 seasons**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "FG%             0.453\n",
       "Distance       12.499\n",
       "2P Freq.        0.762\n",
       "0-3ft           0.295\n",
       "3-10ft          0.150\n",
       "10-16ft         0.118\n",
       "16-3pt          0.199\n",
       "3P Freq.        0.238\n",
       "2P FG%          0.484\n",
       "0-3ft FG%       0.621\n",
       "3-10ft FG%      0.394\n",
       "10-16ft FG%     0.394\n",
       "16-3P FG%       0.398\n",
       "3P FG%          0.355\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total team shooting dataset mean\n",
    "total_tshooting_mean = 0\n",
    "for file in t_shooting:\n",
    "    total_tshooting_mean += t_shooting[file].mean()/len(t_shooting)\n",
    "display(Markdown('**Means for entire \\'Team Shooting\\' dataset across 18 seasons**'))\n",
    "total_tshooting_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Means for entire 'Team Stats' dataset across 18 seasons**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "FG     3054.674\n",
       "FGA    6745.020\n",
       "FG%       0.453\n",
       "3P      576.871\n",
       "3PA    1617.693\n",
       "3P%       0.355\n",
       "2P     2477.803\n",
       "2PA    5127.327\n",
       "2P%       0.484\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total team per game dataset mean\n",
    "total_tstats_mean = 0\n",
    "for file in t_stats:\n",
    "    total_tstats_mean += t_stats[file].mean()/len(t_stats)\n",
    "display(Markdown('**Means for entire \\'Team Stats\\' dataset across 18 seasons**'))\n",
    "total_tstats_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We shall do the same with medians. However, looking at a median across seasons is impractical and we will avoid doing so."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**2018-2019 Season 'Team Per Game' Medians**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "FG     41.350\n",
       "FGA    89.100\n",
       "FG%     0.461\n",
       "3P     11.300\n",
       "3PA    31.750\n",
       "3P%     0.353\n",
       "2P     30.200\n",
       "2PA    57.500\n",
       "2P%     0.518\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**2018-2019 Season 'Team Shooting' Medians**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "FG%             0.461\n",
       "Distance       13.500\n",
       "2P Freq.        0.653\n",
       "0-3ft           0.290\n",
       "3-10ft          0.163\n",
       "10-16ft         0.098\n",
       "16-3pt          0.094\n",
       "3P Freq.        0.348\n",
       "2P FG%          0.518\n",
       "0-3ft FG%       0.659\n",
       "3-10ft FG%      0.401\n",
       "10-16ft FG%     0.407\n",
       "16-3P FG%       0.394\n",
       "3P FG%          0.353\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**2018-2019 Season 'Team Stats' Medians**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "FG     3391.000\n",
       "FGA    7306.000\n",
       "FG%       0.461\n",
       "3P      927.500\n",
       "3PA    2602.000\n",
       "3P%       0.353\n",
       "2P     2474.500\n",
       "2PA    4716.500\n",
       "2P%       0.518\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Medians of the 2018-2019 Season Datasets\n",
    "display(Markdown('**2018-2019 Season \\'Team Per Game\\' Medians**'))\n",
    "display(t_per_game['18-19_team_per_game_stats.csv'].median())\n",
    "display(Markdown('**2018-2019 Season \\'Team Shooting\\' Medians**'))\n",
    "display(t_shooting['18-19_team_shooting.csv'].median())\n",
    "display(Markdown('**2018-2019 Season \\'Team Stats\\' Medians**'))\n",
    "display(t_stats['18-19_team_stats.csv'].median())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time to look at the range, and more importantly IQR of a season. We separate the data into two categories: one for percentages and frequencies, and the other for counts. This way, we prevent one category of skewing the axis heavily in the direction of one statistic, such as for example Average Shot Distance in the 'Team Shooting' dataset relative to the others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**2018-2019 Season 'Team Per Game' IQR**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, \"'Team Per Game' Shot Percentages IQR (%)\")"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Markdown('**2018-2019 Season \\'Team Per Game\\' IQR**'))\n",
    "t_per_game['18-19_team_per_game_stats.csv'].boxplot(column=['FG%','3P%','2P%'])\n",
    "pl.suptitle('\\'Team Per Game\\' Shot Percentages IQR (%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, \"'Team Per Game' Shot Attempts IQR (Count)\")"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t_per_game['18-19_team_per_game_stats.csv'].boxplot(column=['FG','FGA','3P','3PA','2P','2PA'])\n",
    "pl.suptitle('\\'Team Per Game\\' Shot Attempts IQR (Count)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**2018-2019 Season 'Team Shooting' IQR**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, \"'Team Shooting' Percentages IQR (%)\")"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Markdown('**2018-2019 Season \\'Team Shooting\\' IQR**'))\n",
    "t_shooting['18-19_team_shooting.csv'].boxplot(figsize=(16,5), column=['FG%','2P Freq.','0-3ft','3-10ft','10-16ft'\n",
    "                                                                      ,'16-3pt','3P Freq.','2P FG%','0-3ft FG%','3-10ft FG%'\n",
    "                                                                      ,'10-16ft FG%','16-3P FG%','3P FG%'])\n",
    "pl.suptitle('\\'Team Shooting\\' Percentages IQR (%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, \"'Team Shooting' Average Shot Distance IQR (ft)\")"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t_shooting['18-19_team_shooting.csv'].boxplot(column=['Distance'])\n",
    "pl.suptitle('\\'Team Shooting\\' Average Shot Distance IQR (ft)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**2018-2019 Season 'Team Stats' IQR**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, \"'Team Stats' Shot Percentages IQR (%)\")"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEVCAYAAAAM3jVmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAHb5JREFUeJzt3XuYXFWd7vHvSy4QIAISDISEJGjQhqCiLXowowkKxnEm4FFHEhSi0ZDzSMDLGQ0ThEeYjMA4OjqDSjRIZoYE8YJGEoII3UrkAAn3S4uEm2kDyDXQGEISfuePvTrsFNVd1V2VrnTv9/M89aT22mvvWnuvrrdWrdpVUURgZmbFsEujG2BmZn3HoW9mViAOfTOzAnHom5kViEPfzKxAHPpmZgXi0LedmqSpktY2uh3WGJJ2l/QHSSN6se2Rklp3QLP6NYd+nUh6WNI4SVdJ6ki3zZJeyi1/v8FtfIukayU9k26rJb0/retRuEraTVJIGl1l/cck7d/TdtVC0hxJv6lQ50ZJL6b+eULSTyS9rtbHrqfUxk80uh155f5eJH1Y0hpJL0h6UtLifJ+n/tiSzvUGSbdK+kCFh/ocsDIinkz7+JSkxyU9IOnduX2/SdLvJKmzLCJuBl6WdExdDnqAcOjXWUR8MCL2jIg9gUuBCzqXI2JOo9olaRdgOfBL4HXA/sCXgBca1aadqF2fSf3VlB7/gp7uQNLgureqH5F0IvAjsnO3L/BmYDDwW0nDc1Vb07neB1gM/ETSHt3s+hTgv9Nj7AacDRwOfAX4dq7efwCnx6u/bXpp2od1igjf6nADHgbGlZRdAvxzmbofBu4EngWuBw7NrTsLeAh4Hrgb+FBu3RzgOuA/gQ3A/UAzMBv4M/A4cEIX7RsNBDCszLp9gY3Ay0BHuu0LvBu4KT3WeuBbwOC0zc1pfy+k+seTBebKdFxPAdflHuMxYP+etCutnwqsBf4JeCId54m59a8FlqR1DwFfBgQcAbwIbEnte6yL/d8IfCK3/CVgTbo/DPh3YF1q/38Au5a066vpvP8glX8s9e1zqX/el2vnf6X9rCMLr11y/Xot8J107h4A3p/W/RuwNR1LB/Bvqfx7QHt6nJuBd+WOYc90Tp5Nf0NnAGtz68eQvcg+CTwIzMmtezdwW9rvY8DXu+uXdH8w8ChwWkmdQekcfCV3nL8p6bsADu/iMQ5J7VBaHgu0pPt7A0+n+58AvtPFPl5P9lwa1OiM2FluDW/AQL5RJvSBd6UnyNvTk2I28EdeCdOPAweQvQv7ZPqDHZHWzQE2AzPSE+1fgUfIwngoMA14BtitTFsGk70wXQEcB7yuZP22J3Gu7EjgHamdrycLuTlp3W7pCTs6V/9bZKOvwak976niHFXTrs3AfGAI2Qvm88Ceaf3lwE/Igu4NZMF/Yu58/abC428LfbJ3Gqt4JcC/D/w0BcxewNXA2bl2bQHOScc6DPibdP6npP47CDgk1b+K7EVj99S/twEnl/TrSelcfwF4uFwbc2UnkY2Wh6Rzsw4Yktb9O3BNavNY4F5eCehBwF1kI+WhZMH6J+C9af1twMfS/eHAO7s4b/nQf2v6WzigTL3zgWtL+yP1+5fIBhv7dPEYHwFuyS0PIXuu7E/24np96pvbgb266eOXOvvBN4f+jj255UP/R8D8krJHunly/QH4QLo/B7grt+4d6cm2V67sBeBNXexrbAqyh8hGj9cC49O6V4V+me3nAUvT/XKhfwFZAB/cw/NUqV0bSKPiVPZcCppdU/2Dc+tOJ5sD3i5kunnsG9M5e5Zs5LyYbAQ6OIXFgbm6U4C2XLteIAVtKltMmZFxOr7Sup8Crsq18+7cus4R8N65Nn6im2MQ8FfgjWl5PSnE0/KpvBLQ7wXuL9n+a8D30v2byV5E9q1w3vKh/36yd4m7lKn3eeDO3HFuTud6czonx3fzGLPIpoPyZR9MbWwBDgO+C5wIHAu0kr3TfFPJNk8BR9bjOT0Qbp7T73tjgX+S9GznDdgPOBBA0ixJd+bWvQHIX7nweO7+RmBTRGwoKduz3ANHxCMRMScixgMHp+KLu2qopEPTB9OPS3qObOqpu6soFpAFTouktZK+2E3dnrTriYh4Obf8V7Jj3J9sRP2n3LpHSOeyB06JiL0jYnREnBwRTwOjyEaW9+T64hdk7wY6PRYRm3PLY8imZkqNJXuRfCK3r28DI/P7Kjk+6KIfASSdIek+SRtI7+6AEemDzJFkI/9O+ftjgXElf39fJDuXACeTzcf/UdJNVXzQCtk0UefjljqAbOqt028jYm+y6cNfk00ndeUZsncb20TEVRFxZERMIXvX9EaygcaPyN4Bf4NsAJE3nOyFxvAHuY2wDjgrhUznbfeI+LmkQ8imAGYDr01PjrVkT6i6iohHyOaFJ3YWlan2A+BW4PUR8RqyqQx1VT8iNkTE6RExluyt+Zn5Kyx62a7uPEY2wjwoV3YQ2bx/2Tb2wKNk0zevz/XTXhGxb765JdusI5sGK7WObD5+n9y+XhMRb6uyLds9TroaZS7ZVNfeZO8MNpLNfQfwF7LPSjqNKWnLH0r+/oZHxIcBIqItIj5O9uL2HeDnkoZWaN/dZIORj5W0c1Bq429fdUARz5GN/OdIOrSL/d4JvCF/RU5u37uk9s0le2HZFBHrgdVkL1qd9V4PbCL77MJw6DfCQmCupGZl9pQ0TdLuZCO7l8lGRrtImkM20q+ZpJGSzpJ0cHrc1wEzyaYOIHvSvk5SfnQ5HNgQER2SDgM+27kiIjaRTbt0jsxJxzE+PUk3kE29bK2xXV1KbbgC+BdJe6Qn+OnA/+SOaYykIZX2VWbfm8nebXxb0ojUtjEVLv/7IXCKpPdI2iXVPyQiHkrHc4Gk4WndBEmTqmzO4+TOM1m/bCb7OxlK9mK8W2795cB8SXtJOgj4P7l1qwAkfV7ZZbeDJb1Z0ttS+UmS9o2IrWR9GGR/k12KiC1knxGcK+ljknaVNIpsumt3shfxcts9nup8tYv1a9OxH1Fm9eeA30XEvanOayVNIJuCywf8e4FrUhsNh36fi4jfA6cBF5G95fwj2dvSiIhbyd6ariEbaY5P9+vhRWAC2bzn88AdZG+fP5PW3wEsAx5Jb/tfS/aB4mckdQAXAj8u2edZZJfcPStpGtklj537/x3wjYioFN6V2lVJ5+V4j5Bd2fRDssv0IJvffRj4i6T2KveX93my6ao1ZAG4km5ehCPierLR63dT/Wt5ZcQ9nWxU/gfgabJzWW46pJxvAScp+w7DBcCvyM7vA2QB9yTbT6GcSXYOHyH7APlystFu54vZ3wJHpfVPkIVy54v93wH3SXoe+DrwD9UEZkQsJpuDn5ce+8/AocDkiHiim02/CfxvSW/sYv1FZBc0bKPs2v/ZZC92RMSLZH+rq9L+Ts9VP5FXT/cUWuelUGY2QEn6AjA1IqqZn6/XY/4d2Tz75Ii4p4b9DCO7omhSpC9o9WDbZrJLXN/b28cfiBz6ZgOMpDFkH2bfTPbuazlwXkT06YhX0ofJrgL6YV8+rnXPoW82wEh6A9lU3ViyqZb/Ac70vLaBQ9/MrFD8Qa6ZWYE49M3MCsShb2ZWIA59M7MCceibmRWIQ9/MrEAc+mZmBeLQNzMrEIe+mVmBOPTNzArEoW9mViAOfTOzAnHom5kViEPfzKxABje6AaVGjBgR48aNa3QzdpgXXniBPfbYo9HNsF5y//VfA73vbrnllicjYr9K9Xa60B83bhxr1tTrv4Xd+bS2tjJ58uRGN8N6yf3Xfw30vpP0SDX1PL1jZlYgDn0zswJx6JuZFYhD38ysQBz6ZmYF4tA3MysQh76ZWYE49M3MCmSn+3KWmVlvSKrLfiKiLvvZWXmkb2YDQkR0exv7lSsr1hnogQ8OfTOzQnHom5kViEPfzKxAHPpmZgXi0DczKxCHvplZgTj0zcwKpKrQlzRV0n2S1kqaV2b9TElPSLo93T6TW7c1V76sno03M7OeqfiNXEmDgAuBY4B2YLWkZRFxb0nVH0fEqWV2sTEi3lp7U83MrFbVjPSPBNZGxIMR8RJwGXDcjm2WmZntCNWE/oHAutxyeyor9RFJd0r6qaQxufLdJK2RdKOk42tprJmZ1aaaH1wr9ytGpT9Q8StgaURskjQHWAwcndYdFBHrJR0MXCfproh4YLsHkGYDswFGjhxJa2trT46hX+no6BjQxzfQuf/6N/dddaHfDuRH7qOB9fkKEfFUbvEHwPm5devTvw9KagWOAB4o2X4hsBCgubk5Jk+eXPUB9Detra0M5OMb6Nx//djK5e47qpveWQ1MkDRe0lDgBGC7q3AkHZBbnAa0pfJ9JO2a7o8A3g2UfgBsZmZ9pOJIPyK2SDoVuBoYBFwcEfdIOgdYExHLgNMkTQO2AE8DM9PmTcBFkl4me4E5r8xVP2Zm1keq+k9UImIFsKKk7Kzc/TOAM8psdwNweI1tNDOzOvE3cs3MCsShb2ZWIA59M7MCceibmRWIQ9/MrEAc+mZmBeLQNzMrEIe+mVmBVPXlLDOzRnvL137Nho2ba9rHuHnLa9p+r2FDuOPsY2vaR6M59M2sX9iwcTMPn/ehXm9fjx/Lq/VFY2fg6R0zswJx6JuZFYhD38ysQBz6ZmYF4tA3MysQh76ZWYE49M3MCsShb2ZWIA59M7MCceibmRWIQ9/MrEAc+mZmBeIfXDOzfmF40zwOXzyvtp0srrUNAL3/0bedgUPfzPqF59vO869s1oGnd8zMCsShb2ZWIA59M7MCceibmRWIQ9/MrEAc+mZmBeLQNzMrEF+nb2b9Rs3Xya+sbfu9hg2p7fF3Ag59M+sXavliFmQvGLXuYyCoanpH0lRJ90laK+lV34OWNFPSE5JuT7fP5NadLOn+dDu5no03M7OeqTjSlzQIuBA4BmgHVktaFhH3llT9cUScWrLta4GzgWYggFvSts/UpfVmZtYj1Yz0jwTWRsSDEfEScBlwXJX7/wBwTUQ8nYL+GmBq75pqZma1qib0DwTW5ZbbU1mpj0i6U9JPJY3p4bZmZtYHqvkgV2XKomT5V8DSiNgkaQ7ZD5geXeW2SJoNzAYYOXIkra2tVTSrf+ro6BjQxzfQuf/6N/dddaHfDozJLY8G1ucrRMRTucUfAOfntp1csm1r6QNExEJgIUBzc3PU+vOnO7N6/LyrNY77rx9budx9R3XTO6uBCZLGSxoKnAAsy1eQdEBucRrQlu5fDRwraR9J+wDHpjIzM2uAiiP9iNgi6VSysB4EXBwR90g6B1gTEcuA0yRNA7YATwMz07ZPSzqX7IUD4JyIeHoHHIeZmVWhqi9nRcQKYEVJ2Vm5+2cAZ3Sx7cXAxTW00czM6sS/vWNmViAOfTOzAvFv79SZVO4q1Z6LeNWVrWZmNfNIv84iotvb2K9cWbGOA9/MdhSHvplZgTj0zcwKxKFvZlYgDn0zswJx6JuZFYhD38ysQBz6ZmYF4tA3MysQfyPXzAaEar4Nr/MrVhnwX470SN/MBoRK33JvaWnxt+Fx6JuZFYpD38ysQBz6ZmYF4tA3MysQh76ZWYH4ks0eesvXfs2GjZtr2se4ectr2n6vYUO44+xja9qHmRWTQ7+HNmzczMPnfajX27e2tjJ58uSa2lDri4aZFZend8zMCsShb2ZWIA59M7MCceibmRWIQ9/MrEAc+mZmBeJLNntoeNM8Dl88r7adLK61DQC9v2zUzIrLod9Dz7ed5+v0zazf8vSOmVmBOPTNzArEoW9mViAOfTOzAqkq9CVNlXSfpLWSurx0RdJHJYWk5rQ8TtJGSben2/fr1XAzM+u5ilfvSBoEXAgcA7QDqyUti4h7S+oNB04DbirZxQMR8dY6tdfMzGpQzUj/SGBtRDwYES8BlwHHlal3LnAB8GId22dmZnVUTegfCKzLLbensm0kHQGMiYgry2w/XtJtkn4r6W9631QzM6tVNV/OUpmy2LZS2gX4FjCzTL1HgYMi4ilJbwd+IemwiHhuuweQZgOzAUaOHElra2t1rW+Qmr8ctbK27fcYwk5/jgaqjo4On/t+yn2XqSb024ExueXRwPrc8nBgItAqCWB/YJmkaRGxBtgEEBG3SHoAOARYk3+AiFgILARobm6OWr+xuiM9PLm27cfNW17TN3qtserxjWprDPddpprpndXABEnjJQ0FTgCWda6MiA0RMSIixkXEOOBGYFpErJG0X/ogGEkHAxOAB+t+FGZmVpWKI/2I2CLpVOBqYBBwcUTcI+kcYE1ELOtm8/cA50jaAmwF5kTE0/VouJmZ9VxVP7gWESuAFSVlZ3VRd3Lu/s+An9XQPjMzqyN/I9fMrEAc+mZmBeLQNzMrEIe+mVmBOPTNzArEoW9mViAOfTOzAnHom5kViEPfzKxAqvpGrlUv/ehc93XOr7yfiKhcycyshzzSr7OI6PbW0tJSsY4D38x2FIe+mVmBOPTNzArEoW9mViAOfTOzAnHom5kViEPfzKxAHPpmZgXi0DczKxCHvplZgTj0zcwKxKFvZlYgDn0zswJx6JuZFYhD38ysQBz6ZmYF4tA3MysQh76ZWYE49M3MCsShb2ZWIA59M7MCceibmRWIQ9/MrECqCn1JUyXdJ2mtpHnd1PuopJDUnCs7I213n6QP1KPRZmbWO4MrVZA0CLgQOAZoB1ZLWhYR95bUGw6cBtyUKzsUOAE4DBgF/EbSIRGxtX6HYGZm1apmpH8ksDYiHoyIl4DLgOPK1DsXuAB4MVd2HHBZRGyKiIeAtWl/ZmbWABVH+sCBwLrccjvwznwFSUcAYyLiSkn/t2TbG0u2PbD0ASTNBmYDjBw5ktbW1qoa3x91dHQM6OMb6Nx//Zf7LlNN6KtMWWxbKe0CfAuY2dNttxVELAQWAjQ3N8fkyZOraFb/1NraykA+voHO/dd/ue8y1YR+OzAmtzwaWJ9bHg5MBFolAewPLJM0rYptzcysD1Uzp78amCBpvKShZB/MLutcGREbImJERIyLiHFk0znTImJNqneCpF0ljQcmADfX/SjMzKwqFUM/IrYApwJXA23A5RFxj6Rz0mi+u23vAS4H7gVWAp/zlTvWHy1dupSJEyfyvve9j4kTJ7J06dJGN8msV6qZ3iEiVgArSsrO6qLu5JLlBcCCXrbPrOGWLl3K/PnzWbRoEVu3bmXQoEHMmjULgOnTpze4dWY942/kmlWwYMECFi1axJQpUxg8eDBTpkxh0aJFLFjgsYz1Pw59swra2tpob2/fbnqnvb2dtra2RjfNrMeqmt4xK7JRo0bx5S9/mSVLlmyb3pkxYwajRo1qdNPMeswjfbMqpMuRu1w26y880jerYP369Zxyyil88IMfZNOmTey66658+tOf5qKLLmp008x6zKFvVsGoUaO44ooruOqqqzy9Y/2ep3fMquDpHRsoPNI3q2D9+vVccsklzJ07l7a2Npqamjj//POZOXNmo5tm1mMe6ZtV0NTUxOjRo7n77ru59tprufvuuxk9ejRNTU2NbppZjzn0zSqYP38+s2bNoqWlhS1bttDS0sKsWbOYP39+o5tm1mOe3jGrYPr06dxwww3bXb3z2c9+1j/BYP2SQ9+sgqVLl7J8+fLtrt6ZNWsWRx11lIPf+h1P75hV4N/esYHEoW9WQVtbG5MmTdqubNKkSf7tHeuXHPpmFTQ1NbFq1artylatWuWrd6xfcuibVeCrd2wg8Qe5Zkmlb9keffTR2y3PmDGDGTNmvKpeRNS1XWb15JG+WRIRFW9jv3JlxTpmOzOHvplZgTj0zcwKxKFvZlYgDn0zswJx6JuZFYhD38ysQBz6ZmYF4tA3MysQh76ZWYE49M3MCsShb2ZWIP7BNSuMt3zt12zYuLnm/Yybt7ym7fcaNoQ7zj625naY9YZD3wpjw8bNPHzeh2raR2trK5MnT65pH7W+aJjVwtM7ZmYF4tA3MyuQqkJf0lRJ90laK2lemfVzJN0l6XZJqyQdmsrHSdqYym+X9P16H4CZmVWv4py+pEHAhcAxQDuwWtKyiLg3V21JRHw/1Z8GfBOYmtY9EBFvrW+zzXpueNM8Dl/8qjFLzy2utR0AtX22YNZb1XyQeySwNiIeBJB0GXAcsC30I+K5XP09AP/3QbbTeb7tPH+Qa4VXTegfCKzLLbcD7yytJOlzwBeBoUD+PxMdL+k24DngzIi4vsy2s4HZACNHjqS1tbXa9vc7HR0dA/r4dna1nvt69Z//Bvqen3uZakK/3P8W/aqRfERcCFwoaQZwJnAy8ChwUEQ8JentwC8kHVbyzoCIWAgsBGhubo5aR1I7s3qMFK2XVi6v+dzXpf/q0A7rOT/3MtWEfjswJrc8GljfTf3LgO8BRMQmYFO6f4ukB4BDgDW9aq1ZjeoytbKy9i9nmTVKNaG/GpggaTzwZ+AEYEa+gqQJEXF/WvwQcH8q3w94OiK2SjoYmAA8WK/Gm/VErfP5kL1o1GM/Zo1SMfQjYoukU4GrgUHAxRFxj6RzgDURsQw4VdL7gc3AM2RTOwDvAc6RtAXYCsyJiKd3xIGYmVllVf0MQ0SsAFaUlJ2Vu396F9v9DPhZLQ00M7P68TdyzcwKxKFvZlYgDn0zswJx6JuZFYhD38ysQBz6ZmYF4tA3MysQh76ZWYE49M3MCsT/MbpZIpX7Qdky9c7vfn2E/zsJ23l5pG+WRETZ25IlSxg/fjzXXXcd11xzDddddx3jx49nyZIlZeub7cwc+mYVLFiwgEWLFjFlyhQGDx7MlClTWLRoEQsWLGh008x6zKFvVkFbWxuTJk3armzSpEm0tbU1qEVmvefQN6ugqamJVatWbVe2atUqmpqaGtQis95z6JtVMH/+fGbNmkVLSwtbtmyhpaWFWbNmMX/+/EY3zazHfPWOWQXTp08HYO7cubS1tdHU1MSCBQu2lZv1Jw59sypMnz6d6dOn+z/Xtn7P0ztmZgXi0DczKxCHvplZgTj0zcwKxKFvZlYg2tl+K0TSE8AjjW7HDjQCeLLRjbBec//1XwO978ZGxH6VKu10oT/QSVoTEc2Nbof1jvuv/3LfZTy9Y2ZWIA59M7MCcej3vYWNboDVxP3Xf7nv8Jy+mVmheKRvZlYgDv06kbRV0u2527hUfqSkVkn3S7pV0nJJh6d1cyXdLWmFpKGpbJKkbzbuSIpJ0m6SbpZ0h6R7JH0tlbdKui+V/17SG1P5pZLulPQvuX18VdJxjTqGopI0RlKLpLbUd6en8kskPZSej7dK+l+p/PzUd/+V28cnO7cb8Lr6f0F969kN6ChTNhJ4GDgqVzYJOD7dv4PshXcB8PeAgKuBfRp9PEW7pXO/Z7o/BLgJeBfQCjSn8tnAMuDNwKWp7HpgL+AA4FeNPo4i3tK5f1u6Pxz4I3AocAnw0VR+LHBn6qvrU9mlwOHAMOBaYEijj6Uvbv5p5R3rVGBxRNzQWRARq0rqDAF2BzYDnwRWRMQzfddEA4gsBTrS4pB0K/3A63fA58n6apikXYChwFbgHOCsvmmt5UXEo8Cj6f7zktqAA0uq/Q54A/AyMFSSyMJ+M/CPwHciYnPftbpxPL1TP8NyUztXpLLDgFu72eYbwI3AfsDvgZOB7+7YZlpXJA2SdDvwF+CaiLippMrfA3dFRBvwJ7K+vZwsTBQRt/Vpg+1V0rTqEWTv1PI6++554GfAbcBDwAbgHRHxyz5sZkP56p06kdQREXuWlP2cbKT/y7R8E/Aa4NcRcXpJ3bOB28lGlycB64AvRcTLfdF+e4WkvYErgLnAf5JNH2wkm6qbGxHrSur/CjgF+BTwFrIXjB/0ZZsNJO0J/BZYEBE/l3QJ8F6yYH8C+EJE3F2yzQ+BC4G3k6aAIuKf+7Thfcwj/R3rHuBtnQsR8U7gq2TzittIGsUro40zgY8Dm4D39V1TrVNEPEs2lz81FZ0YEW+NiOPLBP5xwBpgD2BiRPwD8ElJu/dlm4tO0hCyEfylEfHz3Kp/TH13TJnAPyLd/SNwUuq7iZIm9E2rG8Ohv2NdCMyUdFSurFwYnEv2YgDZPGOQzT06OPqIpP3SCB9Jw4D3A3+osM0Q4HTgX8n6qvNtc+dcv/WBND+/CGiLiJ5c+XYu2ecwQ4BBqWzAP+8c+jtQRDxGNmr/uqS1km4APko2ZQC8MtrIzQcvAu4ie4ewsm9bXGgHAC2S7gRWk03RXFlhm8+RTd/9lezKEEm6C/h9erdgfePdZBdBHJ37XO1vu9tA0vHA6ohYn/rq/6W+i4i4ow/a3DCe0zczKxCP9M3MCsShb2ZWIA59M7MCceibmRWIQ9/MrEAc+mZmBeLQNzMrEIe+mVmB/H9wknHg23LyIQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Markdown('**2018-2019 Season \\'Team Stats\\' IQR**'))\n",
    "t_stats['18-19_team_stats.csv'].boxplot(column=['FG%','3P%','2P%'])\n",
    "pl.suptitle('\\'Team Stats\\' Shot Percentages IQR (%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, \"'Team Stats' Shot Attempts IQR (count)\")"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t_stats['18-19_team_stats.csv'].boxplot(column=['FG','FGA','3P','3PA','2P','2PA'])\n",
    "pl.suptitle('\\'Team Stats\\' Shot Attempts IQR (count)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to make sure that the data is consistent and reliable, we explored the data through graphs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configure libraries\n",
    "# The seaborn library makes plots look nicer\n",
    "sns.set()\n",
    "sns.set_context('talk')\n",
    "\n",
    "# Don't display too many rows/cols of DataFrames\n",
    "pd.options.display.max_rows = 7\n",
    "pd.options.display.max_columns = 8\n",
    "\n",
    "# Round decimals when displaying DataFrames\n",
    "pd.set_option('precision', 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read all csv\n",
    "stats_game_2000 = pd.read_csv(\"00-01_team_per_game_stats.csv\")\n",
    "stats_shooting_2000 = pd.read_csv(\"00-01_team_shooting.csv\")\n",
    "\n",
    "# All team stats for all years\n",
    "df_stats_team_stats_2000 = pd.read_csv(\"00-01_team_stats.csv\")\n",
    "df_stats_team_stats_2001 = pd.read_csv(\"01-02_team_stats.csv\")\n",
    "df_stats_team_stats_2002 = pd.read_csv(\"02-03_team_stats.csv\")\n",
    "df_stats_team_stats_2003 = pd.read_csv(\"03-04_team_stats.csv\")\n",
    "df_stats_team_stats_2004 = pd.read_csv(\"04-05_team_stats.csv\")\n",
    "df_stats_team_stats_2005 = pd.read_csv(\"05-06_team_stats.csv\")\n",
    "df_stats_team_stats_2006 = pd.read_csv(\"06-07_team_stats.csv\")\n",
    "df_stats_team_stats_2007 = pd.read_csv(\"07-08_team_stats.csv\")\n",
    "df_stats_team_stats_2008 = pd.read_csv(\"08-09_team_stats.csv\")\n",
    "df_stats_team_stats_2009 = pd.read_csv(\"09-10_team_stats.csv\")\n",
    "df_stats_team_stats_2010 = pd.read_csv(\"10-11_team_stats.csv\")\n",
    "df_stats_team_stats_2011 = pd.read_csv(\"11-12_team_stats.csv\")\n",
    "df_stats_team_stats_2012 = pd.read_csv(\"12-13_team_stats.csv\")\n",
    "df_stats_team_stats_2013 = pd.read_csv(\"13-14_team_stats.csv\")\n",
    "df_stats_team_stats_2014 = pd.read_csv(\"14-15_team_stats.csv\")\n",
    "df_stats_team_stats_2015 = pd.read_csv(\"15-16_team_stats.csv\")\n",
    "df_stats_team_stats_2016 = pd.read_csv(\"16-17_team_stats.csv\")\n",
    "df_stats_team_stats_2017 = pd.read_csv(\"17-18_team_stats.csv\")\n",
    "df_stats_team_stats_2018 = pd.read_csv(\"18-19_team_stats.csv\")\n",
    "\n",
    "# Create new dataframe with all team stats with 3PA\n",
    "df_all_team_stats = pd.DataFrame()\n",
    "\n",
    "years = ['2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008','2009', '2010', '2011', '2012', \n",
    "         '2013', '2014', '2015', '2016', '2017', '2018'] \n",
    "df_all_team_stats['Year'] = years\n",
    "\n",
    "# Get sum of all 3PA for team each for all years\n",
    "three_attempts = [df_stats_team_stats_2000['3PA'].sum(), df_stats_team_stats_2001['3PA'].sum(), \n",
    "                  df_stats_team_stats_2002['3PA'].sum(), df_stats_team_stats_2003['3PA'].sum(),\n",
    "                  df_stats_team_stats_2004['3PA'].sum(), df_stats_team_stats_2005['3PA'].sum(),\n",
    "                  df_stats_team_stats_2006['3PA'].sum(), df_stats_team_stats_2007['3PA'].sum(),\n",
    "                  df_stats_team_stats_2008['3PA'].sum(), df_stats_team_stats_2009['3PA'].sum(), \n",
    "                  df_stats_team_stats_2010['3PA'].sum(), df_stats_team_stats_2011['3PA'].sum(), \n",
    "                  df_stats_team_stats_2012['3PA'].sum(),\n",
    "                  df_stats_team_stats_2013['3PA'].sum(), df_stats_team_stats_2014['3PA'].sum(),\n",
    "                  df_stats_team_stats_2015['3PA'].sum(), df_stats_team_stats_2016['3PA'].sum(),\n",
    "                  df_stats_team_stats_2017['3PA'].sum(), df_stats_team_stats_2018['3PA'].sum()]\n",
    "df_all_team_stats['3PA'] = three_attempts\n",
    "\n",
    "# Get sum of all 2PA for team each for all years\n",
    "two_attempts = [df_stats_team_stats_2000['2PA'].sum(), df_stats_team_stats_2001['2PA'].sum(), \n",
    "                  df_stats_team_stats_2002['2PA'].sum(), df_stats_team_stats_2003['2PA'].sum(),\n",
    "                  df_stats_team_stats_2004['2PA'].sum(), df_stats_team_stats_2005['2PA'].sum(),\n",
    "                  df_stats_team_stats_2006['2PA'].sum(), df_stats_team_stats_2007['2PA'].sum(),\n",
    "                  df_stats_team_stats_2008['2PA'].sum(), df_stats_team_stats_2009['2PA'].sum(), \n",
    "                  df_stats_team_stats_2010['2PA'].sum(), df_stats_team_stats_2011['2PA'].sum(), \n",
    "                  df_stats_team_stats_2012['2PA'].sum(),\n",
    "                  df_stats_team_stats_2013['2PA'].sum(), df_stats_team_stats_2014['2PA'].sum(),\n",
    "                  df_stats_team_stats_2015['2PA'].sum(), df_stats_team_stats_2016['2PA'].sum(),\n",
    "                  df_stats_team_stats_2017['2PA'].sum(), df_stats_team_stats_2018['2PA'].sum()]\n",
    "df_all_team_stats['2PA'] = two_attempts\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 826.8x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Shows all 3PA for all teams in each year\n",
    "df_all_team_stats.plot(x='Year', y='3PA', xticks=range(19), title=\"3PA Over Past Two Decades\")\n",
    "\n",
    "f4 = plt.gcf()\n",
    "plt.gca().margins(x=0)\n",
    "f4.canvas.draw()\n",
    "tl = plt.gca().get_xticklabels()\n",
    "maxsize = max([t.get_window_extent().width for t in tl])\n",
    "m = 0.2 # inch margin\n",
    "s = maxsize/plt.gcf().dpi*19+2*m\n",
    "margin = m/plt.gcf().get_size_inches()[0]\n",
    "\n",
    "plt.gcf().subplots_adjust(left=margin, right=1.-margin)\n",
    "plt.gcf().set_size_inches(s, plt.gcf().get_size_inches()[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 826.8x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Shows all 2PA vs 3PA\n",
    "df_all_team_stats.plot(x='Year', y='2PA', xticks=range(19), title=\"2PA Over Past Two Decades\")\n",
    "\n",
    "f4 = plt.gcf()\n",
    "plt.gca().margins(x=0)\n",
    "f4.canvas.draw()\n",
    "tl = plt.gca().get_xticklabels()\n",
    "maxsize = max([t.get_window_extent().width for t in tl])\n",
    "m = 0.2 # inch margin\n",
    "s = maxsize/plt.gcf().dpi*19+2*m\n",
    "margin = m/plt.gcf().get_size_inches()[0]\n",
    "\n",
    "plt.gcf().subplots_adjust(left=margin, right=1.-margin)\n",
    "plt.gcf().set_size_inches(s, plt.gcf().get_size_inches()[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://en.m.wikipedia.org/wiki/2011%E2%80%9312_NBA_season\n",
    "# 2011-2012 --> season was shortened from 82 games to 66 games"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While exploring the data and creating graphs of the shot attempts over time, we found that the shot attempts tended to rise over time for three-pointers, while the number of shot attempts for two-pointers decreased over time. Between the years 2000 and 2010, the number of shot attempts for three-point shots increased steadily, but starting from 2012, the number of three-point attempts skyrocketed and increased heavily. For the number of two-point attempts, between the years 2000 and 2010 the number of attempts stayed relatively the same. However, starting in 2012, the number of two-point attempts began to decrease steadily as three-point attempts started to greatly rise. Furthermore, we found that in the year 2011, the number of two-point and three-point attempts decreased greatly, creating a dip in both graphs. After researching about the basketball season in 2011-2012, we found that the number of games decreased from 82 to 66 that year because of a new deal that was enacted.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis I"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Winning Team: FGA% by Distance\n",
    "\n",
    "While investigating the relation between the shooting average between each distance, we also wanted to analyze each winning team's field goal attempts sorted by distance. In order to further analyze this relation, we extracted data from all the team shooting csv files. Each team shooting csv file contains information about how well each team performed while shooting in that specific season. We first extracted the winning team for each specific season. We then extracted the field goal attempts (FGA) for different distances. These distance categories include 0 to 3 feet, 3 to 10 feet, 10 to 16 feet, 16 feet to the 3 pt line, and anything that would be categorized as a 3pt shot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2000-2001 Winning Team: Los Angeles Lakers\n",
    "winning_team_00_01 = df_00_01TS.loc[df_00_01TS['Unnamed: 1'] == 'Los Angeles Lakers*']\n",
    "lakers_00_01_FGADistance_0_3 = float(df_00_01TS['Unnamed: 7'][14])\n",
    "lakers_00_01_FGADistance_3_10 = float(df_00_01TS['Unnamed: 8'][14])\n",
    "lakers_00_01_FGADistance_10_16 = float(df_00_01TS['Unnamed: 9'][14])\n",
    "lakers_00_01_FGADistance_16_3pt = float(df_00_01TS['Unnamed: 16'][14])\n",
    "lakers_00_01_FGADistance_3pt = float(df_00_01TS['Unnamed: 17'][14])\n",
    "\n",
    "\n",
    "#2001-2002 Winning Team: Los Angeles Lakers\n",
    "winning_team_01_02 = df_01_02TS.loc[df_01_02TS['Unnamed: 1'] == 'Los Angeles Lakers*']\n",
    "lakers_01_02_FGADistance_0_3 = float(df_01_02TS['Unnamed: 7'][14])\n",
    "lakers_01_02_FGADistance_3_10 = float(df_01_02TS['Unnamed: 8'][14])\n",
    "lakers_01_02_FGADistance_10_16 = float(df_01_02TS['Unnamed: 9'][14])\n",
    "lakers_01_02_FGADistance_16_3pt = float(df_01_02TS['Unnamed: 16'][14])\n",
    "lakers_01_02_FGADistance_3pt = float(df_01_02TS['Unnamed: 17'][14])\n",
    "\n",
    "#2002-2003 Winning Team: Los Angeles Lakers\n",
    "winning_team_02_03 = df_02_03TS.loc[df_02_03TS['Unnamed: 1'] == 'Los Angeles Lakers*']\n",
    "lakers_02_03_FGADistance_0_3 = float(df_02_03TS['Unnamed: 7'][14])\n",
    "lakers_02_03_FGADistance_3_10 = float(df_02_03TS['Unnamed: 8'][14])\n",
    "lakers_02_03_FGADistance_10_16 = float(df_02_03TS['Unnamed: 9'][14])\n",
    "lakers_02_03_FGADistance_16_3pt = float(df_02_03TS['Unnamed: 16'][14])\n",
    "lakers_02_03_FGADistance_3pt = float(df_02_03TS['Unnamed: 17'][14])\n",
    "\n",
    "#2003-2004 Winning Team: San Antonio Spurs\n",
    "winning_team_03_04 = df_03_04TS.loc[df_03_04TS['Unnamed: 1'] == 'San Antonio Spurs*']\n",
    "spurs_03_04_FGADistance_0_3 = float(df_03_04TS['Unnamed: 7'][26])\n",
    "spurs_03_04_FGADistance_3_10 = float(df_03_04TS['Unnamed: 8'][26])\n",
    "spurs_03_04_FGADistance_10_16 = float(df_03_04TS['Unnamed: 9'][26])\n",
    "spurs_03_04_FGADistance_16_3pt = float(df_03_04TS['Unnamed: 16'][26])\n",
    "spurs_03_04_FGADistance_3pt = float(df_03_04TS['Unnamed: 17'][26])\n",
    "\n",
    "#2004-2005 Winning Team: Detroit Pistons\n",
    "winning_team_04_05 = df_04_05TS.loc[df_04_05TS['Unnamed: 1'] == 'Detroit Pistons*']\n",
    "pistons_04_05_FGADistance_0_3 = float(df_04_05TS['Unnamed: 7'][9])\n",
    "pistons_04_05_FGADistance_3_10 = float(df_04_05TS['Unnamed: 8'][9])\n",
    "pistons_04_05_FGADistance_10_16 = float(df_04_05TS['Unnamed: 9'][9])\n",
    "pistons_04_05_FGADistance_16_3pt = float(df_04_05TS['Unnamed: 16'][9])\n",
    "pistons_04_05_FGADistance_3pt = float(df_04_05TS['Unnamed: 17'][9])\n",
    "\n",
    "#2005-2006 Winning Team: San Antonio Spurs\n",
    "winning_team_05_06 = df_05_06TS.loc[df_05_06TS['Unnamed: 1'] == 'San Antonio Spurs*']\n",
    "spurs_05_06_FGADistance_0_3 = float(df_05_06TS['Unnamed: 7'][27])\n",
    "spurs_05_06_FGADistance_3_10 = float(df_05_06TS['Unnamed: 8'][27])\n",
    "spurs_05_06_FGADistance_10_16 = float(df_05_06TS['Unnamed: 9'][27])\n",
    "spurs_05_06_FGADistance_16_3pt = float(df_05_06TS['Unnamed: 16'][27])\n",
    "spurs_05_06_FGADistance_3pt = float(df_05_06TS['Unnamed: 17'][27])\n",
    "\n",
    "#2006-2007 Winning Team: Miami Heat\n",
    "winning_team_06_07 = df_06_07TS.loc[df_06_07TS['Unnamed: 1'] == 'Miami Heat*']\n",
    "heat_06_07_FGADistance_0_3 = float(df_06_07TS['Unnamed: 7'][16])\n",
    "heat_06_07_FGADistance_3_10 = float(df_06_07TS['Unnamed: 8'][16])\n",
    "heat_06_07_FGADistance_10_16 = float(df_06_07TS['Unnamed: 9'][16])\n",
    "heat_06_07_FGADistance_16_3pt = float(df_06_07TS['Unnamed: 16'][16])\n",
    "heat_06_07_FGADistance_3pt = float(df_06_07TS['Unnamed: 17'][16])\n",
    "\n",
    "#2007-2008 Winning Team: San Antonio Spurs\n",
    "winning_team_07_08 = df_07_08TS.loc[df_07_08TS['Unnamed: 1'] == 'San Antonio Spurs*']\n",
    "spurs_07_08_FGADistance_0_3 = float(df_07_08TS['Unnamed: 7'][27])\n",
    "spurs_07_08_FGADistance_3_10 = float(df_07_08TS['Unnamed: 8'][27])\n",
    "spurs_07_08_FGADistance_10_16 = float(df_07_08TS['Unnamed: 9'][27])\n",
    "spurs_07_08_FGADistance_16_3pt = float(df_07_08TS['Unnamed: 16'][27])\n",
    "spurs_07_08_FGADistance_3pt = float(df_07_08TS['Unnamed: 17'][27])\n",
    "\n",
    "#2008-2009 Winning Team: Boston Celtics\n",
    "winning_team_08_09 = df_08_09TS.loc[df_08_09TS['Unnamed: 1'] == 'Boston Celtics*']\n",
    "celtics_08_09_FGADistance_0_3 = float(df_08_09TS['Unnamed: 7'][3])\n",
    "celtics_08_09_FGADistance_3_10 = float(df_08_09TS['Unnamed: 8'][3])\n",
    "celtics_08_09_FGADistance_10_16 = float(df_08_09TS['Unnamed: 9'][3])\n",
    "celtics_08_09_FGADistance_16_3pt = float(df_08_09TS['Unnamed: 16'][3])\n",
    "celtics_08_09_FGADistance_3pt = float(df_08_09TS['Unnamed: 17'][3])\n",
    "\n",
    "#2009-2010 Winning Team: Los Angeles Lakers\n",
    "winning_team_09_10 = df_09_10TS.loc[df_09_10TS['Unnamed: 1'] == 'Los Angeles Lakers*']\n",
    "lakers_09_10_FGADistance_0_3 = float(df_09_10TS['Unnamed: 7'][14])\n",
    "lakers_09_10_FGADistance_3_10 = float(df_09_10TS['Unnamed: 8'][14])\n",
    "lakers_09_10_FGADistance_10_16 = float(df_09_10TS['Unnamed: 9'][14])\n",
    "lakers_09_10_FGADistance_16_3pt = float(df_09_10TS['Unnamed: 16'][14])\n",
    "lakers_09_10_FGADistance_3pt = float(df_09_10TS['Unnamed: 17'][14])\n",
    "\n",
    "#2010-2011 Winning Team: Los Angeles Lakers\n",
    "winning_team_10_11 = df_10_11TS.loc[df_10_11TS['Unnamed: 1'] == 'Los Angeles Lakers*']\n",
    "lakers_10_11_FGADistance_0_3 = float(df_10_11TS['Unnamed: 7'][14])\n",
    "lakers_10_11_FGADistance_3_10 = float(df_10_11TS['Unnamed: 8'][14])\n",
    "lakers_10_11_FGADistance_10_16 = float(df_10_11TS['Unnamed: 9'][14])\n",
    "lakers_10_11_FGADistance_16_3pt = float(df_10_11TS['Unnamed: 16'][14])\n",
    "lakers_10_11_FGADistance_3pt = float(df_10_11TS['Unnamed: 17'][14])\n",
    "\n",
    "#2011-2012 Winning Team: Dallas Mavericks\n",
    "winning_team_11_12 = df_11_12TS.loc[df_11_12TS['Unnamed: 1'] == 'Dallas Mavericks*']\n",
    "mavericks_11_12_FGADistance_0_3 = float(df_11_12TS['Unnamed: 7'][7])\n",
    "mavericks_11_12_FGADistance_3_10 = float(df_11_12TS['Unnamed: 8'][7])\n",
    "mavericks_11_12_FGADistance_10_16 = float(df_11_12TS['Unnamed: 9'][7])\n",
    "mavericks_11_12_FGADistance_16_3pt = float(df_11_12TS['Unnamed: 16'][7])\n",
    "mavericks_11_12_FGADistance_3pt = float(df_11_12TS['Unnamed: 17'][7])\n",
    "\n",
    "#2012-2013 Winning Team: Miami Heat\n",
    "winning_team_12_13 = df_12_13TS.loc[df_12_13TS['Unnamed: 1'] == 'Miami Heat*']\n",
    "heat_12_13_FGADistance_0_3 = float(df_12_13TS['Unnamed: 7'][17])\n",
    "heat_12_13_FGADistance_3_10 = float(df_12_13TS['Unnamed: 8'][17])\n",
    "heat_12_13_FGADistance_10_16 = float(df_12_13TS['Unnamed: 9'][17])\n",
    "heat_12_13_FGADistance_16_3pt = float(df_12_13TS['Unnamed: 16'][17])\n",
    "heat_12_13_FGADistance_3pt = float(df_12_13TS['Unnamed: 17'][17])\n",
    "\n",
    "#2013-2014 Winning Team: Miami Heat\n",
    "winning_team_13_14 = df_13_14TS.loc[df_13_14TS['Unnamed: 1'] == 'Miami Heat*']\n",
    "heat_13_14_FGADistance_0_3 = float(df_13_14TS['Unnamed: 7'][17])\n",
    "heat_13_14_FGADistance_3_10 = float(df_13_14TS['Unnamed: 8'][17])\n",
    "heat_13_14_FGADistance_10_16 = float(df_13_14TS['Unnamed: 9'][17])\n",
    "heat_13_14_FGADistance_16_3pt = float(df_13_14TS['Unnamed: 16'][17])\n",
    "heat_13_14_FGADistance_3pt = float(df_13_14TS['Unnamed: 17'][17])\n",
    "\n",
    "#2014-2015 Winning Team: San Antonio Spurs\n",
    "winning_team_14_15 = df_14_15TS.loc[df_14_15TS['Unnamed: 1'] == 'San Antonio Spurs*']\n",
    "spurs_14_15_FGADistance_0_3 = float(df_14_15TS['Unnamed: 7'][28])\n",
    "spurs_14_15_FGADistance_3_10 = float(df_14_15TS['Unnamed: 8'][28])\n",
    "spurs_14_15_FGADistance_10_16 = float(df_14_15TS['Unnamed: 9'][28])\n",
    "spurs_14_15_FGADistance_16_3pt = float(df_14_15TS['Unnamed: 16'][28])\n",
    "spurs_14_15_FGADistance_3pt = float(df_14_15TS['Unnamed: 17'][28])\n",
    "\n",
    "#2015-2016 Winning Team: Golder State Warriors\n",
    "winning_team_15_16 = df_15_16TS.loc[df_15_16TS['Unnamed: 1'] == 'Golden State Warriors*']\n",
    "warriors_15_16_FGADistance_0_3 = float(df_15_16TS['Unnamed: 7'][11])\n",
    "warriors_15_16_FGADistance_3_10 = float(df_15_16TS['Unnamed: 8'][11])\n",
    "warriors_15_16_FGADistance_10_16 = float(df_15_16TS['Unnamed: 9'][11])\n",
    "warriors_15_16_FGADistance_16_3pt = float(df_15_16TS['Unnamed: 16'][11])\n",
    "warriors_15_16_FGADistance_3pt = float(df_15_16TS['Unnamed: 17'][11])\n",
    "\n",
    "#2016-2017 Winning Team: Cleveland Cavaliers\n",
    "winning_team_16_17 = df_16_17TS.loc[df_16_17TS['Unnamed: 1'] == 'Cleveland Cavaliers*']\n",
    "cavaliers_16_17_FGADistance_0_3 = float(df_16_17TS['Unnamed: 7'][7])\n",
    "cavaliers_16_17_FGADistance_3_10 = float(df_16_17TS['Unnamed: 8'][7])\n",
    "cavaliers_16_17_FGADistance_10_16 = float(df_16_17TS['Unnamed: 9'][7])\n",
    "cavaliers_16_17_FGADistance_16_3pt = float(df_16_17TS['Unnamed: 16'][7])\n",
    "cavaliers_16_17_FGADistance_3pt = float(df_16_17TS['Unnamed: 17'][7])\n",
    "\n",
    "#2017-2018 Winning Team: Golder State Warriors\n",
    "winning_team_17_18 = df_17_18TS.loc[df_17_18TS['Unnamed: 1'] == 'Golden State Warriors*']\n",
    "warriors_17_18_FGADistance_0_3 = float(df_17_18TS['Unnamed: 7'][11])\n",
    "warriors_17_18_FGADistance_3_10 = float(df_17_18TS['Unnamed: 8'][11])\n",
    "warriors_17_18_FGADistance_10_16 = float(df_17_18TS['Unnamed: 9'][11])\n",
    "warriors_17_18_FGADistance_16_3pt = float(df_17_18TS['Unnamed: 16'][11])\n",
    "warriors_17_18_FGADistance_3pt = float(df_17_18TS['Unnamed: 17'][11])\n",
    "\n",
    "#2018-2019 Winning Team: Golder State Warriors\n",
    "winning_team_18_19 = df_18_19TS.loc[df_18_19TS['Unnamed: 1'] == 'Golden State Warriors*']\n",
    "warriors_18_19_FGADistance_0_3 = float(df_18_19TS['Unnamed: 7'][11])\n",
    "warriors_18_19_FGADistance_3_10 = float(df_18_19TS['Unnamed: 8'][11])\n",
    "warriors_18_19_FGADistance_10_16 = float(df_18_19TS['Unnamed: 9'][11])\n",
    "warriors_18_19_FGADistance_16_3pt = float(df_18_19TS['Unnamed: 16'][11])\n",
    "warriors_18_19_FGADistance_3pt = float(df_18_19TS['Unnamed: 17'][11])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After extracting the data from each of the individual csv files for each season, we then organized the data into a new dataframe that holds all the field goal attempts (FGA) sorted by distance for each winning team."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "winning_FG_Distance = {'Season':['2000-2001','2001-2002','2002-2003','2003-2004','2004-2005','2005-2006','2006-2007','2007-2008','2008-2009','2009-2010','2010-2011','2011-2012','2012-2013','2013-2014','2014-2015','2015-2016','2016-2017','2017-2018','2018-2019'],\n",
    "                       'Winning Team':['Los Angeles Lakers','Los Angeles Lakers','Los Angeles Lakers','San Antonio Spurs','Detroit Pistons','San Antonio Spurs','Miami Heat','San Antonio Spurs','Boston Celtics','Los Angeles Lakers','Los Angeles Lakers','Dallas Mavericks','Miami Heat','Miami Heat','San Antonio Spurs','Golden State Warriors','Cleveland Cavaliers','Golden State Warriors','Golden State Warriors'],\n",
    "                       'FG% Distance 0-3':[lakers_00_01_FGADistance_0_3,lakers_01_02_FGADistance_0_3,lakers_02_03_FGADistance_0_3,spurs_03_04_FGADistance_0_3,pistons_04_05_FGADistance_0_3,spurs_05_06_FGADistance_0_3,heat_06_07_FGADistance_0_3,spurs_07_08_FGADistance_0_3,celtics_08_09_FGADistance_0_3,lakers_09_10_FGADistance_0_3,lakers_10_11_FGADistance_0_3,mavericks_11_12_FGADistance_0_3,heat_12_13_FGADistance_0_3,heat_13_14_FGADistance_0_3,spurs_14_15_FGADistance_0_3,warriors_15_16_FGADistance_0_3,cavaliers_16_17_FGADistance_0_3,warriors_17_18_FGADistance_0_3,warriors_18_19_FGADistance_0_3],\n",
    "                       'FG% Distance 3-10':[lakers_00_01_FGADistance_3_10,lakers_01_02_FGADistance_3_10,lakers_02_03_FGADistance_3_10,spurs_03_04_FGADistance_3_10,pistons_04_05_FGADistance_3_10,spurs_05_06_FGADistance_3_10,heat_06_07_FGADistance_3_10,spurs_07_08_FGADistance_3_10,celtics_08_09_FGADistance_3_10,lakers_09_10_FGADistance_3_10,lakers_10_11_FGADistance_3_10,mavericks_11_12_FGADistance_3_10,heat_12_13_FGADistance_3_10,heat_13_14_FGADistance_3_10,spurs_14_15_FGADistance_3_10,warriors_15_16_FGADistance_3_10,cavaliers_16_17_FGADistance_3_10,warriors_17_18_FGADistance_3_10,warriors_18_19_FGADistance_3_10],\n",
    "                       'FG% Distance 10-16':[lakers_00_01_FGADistance_10_16,lakers_01_02_FGADistance_10_16,lakers_02_03_FGADistance_10_16,spurs_03_04_FGADistance_10_16,pistons_04_05_FGADistance_10_16,spurs_05_06_FGADistance_10_16,heat_06_07_FGADistance_10_16,spurs_07_08_FGADistance_10_16,celtics_08_09_FGADistance_10_16,lakers_09_10_FGADistance_10_16,lakers_10_11_FGADistance_10_16,mavericks_11_12_FGADistance_10_16,heat_12_13_FGADistance_10_16,heat_13_14_FGADistance_10_16,spurs_14_15_FGADistance_10_16,warriors_15_16_FGADistance_10_16,cavaliers_16_17_FGADistance_10_16,warriors_17_18_FGADistance_10_16,warriors_18_19_FGADistance_10_16],\n",
    "                       'FG% Distance 16-3pt':[lakers_00_01_FGADistance_16_3pt,lakers_01_02_FGADistance_16_3pt,lakers_02_03_FGADistance_16_3pt,spurs_03_04_FGADistance_16_3pt,pistons_04_05_FGADistance_16_3pt,spurs_05_06_FGADistance_16_3pt,heat_06_07_FGADistance_16_3pt,spurs_07_08_FGADistance_16_3pt,celtics_08_09_FGADistance_16_3pt,lakers_09_10_FGADistance_16_3pt,lakers_10_11_FGADistance_16_3pt,mavericks_11_12_FGADistance_16_3pt,heat_12_13_FGADistance_16_3pt,heat_13_14_FGADistance_16_3pt,spurs_14_15_FGADistance_16_3pt,warriors_15_16_FGADistance_16_3pt,cavaliers_16_17_FGADistance_16_3pt,warriors_17_18_FGADistance_16_3pt,warriors_18_19_FGADistance_16_3pt],\n",
    "                       'FG% Distance 3pt':[lakers_00_01_FGADistance_3pt,lakers_01_02_FGADistance_3pt,lakers_02_03_FGADistance_3pt,spurs_03_04_FGADistance_3pt,pistons_04_05_FGADistance_3pt,spurs_05_06_FGADistance_3pt,heat_06_07_FGADistance_3pt,spurs_07_08_FGADistance_3pt,celtics_08_09_FGADistance_3pt,lakers_09_10_FGADistance_3pt,lakers_10_11_FGADistance_3pt,mavericks_11_12_FGADistance_3pt,heat_12_13_FGADistance_3pt,heat_13_14_FGADistance_3pt,spurs_14_15_FGADistance_3pt,warriors_15_16_FGADistance_3pt,cavaliers_16_17_FGADistance_3pt,warriors_17_18_FGADistance_3pt,warriors_18_19_FGADistance_3pt]}\n",
    "winning_avg_FG = pd.DataFrame(winning_FG_Distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Season</th>\n",
       "      <th>Winning Team</th>\n",
       "      <th>FG% Distance 0-3</th>\n",
       "      <th>FG% Distance 3-10</th>\n",
       "      <th>FG% Distance 10-16</th>\n",
       "      <th>FG% Distance 16-3pt</th>\n",
       "      <th>FG% Distance 3pt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000-2001</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001-2002</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002-2003</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016-2017</td>\n",
       "      <td>Cleveland Cavaliers</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017-2018</td>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018-2019</td>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Season           Winning Team  FG% Distance 0-3  FG% Distance 3-10  \\\n",
       "0   2000-2001     Los Angeles Lakers              0.28               0.19   \n",
       "1   2001-2002     Los Angeles Lakers              0.28               0.17   \n",
       "2   2002-2003     Los Angeles Lakers              0.29               0.17   \n",
       "..        ...                    ...               ...                ...   \n",
       "16  2016-2017    Cleveland Cavaliers              0.28               0.11   \n",
       "17  2017-2018  Golden State Warriors              0.27               0.11   \n",
       "18  2018-2019  Golden State Warriors              0.25               0.11   \n",
       "\n",
       "    FG% Distance 10-16  FG% Distance 16-3pt  FG% Distance 3pt  \n",
       "0                 0.14                 0.41              0.34  \n",
       "1                 0.14                 0.38              0.35  \n",
       "2                 0.13                 0.40              0.36  \n",
       "..                 ...                  ...               ...  \n",
       "16                0.09                 0.42              0.38  \n",
       "17                0.14                 0.46              0.39  \n",
       "18                0.12                 0.46              0.39  \n",
       "\n",
       "[19 rows x 7 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "winning_avg_FG"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After creating the data frame, we then created a time series plot that represents how the field goal attempt percentage fluctuated from 2000 to 2018. On the x-axis, there exists a label that shows each NBA season from 2000 to 2018. The y-axis measures the field goal attempt percentage.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Fixes the x axis label of the plot\n",
    "winning_x_axis = ['2000','2001','2002','2003','2004','2005','2006','2007','2008','2009','2010','2011','2012','2013','2014','2015','2016','2017','2018']\n",
    "\n",
    "#Creates a plot showing the field goal attempt averages in each categorized distance\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(winning_x_axis,winning_avg_FG['FG% Distance 0-3'],label='FG% 0-3 ft')\n",
    "plt.plot(winning_avg_FG['FG% Distance 3-10'],label='FG% 3-10 ft')\n",
    "plt.plot(winning_avg_FG['FG% Distance 10-16'], label='FG% 10-16 ft')\n",
    "plt.plot(winning_avg_FG['FG% Distance 16-3pt'], label='FG% 16 ft-3pt')\n",
    "plt.plot(winning_avg_FG['FG% Distance 3pt'], label='FG% 3pt')\n",
    "plt.title('Winning Team FGA% Average by Distance')\n",
    "plt.xlabel('Seasons')\n",
    "plt.ylabel('FG%')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By creating the plot, we are able to visualize and identify a gradual increase of field goal percentage for both shots taken within the distances of: 16 feet to the 3pt line and 3pt shots. We also find a noticeable decrease of field goal attempts for shots taken within the range of 3-10 feet. This visualization depicts the possibility that over the last 20 decades, NBA winning teams may have shifted their field goal attempts to further distances and decreased their likelihood of shooting at shorter distances."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Winning Team: Average Distance\n",
    "\n",
    "In order to determine the distinct differences in terms of distances for every shot made, we have to look at the average distance for every team. However, we are only going to be specifically looking at each winning team's average distance per shot made. By analyzing this data, we are able to determine whether the average distance increases over time. To obtain this data, we extracted the average distance for each winning team located in the team shooting csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculates the average distance for every winnning team from NBA Seasons: 2000 to 2018\n",
    "lakers_00_01_avg_distance = float(df_00_01TS['Unnamed: 5'][14])\n",
    "lakers_01_02_avg_distance = float(df_01_02TS['Unnamed: 5'][14])\n",
    "lakers_02_03_avg_distance = float(df_02_03TS['Unnamed: 5'][14])\n",
    "spurs_03_04_avg_distance = float(df_03_04TS['Unnamed: 5'][26])\n",
    "pistons_04_05_avg_distance = float(df_04_05TS['Unnamed: 5'][9])\n",
    "spurs_05_06_avg_distance = float(df_05_06TS['Unnamed: 5'][27])\n",
    "heat_06_07_avg_distance = float(df_06_07TS['Unnamed: 5'][16])\n",
    "spurs_07_08_avg_distance = float(df_07_08TS['Unnamed: 5'][27])\n",
    "celtics_08_09_avg_distance = float(df_08_09TS['Unnamed: 5'][3])\n",
    "lakers_09_10_avg_distance = float(df_09_10TS['Unnamed: 5'][14])\n",
    "lakers_10_11_avg_distance = float(df_10_11TS['Unnamed: 5'][14])\n",
    "mavericks_11_12_avg_distance = float(df_11_12TS['Unnamed: 5'][7])\n",
    "heat_12_13_avg_distance = float(df_12_13TS['Unnamed: 5'][17])\n",
    "heat_13_14_avg_distance = float(df_13_14TS['Unnamed: 5'][17])\n",
    "spurs_14_15_avg_distance = float(df_14_15TS['Unnamed: 5'][28])\n",
    "warriors_15_16_avg_distance = float(df_15_16TS['Unnamed: 5'][11])\n",
    "cavaliers_16_17_avg_distance = float(df_16_17TS['Unnamed: 5'][7])\n",
    "warriors_17_18_avg_distance = float(df_17_18TS['Unnamed: 5'][11])\n",
    "warriors_18_19_avg_distance = float(df_18_19TS['Unnamed: 5'][11])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After gathering the average distance for every winning team, we organized the data into a new dataframe. This dataframe contains information about each winning team for every NBA season and contains the average distance for every winning team."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "winning_avg_distance = {'Season':['2000-2001','2001-2002','2002-2003','2003-2004','2004-2005','2005-2006','2006-2007','2007-2008','2008-2009','2009-2010','2010-2011','2011-2012','2012-2013','2013-2014','2014-2015','2015-2016','2016-2017','2017-2018','2018-2019'],\n",
    "                       'Winning Team':['Los Angeles Lakers','Los Angeles Lakers','Los Angeles Lakers','San Antonio Spurs','Detroit Pistons','San Antonio Spurs','Miami Heat','San Antonio Spurs','Boston Celtics','Los Angeles Lakers','Los Angeles Lakers','Dallas Mavericks','Miami Heat','Miami Heat','San Antonio Spurs','Golden State Warriors','Cleveland Cavaliers','Golden State Warriors','Golden State Warriors'],\n",
    "                       'Average Distance':[lakers_00_01_avg_distance, lakers_01_02_avg_distance, lakers_02_03_avg_distance, spurs_03_04_avg_distance,pistons_04_05_avg_distance, spurs_05_06_avg_distance, heat_06_07_avg_distance, spurs_07_08_avg_distance, celtics_08_09_avg_distance, lakers_09_10_avg_distance, lakers_10_11_avg_distance, mavericks_11_12_avg_distance, heat_12_13_avg_distance , heat_13_14_avg_distance, spurs_14_15_avg_distance , warriors_15_16_avg_distance, cavaliers_16_17_avg_distance, warriors_17_18_avg_distance, warriors_18_19_avg_distance ]}\n",
    "winning_avg_distance = pd.DataFrame(winning_avg_distance) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>Season</th>\n",
       "      <th>Winning Team</th>\n",
       "      <th>Average Distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000-2001</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001-2002</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002-2003</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016-2017</td>\n",
       "      <td>Cleveland Cavaliers</td>\n",
       "      <td>14.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017-2018</td>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018-2019</td>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>15.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Season           Winning Team  Average Distance\n",
       "0   2000-2001     Los Angeles Lakers              11.9\n",
       "1   2001-2002     Los Angeles Lakers              12.3\n",
       "2   2002-2003     Los Angeles Lakers              11.9\n",
       "..        ...                    ...               ...\n",
       "16  2016-2017    Cleveland Cavaliers              14.4\n",
       "17  2017-2018  Golden State Warriors              14.0\n",
       "18  2018-2019  Golden State Warriors              15.2\n",
       "\n",
       "[19 rows x 3 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "winning_avg_distance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By organizing the data, we are able to efficiently create a line plot that depicts the possible trends of how the winning team's average distance of a shot has changed over the last two decades. In the plot's x-axis, we have each season and on the y axis we have the average distance in feet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(winning_x_axis,winning_avg_distance['Average Distance'],label='Distance (ft)')\n",
    "plt.title('Winning Team: Average Distance')\n",
    "plt.xlabel('Seasons')\n",
    "plt.ylabel('Average Distance (m)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the line plot we can gather that there is a cyclic behavior. Roughly every 3-5 years the winning team's average distance peaks, then decreases, and then creates a higher peak. Comparing both the 2000 season and 2018 season, you could tell that there is a significant increase in the winning team's average distance. This difference is roughly 3 feet. After analyzing this visualization, the evidence proposes that the winning team's average distance has significantly increased over the last 2 decades."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis II"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then need to read in the datasets and clean the data.\n",
    "We can remove all of the columns we do not need so we are left with a dataframe that only consists of the team and the statistics of their 2P shots made, midrange shots made, and 3P shots made.\n",
    "Then, we need to rename the columns so they are readable and reset the indices in the dataframe to finish cleaning the data. The following cells read in the datasets and clean the data for the years 2000 to 2018."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2000 = pd.read_csv('00-01_team_shooting.csv')\n",
    "df_2000 = df_2000[['Unnamed: 1', 'Unnamed: 12', 'Unnamed: 15', 'Unnamed: 17']]\n",
    "df_2000.columns = ['Team', '% 2P Shots Made', '% Midrange Shots(10-16ft) Made', '% 3P Shots Made']\n",
    "df_2000 = df_2000.drop([0, 1])\n",
    "df_2000 = df_2000.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2001 = pd.read_csv('01-02_team_shooting.csv')\n",
    "df_2001 = df_2001[['Unnamed: 1', 'Unnamed: 12', 'Unnamed: 15', 'Unnamed: 17']]\n",
    "df_2001.columns = ['Team', '% 2P Shots Made', '% Midrange Shots(10-16ft) Made', '% 3P Shots Made']\n",
    "df_2001 = df_2001.drop([0, 1])\n",
    "df_2001 = df_2001.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2002 = pd.read_csv('02-03_team_shooting.csv')\n",
    "df_2002 = df_2002[['Unnamed: 1', 'Unnamed: 12', 'Unnamed: 15', 'Unnamed: 17']]\n",
    "df_2002.columns = ['Team', '% 2P Shots Made', '% Midrange Shots(10-16ft) Made', '% 3P Shots Made']\n",
    "df_2002 = df_2002.drop([0, 1])\n",
    "df_2002 = df_2002.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2003 = pd.read_csv('03-04_team_shooting.csv')\n",
    "df_2003 = df_2003[['Unnamed: 1', 'Unnamed: 12', 'Unnamed: 15', 'Unnamed: 17']]\n",
    "df_2003.columns = ['Team', '% 2P Shots Made', '% Midrange Shots(10-16ft) Made', '% 3P Shots Made']\n",
    "df_2003 = df_2003.drop([0, 1])\n",
    "df_2003 = df_2003.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2004 = pd.read_csv('04-05_team_shooting.csv')\n",
    "df_2004 = df_2004[['Unnamed: 1', 'Unnamed: 12', 'Unnamed: 15', 'Unnamed: 17']]\n",
    "df_2004.columns = ['Team', '% 2P Shots Made', '% Midrange Shots(10-16ft) Made', '% 3P Shots Made']\n",
    "df_2004 = df_2004.drop([0, 1])\n",
    "df_2004 = df_2004.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2005 = pd.read_csv('05-06_team_shooting.csv')\n",
    "df_2005 = df_2005[['Unnamed: 1', 'Unnamed: 12', 'Unnamed: 15', 'Unnamed: 17']]\n",
    "df_2005.columns = ['Team', '% 2P Shots Made', '% Midrange Shots(10-16ft) Made', '% 3P Shots Made']\n",
    "df_2005 = df_2005.drop([0, 1])\n",
    "df_2005 = df_2005.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2006 = pd.read_csv('06-07_team_shooting.csv')\n",
    "df_2006 = df_2006[['Unnamed: 1', 'Unnamed: 12', 'Unnamed: 15', 'Unnamed: 17']]\n",
    "df_2006.columns = ['Team', '% 2P Shots Made', '% Midrange Shots(10-16ft) Made', '% 3P Shots Made']\n",
    "df_2006 = df_2006.drop([0, 1])\n",
    "df_2006 = df_2006.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2007 = pd.read_csv('07-08_team_shooting.csv')\n",
    "df_2007 = df_2007[['Unnamed: 1', 'Unnamed: 12', 'Unnamed: 15', 'Unnamed: 17']]\n",
    "df_2007.columns = ['Team', '% 2P Shots Made', '% Midrange Shots(10-16ft) Made', '% 3P Shots Made']\n",
    "df_2007 = df_2007.drop([0, 1])\n",
    "df_2007 = df_2007.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2008 = pd.read_csv('08-09_team_shooting.csv')\n",
    "df_2008 = df_2008[['Unnamed: 1', 'Unnamed: 12', 'Unnamed: 15', 'Unnamed: 17']]\n",
    "df_2008.columns = ['Team', '% 2P Shots Made', '% Midrange Shots(10-16ft) Made', '% 3P Shots Made']\n",
    "df_2008 = df_2008.drop([0, 1])\n",
    "df_2008 = df_2008.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2009 = pd.read_csv('09-10_team_shooting.csv')\n",
    "df_2009 = df_2009[['Unnamed: 1', 'Unnamed: 12', 'Unnamed: 15', 'Unnamed: 17']]\n",
    "df_2009.columns = ['Team', '% 2P Shots Made', '% Midrange Shots(10-16ft) Made', '% 3P Shots Made']\n",
    "df_2009 = df_2009.drop([0, 1])\n",
    "df_2009 = df_2009.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2010 = pd.read_csv('10-11_team_shooting.csv')\n",
    "df_2010 = df_2010[['Unnamed: 1', 'Unnamed: 12', 'Unnamed: 15', 'Unnamed: 17']]\n",
    "df_2010.columns = ['Team', '% 2P Shots Made', '% Midrange Shots(10-16ft) Made', '% 3P Shots Made']\n",
    "df_2010 = df_2010.drop([0, 1])\n",
    "df_2010 = df_2010.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2011 = pd.read_csv('11-12_team_shooting.csv')\n",
    "df_2011 = df_2011[['Unnamed: 1', 'Unnamed: 12', 'Unnamed: 15', 'Unnamed: 17']]\n",
    "df_2011.columns = ['Team', '% 2P Shots Made', '% Midrange Shots(10-16ft) Made', '% 3P Shots Made']\n",
    "df_2011 = df_2011.drop([0, 1])\n",
    "df_2011 = df_2011.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2012 = pd.read_csv('12-13_team_shooting.csv')\n",
    "df_2012 = df_2012[['Unnamed: 1', 'Unnamed: 12', 'Unnamed: 15', 'Unnamed: 17']]\n",
    "df_2012.columns = ['Team', '% 2P Shots Made', '% Midrange Shots(10-16ft) Made', '% 3P Shots Made']\n",
    "df_2012 = df_2012.drop([0, 1])\n",
    "df_2012 = df_2012.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2013 = pd.read_csv('13-14_team_shooting.csv')\n",
    "df_2013 = df_2013[['Unnamed: 1', 'Unnamed: 12', 'Unnamed: 15', 'Unnamed: 17']]\n",
    "df_2013.columns = ['Team', '% 2P Shots Made', '% Midrange Shots(10-16ft) Made', '% 3P Shots Made']\n",
    "df_2013 = df_2013.drop([0, 1])\n",
    "df_2013 = df_2013.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2014 = pd.read_csv('14-15_team_shooting.csv')\n",
    "df_2014 = df_2014[['Unnamed: 1', 'Unnamed: 12', 'Unnamed: 15', 'Unnamed: 17']]\n",
    "df_2014.columns = ['Team', '% 2P Shots Made', '% Midrange Shots(10-16ft) Made', '% 3P Shots Made']\n",
    "df_2014 = df_2014.drop([0, 1])\n",
    "df_2014 = df_2014.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2015 = pd.read_csv('15-16_team_shooting.csv')\n",
    "df_2015 = df_2015[['Unnamed: 1', 'Unnamed: 12', 'Unnamed: 15', 'Unnamed: 17']]\n",
    "df_2015.columns = ['Team', '% 2P Shots Made', '% Midrange Shots(10-16ft) Made', '% 3P Shots Made']\n",
    "df_2015 = df_2015.drop([0, 1])\n",
    "df_2015 = df_2015.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2016 = pd.read_csv('16-17_team_shooting.csv')\n",
    "df_2016 = df_2016[['Unnamed: 1', 'Unnamed: 12', 'Unnamed: 15', 'Unnamed: 17']]\n",
    "df_2016.columns = ['Team', '% 2P Shots Made', '% Midrange Shots(10-16ft) Made', '% 3P Shots Made']\n",
    "df_2016 = df_2016.drop([0, 1])\n",
    "df_2016 = df_2016.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2017 = pd.read_csv('17-18_team_shooting.csv')\n",
    "df_2017 = df_2017[['Unnamed: 1', 'Unnamed: 12', 'Unnamed: 15', 'Unnamed: 17']]\n",
    "df_2017.columns = ['Team', '% 2P Shots Made', '% Midrange Shots(10-16ft) Made', '% 3P Shots Made']\n",
    "df_2017 = df_2017.drop([0, 1])\n",
    "df_2017 = df_2017.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2018 = pd.read_csv('18-19_team_shooting.csv')\n",
    "df_2018 = df_2018[['Unnamed: 1', 'Unnamed: 12', 'Unnamed: 15', 'Unnamed: 17']]\n",
    "df_2018.columns = ['Team', '% 2P Shots Made', '% Midrange Shots(10-16ft) Made', '% 3P Shots Made']\n",
    "df_2018 = df_2018.drop([0, 1])\n",
    "df_2018 = df_2018.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An example of the dataframes is as follows (taken from the 2018 data):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>Team</th>\n",
       "      <th>% 2P Shots Made</th>\n",
       "      <th>% Midrange Shots(10-16ft) Made</th>\n",
       "      <th>% 3P Shots Made</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>.518</td>\n",
       "      <td>.374</td>\n",
       "      <td>.352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Boston Celtics*</td>\n",
       "      <td>.527</td>\n",
       "      <td>.448</td>\n",
       "      <td>.365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brooklyn Nets*</td>\n",
       "      <td>.513</td>\n",
       "      <td>.423</td>\n",
       "      <td>.353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Utah Jazz*</td>\n",
       "      <td>.541</td>\n",
       "      <td>.398</td>\n",
       "      <td>.356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Washington Wizards</td>\n",
       "      <td>.543</td>\n",
       "      <td>.386</td>\n",
       "      <td>.341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>League Average</td>\n",
       "      <td>.520</td>\n",
       "      <td>.413</td>\n",
       "      <td>.355</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>31 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Team % 2P Shots Made % Midrange Shots(10-16ft) Made  \\\n",
       "0        Atlanta Hawks            .518                           .374   \n",
       "1      Boston Celtics*            .527                           .448   \n",
       "2       Brooklyn Nets*            .513                           .423   \n",
       "..                 ...             ...                            ...   \n",
       "28          Utah Jazz*            .541                           .398   \n",
       "29  Washington Wizards            .543                           .386   \n",
       "30      League Average            .520                           .413   \n",
       "\n",
       "   % 3P Shots Made  \n",
       "0             .352  \n",
       "1             .365  \n",
       "2             .353  \n",
       "..             ...  \n",
       "28            .356  \n",
       "29            .341  \n",
       "30            .355  \n",
       "\n",
       "[31 rows x 4 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2018"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are ready to plot the data and run linear regressions to visually determine if there is a trend in the data over the past two decades. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5de359bd30>]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#add the years to the dataframe and set the shot values as numeric values\n",
    "percent2PData = {'year' : [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018],\n",
    "                 '% 2P Shots Made': [df_2000.iloc[-1]['% 2P Shots Made'], df_2001.iloc[-1]['% 2P Shots Made'], df_2002.iloc[-1]['% 2P Shots Made'], df_2003.iloc[-1]['% 2P Shots Made'], df_2004.iloc[-1]['% 2P Shots Made'], df_2005.iloc[-1]['% 2P Shots Made'], df_2006.iloc[-1]['% 2P Shots Made'], df_2007.iloc[-1]['% 2P Shots Made'], df_2008.iloc[-1]['% 2P Shots Made'], df_2009.iloc[-1]['% 2P Shots Made'], df_2010.iloc[-1]['% 2P Shots Made'], df_2011.iloc[-1]['% 2P Shots Made'], df_2012.iloc[-1]['% 2P Shots Made'], df_2013.iloc[-1]['% 2P Shots Made'], df_2014.iloc[-1]['% 2P Shots Made'], df_2015.iloc[-1]['% 2P Shots Made'], df_2016.iloc[-1]['% 2P Shots Made'], df_2017.iloc[-1]['% 2P Shots Made'], df_2018.iloc[-1]['% 2P Shots Made']],}\n",
    "df_2PMade = pd.DataFrame(percent2PData)\n",
    "df_2PMade['% 2P Shots Made'] = pd.to_numeric(df_2PMade['% 2P Shots Made'])\n",
    "\n",
    "#plot points\n",
    "df_2PMade.plot.scatter(x='year', y='% 2P Shots Made',)\n",
    "\n",
    "#plot regression line\n",
    "a1, b1 = np.polyfit(df_2PMade['year'], df_2PMade['% 2P Shots Made'], 1)\n",
    "x1 = np.arange(2000, 2019, 1)\n",
    "y1 = a1 * x1 + b1\n",
    "plt.plot(x1, y1, color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There appears to be a strong trend of the data as linear regression shows a promising upward trend, but we will need to perform more tests to be sure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5de3595898>]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#add the years to the dataframe and set the shot values as numeric values\n",
    "percentMidrangeData = {'year' : [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018],\n",
    "                       '% Midrange Shots Made': [df_2000.iloc[-1]['% Midrange Shots(10-16ft) Made'], df_2001.iloc[-1]['% Midrange Shots(10-16ft) Made'], df_2002.iloc[-1]['% Midrange Shots(10-16ft) Made'], df_2003.iloc[-1]['% Midrange Shots(10-16ft) Made'], df_2004.iloc[-1]['% Midrange Shots(10-16ft) Made'], df_2005.iloc[-1]['% Midrange Shots(10-16ft) Made'], df_2006.iloc[-1]['% Midrange Shots(10-16ft) Made'], df_2007.iloc[-1]['% Midrange Shots(10-16ft) Made'], df_2008.iloc[-1]['% Midrange Shots(10-16ft) Made'], df_2009.iloc[-1]['% Midrange Shots(10-16ft) Made'], df_2010.iloc[-1]['% Midrange Shots(10-16ft) Made'], df_2011.iloc[-1]['% Midrange Shots(10-16ft) Made'], df_2012.iloc[-1]['% Midrange Shots(10-16ft) Made'], df_2013.iloc[-1]['% Midrange Shots(10-16ft) Made'], df_2014.iloc[-1]['% Midrange Shots(10-16ft) Made'], df_2015.iloc[-1]['% Midrange Shots(10-16ft) Made'], df_2016.iloc[-1]['% Midrange Shots(10-16ft) Made'], df_2017.iloc[-1]['% Midrange Shots(10-16ft) Made'], df_2018.iloc[-1]['% Midrange Shots(10-16ft) Made']]}\n",
    "df_MidrangeMade = pd.DataFrame(percentMidrangeData)\n",
    "df_MidrangeMade['% Midrange Shots Made'] = pd.to_numeric(df_MidrangeMade['% Midrange Shots Made'])\n",
    "\n",
    "#plot points\n",
    "df_MidrangeMade.plot.scatter(x='year', y='% Midrange Shots Made')\n",
    "\n",
    "#plot regression line\n",
    "a2, b2 = np.polyfit(df_MidrangeMade['year'], df_MidrangeMade['% Midrange Shots Made'], 1)\n",
    "x2 = np.arange(2000, 2019, 1)\n",
    "y2 = a2 * x2 + b2\n",
    "plt.plot(x2, y2, color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to the graph for 2P shots, there appears to be a strong trend of the data as linear regression shows a promising upward trend, but we will need to perform more tests to be sure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5de34c14e0>]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#add the years to the dataframe and set the shot values as numeric values\n",
    "percent3PData = {'year' : [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018],\n",
    "                 '% 3P Shots Made': [df_2000.iloc[-1]['% 3P Shots Made'], df_2001.iloc[-1]['% 3P Shots Made'], df_2002.iloc[-1]['% 3P Shots Made'], df_2003.iloc[-1]['% 3P Shots Made'], df_2004.iloc[-1]['% 3P Shots Made'], df_2005.iloc[-1]['% 3P Shots Made'], df_2006.iloc[-1]['% 3P Shots Made'], df_2007.iloc[-1]['% 3P Shots Made'], df_2008.iloc[-1]['% 3P Shots Made'], df_2009.iloc[-1]['% 3P Shots Made'], df_2010.iloc[-1]['% 3P Shots Made'], df_2011.iloc[-1]['% 3P Shots Made'], df_2012.iloc[-1]['% 3P Shots Made'], df_2013.iloc[-1]['% 3P Shots Made'], df_2014.iloc[-1]['% 3P Shots Made'], df_2015.iloc[-1]['% 3P Shots Made'], df_2016.iloc[-1]['% 3P Shots Made'], df_2017.iloc[-1]['% 3P Shots Made'], df_2018.iloc[-1]['% 3P Shots Made']]}\n",
    "df_3PMade = pd.DataFrame(percent3PData)\n",
    "df_3PMade['% 3P Shots Made'] = pd.to_numeric(df_3PMade['% 3P Shots Made'])\n",
    "\n",
    "#plot points\n",
    "df_3PMade.plot.scatter(x='year', y='% 3P Shots Made')\n",
    "\n",
    "#plot regression line\n",
    "a3, b3 = np.polyfit(df_3PMade['year'], df_3PMade['% 3P Shots Made'], 1)\n",
    "x3 = np.arange(2000, 2019, 1)\n",
    "y3 = a3 * x3 + b3\n",
    "plt.plot(x3, y3, color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unlike the graphs for 2P shots and midrange shots, there does not appears to be a strong trend of the data. There does seem to be a slight favor for increasing percentage of 3P shots made, but we will need to perform more tests to be sure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f5de34f5da0>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot all three data sets in one graph\n",
    "plt.plot( df_2PMade['year'], df_2PMade['% 2P Shots Made'], marker = 'o', color = 'red', label = '2P')\n",
    "plt.plot( df_MidrangeMade['year'], df_MidrangeMade['% Midrange Shots Made'], marker='o', color = 'green', label = 'Midrange')\n",
    "plt.plot( df_3PMade['year'], df_3PMade['% 3P Shots Made'], marker='o', color = 'blue', label = '3P')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overlaying all of the data from the three different distances from the hoop more easily illustrates the changes within the distances and well as the relative change between the distances. It appears as if the 2P shots distances are experiencing the largest change, with an increase in shots made as time progresses. The midrange shot also seems to have an increase in popularity in term of shots made as it begins at 0.385% in 2000 and becomes 0.413% in 2018. However, the 3P shot experiences the least amount of change out of the three shot distances - it changes only from 0.354% made in 2000 to 0.355% made in 2018. \n",
    "\n",
    "We can run a ttest on the separate three separate data for 2P, midrange, and 3P shots to determine whether or not there is a significant difference between the average percent of shots made in the past versus the current time. We will split the data into two groups for all of the shot distances: one group ranging from 2000 to 2009 and another group ranging from 2010 to 2018."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-3.2673799697014316, pvalue=0.0045385633838504243)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#run a ttest on the first half versus second half of data\n",
    "ttest_ind(df_2PMade['% 2P Shots Made'][:10], df_2PMade['% 2P Shots Made'][10:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the pvalue of 0.00454 (0.00454 < 0.01) we can see that there is a significant difference in the data. The percentage of 2P shots made has increased since the past two decades."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-4.0312731576558738, pvalue=0.00086647498885918431)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#run a ttest on the first half versus second half of data\n",
    "ttest_ind(df_MidrangeMade['% Midrange Shots Made'][:10], df_MidrangeMade['% Midrange Shots Made'][10:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the pvalue of 0.000866 (0.000866 < 0.01) we can see that there is a significant difference in the data. The percentage of midrange shots made has increased since the past two decades."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-0.046343414364713287, pvalue=0.9635765983017327)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#run a ttest on the first half versus second half of data\n",
    "ttest_ind(df_3PMade['% 3P Shots Made'][:10], df_3PMade['% 3P Shots Made'][10:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the pvalue of 0.964 (0.964 > 0.01) we can see that there is NOT a significant difference in the data. The percentage of 3P shots made has stayed fairly consistent the past two decades. However, this does not indicate that the number of 3P shots ATTEMPTED has stayed consistent over the years and this ran analysis for the number of 3P shots attempted."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis III"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this final analysis, we will focus on the relationships between both the percentages and the raw number of three-pointers when compared to the amount of three-pointers attempted, made, and all shots attempted.\n",
    "\n",
    "Before any analysis can be done, we need to set some parameters in order for the following cells to display correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configure libraries\n",
    "# The seaborn library makes plots look nicer\n",
    "sns.set()\n",
    "sns.set_context('talk')\n",
    "\n",
    "# Round decimals when displaying DataFrames\n",
    "pd.set_option('precision', 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we can preview what the data looks like, in order to get a good idea at what columns we want to focus in on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>Rk</th>\n",
       "      <th>Team</th>\n",
       "      <th>G</th>\n",
       "      <th>MP</th>\n",
       "      <th>...</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Sacramento Kings*</td>\n",
       "      <td>82</td>\n",
       "      <td>20080</td>\n",
       "      <td>...</td>\n",
       "      <td>432</td>\n",
       "      <td>1221</td>\n",
       "      <td>1596</td>\n",
       "      <td>8343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Milwaukee Bucks*</td>\n",
       "      <td>82</td>\n",
       "      <td>19780</td>\n",
       "      <td>...</td>\n",
       "      <td>386</td>\n",
       "      <td>1123</td>\n",
       "      <td>1928</td>\n",
       "      <td>8260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Los Angeles Lakers*</td>\n",
       "      <td>82</td>\n",
       "      <td>19905</td>\n",
       "      <td>...</td>\n",
       "      <td>490</td>\n",
       "      <td>1184</td>\n",
       "      <td>1872</td>\n",
       "      <td>8251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28.0</td>\n",
       "      <td>New York Knicks*</td>\n",
       "      <td>82</td>\n",
       "      <td>19905</td>\n",
       "      <td>...</td>\n",
       "      <td>346</td>\n",
       "      <td>1189</td>\n",
       "      <td>1863</td>\n",
       "      <td>7275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29.0</td>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>82</td>\n",
       "      <td>19855</td>\n",
       "      <td>...</td>\n",
       "      <td>379</td>\n",
       "      <td>1292</td>\n",
       "      <td>1902</td>\n",
       "      <td>7181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>League Average</td>\n",
       "      <td>82</td>\n",
       "      <td>19846</td>\n",
       "      <td>...</td>\n",
       "      <td>431</td>\n",
       "      <td>1234</td>\n",
       "      <td>1833</td>\n",
       "      <td>7774\\</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Rk                 Team   G     MP  ...    BLK   TOV    PF    PTS\n",
       "0    1.0    Sacramento Kings*  82  20080  ...    432  1221  1596   8343\n",
       "1    2.0     Milwaukee Bucks*  82  19780  ...    386  1123  1928   8260\n",
       "2    3.0  Los Angeles Lakers*  82  19905  ...    490  1184  1872   8251\n",
       "..   ...                  ...  ..    ...  ...    ...   ...   ...    ...\n",
       "27  28.0     New York Knicks*  82  19905  ...    346  1189  1863   7275\n",
       "28  29.0        Chicago Bulls  82  19855  ...    379  1292  1902   7181\n",
       "29   NaN       League Average  82  19846  ...    431  1234  1833  7774\\\n",
       "\n",
       "[30 rows x 25 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_csv(\"00-01_team_stats.csv\")\n",
    "df_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That is quite alot of data. For the purposes of looking at the league generally, we will only need the league averages from each DataFrame. Here, we will extract the League Average row from every single DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>Rk</th>\n",
       "      <th>Team</th>\n",
       "      <th>G</th>\n",
       "      <th>MP</th>\n",
       "      <th>...</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>League Average</td>\n",
       "      <td>82</td>\n",
       "      <td>19846</td>\n",
       "      <td>...</td>\n",
       "      <td>431</td>\n",
       "      <td>1234</td>\n",
       "      <td>1833</td>\n",
       "      <td>7774\\</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Rk            Team   G     MP  ...    BLK   TOV    PF    PTS\n",
       "29 NaN  League Average  82  19846  ...    431  1234  1833  7774\\\n",
       "\n",
       "[1 rows x 25 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load in all the dataframes for the years 2000-2019\n",
    "#Use the tail function in order to only grab the league average (the only row we are concerned with)\n",
    "df_0 = pd.read_csv(\"00-01_team_stats.csv\").tail(1);\n",
    "df_1 = pd.read_csv(\"01-02_team_stats.csv\").tail(1);\n",
    "df_2 = pd.read_csv(\"02-03_team_stats.csv\").tail(1);\n",
    "df_3 = pd.read_csv(\"03-04_team_stats.csv\").tail(1);\n",
    "df_4 = pd.read_csv(\"04-05_team_stats.csv\").tail(1);\n",
    "df_5 = pd.read_csv(\"05-06_team_stats.csv\").tail(1);\n",
    "df_6 = pd.read_csv(\"06-07_team_stats.csv\").tail(1);\n",
    "df_7 = pd.read_csv(\"07-08_team_stats.csv\").tail(1);\n",
    "df_8 = pd.read_csv(\"08-09_team_stats.csv\").tail(1);\n",
    "df_9 = pd.read_csv(\"09-10_team_stats.csv\").tail(1);\n",
    "df_10 = pd.read_csv(\"10-11_team_stats.csv\").tail(1);\n",
    "df_11 = pd.read_csv(\"11-12_team_stats.csv\").tail(1);\n",
    "df_12 = pd.read_csv(\"12-13_team_stats.csv\").tail(1);\n",
    "df_13 = pd.read_csv(\"13-14_team_stats.csv\").tail(1);\n",
    "df_14 = pd.read_csv(\"14-15_team_stats.csv\").tail(1);\n",
    "df_15 = pd.read_csv(\"15-16_team_stats.csv\").tail(1);\n",
    "df_16 = pd.read_csv(\"16-17_team_stats.csv\").tail(1);\n",
    "df_17 = pd.read_csv(\"17-18_team_stats.csv\").tail(1);\n",
    "df_18 = pd.read_csv(\"18-19_team_stats.csv\").tail(1);\n",
    "\n",
    "#Preview a dataframe to see what data is contained inside\n",
    "df_0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far, everything looks good! Rather than having the DataFrame's contain all the data, we now have isolated each DataFrame to only contain the information related to our analysis: League Average.\n",
    "\n",
    "The next step would be to get all of these into one cohesive DataFrame, thus allowing us to do analysis and create visualizations much easier with the tools we have. We will achieve this by creating a list of all the DataFrame names, and then concatenate them. One thing to also note is that in the above preview printout, it still has it's original index (29 since it was at the tail of a larger DataFrame). We can resolve this by setting the ignore_index flag to True."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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>Rk</th>\n",
       "      <th>Team</th>\n",
       "      <th>G</th>\n",
       "      <th>MP</th>\n",
       "      <th>...</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>League Average</td>\n",
       "      <td>82</td>\n",
       "      <td>19846</td>\n",
       "      <td>...</td>\n",
       "      <td>431</td>\n",
       "      <td>1234</td>\n",
       "      <td>1833</td>\n",
       "      <td>7774\\</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>League Average</td>\n",
       "      <td>82</td>\n",
       "      <td>19816</td>\n",
       "      <td>...</td>\n",
       "      <td>428</td>\n",
       "      <td>1186</td>\n",
       "      <td>1741</td>\n",
       "      <td>7829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>League Average</td>\n",
       "      <td>82</td>\n",
       "      <td>19842</td>\n",
       "      <td>...</td>\n",
       "      <td>411</td>\n",
       "      <td>1223</td>\n",
       "      <td>1784</td>\n",
       "      <td>7797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>League Average</td>\n",
       "      <td>82</td>\n",
       "      <td>19813</td>\n",
       "      <td>...</td>\n",
       "      <td>389</td>\n",
       "      <td>1144</td>\n",
       "      <td>1632</td>\n",
       "      <td>8658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>League Average</td>\n",
       "      <td>82</td>\n",
       "      <td>19795</td>\n",
       "      <td>...</td>\n",
       "      <td>395</td>\n",
       "      <td>1170</td>\n",
       "      <td>1628</td>\n",
       "      <td>8719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>League Average</td>\n",
       "      <td>82</td>\n",
       "      <td>19815</td>\n",
       "      <td>...</td>\n",
       "      <td>406</td>\n",
       "      <td>1155</td>\n",
       "      <td>1714</td>\n",
       "      <td>9119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Rk            Team   G     MP  ...    BLK   TOV    PF    PTS\n",
       "0  NaN  League Average  82  19846  ...    431  1234  1833  7774\\\n",
       "1  NaN  League Average  82  19816  ...    428  1186  1741   7829\n",
       "2  NaN  League Average  82  19842  ...    411  1223  1784   7797\n",
       "..  ..             ...  ..    ...  ...    ...   ...   ...    ...\n",
       "16 NaN  League Average  82  19813  ...    389  1144  1632   8658\n",
       "17 NaN  League Average  82  19795  ...    395  1170  1628   8719\n",
       "18 NaN  League Average  82  19815  ...    406  1155  1714   9119\n",
       "\n",
       "[19 rows x 25 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Concatenate all the dataframes together to make one cohesive dataframe\n",
    "list_of_names = [df_0, df_1, df_2, df_3, df_4, df_5, df_6, df_7, df_8, df_9, df_10, df_11, df_12, df_13, df_14, df_15, df_16, df_17, df_18]\n",
    "league_averages = pd.concat(list_of_names,ignore_index=True)\n",
    "\n",
    "#Print it out to make sure everything looks good\n",
    "league_averages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Everything looks good so far. However, by doing all this data cleaning, we have lost some information: what year pertains to what data. Here we will add in a new column to represent this information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Rk</th>\n",
       "      <th>Team</th>\n",
       "      <th>G</th>\n",
       "      <th>...</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>League Average</td>\n",
       "      <td>82</td>\n",
       "      <td>...</td>\n",
       "      <td>431</td>\n",
       "      <td>1234</td>\n",
       "      <td>1833</td>\n",
       "      <td>7774\\</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>League Average</td>\n",
       "      <td>82</td>\n",
       "      <td>...</td>\n",
       "      <td>428</td>\n",
       "      <td>1186</td>\n",
       "      <td>1741</td>\n",
       "      <td>7829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>02-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>League Average</td>\n",
       "      <td>82</td>\n",
       "      <td>...</td>\n",
       "      <td>411</td>\n",
       "      <td>1223</td>\n",
       "      <td>1784</td>\n",
       "      <td>7797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>League Average</td>\n",
       "      <td>82</td>\n",
       "      <td>...</td>\n",
       "      <td>389</td>\n",
       "      <td>1144</td>\n",
       "      <td>1632</td>\n",
       "      <td>8658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>League Average</td>\n",
       "      <td>82</td>\n",
       "      <td>...</td>\n",
       "      <td>395</td>\n",
       "      <td>1170</td>\n",
       "      <td>1628</td>\n",
       "      <td>8719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>League Average</td>\n",
       "      <td>82</td>\n",
       "      <td>...</td>\n",
       "      <td>406</td>\n",
       "      <td>1155</td>\n",
       "      <td>1714</td>\n",
       "      <td>9119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  Rk            Team   G  ...    BLK   TOV    PF    PTS\n",
       "0   00-01 NaN  League Average  82  ...    431  1234  1833  7774\\\n",
       "1   01-02 NaN  League Average  82  ...    428  1186  1741   7829\n",
       "2   02-03 NaN  League Average  82  ...    411  1223  1784   7797\n",
       "..    ...  ..             ...  ..  ...    ...   ...   ...    ...\n",
       "16  16-17 NaN  League Average  82  ...    389  1144  1632   8658\n",
       "17  17-18 NaN  League Average  82  ...    395  1170  1628   8719\n",
       "18  18-19 NaN  League Average  82  ...    406  1155  1714   9119\n",
       "\n",
       "[19 rows x 26 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Insert a row that will let us know what year the league average is for.\n",
    "league_years = [\"00-01\", \"01-02\", \"02-03\", \"03-04\", \"04-05\", \"05-06\", \"06-07\", \"07-08\", \"08-09\", \"09-10\", \"10-11\", \"11-12\", \"12-13\", \"13-14\", \"14-15\", \"15-16\", \"16-17\", \"17-18\", \"18-19\"]\n",
    "league_averages.insert(loc=0, column=\"Year\", value=league_years)\n",
    "\n",
    "#Print it out to see the difference\n",
    "league_averages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again, there is quite abit of data. We will narrow this down to columns we need for our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>Year</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3P</th>\n",
       "      <th>3PA</th>\n",
       "      <th>3P%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00-01</td>\n",
       "      <td>2926</td>\n",
       "      <td>6609</td>\n",
       "      <td>0.44</td>\n",
       "      <td>397</td>\n",
       "      <td>1124</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01-02</td>\n",
       "      <td>2966</td>\n",
       "      <td>6664</td>\n",
       "      <td>0.45</td>\n",
       "      <td>428</td>\n",
       "      <td>1209</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>02-03</td>\n",
       "      <td>2929</td>\n",
       "      <td>6624</td>\n",
       "      <td>0.44</td>\n",
       "      <td>421</td>\n",
       "      <td>1204</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16-17</td>\n",
       "      <td>3202</td>\n",
       "      <td>7004</td>\n",
       "      <td>0.46</td>\n",
       "      <td>792</td>\n",
       "      <td>2214</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17-18</td>\n",
       "      <td>3248</td>\n",
       "      <td>7057</td>\n",
       "      <td>0.46</td>\n",
       "      <td>860</td>\n",
       "      <td>2378</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18-19</td>\n",
       "      <td>3369</td>\n",
       "      <td>7315</td>\n",
       "      <td>0.46</td>\n",
       "      <td>932</td>\n",
       "      <td>2625</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year    FG   FGA   FG%   3P   3PA   3P%\n",
       "0   00-01  2926  6609  0.44  397  1124  0.35\n",
       "1   01-02  2966  6664  0.45  428  1209  0.35\n",
       "2   02-03  2929  6624  0.44  421  1204  0.35\n",
       "..    ...   ...   ...   ...  ...   ...   ...\n",
       "16  16-17  3202  7004  0.46  792  2214  0.36\n",
       "17  17-18  3248  7057  0.46  860  2378  0.36\n",
       "18  18-19  3369  7315  0.46  932  2625  0.35\n",
       "\n",
       "[19 rows x 7 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Keep just the relevant rows by making a subselection.\n",
    "league_averages = league_averages[[\"Year\", \"FG\", \"FGA\", \"FG%\", \"3P\", \"3PA\", \"3P%\"]]\n",
    "\n",
    "#Print out to check what the DataFrame now looks like\n",
    "league_averages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Legend For All The Abbreviations Used - \n",
    "\n",
    "FG: Shots made overall\n",
    "\n",
    "FGA: Shots attempted overall\n",
    "\n",
    "FG%: Percentage of shots made overall vs. shots attempted\n",
    "\n",
    "\n",
    "3P: Three pointer shots made\n",
    "\n",
    "3PA: Three pointers attempted\n",
    "\n",
    "3P%: Percetange of three pointer shots vs. three pointers attempted.\n",
    "\n",
    "\n",
    "That means 3P% represents the percentage of three pointers from three pointers attempted, NOT the percentage of three pointers vs. all shots attempted.\n",
    "\n",
    "With that clarified, we can now start with some visualizations. Here is the percentage of three-pointers made vs. all three-pointers attempted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#gca = get current axis\n",
    "ax = plt.gca()\n",
    "ax.set_ylim([0,1])\n",
    "\n",
    "#Change size of plot labels\n",
    "ax.xaxis.set_tick_params(labelsize=12)\n",
    "ax.yaxis.set_tick_params(labelsize=12)\n",
    "\n",
    "#Set the axis labels\n",
    "plt.xlabel('Years', fontsize=18)\n",
    "plt.ylabel('Percentage of Three-Pointers Made', fontsize=12)\n",
    "\n",
    "#Plot using MatPlotLib\n",
    "league_averages.plot(kind='line',x='Year',y='3P%',ax=ax,marker = \"o\")\n",
    "\n",
    "#Set the title, legend\n",
    "plt.title('Percentage of Three-Pointers Made vs. Three-Pointers Attempted', fontsize=20)\n",
    "plt.legend(['Percentage of Three-Pointers Made Per Year'], loc='upper right', fontsize=10)\n",
    "\n",
    "#Set range for ticks\n",
    "plt.xticks(np.arange(0,19), league_averages[\"Year\"])\n",
    "plt.xticks(rotation=60)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we can see that the amount of three point shots made vs. the amount of three points shots attempted stays extremely consistent. This can be even more supported by taking a look at just that column.\n",
    "\n",
    "This result is not surprising, as this conclusion closely follows the conclusion found in our second analysis. There is no large increase in player's ability to shoot three-pointers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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>Year</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3P</th>\n",
       "      <th>3PA</th>\n",
       "      <th>3P%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00-01</td>\n",
       "      <td>2926</td>\n",
       "      <td>6609</td>\n",
       "      <td>0.44</td>\n",
       "      <td>397</td>\n",
       "      <td>1124</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01-02</td>\n",
       "      <td>2966</td>\n",
       "      <td>6664</td>\n",
       "      <td>0.45</td>\n",
       "      <td>428</td>\n",
       "      <td>1209</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>02-03</td>\n",
       "      <td>2929</td>\n",
       "      <td>6624</td>\n",
       "      <td>0.44</td>\n",
       "      <td>421</td>\n",
       "      <td>1204</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16-17</td>\n",
       "      <td>3202</td>\n",
       "      <td>7004</td>\n",
       "      <td>0.46</td>\n",
       "      <td>792</td>\n",
       "      <td>2214</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17-18</td>\n",
       "      <td>3248</td>\n",
       "      <td>7057</td>\n",
       "      <td>0.46</td>\n",
       "      <td>860</td>\n",
       "      <td>2378</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18-19</td>\n",
       "      <td>3369</td>\n",
       "      <td>7315</td>\n",
       "      <td>0.46</td>\n",
       "      <td>932</td>\n",
       "      <td>2625</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year    FG   FGA   FG%   3P   3PA   3P%\n",
       "0   00-01  2926  6609  0.44  397  1124  0.35\n",
       "1   01-02  2966  6664  0.45  428  1209  0.35\n",
       "2   02-03  2929  6624  0.44  421  1204  0.35\n",
       "..    ...   ...   ...   ...  ...   ...   ...\n",
       "16  16-17  3202  7004  0.46  792  2214  0.36\n",
       "17  17-18  3248  7057  0.46  860  2378  0.36\n",
       "18  18-19  3369  7315  0.46  932  2625  0.35\n",
       "\n",
       "[19 rows x 7 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "league_averages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, let's try to approach this from a different angle. Let's see, for example, the percentage of three point shots made vs. the total number of shots made."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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>Year</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>...</th>\n",
       "      <th>3PA</th>\n",
       "      <th>3P%</th>\n",
       "      <th>3P%/Total FG</th>\n",
       "      <th>3PA/Total FGA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00-01</td>\n",
       "      <td>2926</td>\n",
       "      <td>6609</td>\n",
       "      <td>0.44</td>\n",
       "      <td>...</td>\n",
       "      <td>1124</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01-02</td>\n",
       "      <td>2966</td>\n",
       "      <td>6664</td>\n",
       "      <td>0.45</td>\n",
       "      <td>...</td>\n",
       "      <td>1209</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>02-03</td>\n",
       "      <td>2929</td>\n",
       "      <td>6624</td>\n",
       "      <td>0.44</td>\n",
       "      <td>...</td>\n",
       "      <td>1204</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16-17</td>\n",
       "      <td>3202</td>\n",
       "      <td>7004</td>\n",
       "      <td>0.46</td>\n",
       "      <td>...</td>\n",
       "      <td>2214</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17-18</td>\n",
       "      <td>3248</td>\n",
       "      <td>7057</td>\n",
       "      <td>0.46</td>\n",
       "      <td>...</td>\n",
       "      <td>2378</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18-19</td>\n",
       "      <td>3369</td>\n",
       "      <td>7315</td>\n",
       "      <td>0.46</td>\n",
       "      <td>...</td>\n",
       "      <td>2625</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year    FG   FGA   FG%      ...         3PA   3P%  3P%/Total FG  \\\n",
       "0   00-01  2926  6609  0.44      ...        1124  0.35          0.14   \n",
       "1   01-02  2966  6664  0.45      ...        1209  0.35          0.14   \n",
       "2   02-03  2929  6624  0.44      ...        1204  0.35          0.14   \n",
       "..    ...   ...   ...   ...      ...         ...   ...           ...   \n",
       "16  16-17  3202  7004  0.46      ...        2214  0.36          0.25   \n",
       "17  17-18  3248  7057  0.46      ...        2378  0.36          0.26   \n",
       "18  18-19  3369  7315  0.46      ...        2625  0.35          0.28   \n",
       "\n",
       "    3PA/Total FGA  \n",
       "0            0.17  \n",
       "1            0.18  \n",
       "2            0.18  \n",
       "..            ...  \n",
       "16           0.32  \n",
       "17           0.34  \n",
       "18           0.36  \n",
       "\n",
       "[19 rows x 9 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "league_averages['3P%/Total FG'] = league_averages[\"3P\"] / league_averages[\"FG\"]\n",
    "league_averages = league_averages.copy()\n",
    "league_averages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#gca = get current axis\n",
    "ax = plt.gca()\n",
    "ax.set_ylim([0,1])\n",
    "\n",
    "#Change size of plot labels\n",
    "ax.xaxis.set_tick_params(labelsize=12)\n",
    "ax.yaxis.set_tick_params(labelsize=12)\n",
    "\n",
    "#Set the axis labels\n",
    "plt.xlabel('Years', fontsize=18)\n",
    "plt.ylabel('Percentage of Three-Pointers Made', fontsize=12)\n",
    "\n",
    "#Plot using MatPlotLib\n",
    "league_averages.plot(kind='line',x='Year',y='3P%/Total FG',ax=ax,marker = \"o\")\n",
    "\n",
    "#Set the title, legend\n",
    "plt.title('Percentage of Three-Pointers Made vs. All Shots Made', fontsize=20)\n",
    "plt.legend(['Percentage of Three-Pointers Made Per Year'], loc='upper right', fontsize=10)\n",
    "\n",
    "\n",
    "#Set range for ticks\n",
    "plt.xticks(np.arange(0,19), league_averages[\"Year\"])\n",
    "plt.xticks(rotation=60)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we can observe a slight increase in three pointers made per year in comparison to all shots made. Once again, this result is not surprising, as statistically speaking, the higher raw frequency of an event occuring, the higher percentages it is likely to yield (i.e if you flip a coin 10 times vs 100 times you will get closer to the 50/50 split in a higher number of trials).\n",
    "\n",
    "However, in order to answer our question, we should also look at the percentage of 3-pointers attempted against all shots attempted, as our analysis is not so focused on whether players have been able to make more three-pointers, but simply that more three-pointers are being attempted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>Year</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>...</th>\n",
       "      <th>3PA</th>\n",
       "      <th>3P%</th>\n",
       "      <th>3P%/Total FG</th>\n",
       "      <th>3PA/Total FGA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00-01</td>\n",
       "      <td>2926</td>\n",
       "      <td>6609</td>\n",
       "      <td>0.44</td>\n",
       "      <td>...</td>\n",
       "      <td>1124</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01-02</td>\n",
       "      <td>2966</td>\n",
       "      <td>6664</td>\n",
       "      <td>0.45</td>\n",
       "      <td>...</td>\n",
       "      <td>1209</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>02-03</td>\n",
       "      <td>2929</td>\n",
       "      <td>6624</td>\n",
       "      <td>0.44</td>\n",
       "      <td>...</td>\n",
       "      <td>1204</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16-17</td>\n",
       "      <td>3202</td>\n",
       "      <td>7004</td>\n",
       "      <td>0.46</td>\n",
       "      <td>...</td>\n",
       "      <td>2214</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17-18</td>\n",
       "      <td>3248</td>\n",
       "      <td>7057</td>\n",
       "      <td>0.46</td>\n",
       "      <td>...</td>\n",
       "      <td>2378</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18-19</td>\n",
       "      <td>3369</td>\n",
       "      <td>7315</td>\n",
       "      <td>0.46</td>\n",
       "      <td>...</td>\n",
       "      <td>2625</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year    FG   FGA   FG%      ...         3PA   3P%  3P%/Total FG  \\\n",
       "0   00-01  2926  6609  0.44      ...        1124  0.35          0.14   \n",
       "1   01-02  2966  6664  0.45      ...        1209  0.35          0.14   \n",
       "2   02-03  2929  6624  0.44      ...        1204  0.35          0.14   \n",
       "..    ...   ...   ...   ...      ...         ...   ...           ...   \n",
       "16  16-17  3202  7004  0.46      ...        2214  0.36          0.25   \n",
       "17  17-18  3248  7057  0.46      ...        2378  0.36          0.26   \n",
       "18  18-19  3369  7315  0.46      ...        2625  0.35          0.28   \n",
       "\n",
       "    3PA/Total FGA  \n",
       "0            0.17  \n",
       "1            0.18  \n",
       "2            0.18  \n",
       "..            ...  \n",
       "16           0.32  \n",
       "17           0.34  \n",
       "18           0.36  \n",
       "\n",
       "[19 rows x 9 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "league_averages['3PA/Total FGA'] = league_averages[\"3PA\"] / league_averages[\"FGA\"]\n",
    "league_averages = league_averages.copy()\n",
    "league_averages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#gca = get current axis\n",
    "ax = plt.gca()\n",
    "ax.set_ylim([0,1])\n",
    "\n",
    "#Change size of plot labels\n",
    "ax.xaxis.set_tick_params(labelsize=12)\n",
    "ax.yaxis.set_tick_params(labelsize=12)\n",
    "\n",
    "#Set the axis labels\n",
    "plt.xlabel('Years', fontsize=18)\n",
    "plt.ylabel('Percentage Of Three Pointers Attempted', fontsize=10)\n",
    "\n",
    "#Plot using MatPlotLib\n",
    "league_averages.plot(kind='line',x='Year',y='3PA/Total FGA',ax=ax,marker = \"o\")\n",
    "\n",
    "#Set the title, legend\n",
    "plt.title('Percentage of Three-Pointers Attempted vs. All Shots Attempted', fontsize=20)\n",
    "plt.legend(['Percentage of Three-Pointers Attempted Per Year'], loc='upper right', fontsize=10)\n",
    "\n",
    "\n",
    "#Set range for ticks\n",
    "plt.xticks(np.arange(0,19), league_averages[\"Year\"])\n",
    "plt.xticks(rotation=60)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see quite a large increase in slope, with the actual percentage nearly doubling from 20% to 40% between the years 2000 to 2019. This is an absoulutely alarming amount, and nearly a 100% increase in three-pointers between these two points in time.\n",
    "\n",
    "It is important to note here that this is the percentage, which means this is the ratio of three-pointers attempted vs. all shots attempted. Below, we will investigate further into the raw numbers, but since the percentage has been increasing over time, that suggests that although both raw values have been rising (i.e the number of shots attempted have been rising overall), the number of three-pointer attempts has increased in terms of the percentage of total shots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#gca = get current axis\n",
    "ax = plt.gca()\n",
    "ax.set_ylim([1000,3500])\n",
    "\n",
    "#Change size of plot labels\n",
    "ax.xaxis.set_tick_params(labelsize=12)\n",
    "ax.yaxis.set_tick_params(labelsize=12)\n",
    "\n",
    "#Set the axis labels\n",
    "plt.xlabel('Years', fontsize=18)\n",
    "plt.ylabel('Number Of Shots Attempted', fontsize=14)\n",
    "\n",
    "#Plot using MatPlotLib\n",
    "league_averages.plot(kind='line',x='Year',y='3PA',ax=ax,marker = \"o\")\n",
    "\n",
    "#Set the title, legend\n",
    "plt.title('Number of Three-Pointers Attempted vs. Time', fontsize=24)\n",
    "plt.legend(['Three-Pointers Attempted Per Year'], loc='upper right', fontsize=15)\n",
    "\n",
    "\n",
    "#Set range for ticks\n",
    "plt.xticks(np.arange(0,19), league_averages[\"Year\"])\n",
    "plt.xticks(rotation=60)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When looking at the raw number of three-pointers attempted vs time, we can clearly see a very sharp increase in the amount of three-pointers attempted. While it is helpful to look at raw numbers to see the general trends, more importantly, our previous analysis on the percentages shows that this increase is truly an increase in the amount of three-pointers attempted in relation to shots attempted, rather than just due to the fact that overall more shots are being attempted year by year, then by chance the number of three-pointers being attempted also increases (confounding variable which is accounted for by looking at percentages).\n",
    "\n",
    "Furthermore, by looking at the raw values, we can see oddity, which is the sharp decrease in 2011-2012. This is due to the NBA Lockout of 2011-2012, where team owners and players were all held to the new standards set by the CBA (collective bargaining agreement). This led to a period of two months of inactivity in the league, thus causing a sharp decrease in the raw values.\n",
    "Source: https://www.nba.com/history/season-recap/2011-12\n",
    "\n",
    "Interestingly enough, due to the consistent nature of the ratios of three-pointers attempted (i.e even with two months of gametime misssing) the trend is still overall increasing when looking at percentages. When looking at raw numbers, this may have seemed like it would have been something to remove in our analysis, but this simply shows that the league is overall extremely consistent in their shooting behaviors within the league (i.e it's not like there's a sudden increase of three-pointers attempted near the end of the season that we would have captured by losing 2 months of gametime).\n",
    "\n",
    "Once again, we can also run this analysis on the amount of three pointers made vs. time to see if this trend also follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eEB8fL+ua9by2tgW7XQzOAg0aNDD5YL366qtA5jqpGzduULNmzQKdqzDw9/c3aXQM80yJiYlG06tWrZprXsfA3bt3uXfvHgDjxo0z+UI0OCxkdSI5efKknGZsIbUBwwddXg4o5jA4Xxjj7bffpnfv3vJvQ1v7+Pjg5uZmtExQUBC2trZotVouXLiQaz7IHE/TFuYw1GdsbCyNGzc2mS8pKQkwXZ8BAQEmyzZr1oxNmzbx8ccf07dvX1q3bo2fn59V1+w1btyYsmXLcvz4cdlQb926FYCuXbtaJEOr1bJ582Z27drFpUuXiI+Pz+U0Y5j7NtzbFy9eRJIklEqlSf+AKlWqULFixVx1p9VqZce30NBQZsyYYbS8wZAanhlzlGjD5ujoyIgRI/jmm29YunQpffv2xdXVtdDPW7ZsWaPHDS+uMmXKmHw4DS9+U96cWSfszaU9evRINmyGL8SkpCT5wTTHkydPch0z9XK2BIOnlzndK1SokCt/ccHcZLu9vT1gur3M1VvWL3dLrtmw7CFrWZ1Ox8OHD/NV1tSL+7PPPjPqeJR1gbatrS0eHh7Url2brl27EhwcnC2vJW1tb2+Pu7s7Dx8+zHdbP01bmMNQnxkZGfmuz6yYa++PP/6YGzducOrUKRYvXszixYuxt7cnICCA9u3b071796cODmBjY0Pnzp1ZtmwZW7duJSgoiOjoaHx9fY16cudEo9EwZMgQ+WMYMh1ssjo9Geon63vC0I6urq5ml+WUL18+l2EzOIdBZs8vL0zVfVZKtGED6NOnD8uWLePu3bssW7aMcePGFbVKhUbW4cesGDy85s2bZ/br3hxPO4QBma68pjBl6J93zNWboV0A/v77b3moyhIMbV27dm15GMdSTL24Tb0wCrKQ3VxbF0cMbdGmTRvmzp1bYDnm2tvDw4M1a9Zw5MgR/vvf//L3338TGRnJsWPHOHbsGMuWLWPVqlXZPvIKQpcuXVi2bBlbtmyRh4st7a3NmzePU6dO4eHhwcSJE2natGm20RudTkft2rUB0+8bcxgrk/U52LJlCz4+PvmWm5MSO8dmwM7OTh7TX7FiRZ5fiIYbM+t6s6wkJydbV8F8EhsbazItaw8g65ejwdU7P+tArIlBF8NDZoysX3FP0zt8nsj6wjBEV8lv2Zs3b+a7h3Lp0iWj/6wRH9OStk5LS5O/zItLWxuekfy2Q35RKBQ0atSIyZMns3nzZo4ePcqXX36Ju7s7t27d4ttvv33qcxiChN+4cYPNmzdjY2Mjz0/lhWGd2pQpU+jatWuuKQlTH0WGdkxKSjI64mPA2Pyiu7u7/N611juqxBs2yHSaqFq1KhqNhsWLF5vNaxiqvH//vtH0f/75x+r65Yd//vnH5I1z/PhxAEqVKpUtsKlh3H/37t2Fr6AR/Pz8gMwJZ1O6Hz16FMhcN2SIF1jSqVKlivxC3bNnT77KGto0JSXFosn0Z4WhraOiokw+Q8ePH5eNseHrvzAxDKGZ62EY6vPGjRsmHbcKAzc3N3r37s37778P/PsMPy2GHlpGRgaNGze2eB2joc1MheU6cuSI0eO+vr4oFAr0ej1///230Ty3bt0y+sGjUqnw9/cH8v8cmOKFMGy2traMHj0agNWrV5vt9RgcP/bv358rLT09nRUrVhSOkhby5MmTbNG5DaSnp7N8+XIA2rVrl21or1u3bgCcO3cuzwgAppxWnoY2bdqgVCqJj49n3bp1udKfPHnC0qVL5bzWGPZ8XjC0zZo1a8x+rUqSlG1+tEaNGvLLeMaMGWa9flNTU5/Z0GDjxo1xcXEhIyODJUuW5ErX6XTMmzcPgMDAQJPz0dbEMMRrbn75tddeo1KlSkDm7g7mPHML8ozo9XqzPWvD3Jq12qlLly4MHjyYwYMHm/VGzImhrox5lmo0GubPn2+0nLu7u+wAtmTJEqMfEeY6FYbnYM+ePfJHriksqf8XwrBBpqtrzZo1SU1Nld1SjdGhQwcA1q9fz8aNG+Ub7cqVKwwbNsysUXwWuLq68uOPP7JixQp5TuTWrVuMHDmSa9euYW9vz/Dhw7OVadasmbxa/9NPP2X27NnZriMhIYF9+/YxcuTIQtnGx8vLS45m8P3337Nu3Tq5Xm/cuMHw4cOJiorC0dGRkSNHWv38xZnhw4dTpUoVUlJS6N+/P5s3b84Wnuru3busX7+e7t27s3fv3mxlJ0+ejJ2dHZcvX6Zfv3789ddf8stTr9dz5coV5s2bR+vWrZ/Zfevk5CR7H69cuZL58+fL13P//n0++OAD/v77b5RKJePHj38mOlWrVg2VSkVSUpLJUQuVSsXkyZNRKBQcPnyYwYMHc+bMGfkFrdVqOXfuHDNmzMgWHcdSkpOTadu2LfPnz+fSpUuy4dTr9Rw5coQffvgBgCZNmhTwKrPj6enJJ598wieffGLSS9EYBsei0NBQIiIi5Os/e/Ys77zzjlnnjtGjR6NQKDhy5AgTJ06Uhy2TkpKYOXMm69atMzmP/NZbbxEQEIBer+fdd99lxYoV2c4VFxfH9u3bCQkJMfphn5MS7zxiQKlUMnbsWMaOHWs2X8+ePQkPD+fMmTN8+umnfP755zg4OJCcnIy7uzvffvst77333jPSOjetWrVCo9Hw7bff8t133+Ho6Ci7N9vY2DBt2jSqVq2aq9x//vMf9Ho9+/btIywsjLCwMFxdXZEkKdu8YWHtQzZx4kRu3brF4cOH+fzzz/nqq6+y6W5nZ8eMGTNemGFIA6VKlWLp0qXyh8nEiRP59NNPKVWqFKmpqdkcOnI62NSpU4e5c+fy4YcfcuHCBQYNGoRKpcLZ2RmNRpPNRftZOucMGTKEa9euER4ezqxZs5gzZw4uLi4kJibKLuGTJ0+Wl6cUNk5OTnTq1Inw8HDGjh2Lq6urvEvExx9/LO8p16pVK7755humTp3K0aNH6dWrF/b29jg6OpKUlPTU6ytv377NrFmzmDVrltxOWeVWqVKlwHsIWovx48dz+PBh7t69S0hICPb29tjY2JCSkoKDgwNhYWHy+uCcBAYG8tFHH/Hdd98RHh7Oli1bKFWqFMnJyeh0OgYNGsT58+eNrgFUqVTMmzeP0aNHc/LkSb799lumTZtGqVKlyMjIyDYiYYkj0wtj2ADatm2Ln5+f2SCsKpWKZcuWMW/ePHbt2kVsbCyOjo60bdu2SA2aAYVCwY8//sjKlSvZtGkT0dHRuLm5Ub9+fUaNGiUvUs2Jk5MTYWFh/PHHH2zcuJEzZ87w6NEjlEol1apVk4Pu5mdNUX5wdHRk8eLFbNq0ifDwcC5dusSTJ0/w8vKiUaNGDB06lJdeeqlQzl3cqVatGuHh4fz666/s2rWLy5cvk5SUhL29Pd7e3gQHB9OuXTujX97Nmzdn9+7drFy5kgMHDhAVFUVSUhKurq68/PLLNG3alPbt2+Pl5fXMrsfGxob//Oc/tGzZkvXr13Pu3Dk0Gg1ly5YlODiYQYMGyXMqz4ovvviC8uXLs2fPHu7cuSPH5cw5hNujRw8aNmzIzz//zF9//cXt27flj9qaNWvy+uuv5xlE2BguLi4sXLiQv/76i1OnTnHv3j0eP36Mo6MjL7/8Mq1bt6Z///758owtDKpUqcKGDRuYPXs2hw8fJjExEXd3d1q1asWIESOoVauW2fJDhw5FrVazZMkSzp07h1arxd/fn759+9K1a1c5KLgxPD09WbVqFTt37mTbtm2cP3+ehIQEVCoV1atXp379+rRt2zZboAtTKKSC+GwKBAKBQFBMeWHm2AQCgUDwYlCkhm3VqlV0794df39/eZ8lA0eOHKF9+/bUq1ePkJCQbDu+pqenM2nSJOrXr0/jxo1lb0BLygoEAoGgZFOkhq1cuXKMGjUqV3DdR48eMXr0aMaNG0dERAT+/v7yOg/IjIQQFRXF77//zs8//8ySJUs4cOCARWUFAoFAULIpUsPWtm1bWrdunSvi/N69e6lVqxYdOnTA3t6eMWPGEBkZKa/zCQ8PZ9SoUbi5uVGjRg169uwphxXKq6xAIBAISjbFco7typUr2QJ2Ojk5UbVqVa5evUpCQgKxsbHZ4on5+PjIoXDMlRUIBAJByadYuvunpKTkiiHn4uKCRqOR3XOzRul3dXWVF4GaK1sQhNOoQCAQ5J+iDGxeLA2bk5NTrmDDGo0GZ2dneUuE5ORkeZuK5ORkeTsLc2ULgiRBXFz+Ax+7uTkCkJBgOiCokPFsZRQHHYQMIaOwZRQHHTw9XSjKDTuK5VBkrVq1iIyMlH+npKQQHR1NzZo1cXNzo2zZstnSIyMj5Y0pzZUVCAQCQcmnSA2bVqslLS0NvV6PTqcjLS0NrVZLmzZtuHLlCrt37yYtLY2wsDC8vb2pUaMGkBm5ev78+SQkJHDt2jU2bNggB9HMq6xAIBAISjZFatjmz59P3bp1WbRoEVu3bqVu3brMnz+f0qVLM2fOHH744QeCgoI4e/YsM2fOlMuNHTuWKlWq0KJFC0JCQhgyZAjNmjUDyLOsQCAQCEo2IqRWHuj1kphjKyEyioMOQoaQUdgyioMOnp4uKJXPkfNISkoKp0+f5uHDhzRq1MjiDewEAoFAIHgW5MuwrV69mpkzZ5KcnIxCoWDZsmWUKVOGR48e0bx5cyZPnkzv3r0LS1dBAcncmiaejIwM9Hp9vssnJWVu/JmeXvBtO4qDjOKgg5AhZBS2jMLUQalU/m/LHbcidefPC4vn2Hbv3s2XX35Jw4YN+frrr7Ot7ypdujRNmzY1uuu0oGiRJIl79+6SnJyATpeRdwEjaLU6tNqn24uqOMgoDjoIGUJGYcsoTB10ugySkxOIj39YrNf4WtxjW7p0KQ0bNiQsLIzHjx8zefLkbOn+/v5s2LDB6goKno7Hjx/9b38uD5ydSxVIho1N5peZTlfwG7k4yCgOOggZQkZhyyhsHTSaRJKSHqPRJODi4p4rvThgcY/t8uXLtGnTxmR62bJliYuLs4pSAuuRlpaGra2qwEZNIBC8WOj0ehI16TxKTCMpJR1djukLZ+dS2Nqqsu3QXtywuMemVCrNzs8YdpoWFC90Oh1KZbFchy8QCIoRkiQRn5xOoiZd/q1QKHiUmEYpZzvcXezkeTWFwqZA8/XPCovfeD4+Phw6dMhoml6vZ9euXdSpU8dqigkEAoHg2WEwapIkyfNnhr8TNenEJ6cXsYaWY7Fh69+/PwcOHGDWrFkkJCQAmRd9/fp1xo0bx9WrVwkJCSk0RQUCgUBQOBiGH005hBiMW85hyeKKxYatY8eOjBgxggULFtCxY0cAhg4dSqdOndi7dy+jR4+mefPmhaao4MWlSZNAmjQJ5LXXGvDaaw3k31n/nTx5gp07t9GkSaC8A0Rxw6Cf4TreeKMNH3wwmkuXIvMunIWTJ0/w2msNuHYtf1sxXbhwjqVLF+arTGFw8uQJmjQJpGnTIO7fv5crfdq0L2nSJJDRo4db5XyTJ39sFVmG9jP8K2j7WcqhQwdo0iSQ48eP5krbs2cXTZsGcebMKaucKyVVa9V8RU2+1rG9//77tG3blm3btnH9+nUkSaJatWp06dJFDEO+IGhSMzgRGUuCJh03ZzsCfcrh7KAq1HMuWLAcyPTUSktLZfTodxk4cAivvdZEzvPyyy9z797dQtXDWsyduwB7ewdiYx+wYsUSxo4dwS+//EqZMmUtKu/t7cPixT/h5VU5X+e9ePE8y5cvZsiQEQVR2+o4ODiyf/9eQkIGyMcyMjI4cOAPHB2dilAz88yevQA7O3vi4h4WqP0spUmTZjRt2pyZM6ezYsVa7OzsAEhJ0RAWNosOHd6gXr1XrHIunV7K031fkiR0+uLr4p+VfEce8fPzw8/PrzB0ERRjJEli84Hr7IqIRqGA9Aw9diolq/ddoX1wVbo2fbnQFmz6+2d+NNnYKOTemJdXZfn406LValEoFNjY2FhFXl74+vrh5OSETifh4+PLW291Zs+e3+jbd0DehQFnZxf52p/GpftpSUtLxcmp4A5jjRs3Zf/+PdkM27FjR9DrdbzySgNSUgq2h2Jh4+NTW94+qyDtZ4y0tFTs7R1yHR83bgIhIT1Zvfpn3nlnKABLlixAq81g1KhxBT5fTmyUChQKRS5IGDU1AAAgAElEQVTjptfp0EsStra2mc9IEYbJyg/Fcj82QfFj05/X2R0RTYb23zH29IzMv3dHRAPQrVn1ItEtJ3fv3mHOnJmcO3eWcuXKM2LEezRv3lJOHz16OO7u7gQFvcovv6zg3r27bNiwlfLlK3D9+lXmz5/D6dOZQzwNG77G++9PwNPz39BxCQkJzJs3h4MH/0SjSUat9mHMmA/w8/PPt67ly1fA3d2Du3f/7W3+/fdxFiyYy7VrV3B2dqF585aMGjVWfpmePHmCsWPfZdWqdbz0UuauFU2aBDJ27Ic8fvyIbds2AwpatGjNmDHvY2dnx86d2/jhh+/kvACvvNKAefMWAeR53YZzfv/9HDZtWs/Jkydo2bINkydPZevWcNas+YU7d27j6OjAyy/X4IMPPqF6dfM7arRu3ZaJEz/k1q1oqlSpCsD+/Xto2vR1UlOfkHVE+eHDhyxaFMapUyeJi3tIuXLladmyNYMGDUOl+nfE4P79e3z33becPPk3pUuXZuDAIUbPbex6P/ro42ztbAnG2u/atauEhc3Od11OmvR5LvkVKlTgnXeGsmzZItq27cCTJ0/YuHE9EyZMwt393zVkqampLF48j/3795KQEM/LL1dn5MgxBAW9KufZvj2c7du3cvPmDZRKJbVqeTN69Dhq1fLG1kaJJEnM/eErHsU9oG3H7mxa/xP37sYw9Zu51KjpA4CTw/NhMkxqOWnSpHwLUygUfPvtt0+lkODZoNXpiU9KsyhvaoaOnUej0JroHaRr9fx2LIpA77I42hu/pZT/W/Cp10m4u9pja1N4SxC++OIz3nyzG337DuDXX9cxdeqnrF+/hYoVK8h5/vnnDLdvxzBy5BgcHBxwcXEhJuYWI0cOwcenNlOmfIler2PJkgV88skHLF68AoVCQXp6OmPHjiQpKZlRo8bi4VGa8PBfGT9+FGvXbsr3izElRUNiYgKenp4A3LhxnQ8/HENQUEO+/no6sbH3WLBgLnfu3GbmzDlmZa1du4oGDYKYMuUrrl27wsKFYVSoUIF+/Qby2mtN6NOnP2vXrpKHdkuVcgGw6LoNhIZ+RceOnenVqy92dnacOnWS6dO/ZejQd/Hzq0NKioZz5/5Bo8k7cHilSl74+vqxd+9uBg8eRlpaKocOHeCrr0LZvj08W96EhHhKlXJjzJj3cXV15dataJYtW0R8/GM+/vgzIHNUYdKkD4mPj2fixCnY29uxdOlCEhMTqVy5iizL1PVOmPA+S5f+bEGr/UvO9rt16xYjRgwuUF2aonfvfuzevZNZs74jOTkZP786dOrURU6XJImJEz/g+vVrDBkyAi+vSuza9RsTJoznp5/W8NJLLwNw7949OnV6k0qVvEhPT2PXrh2MGjWUJcvWgurfda737sawcd1yur01ANdSpfD0LIdCoaCUsx02z8nSIZOGbfPmzbmOGRolZ3fV0IUVhu35QKvTM3nxMWLjCx79O7dMianLj1uUt5y7I18Pa1hoxq1Xr7688Ubmg+/t7cObb7bjr78O0qNHTzlPUlIyy5b9ks0QzZz5H0qX9mTGjNlyL6BGjVr06/cWR44cplGjJuzatZPr16+xcuV6uZcRGBhM3749WLv2F957L+/hIb1eh1arJTb2AWFhPwKZw3IAP/20mAoVKhIaOlMeGnV1dWPq1EmcO3cWf/+6JuVWrFiJzz77PyCzl/DPP2f488/f6ddvIB4eHlSsWBHIPrQLsHz5ojyv20CLFq0YNmyk/Hvt2pXUrFmLkJBB8rEmTSx3Imvdui3bt29h8OBhHDp0EHt7OwIDg3MZtho1ajJ69Hj5d5069XBwcCQ09Evef/9jbGzsOHLkMJcvX2Lhwp/k3rO3ty+9e3fNZtjMXe9ffx3i1VebYA69Xo9WqyUu7mGu9lu2bBGenmUKVJemsLW15cMPJzF69DCUSiXLl/+SzUAeOXKYEyci5Ou2sVHQsOFrREXdZOXKZUyZ8hUAQ4e+K5fR6XQEBb1K7z7d2LZjB2906YONjQJbWyVJSQlM/nIWlbyqyucxrGN7XjBp2LLuQg3w6NEjhg4dSqVKlRg6dKi8I/WVK1dYsmQJd+/eZcmSJYWrrUBgAcHB/w6/uLm54+7uQWxsbLY83t4+uXpXJ05E0L79GygUCrTaTO+vihUrUaFCRSIjL9CoURNOnIjAx8eXihUryXkAAgLqExl5Ach88WVdvGpjY5PtRdSmzevy3+7u7kycOIVatbwBuHDhAi1atMw23/f665m/z549bdawBQU1zPb7pZeqExl50WT+/Fy3gawOOwC1ankTFjab2bO/p1mzFvj51ck2NJgXLVu2ISzsR65evcL+/Xto3rwltra5X0uSJLFhwxq2bt3MnTt3SE//d7Th/v17VKtWlQsXzlO6tGe2IeEKFSqiVvtYdL0VK1YkMvJinoatffvX5b9ztt/x48fo2LFgdWmOevUC8POrg5ubG9Wr18xxPceoWLES3t4+aLVaJCnzXgsMDObAgd/lfNeuXWXRojDOnz9HfPxj+fjdOzGobJWUL+2EvUpJpUpe1PP3RquTUCoyhx+fl56aAYsHTENDQyldujRz587NdvyVV14hLCyMIUOGEBoayvTp062upMC62Noo+XpYQ4uHIiMiY9l66AbpWtNrWOxslXRu9BINa5c3mv4shyJdXFyz/VapVKSnZ19cWrp06Vzl4uPj+eWXFfzyy4pcabGx9+U85879w+uvv5orj8FLcdq0L/ntt+3y8U8/nUrHjp3l3/PnL8HBwQFXVzfKlSufLTJMXNxDPDw8s8m1sbHBzc2dxMREk9cM4Oqa/bptbW2zGQBTWHLdBkqXzq5bcHBDPvtsKuvXr2XDhrU4OjrRtm0H3ntvnEWRiMqWLUfdugFs2bKZo0f/YsaMH43mW79+NWFhP9Kv30BeeaUBrq6uXLx4gZkz/yNfY1xcHB4eHrnKeniUzuaIYu56jS0/yElY2GLs7R1wd3fP1X7x8fGsXLmClSvzX5d5YWtra/SjISEhnrt37xi9Jw1tkJiYyAcfvEf58hUZN+5DnF1Lk6pVsGBOKDpdBhVKO2Hzv2eydOnSlHLO7J0VpXPS02CxYfvzzz8ZO3asyfSWLVsye/ZsqyglKHxsbZSUcbfMo61lfS+2HLphNo8EtKjvhZMJ139rBGa1JsY8OEuVcqNZs9fp3LlrrjQ3N/f/5SmFr29tPvxwYq48KlXmy2Dw4OH06NFLPl6xYqVs+dRqb9krMieenmV4/PhRtmM6ne5/c0yFE+/Tkus2YMzxtVOnzrRv/waPHz/mzz//y5w5M3F2dmbkyDEWnb9Nm7Z8//10PDxKExBQ32ie33/fT4sWrRgx4j352M2b2e9JT09PHj9+nLMojx8/wt7eXv5t6nqVSkU2hwxT1KrlLTvy5KRUKTeaN28hD4VnxZK6LAiurm5UrOjFV19NA5A3+NTrJRSKTGN19uwp4uLimD9/GQ6uniT+L4pISkoSjnY2slH7n2bWUawIsdiwpaenc//+fZPp9+7dy/VVLCgZODuq6NCwKruORRvttdmplLQLqmrSqD0vNGgQxPXr1/D29jW5dCEwMJh582ZTvnwFPDxy9/rAMKxVyWhaXtSu7ceBA38wYsR78nDkn3/+jk6no27dgALJNGBrm9k+aWlp2V70lly3JXh4eNC1aw8OHPidmzevW1yuRYvWHDt2hAYNgk3GNU1LS5U/HAzs2fNbtt++vrVZunQR58+fk4cj7927x+XLkdSpU0/OZ+p6rfHxFRgYxPXrV5+6LvN7zvDwX3F1LYWXV2Wj15GWltmrTU7Tk67IfE9fu3SG+MePivW+agXFYsNWv359Vq5cSdOmTQkKCsqWFhERwapVq6hf3/jXluD5p3vz6kgS8jq2jAw9KpUSSYJ2QZnr2J53Bg8ezvDhA5kwYRydOr2Jm5s7Dx8+4PjxY3To8Ab16wfSoUMnNm/+lTFjRtCnT38qVfIiMTGBCxfO4+npSe/e/Z5Kh4EDhzB4cD8mTfqIbt16EBsby/z5cwgOfs3s/JolVKv2EgAbNqyhQYMgXF1dqFbtJYuu2xSLFy8gMTGRgID6uLm5c+XKJU6fPsm77462WC8PDw/+85+ZZg1KUFBDfv11HbVr++PlVZk9e34jJiYmW55GjZpQs6aaKVM+YeTIMdjZ2bN06YJcHyCmrvfEiWN06tSZevUaWKx7ToYOHcGQIQMKVJcFpVGjptSrV59x40bSr99Aqld/maSkJNlPYujQd/Hzr4udnT0/zpxG+05vkZTwgF/XrZC9OUsaFhu2SZMm0bdvXwYMGIC/vz/Vq1dHoVBw7do1zp07h4uLCxMn5h6eEZQMFAoF3ZpVp21wlWyRR4J8yj33PTUDVatWY+HCn1i8eB7Tp39LWloaZcuWpUGDINmrzt7enrCwhSxcuIBlyxbx6FEcHh6l8fX1o0mTZk+tQ/XqNZgxYzYLF4bx2Wcf4+TkTOvW7Rg1yvQ0gKXUq/cKffuGsGHDWhYuDCMgoD7z5i2y6LpN4etbm7VrV7Nv325SUlIoX74CgwYNp2fPt59a36y8884w4uPjWbx4PgDNm7dg/PiP+OST9+U8CoWC0NCZTJ/+DdOmfYWHhwchIYM4ceIY8fHxcj5T1xsYGJzn9eZF1arVWLz4JxYuzH9dFhSlUsn06T/w009LWL36Zx48iMW1lBs1aqjp3bsPer0EqlKMfn8Ka1ct4ofpk6latRqTJn3O4sULCkWnokYh5WMb1JiYGH744Qd+//13OQKEk5MTLVq0YPz48VSpUjgNV5To9RJxcXmvycmJm1vm/FVCQsFd6q0hIynpIVqtDg8P404dllAcNk+0hozioIOQIWQUhgxTW85IgI3yX1kerva4udibkWSZDnFxmdNSnp7G3yueni7yXF9RkK9l5JUrV+b7779HkiTi4uKQJAlPT0+x35dAIBAUIVm3nDFg+Funy/zt6eaAq9PzsxbtaShQfBSFQkGZMvmLsCAQCAQC65PXljMAKJ6fcFjWIN9XqtPpuH79OgkJCUYrMqdjiUAgEAgKD0u2klGgICVVK3psxli0aBGLFy8mOdn0nNPFi3lHOhAIBAKBdShpW85YA4snxzZs2MDMmTPx8fFh/PjxSJLEwIEDGTJkCG5ubvj7+4s4kQKBQPCMMWw5Y47nacsZa2CxYVuzZg0BAQGsXLmSXr0yoyo0b96cjz76iK1bt3L79m10hllKQbHBxsYmW9xCgUBQsnBysM2zx2bIZy0kSVesnQYt1uz69eu0b98e+DcckcGQlStXjl69evHzz/nb8kFQ+Njb26PVZqDRmI8zKBAInj8kSeJRovl4oNbeckajSUSrzchXsOtnjcUmXKlUygE1DXHSEhIS5HQvLy+ioqKsrJ7gafHwKE1aWhqJiY958iQZhSL/u0Qbnoen6fgVBxnFQQchQ8iwlgwJidQ0HTqdHgWZMSL1hp6bhBzy0c7WBn26krg4y4ciTekgSTq02gzs7Z1wdnazWN6zxmITXqlSJTmEjZ2dHRUrVuTEiRNy+j///IObW/G90BcVhUJBhQoVcXFxw8amYF9YtrY22Nrm3yAWNxnFQQchQ8iwhoy0dC1/X3rA8cj7nLzygEdJaTg52OJgZ8uTdB0JKekkpWTgYGeLncqG/AY2NqWDjY0KFxc33N3LFOsYkxb32AIDA/njjz/48MMPAWjfvj0rVqwgNTUVSZLYunUrPXr0KDRFBQVHoVDg4pJ31HJTFJcoKk8rozjoIGQIGU8rI+peErM2niEhOR0bpYLBHX15pfa/u8OraxWP6yhKLDZsAwYMwMfHh9TUVBwcHBgzZgw3btwgPDxzp9vGjRvLRk8gEAgE1ufstTjmbzlHWroOR3tbRnevg2+13HvQvehYbNiqV69O9erV5d9OTk4sWLCApKQklEolzs7OhaKgQCAQCODP07dZufsyeknCs5Q943vWw6usS1GrVSx5av/PnLv2CgQCgcB6SJLE5oPX2f5XpnNe1fIujHurHh6ueQczflEptgsRYmJiGDZsGEFBQTRu3Jgvv/wSrTYzdMzFixfp3r079erVo3v37tminUiSxHfffUfDhg1p2LAh06dPt2iNh0AgEBQ1mtQM9kZEs2H/Ff48fZsETRpLtl+QjZp/9dJ80re+MGp5YLbH5uvrmy9hCoWCCxcuPJVCBr744gs8PT05dOgQiYmJDB48mNWrV9OnTx9GjRrFwIED6du3L2vXrmXUqFHs3r0bOzs71q1bx759+9iyZQsKhYJBgwZRpUoV3n7buvtDCQQCgbWQJInwgzfYFRGNUqEgLUOHna2Sn3ddwvBZ3qxeJULaqa22Hq0kY9awSZKEg4MDDRo0eOaL8WJiYujfvz/29vaULVuWJk2acPXqVSIiItBqtQwcOBCFQsGAAQNYtmwZR48epVmzZoSHhzN48GAqVMj0Eho0aBAbNmwQhk0gEBRbwg/eYHdENBnafxeOpWf526eqOwPbexdrF/vihFnDplaruXz5MhcvXqRz58706NEDtVr9TBQbMGAAO3bsIDg4mMTERA4ePMi4ceO4evUq3t7ZG9jb25urV6/SrFkzrly5go+Pj5zm4+PDlStXCqyHQvGv62t+MKwBKUhZIaNwZBQHHYQMISMnyU8y2JXDqOXk2p1EVPYqnB3z7mAUh7ooavtrtk+7detWNm7cSLt27QgPD6dLly689dZbrF27lqSkpEJVLDg4mKtXr9KgQQOaNWuGv78/rVu3RqPR5HJYcXFxQaPRAJCSkoKLy7+eQq6urqSkpIh5NoFAUCw58s9dlHlYAqVCwV//3H1GGj3/5OkV6efnh5+fH5MmTWLv3r1s3LiRL7/8ktDQUFq1asVbb73Fa6+9ZlWl9Ho9Q4YMoXfv3qxduxaNRsOnn37Kd999R7ly5XJtm6PRaOTlBk5OTrKRA0hOTsbJyanAXXhJKtgixedlseeLJKM46CBkCBk5ufcwmbQM8wHk0zN03HuYbJHc4lAXnp4uRdprs3gW0s7Ojk6dOrFs2TL279/P8OHDOXv2LIMHD2bhwoVWVSo+Pp67d+/Sv39/7Ozs8PDwoEePHhw4cICaNWty6dKlbD2wS5cuUbNmTQBq1apFZGSknBYZGUmtWrWsqp9AIBBYCzdnO+xszb+KVSolbs4vxiah1qBA7jVKpRKlUolCoUCS8t7kLr+ULl2aypUrs2bNGrRaLYmJiWzevBlvb2+Cg4OxsbHh559/Jj09nVWrVgHw6quvAtClSxeWL1/O/fv3uX//PsuXL6dbt25W1U8gEAisRX11WTJ05iMeSxIE+ZR7Rho9/1i8QDs9PZ19+/axceNGjh49io2NDa1bt2bq1Kk0atTI6orNnTuXb7/9lsWLF6NUKmnYsCGffvopdnZ2hIWFMXnyZL7//ntq1KhBWFgYdnaZXzN9+vTh1q1bdO7cGYC33nqLPn36WF0/gUAgsAa/HYvGXN/ATqWkXVBVnByK7zYxxQ2FlEd369y5c2zatIkdO3aQkJBA7dq16dGjB507d6ZUqVLPSs8iQ6+XiItLzjtjDorDOLeQUfx0EDKEjKwc/ucuS3dkBph4uaIrMQ80KBUK0jN0qFRKJAnaB1ela9OXLfYTKA514enpgrIId+w222N78803uXLlCu7u7nTp0oUePXrg7e39rHQTCASCEsvVmARW7Mr0B/CvXppxb9UlNV3H+ah44pPSsLdVEORTTvTUCoBZw3b58mUcHByoXbs20dHR/PDDD2aFKRQK5s+fb1UFBQKBoKQRl5DK3E1n0eokKno68e6b/tgolTg7KGkTXBV4freMKQ7kOceWmprK4cOHLRImVsULBAKBeVLTtczeeJbElAycHWwZ26MuTg5PHY9ekAWztZnVbV4gEAgET4dekli6/SK3YpNRKhSM7OpP+dJORa1WiUNE0xQIBIJnxNZDN/j78gMA3m5di9ovlS5ijUomwrAJBALBMyDi4n22Hr4JwOuveNGyvlfRKlSCEYZNIBAICpkbdxNlt36fqu70bV1L+CQUIsKwCQQCQSHyOCmNORvPkqHVU9bdgVHd6mBrI169hYmoXYFAICgk0jN0zN30D/HJ6TjY2TD2rXq4WLD1jODpEIZNIBAICgFJkvhpVyQ37iaiAEa86YdXGeeiVuuFQBg2gUAgKAR2Ho3i6Pn7APRsUZN6NcsUsUYvDhavCtTpdKSnp+Po+O+OqomJifz6668kJCTQsWNHEW5LIBC8sGhSM4i49ID4pDTux2n4/dRtABr7V6BdcJUi1u7FwmLD9vnnn3PmzBm2b98OQEZGBn379uXq1asALF++nHXr1uHr61s4mgoEAkExRJIkwg/eYFdENEqFItumoe4udoS08xYekM8Yi4ci//77b1q2bCn/3r17N1evXuXzzz9n7dq1lClThkWLFhWKkgKBQFBcCT94g90R0WRo9bl2wtakatlxJKqINHtxsdiwPXjwgMqVK8u///jjD2rVqkXfvn0JCAigV69enD59ulCUFAgEguKIJjWDXRHRpGuNbxSaodWzKyKalNSMZ6zZi43Fhk2SJHS6f79GIiIiaNiwofy7bNmyxMXFWVc7gUAgKMaciIwlr1FGhQKOR8Y+G4UEQD4MW+XKlTl06BCQOSz54MGDbIYtNjYWV1dX62soEAgExZSE5HTSM4z31gxkZOhJ0KQ/I40EkA/nke7duxMaGsobb7zB/fv38fT0pEmTJnL6mTNnqF69eqEoKRAIBMWNhOQ0i3piKpUSN2e7Z6CRwIDFhu2dd95Bo9Gwf/9+fH19+eCDD2TX/8ePH3PmzBkGDx5caIoKBAJBceH01Ycs33mRpJS8584kCYJ8yj0DrQQG8rW73Xvvvcd7772X67iHhwdHjhyxmlICgUBQHEnL0LHuv1f5439r1JzsbfGp5s6564+MOpDYqZS0C6qKk4MIo/UssXiObcCAAWaN19GjRxkwYIBVlBIIBILixs17iXyx/Lhs1HyquvPlkGDe61aHdsFVUdkqsVfZoCDToKlsM41a16YvF63iLyAW99giIiLo2bOnyfRHjx5x/PhxqyglEAgExQW9XuK3Y1GEH7yBTi9ho1TQvVl12gVXRanMdIns1qw6bYOrcD4qnvikNOxtFQT5lBM9tSIiX0OR5khMTMTOTkyQCgSCkkNcQipLtl/g0q14ACqUdmLEm35Uq5DbA9zZQUWb4KoAJCQ8eaZ6CrJj1rBFRkYSGRkp/z5x4kS2tWwG4uPjWbNmDTVq1LC+hgKBQFDIZI3zaG+rINCnHP9cj2Pl7ss8SdMC0OIVL3q1rIm9yqaItRXkhVnDtm/fPubOnQuAQqFg3bp1rFu3zmheZ2dnPvvsM+trKBAIBGYwZpScLRwCNBbn0c5Wyc+7LyFJmXlcnVQM6uhLgIjO/9xg1rB169aN4OBgJEli4MCBjBgxgsaNG2fLo1AocHJyombNmtjb2xeqsgKBQGDAqFFSKVm97wrtgzOdNvIKPpw1zqOBrN6N5dwdmRTSQKxDe84wa9i8vLzw8vICYNq0aQQFBWWLFykQCARFhVGj9L8oILsjooFMpw5TJGjS+O1YFFqdZDLP4+Q0VDYiMv/zhsXOI926dcv2+9GjRwCULl3auhoJBAJBHhiCD2eYCD6crtXz27EoKpVx5kmalvjkNOKT03iclP6//9NIfpL34mpDnMfmAV7WvgRBIZIvr8j79+8zc+ZM9u/fj0ajAcDFxYVWrVrx/vvvU758+UJRUiAQCLJiSfBhrU5i4dbzT3UeEefx+cRiw3bnzh169erFw4cP8fX1pWbNmgBcu3aN8PBwDh8+zPr166lYsWKhKSsQCAQACZq8gw8DKJUKypRywN3FDndXe9xd/vfP1Y6oe0n89+Rtk70+EHEen1csNmw//vgjiYmJLFy4kObNm2dL+/PPPxkzZgw//vgjoaGhVldSIBAIspKWrkMBmJ4dAztbJX1a1eL1V4wPI9ap7sl/T942ex4R5/H5xOKQWocPH6Zv3765jBpA8+bNefvttzl48KBVlRMIBIKsJD/J4KffIvntWLRZowaZRi/Y17RRcnZQ0T64Kna2xl+Ddiol7YNFnMfnEYt7bAkJCVSrVs1kerVq1UhMTLSKUgKBQJAVSZL469w91v33quz0UcrZjiepWjJ0BQ8+bIjjaFgykJ6hQ6VSIkmIOI/PMRYbtgoVKhAREcHbb79tNP3EiRNUqFDBaooZ2LFjB3PnzuXu3buUKVOG0NBQAgMDOXLkCF988QV3796lbt26hIaGyksT0tPTmTp1Krt378bR0ZGhQ4cyaNAgq+smEAgKnzsPNazac4nI6MywVg52NnRvVp0Wr3ix9fDNpzJKCoVCxHksgVhs2Nq3b8+SJUuoXLkyw4cPl3fLTk5OZtGiRfz2228MHz7cqsodPnyYGTNm8MMPP1C3bl0ePHgAZC41GD16NF9//TUtW7Zk1qxZvP/++6xfvx6AOXPmEBUVxe+//87Dhw8ZMGAANWrUoFmzZlbVTyAQFB7pGTq2H7nJb0ej0ekzBx6DfMrRp1UtPFwzg0FYyyiJOI8lC4sN26hRozhx4gSLFy9m2bJllCuXOXYdGxuLTqejfv36jBw50qrKzZkzh1GjRhEQEAAgLydYt24dtWrVokOHDgCMGTOGV199lWvXrlGjRg3Cw8OZNm0abm5uuLm50bNnTzZv3iwMm0BQzDAVDuuf63Gs2nOJB/GpAJR1d6B/W2/qVPfMJUMYJUFOLDZsjo6OrFq1io0bN7Jv3z5iYmKQJIkmTZrQunVrunXrhq2t1TYLQKfTce7cOVq2bEmbNm1IS0ujdevWfPzxx1y5cgVvb285r5OTE1WrVuXq1auUKVOG2NhYfHx85HQfHx/2799fID0UCnBzc8x3OVvbzECpBSkrZBSOjOKgg5CRicKetgIAACAASURBVCRJrN17mS0Hr6NUKkhL12GvsmH1viuU83Dk9oPMdbK2Ngq6NqtB9xbmgw8/7/VhTRnFQYe81hgWNvmyRDY2NvTq1YtevXoVlj4yDx8+JCMjg127dvHLL79ga2vLqFGjmD9/PikpKbkinri4uKDRaEhJSQGQh0oNfxsWlAsEgqJn7d7LbDt0I9sasrSMzJ1DDEbNr3pphnepQ+VyLkWio+D5xXpdLCvj4OAAQEhIiDzsOWjQIObPn09gYCDJycnZ8ms0GpydnXFycgIy5/4MQZmTk5NxdnYukB6SVLDhDcOXztMMjQgZ1pVRHHQQMjKHH7ccvG52YbSNUsG7nWvjbG9jkeznuT6sLaM46ODp6VKkvbZ8GbaUlBS2b9/OzZs3iY+PR5KyryRRKBR8++23VlHMzc2NChUqGI3OXatWLTZv3pxNr+joaGrWrImbmxtly5YlMjJS3okgMjJSjpQiEAiKFkvCYdnYKDhx6YGI0SgoEBYbtrNnzzJ8+HDi4+NN5rGmYQPo3r07K1eupGnTptja2rJixQpef/112rRpw/Tp09m9ezevv/46YWFheHt7yxuddu3alfnz5+Pv78/Dhw/ZsGGDVfUSCAQFx5JwWCJGo+BpsNiwTZs2Da1Wy6xZs3j11Vdxd3cvTL2ATE/Mx48f065dO+zt7enQoQMjR47E3t6eOXPm8OWXXzJhwgTq1avHzJkz5XJjx45l6tSptGjRAgcHB4YNGyY8IgWCYoKbsx12KqVZ4yZiNAqeBoWUczzRBHXr1mXEiBG89957ha1TsUKvl4iLS847Yw6Kwzi3kFH8dBAyMufYPph72HzwYVslP4xubPF6tOe5Pqwtozjo4OnpglJZdJNsFseKdHFxeSa9NIFAULJxdlDRor7puTMRo1HwtFhs2Nq0acOhQ4cKUxeBoMSjSc1gb0Q0G/Zf4c/Tt9Gk5r3ZZUnEIcuaNHuVDQoyDZrKViliNAqeGovn2CZMmMCQIUP46quvGDhwIFWqVDHqsSgQCHIjSRLhB2/IcQ3TMnTYqZSs3neF9sGZL/IX5XlKTdey/+8YANoGVaZ6ZQ8Ro1FgVUwaNh8fn1wPmiRJnD17ltWrVxsto1AouHDhgnU1FAiKCabCP1lC+MEb7I6IzjavZHCe2B0RDWTGPXwR+PP0HTSpWuxslXR89SWqVHIDRDgsgfUwadi6du36wnxBCgTmeNreliY1g105jFpW0rV6dkVE0y64yjPrrTyNkX4aMrR62ZA3rVeJUsLzUVAImDRsYidsgSATS3tbaRk64pPTiE9K43FyGvFJ6cQnp3H5VjxaI3uG5eTYhfu0qF85z3xPY5SKekj0r3N3iU9Ox0apoP3/AhcLBNam2IbUEgiKA5b0trb9dZN9J27xJF1X4PNkaPWs3neF45Gx1KzsTq3KbtSo5IaTw7+PqDWMUlEOier0en47mnmOV/3K4+nmUCjnEQgKbNhOnTrFpk2buH//PjVr1uSdd96RYzoKBCUFS8I/AbmMmoujCg9Xe9xd7HmSpuXG3UR5TzFT6PQSkdHx8oaaCsCrrAu1KrtRq7Ib1+4kcPDM3QIbpaIeEj0R+YDY+CcogI6vVrO6fIHAgFnDtnjxYhYuXMiOHTvkvdAAtm3bxsSJE9HpMh/mAwcOsGPHDjZt2oSnZ+79kgSC55V7j1LyDP8EEOxTjtaBVXB3tcPN2R6V7b8raQwLks0ZNlsbBSHtvIm+n8zVmASiY5OQJIh5kEzMg2R+P3Xb7PnTtXp2Ho3CzlYJCkjL0JOh1ZGeoSc9Q0eaVs/dOE2eQ6IKBRyPjLV6jEZJkthxJAqA+t5lqehZsKDkAoElmDVsx44dw8fHJ5tR02q1hIaGolQq+b//+z/q1avH3r17mTt3LkuXLuXjjz8udKUFgsLmcVIaO49E8fupmDzz2qmU+L7kQc3KbkbTnR1UtA+uyu6IaNKN9JbsVJlrt5rWrSQfe5Km5frdRK7GJHA1Jp5Lt+LR6vLu8W08cD1Pfc1RWDEaz16LI+ZBZgSfTq+J3pqgcDFr2K5fv07nzp2zHTt+/DhxcXH079+fnj17AqBWq7lw4QIHDx4Uhk3wXPM4KY2dR6P48/Qdixw+IHNroyAf88PwhgXHhvmx9AwdKpUSScLogmRH+/9n777Dori+PoB/d4GlVxVEqqLSFMWCCKjRiCWa2CJGIxoVoxGJYjQJ0VhjSWKJGGMXY9RYUDQoikIEC6iAioKg9CZFellgWfa+f/DbeSWyuwMsQsz9PE+eyOzO4czdYc7OzL13FGFrrgdb84bnDl66k4pLd9Jl5qKlpoTOOqrgKXLBU1Jo+O9//y4o4eN5ZqnUM8e2mqPxyr2GszVbc12Yd9WSe3yKep3UwlZcXAxj48a9tB49egQOh4P333+/0XIHBwdERETIP0OKkgNZPQmbKmha6jx8MMQUZXwBQqOzpZ5tybonxeFwMGV4D4xxMEF8RmmzByTraCjLnDiYp8TFlOE9JF5GZHNJlE2Rbq4XWaVIzi4DAEwYai7X2BTVFKmFTUVFhXkitdiTJ0/A4XBgZ2fXaLmmpiZzz42iOgpZPQlH9O+Ga/czEfZ6QVNTwnhHM7xnbwRlJQUQQqDI5bI+25JGXUUJrv/r5t6cAcmDrPRxKiRJxrZKL0qyLokCwOiBxnLvOHI5Mh0AYNFNC5amdL5Zqu1JLWzGxsaIjIzE3LlzAQC1tbWIiYlB796933gidWFhIe04QnU40rq3X7mXjiuR6RCfwGipKWHcEDOMHNBQ0MRae7YlD2zv08nKp6lLooqKXKZ9yvnyvb+WkVeBuNRiAA1na3TSB+ptkFrYJk2ahC1btuDHH3+Eo6Mj/vrrL1RWVmL8+PFvvPfhw4cwNaUDLqmOQ1b3dtH/FmuoKuIDR3OMtDeCMk+hyfcCLT/bkpfm3qdriqQiXVElwIXbabj7NA99unfCEBsDmbHYEN9bM+qiDrue9Isv9XZILWwzZszAlStX4Ofnh2PHjoEQAhsbG8yZM6fR+169eoU7d+7Ay8urTZOlqOZgMwZNgcvBJOceeH+Q7Bk/2ps8zxz/WaRFhCAppxxPU4twPDgRPbppoYuOaqvyzSvmIyaxAEDDuDUuPVuj3hKphY3H4+HkyZMIDQ1Feno6TE1N8f7770NJqfEfUVFREVasWIFx48a1abIUxRYhBBn5lTLHoIlEBFW1/65Hx7TFmSOXw8GCCdZYd/QByqoEOPhXPL75dAAUFVg/2eoNV+9lgADorK0CB2s6eQP19siceURBQQFjxoyR+h4rKytYWVnJLSmK+ie28yMWllXjXnw+IuPzkFvEbyJSY23Vvf3fSEudB48PbbDz9GOkvCzHpTtpmDbCokWxistrEBGXBwAY72gGBW7LCyRFNRedK5Lq0NjMj8ivFSI6sQCR8fl4kVXazPjy797+b2Zrrodxjqa4ei8TQZEZsDHThfX/xtI1R/CDLNSLCLTVeXDp27UNMqUoyWhhozo0ab0ar97PQFRiAQrLqhvNyqGlzsMQawM49emKmOcFuB6V1aqehP81U4b1QGJGKdJyy3Hw8jNsmO8ALTX2Z7UVfAHCYxumABvjYAIlRckdciiqLdDCRrW5lj5mRVavRmE9QV5xw+VGnhIXA3t3wVDbrrA212UufZkaaIDD4chlDNp/haICF4s+ssF6vyiUVQpw9EoCln1sx7qrfkh0NgR1IqgpK+I9Oc85SVFs0MJGtZmWPmZFJCIoLq/BjZhsECJ9fkQOB3Dpa4iZo3tBhffm7twRxqD9G+nrqmHOWEscDHyGJylFCInJhusgE5nrVdcKERrTML/m+wONoapMDzHU20f3OqrNyHr2V1VNHfr37Iz8kmoUlFQjv4SPgpJqvCqtlvmIFwYBOmmrNFnUXtfeY9D+jRxtuyI+rRh34/Jw7mYyLE10YGqgKXWdsMc54NcKwVPiYvS/YAgF9W6S2FXJ2toagYGBzM8+Pj6IjY19K0lR/37iy4iSpm4SCEX4+2EOdp6NxckbL3AjOgtPUoqQV8xnX9RAezW2tVmuvWGgqwphPcH+S/GolfIw1TphPa4/yAIAjOhnBM1m3JejKHmS+DWXy+U2mvsxICAATk5O6Nev31tJjPp3i/7fwFxZOAC66KrCQFcN+rqq0NdVhYGuKvR11aCqrICv90VKvMcG0F6NbU1VWRGLJtli8/EY5BXzcSrkBeZ9YN3ke+88zUNZlQAKXA7GOsi+bElRbUViYTM0NERMTAwmT57MLKPzvFGyiAhBfFoxrt6T3OnjdR86m2PyMMlPfZbH/IhU65h31cLH71ngzN/JuP0kF7bd9eBg3XjKrXqRCFf/N32WU5+u0NNSaY9UKQqAlMI2adIk7N27F8HBwdDUbLiuvmXLFuzatUtiMA6Hg5CQEPln+R/U0p6E7aVWUI+I+DyERGexGhgNNBQmXU1lqe+Rx/yIVOu5DjZBfHox4lKL8fu1RHQ3bDzlVlRCAQrLasBBw4BsimpPEgvb0qVL0a1bN0RERKCwsBAvX76Ejo4OncG/jbW0J2FbkVVgi8trEPowG7cev0RVjZBZ3ttYGykvy1v97C/aq7FjaJhyywbrjj5A+WtTbgENvVjFkx0PtNJHVz219kyVoiQXNg6Hg2nTpmHatGkAGqbN+uKLL954ojYlX7J6EgLAlOGSL929rjVnfbIKbJ8eegiJzkbM81cQ/a9LvqICF462BnAdZAITfQ0E3EqV22VE2qux/Wmr8+Ax0Ro7z8Qi5WU5/MOS0cNYF/FpRch5VQUAmEDP1qgOgHV3/+PHj6Nnz55tmct/nqwByQKhCNceZGKsg4nUgiCPsz5pBfZyRDoCI9KZ5drqPIwcYIT3+htB67UeivQy4runT/dOGOtgguAHWbgelQ2lRy+ZfYTDAR6+eMUMiqeo9sK6sDk4OCA3Nxfh4eGoqKiApqYmevfuDUNDw7bM7z+FzWNWCAGu3s9kioiS4psjNlp71ierwIovLpp00cDYISYYbGXQZB70MuK7SZHLBQcN+8Hr+wghzb+qQFFtgVVhi4yMxI4dOxAfH//Ga7a2tvjqq68wdOhQuSf3X1NYViPzMSvCehGuRGbgSmTDPQ1VZUVoqfOgraYELXUeVJUVcTcuDyIJ97YEQhGC7mUAHAKRCKiprUeNQIhqQcP/awT1KCqvkdmjUUmRi5EDjODUR/YXG3oZ8d1RVVOH69FZkHTnlO1VBYpqSzIL2+nTp7Fx40YQQtC/f3/Y2tpCQ0MDlZWVePbsGR49eoQFCxZg3bp1mDFjxtvI+Z1CCEHqy3KEP36JyGd5zV6/ulaI6loh8ovZr1MvIgi8m9Hs3/U6oVCEcr6gVTGofx82VxU4HCAqsQAj6DyRVDuRWtgSExOxadMm9OrVCzt27GjyHltycjJWrVqFjRs3ol+/fvS5bGDXaYNfU4fI+HyEP85B9v9uvLOhpMjFloWOENaLUFYlQHmVAOX8//2/SoDnmaXILZbd3V5XUxmGndSgwlOECk8BKjwFqCo3/Du7oBIxL141mjH/jTzojB//SWVVAplXFerqGvZNimovUgvb0aNHoaOjg99//x06OjpNvqdnz57w8/PDhAkTcOzYMWzbtk2uCaanp+PDDz/E2LFjsX37dgBAYGAgdu7ciZKSEjg5OWHLli1MfqWlpVi9ejXu3r0LXV1drFix4q315JTVaWOSizlSX1Yg/HEOohILGvUWNNHXwHv9u+FVaQ3+fpgttSdhJ+2Gwa8GTXSrDn+cgz9Dk6QefHhKXHzkbC7xG3VVTR0eJhUCEi840Rk//qu01XngKXGl7l/0Sw/V3qQWtqioKEydOlViURPT0dHBlClTcOXKFbkmBwAbN25E3759mZ+TkpKwdu1aHDx4EDY2Nli7di02bNjADBzfuHEjlJSUcPfuXSQkJGDRokWwsrJCr1695J7bP0nrtBF0LwM3H+egkl/HvKaspIAhNvoY0d8I5l01weFwQAiBkiK3xT0JB1np41RIktT3yCpK6ipKdMYPqkny2L8oqq1JLWyFhYUwNzdnFah79+4oLCyUR06MK1euQFNTE/b29sjIaLgnFBgYiFGjRmHw4MEAgGXLluGDDz5AZWUluFwurl+/jsDAQKirq2PQoEEYNWoULl26hJUrV8o1t3+S1ZOwXkSYomZqoIH3+hthiI3BG4/1aG1PQnkVJdpVn2oK/dJD/RtILWxqamooKytjFaisrAxqavKbcaCyshK+vr44duwY/P39meVJSUmwt7dnfjY1NYWSkhLS09PB5XLB5XLRvfv/H3StrKwQFRXV4jw4HEBbW1Xm+x48fwWujLvqClwOJo+wwKwxljLjaWurwtRIFwAgFEqeUb0pcyfaQFlZEZdup4LL5UAgqAdPSQEiQvCRSw984tqb1Tijzz60xceje+PBs3yUlNdAS50Hp76GUFdt/kFL8X9PUWbTlm0VoyPk8C7EkNf+1do8aIyOm0N7D2OUWtisrKxw/fp1zJ8/X2ag69evw9JS9gGbrV9++QXTpk17Y5wcn89n5q4U09DQQFVVFRQUFN54TVNTE1VV7DtntFRpRS1q66QXIJGIQElB4pOC5IbD4WDmGEt8OKxHq4uShqoSxgxpmE2iuQWWejfJc/+iqLYgtbBNnjwZPj4+8PX1xZdffinxfXv27EFsbCy2bNkil6QSEhIQGRmJgICAN15TU1NDZWVlo2WVlZVQV1cHl8uV+FpLEcJu7JWyIofVTXVlRQ7rsVzib0utGfs1aqAxE0MoEKJMIJSxRtvk0RFidIQc3rUYdP/qeDE6Qg6dOmm061mbzMIWGBiIffv2ISIiAtOnT4eNjQ0zji0+Ph7+/v6IjY2Fo6Njo0fctMb9+/eRk5ODkSNHAmg4S6uvr8eUKVMwbNgwJCYmMu/NyspCXV0dzM3NmWfIpaenM/cGExMT38pUYPSmOkVRVMcgtbBxOBzs3bsXa9euRWBgYJNP0CaEYMKECdi4caPc5oebMWMGJkyYwPx89OhR5OTkYP369SgqKsKMGTMQHR0NGxsb7N69G66urtDQ0AAAuLq6wtfXFz/88AMSEhIQGhqK06dPyyUvaehNdYqiqI5B5swjqqqq+Pnnn+Hh4YHr168jKSkJlZWV0NDQQK9eveDq6ir3QdmqqqpQVf3/m5Zqamrg8XjQ09ODnp4eNmzYgJUrV6K0tBRDhw7F1q1bmfeuW7cO3333HZycnKCjo4P169e/la7+AO1JSFEU1RFwCCGSR+FSEIkIiooqZb/xNVU1dXKZ9LcjXCt/l2J0hBxoDBqjrWN0hBw6ddIAl9t+N9lYz+5PsUcn/aUoimo/bd/3nKIoiqLeIlrYKIqiqHcKLWwURVHUO4UWNoqiKOqdwqqw1dTU4OLFi02OY6MoiqKojoRVYePxeFizZg2ePXvW1vlQFEVRVKuwKmxcLheGhoZvzMNIURRFUR0N63tskydPxl9//QWBgD7ynaIoiuq4WA/QHjBgAG7cuIFJkyZh1qxZMDMzazTtlZj4AaAURVEU1R5YF7Z58+Yx/968efMbEx4TQsDhcJCQkCC/7CiKoiiqmVgXttcnGqYoiqKojop1YZsyZUpb5kFRFEVRckEHaFMURVHvlGYVttzcXPj4+GD48OHo06cPIiMjAQDFxcXw8fHBkydP2iRJiqIoimKLdWHLysrCtGnTcP36dfTq1Qv19fXMa3p6eoiLi4O/v3+bJElRFEVRbLG+x/bLL7+Ay+Xi8uXLUFZWhpOTU6PXR4wYgZs3b8o9QYqiKIpqDtZnbBEREZg5cyYMDQ3f6OoPAN26dUNeXp5ck6MoiqKo5mJd2CorK6Gvry/x9bq6ukaXJymKoiiqPbAubIaGhkhKSpL4emxsLExNTeWSFEVRFEW1FOvC5urqivPnz+PFixfMMvElyeDgYFy7dg3jx4+Xf4YURVEU1QysO4988cUXCAsLg5ubGwYNGgQOh4NDhw5h165dePLkCaytrTF//vy2zJWiKIqiZGJ9xqahoYEzZ87g448/RlxcHAghuHv3LtLS0jBr1iwcP34cysrKbZkrRVEURcnE+owNaChua9aswZo1a1BcXAxCCPT09JrsJUlRFEVR7aFZhe11enp68syDoiiKouSi2YUtKCgIISEhyMrKAgCYmJhg9OjR+OCDD+SeHEVRFEU1F+vCVl1djSVLluDevXsghEBLSwuEEDx9+hRXr17FmTNnsG/fPqipqbVlvhRFURQlFevOIzt37kRkZCRmz56N27dv48GDB4iKisLt27cxe/Zs3L9/H7t27WrLXCmKoihKJtaF7erVqxg3bhxWr16NLl26MMu7dOmC1atXY8yYMbh69WqbJElRFEVRbDVrSq0hQ4ZIfN3R0RGVlZVySYqiKIqiWop1YbO0tERGRobE1zMyMtC7d2+5JEVRFEVRLcW6sC1fvhxnz57F33///cZrISEhOHfuHLy9veWaHEVRFEU1l8RekT4+Pm8sMzY2hqenJ7p37w4LCwtwOBwkJycjLS0NvXv3RmBgIIYOHdqmCVMURVGUNBILW0BAgMSVUlNTkZqa2mjZ8+fP8eLFC2zZskUuiQkEAqxfvx6RkZEoLS2FmZkZvL29MWLECABAZGQkNmzYgNzcXNjZ2WHbtm0wMjJi1l23bh2Cg4OhqqoKDw8PzJs3Ty55URRFUR2bxMKWmJj4NvN4g1AohKGhIf744w9069YN4eHhWL58OQIDA6GmpoalS5fihx9+wKhRo/DLL7/A29sbZ8+eBQDs2bMHGRkZuHnzJgoLCzFnzhxYWFhg+PDh7bpNFEVRVNtjfY/tbVNTU4OXlxeMjY3B5XIxcuRIGBsbIz4+Hjdu3ECvXr0wfvx4KCsrw8vLC4mJiUhJSQEAXLx4EUuWLIG2tjYsLCwwffp0qWegFEVR1LujxXNFvm2FhYVIT09Hz5498eeff8LS0pJ5TU1NDaampkhOTkbnzp1RUFAAKysr5nUrKyuEhoa26PdyOIC2tmqz11NUVADQsnVpjLaJ0RFyoDFojLaO0RFyaO958ZtV2B4+fIiTJ08iIyMDpaWlIIQ0ep3D4SAkJESuCQJAXV0dVq5ciSlTpsDCwgJ8Pv+NSZg1NDRQVVUFPp8PANDU1GRe09TURFVVldzzoiiKojoe1oXt7NmzWLduHZSUlNC9e3cYGhq2ZV4MkUiEr7/+GkpKSvj+++8BNJyh/XMweFVVFdTV1Zm5KisrK5nnw1VWVkJdXb1Fv58QoKysutnrib/ptGRdGqNtYnSEHGgMGqOtY3SEHDp10mjXszbWhW3//v2wtrbG4cOH39ojawghWL16NQoLC3Ho0CEoKSkBAHr16tXonhmfz0dmZiZ69uwJbW1tdOnSBYmJiXB2dgbQ0BGmZ8+ebyVniqIoqn2x7jxSVFSEadOmvdXnsK1btw4pKSnYv38/VFRUmOWurq5ISkpCcHAwamtrsXfvXlhaWsLCwgIAMHnyZOzbtw9lZWVISUnBuXPnMGXKlLeWN0VRFNV+WBc2CwsLlJeXt2UujeTk5ODMmTNISEiAi4sL7O3tYW9vj7/++gt6enrYs2cPdu3ahcGDB+PJkyfYuXMns+6XX34JExMTjBw5Eu7u7liwYAHt6k9RFPUfwfpS5OLFi7Fp0yZMnToVBgYGbZkTAMDIyAjPnz+X+LqTkxOuXbvW5Gs8Hg9bt27F1q1b2yo9iqIoqoNiXdjGjBmD6upqfPDBBxg9ejSMjIzA5TY+4eNwOPD09JR7khRFURTFFuvClpaWBl9fX1RVVeHSpUtNvocWNoqiKKq9sS5sGzZsQHFxMVavXo1BgwZBS0urLfOiKIqiqBZhXdhiY2Mxf/58uLu7t2U+FEVRFNUqrHtFqqurv9Wu/hRFURTVEqwL2/jx43H9+vW2zIWiKIqiWo11Yfvkk09QVVWFJUuWIDIyEllZWXj58uUb/1EURVFUe2J9j23ChAngcDiIi4vDzZs3Jb4vISFBLolRFEVRVEuwLmyenp7gtPezCCiKoihKBtaFzcvLqy3zoCiKoii56LBP0KYoiqKolmB9xhYVFcXqfYMHD25xMhRFURTVWqwLm7u7O6t7bLTzCEVRFNWeWBe2pmbKFwqFyMrKwoULF2BsbIwZM2bINTmKoiiKai7WhU3agzoXLFhAH+RJURRFdQhy6Tyira2N6dOn4/Dhw/IIR1EURVEtJrdekVpaWsjKypJXOIqiKIpqEbkUttraWvz111/o3LmzPMJRFEVRVIuxvsfm4+PT5PKysjI8fvwYxcXF+Prrr+WWGEVRFEW1BOvCFhAQ0ORybW1tdO/eHT4+Pvjwww/llhhFURRFtQTrwpaYmNiWeVAURVGUXNAptSiKoqh3Ci1sFEVR1DtF6qXIxYsXNysYh8PBvn37WpUQRVEURbWG1MIWFhbWrGD0eW0URVFUe5Na2Nh0GLl//z62b9+Op0+fokuXLnJLjKIoiqJagnWvyH968eIFtm/fjtu3b0NdXR3Lli3DvHnz5JkbRVEURTVbswtbbm4udu/ejcDAQHC5XLi7u+OLL76Arq5uW+RHURRFUc3CurCVlZVh//79OHXqFAQCASZMmIDly5fD2Ni4LfOjKIqiqGaRWdgEAgGOHTuGw4cPo7y8HM7Ozli5ciWsra3fRn4URVEU1SxSC5u/vz/27NmDgoIC2NjYYOXKlRg6dOjbyo2iKIqimk1qYVuzZg04HA769OmD8ePHIzExUWpPSQ6Hg88++0zeOVIURVEUazIvRRJC8PTpUzx9+lRmMFrYKIqiqPYmtbAdP378beVBURRFUXIhtbA5ODi8rTzkrrS0FKtXr8bdu3ehq6uLFStW0MfqUBRF/Qe0eIB2R7dx40YoKSnh7t27SEhIwKJFi2BlZYVevXq1d2oURVFUG+IQQkh7JyFvfD4fDg4OjJClxAAAIABJREFUCAwMRPfu3QEAq1atgoGBAVauXNmsWO9g81AURbW59pw7+J08Y0tPTweXy2WKGgBYWVkhKiqq2bHoxM4URVH/Lu/k89j4fD40NTUbLdPU1ERVVVU7ZURRFEW9Le9kYVNTU0NlZWWjZZWVlVBXV2+njCiKoqi35Z0sbObm5qivr0d6ejqzLDExET179my/pCiKoqi34p0sbGpqanB1dYWvry/4fD5iYmIQGhqKSZMmtXdqFEVRVBt7J3tFAg3j2L777jtERERAR0cHX331FR3HRlEU9R/wzhY2iqIo6r/pnbwUSVEURf130cJGURRFvVNoYaMoiqLeKbSwURRFUe8UWtgoiqKodwotbBRFUdQ7hRY2iqIo6p1CCxtFtRIdCkpRHQstbFSLiESidv398igmr169QlFRUavjvP5oo9bmJY92be/PRl5a25btvb48ySMXeewXHalNpKGF7S1qzU5RU1OD/Px8VFRUtDhOQUEBUlJSUFBQAKBlO3pkZCSqq6vB5XJbHOPixYsQCoWtao99+/YhLS0NdXV1jZY3J+batWtx4sSJFq0r9uTJE4SEhCA4OBi1tbVMkWMbq6CgAImJiYiNjUVlZWWL2jUnJwdxcXGIjo5ucYyWvL8tEUJa/SxEDoeD+vr6Vq3fGi9fvmx1m545cwZ8Pr/VudTV1TH7RUv/7kQiUbP37/aisH79+vXtncS7LCkpCXl5edDX12+0UzRnR71x4wYOHjyIjRs3Ij09HT179oSenl6z8ggMDMTBgwexd+9eJCYmwsXFBaqqqs2KcfHiRSxbtgyvXr0CAPTo0aPZf3AnT57E6dOnMXPmzBb/sZ48eRKnTp3CvHnzoKamBqDhsUQ8Ho91zJMnT+L3339HYmIieDwe+vfv3+x8/P398csvvyA6Ohq5ubkAAEtLSwDsDooXL17E7t27cfjwYSQnJ+PChQtQVFSElZUV61wuXLiA3bt349SpU8jOzsbJkyfB5/Nhb2/f7O0Rv//1A1hz5ObmIjo6Gk+fPkVZWRmMjIyaHePFixe4d+8ewsLCQAhBt27dmL8Xtn83ISEhuHPnDvr168cczOvr65l/y/Lnn38iMTERtra2zc5f7MKFCwgODoa1tTU0NDQavcZ2OwICArBmzRoYGBigb9++zVpXLDo6GhcuXICfnx9KSkrQr1+/Zh+Hnj9/jlu3buHKlStISkqCra0tFBQUWpTP20ILWxsihMDNzQ2pqamoqKiAtrY2tLW1m/VHKhKJ4O7uDnd3d3h4eODRo0f4448/MHbsWKioqLCOMX/+fHz11VdYuHAh/Pz8UFBQADU1Ndy/fx88Ho9VoRQIBHjw4AF69eqFR48e4eHDhzAyMoKuri4ePnwIQ0NDqQdFkUiEzz//HJs2bYKJiQmys7MRHh6OmJgYiEQiaGlpQUlJSWoOhBAsX74cmzdvRs+ePRESEoJDhw7h6tWruHv3Ljp16oSuXbvKbI9FixbB398fLi4uOHHiBExNTWFsbMz6cyGEwMPDAzt37sSCBQtQWFiIkJAQmJmZISAgADU1NTAzM5O6/vz587FmzRosXrwYhoaGCAsLw8WLF5GamopevXpBV1dXZg5z587Fxo0b8cUXX8Da2hp6enq4dOkSrl69ih49esDAwIDVtmzevBmqqqowMjJi9k+A/VmL+LN99OgRkpOTkZiYiF69ejXrC5hIJMK8efNQUVGBx48f49WrVxg1ahTq6uqgoKDA+u9m8eLFuHz5Mm7evAldXV1YWFiAy+WyKm4ikQhz587F1KlTYW5uziyvra2FoqIi6+2YO3cu5s+fj759+6KqqgoZGRnIzMyEoaEhq+0Q/82+//77CAoKQqdOndC7d+9mFRHxsaNTp06wsLBAaGgozM3N0a1bNwANn62sLzEikQizZs1CcXExACA4OBiHDx+Gjo4OrK2tO2RRA2hha1O///47kpKSYGdnh8ePHyMpKQnV1dXQ19eHiooKAKC4uBgKCgrMN6B/2rt3LxQVFbFixQro6+tj7NixCAwMRI8ePZgDZ3l5OTgcjsQYO3bsgJKSEpYsWYLa2lrs3r0bY8aMQXZ2NmJiYvDs2TMMHjwYysrKErdFJBJBV1cXMTEx0NDQwODBg5lv1/v370daWhrGjx8vdUffsmULRCIRli1bhqKiIixduhQZGRm4d+8eIiIioK6uDisrK6ltWlBQgKioKEydOhW1tbX4/PPPMXr0aOjr6wMAwsPDYW9v/8a35NetWbMGOjo6mDNnDjQ0NJCQkICTJ09i6NCh6NSpk9TfL3br1i3Ex8dj6dKlUFZWxoABA/Djjz8iPz8f1dXV8Pf3R7du3dC9e/cm179z5w5evHgBb29vaGpqokePHtDS0gIA8Hg8lJaWYuDAgVJzePr0KWJiYvD5559DV1cXBgYGsLa2hr29PfLy8hAbG4uhQ4fK/LKwfft2+Pv7IzU1Fc+ePUP37t2ZL2BCoZDVmc4PP/yAuro6+Pn5YcCAAQgPD0dkZCQmTpzIFCZZNm3aBADYs2cP1NXV4evrCx6Ph5s3byIgIAB9+vRh2qgp4mKhpaUFTU1NvPfee/jtt99w+/ZtDB8+HGfOnIGamhp0dHQkbtOGDRvQqVMneHp6oqSkBBcvXsSWLVvw/PlzpKeno1evXuDxeFK34/z58ygtLYW3tzfy8/OxcuVKBAcH48KFC7hy5QoGDx4s80vLhg0boKOjg927d6Ourg7+/v7Mly+2Z5++vr5QUFDAzz//jH79+iE7OxuPHz/GjRs3sHfvXlhYWMDY2FhqjF9//RVAw6V/Z2dnDBgwAOfOnUNwcDBiY2NbdOXnbaD32NqIUChETU0N5syZg2+++QZz5sxBXV0dAgMDcfjwYURHR0MkEsHHxwcvX75sMoZAIEBxcTH69esHoOE+GwAMHDgQV65cYd63Zs0a5OXlSYxRVlaGL774AkDDQWPGjBnw8PDA4sWLMX36dAQFBeHhw4dSt4fL5UJZWRmenp7g8/kYN24cVq1ahU6dOiEjIwMCgQDnz5+XeE+hpqYGd+/ehVAoREJCAnx8fNC3b18cPnwYgYGBmDRpEjZt2oSMjAypeRgYGMDQ0BB///03oqOjMW7cOCxYsAALFy7EpEmTkJGRIXVbKisrERYWhrVr1wIA9PT0sHXrVtjb22P79u3Izs4GAJn3ZqysrMDj8RAYGIinT59i7dq1MDExga+vL3x8fGBnZ4f4+HiJ69va2oLD4SAmJoZZxuVyIRAI8MEHH+DgwYONHpTblD59+sDAwAABAQHMMiUlJVhZWeGjjz7CnTt3EBkZKTVGeXk5njx5gqVLl2LmzJkoKSnBt99+iyNHjqC6upo5S7l586bE+yolJSWIiYmBt7c3AMDExARff/01CgsLUV5ezhTW27dvS4xRVFSEiIgIrF69GkDD5cD33nsPLi4ucHZ2hkAgwN69e6Vui/hLlb29PRISEqCjo4OffvoJXbp0wfjx47Ft2zYAkFhkCwoKcP78eYwZMwZAQ6GNioqCg4MD9PT0cO/ePZntCQBmZmYQCAQAgB9//BHm5ub48ccfce7cOVhYWODnn39mXm9KdnY2/vrrL6YtJk+eDDMzM+zduxeVlZWsviSIRCJUVlYyBVRZWRk1NTV49uwZRo0aBUdHR3h6eiIrK0tiDEIIBAIBU/xUVVXRv39/zJkzB5s3b4aKigru3bsnM5f2QM/Y2giXy0W/fv2goaEBXV1dmJiYYNiwYaivr0dsbCxSUlLw22+/MWcuTVFQUICdnR3U1dVhaGgILpcLDocDDoeD8+fPY9asWdi1axcePXrEFK6mYgwbNgxaWlrg8Xjgcrn47LPPmBvrZmZmSElJgYaGBnMd/5/E34Tr6urQpUsXBAcH49WrV3B2dsaBAwdgb2+Prl27QiAQYMiQIU3GUFRUxOTJk5Gamort27ejoKAAhw8fZg6cffv2xaNHj2BhYQETE5MmY4i/qQoEAmzdupXp0Ths2DAoKiqic+fOSE9PR1VVlcQ8eDwe3N3doaOjA6FQCJFIBC6XCxMTEwQFBSE7Oxvvvfee1G/E9fX1UFNTQ2ZmJq5fv47s7GzExsbCy8sL3bt3h7KyMmJiYlBQUIDRo0c3ub6KigqePXuGDRs2oLCwEI8ePcKxY8fg6emJESNGIC4uDmZmZhLbQpw3n8/Hrl27UFJSAhcXF+Z1AwMDpKSkoK6uDoMGDZK4LWVlZVBTU4OTkxMGDx6M7t27Q1VVFREREbhy5QoMDAxw7tw5hIeHS3ye4atXr/Dq1SvY2Nigc+fOIIRAW1sbly9fZi6D/frrr4iIiMC4ceOajJGfnw9zc3M4ODggLy8PERER+PXXX6Gvrw9TU1PU1NQgPDwcI0eOhLq6usTtAQBtbW0YGBjg8ePHmDZtGt5//31cvHgR2traOHHiBCwsLNCjR4831ktJSUF2djYqKytx7do1REZG4sSJE3BxcYGDgwMePHiAiIgITJgwQer+oa6ujhs3bqCmpgYvX76Ep6cnTExMoKysDBMTE4SGhmLQoEESz9qio6Nha2sLZ2dn1NfXQ0NDA8OHD8fly5cREhKCwYMHQ1NTU+plRPEVnKCgIGRmZiIqKgpHjx7F+fPnYWdnBxcXF0RERKBHjx4wNTWVGKO+vh5BQUGwsLAAh8MBj8eDj48P3NzcoKioiBs3bmDcuHGsiu1bRag2V19fT+rq6pify8rKyPbt24mlpSVJTk6WuJ5IJGpyeXl5OZkzZw65c+cOcXBwIAkJCVJ/t7TYlZWVxMHBgSQlJbHZFEIIIXFxccTT05P8/PPPZNy4cYSQhm2qqKhgtX5SUhIJCAggfD6fyY/P5xNnZ2eSlpbGKkZ4eDiZOHEisbS0JH5+fiQuLo6EhYWR/v37S20PoVAo8bVnz56RkSNHkm3btjX6vKQpKioiAoGAbN68mWzatIkQQsiLFy/I4MGDJebxeg4PHjwgixYtIlu3biVBQUGEEEJKSkrIoEGDSEpKCqscoqKiiKurK3FyciInT54k9+/fJzdu3CB2dnZS20Ksurq60fby+XwSFRVF9u7dS6ZPn04sLS1lxikqKiK1tbWEEMLE2rVrF1m1ahXh8/mkf//+5NmzZ1JjCAQC5v+FhYVMboQQkpeXR8aPH0/y8/Nlbo9IJCLFxcVk7ty5JCYmhoSGhpKxY8eSjIwM8scff5CsrCyJ6+bn55MjR46QmTNnEj8/P0IIIVVVVYQQQhISEsjs2bOl7ufiv6s//viDTJw4kTg6OpINGzaQsrIyQkjDZz9ixAiSnZ0tczvE8cT7S0JCAnF3dyd79uxhtV5FRQXx8/Mjx44dI7t27SIeHh6kqKiIENLwGQ0ZMoSkpqZKjVFWVka+//57MmnSJOLu7k7c3NzIqlWrCCGEpKWlETc3N1JeXs5qW94m+qDRNhAeHo7MzEzk5uZiypQp6NWrF4CGy5NAw9nLl19+CUVFRezcubPJGIGBgUhOTkZWVhbc3NzQp0+fRveNfvjhB5w4cQKzZ8/GmjVrpMbIzs7G9OnTYWNj0+geRVZWFjZv3gxtbW38+OOPrGKI8zh69Ch8fX2xadMmmU8mF7fHy5cvMWnSJOY+mviso7q6Gt7e3tDS0sJPP/3EOkZubi5Onz6NP//8E507d0bXrl0xZMgQLFq0qFnbAjScRSkoKODkyZMAgE8//VRqjIyMDLi5uaF///5QU1NDeHg4li9fDh0dHZiamqJv375YuXLlG+ufOnUKT548QXZ2NqZNmwYnJ6dGHTxqamqYS1A7duyQ2q719fXgcDhMx4jTp0/j999/h66uLrS1tTF06FDMmzdP4vqERUeMDz74AA4ODmjJhZ34+Hjs2bMHQqEQurq6+Pnnn5sdQ2zZsmVQVVVlLidKI96u8PBwHD9+HM+fP8e3336LiRMnMp9zU+sQQpgzsfT0dHC53EZnM97e3lBTU8PmzZtZ5Xzz5k38+OOPKCoqwscff4zq6mpkZGSgU6dO2L59O6tt+Kdbt27h888/x44dOzBhwgRWeQAAn89n7kcbGBjg0qVLUv/u/+nBgwfIzc2FpaUlTE1NoaamhsWLFzcrxttEL0XK2ZEjR3DkyBFUVVWhoqICmzdvRnx8POzt7Zmb1sXFxXj8+DHWr1/fZE+rQ4cO4ffff2duFO/evRvx8fHQ19dnrneXlpbi3r17jS7nSYohFAqxe/duPHv2jIkhFAoRExODuLg4/PTTT6xjPH36FAYGBhg5ciRsbGwwfvx41u1RWVmJLVu2IC4uDvb29tDW1kZdXR0iIyNx+/Zt7N69u8mODk3FSEhIwLBhwzBmzBgsXLgQdnZ2mD9/PhwcHJrMo6lteb1NxQc0Ozs79OnTp8mDyusxRCIRfH198fTpU+jr68PZ2Rnz5s2Dvr4+Zs2ahbFjx74R4+DBgzh9+jRzOS0gIADR0dEQCoUwMTEBj8dDfn4+hEIhvLy8mjwAnz59Gvv374etrS10dXWZHnZcLhd2dnZwd3fH4MGD4e7uDnt7e4mfS2VlpcTOQuKDalpaGg4ePAg/P78mP5f8/HwAkNiZQl1dHfv27UNWVhaOHz/e5D4mK8arV69w5swZXL9+HYcOHWKVh7jdzc3Nce/ePejo6DD3/5q6hChuC/GlNy6XCx0dHWhrawNouA/p7++P8+fP4+jRo03mIB5uAvz/JfPu3btj9uzZ4PF4yMrKQn19PQYPHozly5c3+dm+vh2SvnCYmZmhd+/eeP/992XGEKuvr4eysjLq6+tx4cIFZGZmolu3bli7dm2TMVJTU1FWVgYdHR1mmZGREaysrJhLzY8ePUJAQAD27dsns3NSu2ivU8V3UW1tLRk6dGijyzYZGRlkwYIFpG/fvuTs2bPMckmXugQCARkxYgSJjY1llhUVFZFvv/2W9O/fnxw4cIBZLr5c05oYlZWVzY5hb29Pjh07JqkZGGzbo6qqirx69apZMebPn0/69u1L/vzzz0bvb+rybXPaQxJZMX777Tep69fV1ZExY8aQ+/fvM8tKSkrIL7/8QsaMGUO2bdsmdRsIaWgLFxcXMnLkSNK3b1+ybds25rKYSCRiLuXJilNdXU1GjhxJfvrpJ6mX1ZKTk8njx4+bfK2mpoYMHz6cfP/99yQhIeGN3y2+xLxnzx5y8uTJFsUQb/PZs2dJWFhYi2LU1tY2aqN/YtMWAoGAHDhwgAQEBDT5uqQYr+ci69I2m7YQk/S5solRWVlJSkpKpObh7OxMVqxYQUJCQiT+XRYVFZHMzEyp29Se6BmbHBUVFSEyMhL9+vVjbvpra2vjo48+gqGhIf744w/Y2NjAwMBA4s3n6upq3Lt3D926dUPv3r0BNPRGGj16NOzs7LBnzx4YGRmhR48ezODk5sbw9fWFsbExevToIfGbsrQYffr0wa5du5g8WtsexsbGErdFUoxJkybB0NAQJ06cgLW1NTN2ralvumzaVNweksiK8euvv8psj5iYGCgpKaF///4AABUVFTg6OmLAgAH45ZdfwOPxYGdnJ/Hb+s2bN5nLr4MHD8bRo0dx5MgRaGtrw8rKCoqKiggKCkJtbW2jCQH+aceOHcjKykJGRgYOHToEbW1t2NjYAGg8kFlPT0/imEBfX1/k5eWhrq4O586dQ21tLQwNDaGurs50cAIABwcHiZ2SZMUAGjo/2draNhpT1pwYCgoKUFJSapRTc9tCQUEBAwcOlDgURVIMBQUFZqiDrO75bNpC3FlE0ucqLQYApvOHeKhRU/bv34+CggIYGBggNDQU+fn54PF40NbWZo4VVVVVzJjcjooWNjlSV1dHcnIyIiMj0aNHD+jp6TE7oampKaKiolBfX48BAwZIjMHj8ZCbm4vAwEDo6+vD0NCQuVxgYmKCnJwcFBcXY+jQoe0Ww9TUlFUMNu0hFAqltgebGCKRqMO3KYfDQXl5OY4fPw5lZWWYm5szBwpxEUpLS8OIESMkHrj09fVhYmLCDDp2d3cHj8fDTz/9xAy+Xbx4MZycnNCzZ88mY/D5fAQFBeHjjz/Gxo0bmfWvX7/eaFyTj48Phg8fzvTEfV1tbS2uXbsGV1dXrF69multGBYWxgzy5nK5WLZsGZydnZs8kLKNsXz5cjg5ObUoRrdu3cDlcvHVV1/BycnpjUuv8mgLWTHEX8ZWr16NYcOGNXnpj21bSNoONjHEl9u9vb3h7OzcZAyBQIDbt2/DyckJy5cvR5cuXRAWFob79+8zE0yoqanhyy+/xJAhQ6SOFW1vtLDJmYGBAcLCwvDw4UPo6OhATU0NampqUFJSwoMHD1BSUoKRI0dKjdGjRw8kJSUhOjqaGUvUuXNnAEBYWBhevXrFjLXp6DFktUdpaanM9nhX2tTGxgY1NTW4f/8+UlNTQQhhDnyXL19GQUGB1A4BPB4PxsbGUFZWZgZO9+vXD/PmzUNWVhbWrVuHUaNGYdmyZRJjKCkpwc7ODjo6OtDT00O/fv3g4eGBhIQEbNq0Cbm5uQgKCkJsbCwzLOSfFBUVmSEkurq6sLS0xKeffoqcnBwcOXIEqampOHfuHDIzMzF//vwm83gbMdLS0nDu3DlkZGQ0GUMebcE2xuPHj/HZZ5+1qi0kbYe8YigoKMDZ2Zk5GzM3N8eHH36IqqoqXLt2DSkpKTh27BjKy8vh4eHRZIyOgvaKbAOFhYXYsWMHYmJi4ODgAFVVVfB4PJw+fRrnz5+XeFnldaWlpTh27Biio6PRuXNn1NXVQUdHByEhITh79qzU6Zo6Wgx5tMe70qZVVVUIDAzEgwcPUFFRgaKiIhgbG+Px48f4448/WOUA/H8vvrq6OigrKzNj965fv85MmcSGUChkOnW8evUKixYtwrNnzxASEiJxVgryjx57r8eoqKjAV199hVu3bv0rYrS2LeQRo6O0hbiXclMxampqsGnTJpw/fx6hoaEtmgf0baKFTc4EAgFziSktLQ1nzpxhZtYePnw4hg0bJjNGXV0d09OosLAQ165dQ0VFBerr6zFs2DBmJpJ/Qwx5tMe70qavry8QCBAREcH0gnR0dISFhYXMHMTd1V8/6AANl7rKysqYKZCaQ/yEBCUlJbi7u8PS0lLiEBJJBAIBOBwOlJSUMG/ePJiZmTV7iEBHiCGPtniX2lMkEkEkEkFRURELFy6EoaEhNm7c2KwY7aJ9+qy8u8Q9lr7//nupg6/ZxPjiiy/Iw4cP34kY8miP5sZ4fXB6S7eltTFe78Em/rebm5vEXn5s43l6epJbt261KMbrxAOgCSEkOzubODs7Sx3E3lQu4gHMhDQMpJ4+ffq/LgYhrWsLecToKG0hjvF6r8fCwkLy5ZdfSpzwoaOhhU2OxDvO9evXibOzM7NcUvfcpoh3nBs3bjSK0ZwdSjwDRGtiiLsKtyaGuD3+GUNae/zzNUntIS3GP/+AW9KmtbW1jYZCSNoWtu3xens6OTk1e/3Xc/jn/iXuSi5rPxP/LnEuf//9N5k5c2aT75FF/DuDg4PJyJEjG70mKw/x662JIc6zNTFeX18ebdFe7SmPthC//vp++t577zUrRkdCC1srFBYWkocPH5LIyMhGyz08PJgxWmynZvonT09P1jHKy8tJfHx8ozFWzY2Rm5tLbt++/ca2LF68mJw5c4ZVjMjIyCbHxc2fP591jFOnTpHnz5+/sfyzzz5jvS0//fQT8fPzIzU1NY2We3l5kdOnT7OKsXTpUjJixAhmqrHXz5TY5BEfH08OHz5M/P39Gy1fsWIF6xxKSkrI06dPSVRUVKPlS5YsYd2e0jg7O5MLFy4QQhqKZnPPTgghxMXFhdnGuro61jFef9/bjCHp4NyctiguLm51jIyMDIkx2LZFWFgYs4+/vl0tbc/XOTk5NYrxbzlTE6OFrYWePn1KpkyZQmbNmkWGDBlCxowZQ86cOUNevnxJ4uPjWcXIzMwk4eHhbyyvr68nOTk5rGIkJCSQ+fPnk+nTp5OJEyeSTZs2kdraWlJXV8c6RlxcHHFzcyNLly4lzs7O5MqVK8xr4j9iWd/W0tLSiKWlJXFzcyMhISGNtuXRo0es8oiJiSG2trbMGSch/3/2yXYuy4cPH5JBgwYxc/EJhUKSk5NDampqWM9DGRsbSwYMGEA8PT0bDZwWx2Kz/qhRo4iXlxcZOHAgcXd3Z74Jsz3IxMfHEzc3N+Lm5kYcHBzImDFjyIULF0hFRYXEgflNKSwsJBEREWTbtm0kLCyM5OTkEKFQSOLj48nChQtZxRAKhU1+/s+fPyeLFi1iFaOkpIQ8fPiQ7N27t9FnmZiYyDpGfn4++fvvv8natWvJixcvmOUJCQmsYiQnJ5Pjx4+TnJycRgfq6Oho4uHhwSqHuLg4sn79emZ9cbtERUWR+fPns4oRGxtLxo4d+8b8m8nJycTT05N1DEtLS7J06VJSWlrKLG/OZ5KZmUkOHDhAPv3000ZfwDIzM4mXlxerGB0VLWwtNHHiRLJv3z5SUlJCRCIROXjwIBk5ciTx8PBgXVAmTZpELC0tiYeHR5MHbvEfjbRvS5MmTSIHDhwgycnJ5Pbt22TOnDlN3oeSFmPy5MnkwIEDhM/nEy8vL7JkyRISGRlJtmzZQnJzc1ltS3p6Ovnkk0/IjBkziK2tLZk9ezZz5nXlyhVWEyRPnTqVHDx4kBDSMJHwqVOniLe3N9m6dWujexfSLFu2jOzcuZMQ0jBR8sqVK0n//v3JokWLJM4c8U+TJ08mR44cIUlJSWTgwIFkw4YNzTozmjJlCjl69CghpGFC4YULF5L79++T9evXkytXrjS6/yHJxIkTyYEDB0hOTg7Jy8sjHh4ezIGsOZPOzpo1i8ybN48sXryYODo6Eg8PD/Lnn3+SlJQUwufzCSGyi+2OHTvIjRs3mvwMxe0iK8bixYvJ3Llzydy5c8mAAQOanIVHVgx3d3fi4+NDZs+eTUahhE26AAAWjklEQVSPHk1SUlKY/Vq8rrQY33//PbG0tCSrVq0ioaGhTEEQTxjMJofx48eT/fv3Mz/z+Xzmy5f47ElWjA8//JCZ7aaqqorExMSQ2NhYkpyczMSSFWPatGnkm2++IV5eXszZuxjb9pw6dSr5/vvvyebNm4mDgwM5ePDgG19gWnK21xHQwtYCRUVFxN3d/Y1ZwisqKoiXlxdxdnYmT548kRojMjKSTJo0icTGxhJPT09ibW1NNm7c2Ojbl7Spbwgh5ObNm8zs+oQ0FC9vb2/yww8/MMtkHUSDgoLImDFjmJ8dHByIj48PuX//Ppk/fz7p378/uXPnjtQYYjdu3CCnTp0i1dXVxMvLi1hbW5OpU6eSjz/+WOa6ly9fJpaWlszP06ZNI15eXmTnzp1k3rx5TX7DbcquXbuYojJy5Ehy4sQJkpSURA4cOECGDRtGTpw4IXX9s2fPkuHDhzM/37t3j8ycOZNcunRJ5u8mpOGS7ieffMLMok4IIcOHDycrV64kXl5e5P3335c5O3tGRgaZMWMGc5ARiUTkzp07ZMWKFeSTTz4h7u7uMvcNQgi5du1ao/skfD6f/Prrr2TatGnEx8dH4nRqr7ty5QqxtLQkI0aMIN999x2Ji4trdEbNxoULF8i4ceOIUCgkdXV15OzZs2TRokVvxJF2VeD8+fPMfpqenk4GDRpEDh48SL799lvy3Xffkby8PJl5hIaGkvHjxxNvb28yYsQI8s0335CsrCxy+fJlVl96/P39ycSJE5mf9+3bRxYtWkRcXFwandlLEx8f3yiGh4cHcXNzI6NGjSIrVqwg169fZ5XH2LFjCSGEHD9+nNja2r5R3GS5du0acXV1ZX6OiIggq1atIhs2bCDLli0jp0+fbtVl7vZGHzTaAlpaWlBTU8PatWsbPeBTQ0MDvr6+GDhwIJ49eyY1ho6ODsaNGwczMzP8+uuvOHHiBO7fv48xY8YwM8y7ubnh4MGDEmNUVlZi4sSJEAgETPf3mTNn4tatW6isrAQAfPHFF/D395cYo76+Hl9//TUA4OTJkzAwMMCWLVvg4OCAI0eOwMHBAU+fPpW6LYQQiEQi2NnZISgoCHFxcfD19YWfnx+Sk5ORlpYGPz8/qQ/vFAqF6NatGxYvXgwvLy9oa2vD19cX3t7e+O2339C5c2ckJiZKzQNomJ1DPLbNzs4On376KXr27InPP/8cixYtkhkjISGBmUG+trYW9vb2cHBwwLZt2xARESHz9+vp6UFFRQVLlizB/fv34efnh5qaGvz888/w9fXF8uXLERcXh4qKCqnboKKigl9++QWlpaXgcDgwNDREfn4+du7cCT6fj+TkZJm5KCsro0+fPszPqqqq8PT0xLZt2/DixQssXrwYfD5f4sM/ASAoKAirVq3C/v37kZeXh+XLl+Pw4cPIyspinlZx+/ZtiQ+YBYCAgADMnz+feVL88OHDkZWVhefPnzPvSUxMlPqkAT8/P2Yi41OnTqFz585wdnbG7Nmz8fz5c3z22WcoLi6W2h6jRo2Cq6srZs+ejRMnTqC0tBTz5s3DN998I3U9sR9++AFOTk4AgMOHDyMsLAyjRo3C5s2bERoaikWLFqG6ulpqDCMjI3Tu3BlFRUUICAhARUUFzpw5g+PHj8PExAQbNmxAamqqzDzEObu7u2PNmjUIDAxkHrAr7bMQq6mpga2tLfNzXl4eQkJCYG9vD2NjY+zfvx9xcXEy43RUdOaRFuByubC1tcWLFy+QmpoKJSUl6OjoMGOUsrKyEBUVhQ8++EBijM6dO8PW1paZlsbQ0BCzZs2ClpYWfH19cejQIQiFQvz2228SY3Tr1g2KioowMzODgoIC6uvrYWRkhICAAIwfPx4PHz7EuXPnpD4io3fv3jAwMACPx2Nm61dVVQWfz4eSkhKysrKQlpYGV1dXiTHEByQNDQ2oqqri8OHDmDp1Kk6cOAEDAwPMnDkTp0+flvgoGKDhidTDhg1DeXk5QkND4e3tDXNzcwgEAigrKyM1NRX5+fkyx6z17dsXxcXFiIqKwrNnz2Bvb888FiYvLw/BwcFwc3OTuP7AgQOZ8WSKiopQUFCAo6MjKioq4O/vjz59+qBLly4S11dQUICNjQ1qa2sRGBiIvLw8jBw5Eo6OjgAaHocSEhIicRYK8e8FgLi4OGRmZuLixYu4cOECHBwc4OrqiqdPn6K+vp6Zc1ISQgh2796NiooK2NraMtMo6enpYfjw4QgNDcXQoUOlzvmnr68PVVVVODg4YNKkSTAwMMCpU6dw48YN6OvrIzIyEitXroSXl1eT65eXl6Ompga9e/eGkZEROBwONDQ0EB0djdzcXLi4uODq1av4/PPPsWTJkiZjVFZWwtDQEO+99x5EIhF27dqFXbt2wcrKCvr6+nBxcUFISAicnZ3RqVOnJmOI51msra3FoUOH4O7ujokTJyI8PBzl5eV4/vw5dHR0mEdM/ZN4bNqJEycQHByMsLAwHD58GE5OTjAzM4OdnR1CQ0MxevRoifOekv89hSE8PBxXrlwBALi4uDCPk3J0dER8fDy6dOkicc7RlJQUdO3aFVOmTGFmn+natSsePHiAwMBAjBgxotGjqSQhhGDbtm1ISEhATU0NNm7ciM2bN2PixIlwcnLCs2fPIBKJZO5jHRUtbM1QWVmJ4OBgnDx5EuXl5cjJyUFWVhZycnKQmpqKnJwcCIVCbNu2DTNnzoS1tbXEGKdOnUJOTg5evnzJPJ0WAPr06QMPDw/s2rUL27Zta3LQrjhGYGAgamtrkZeXh+7du0NBQQEcDgcxMTHgcDjYuXMnlixZwkzq2lSM06dPIycnBzk5OejduzfzRyku0mvWrMH06dNhaWkptT3Ez58bP3488vLyEB8fj7Nnz2Lz5s1wcXHBrFmzpLbp6dOnUVZWBl1dXXh4eDBPfRbPrbdhwwZ89NFHzATETcU4fvw4srKyUFdXBzU1NeTm5uLatWsoLCxEaWkpfvzxR3h4eEj9XPz9/ZGRkYGcnBz06tWr0byUjx49wo0bNzBx4sQ35vx7/XMVH4i/+eYbdOvWDUeOHIGJiQnU1dWxcuVKmTmcPXsWKioqSEpKgoqKCrp06QJHR0fm2WqbN2/G6NGjJR6ExXR1ddG7d2/cunULSUlJUFBQYKbw0tDQwO7duzFkyBCps2p069aNOcgSQtCzZ0/MmjULZWVl2L9/Py5fvoytW7c2uT1Aw1mjnp4ejIyMoKKiwsyQoaWlhevXr2Py5MlYtmwZvvrqK4kxeDweTExMoKKiAgUFBcycOROdOnViDuyampo4duwYnJycJM66Iv695ubmiIiIQHZ2NjQ0NHDs2DGcOHECIpEIJiYmEmd+UVBQwODBgzF58mS8ePECxsbGmD59OpNDly5d8Mcff8DR0RH6+vpNxhBPyjxkyBBERUXhzp07yMjIwPTp05n37NixA87OzhILm56eHmxtbZn9j8PhQE1NDePGjcOtW7cQEREBBwcHicVVrEuXLhgxYgQKCgrA5/PB4/Hw6aefMl+0f/75Z7i6ukqcc7SjozOPNMOCBQvA4/GgoKAAPT09VFdXIzs7GzweD6ampkhOToZQKMTQoUOxYsUKqTEUFRWhpaWF/Px8KCoqYvbs2XBxcQHQsHP7+/sjMjJSZh7a2tpMjE8//RTDhg1DYGAgVq1ahf79++P06dOs8sjLy4OSkhITo6KiAgcOHEBERAQuXLggMYaysjIUFRWhqamJ3NxcqKqqQlVVFUFBQVi4cCG8vb3fmCVD0rbo6Ojg1atXABoe9Dl8+HDU1dXh119/RXh4OC5evCg1D3F7lJeXo7y8HGZmZujcuTMiIyOhpqYGFxcXiWdK4jyUlJSgqanZ5OeSmZkJPz8/rFu3TuZ2vHz5EioqKnB2dkZKSgpSU1NRXl4OW1tbbNq0SWoOXC4XnTp1Ap/PR0lJCRYuXMic8f3000+4e/cuLl261GQMAMjJyWGmPKqtrUVwcDDCw8NRV1fHTIibl5eHuLg4iXFejyE+RIifVyY+qG7ZsgVPnjyRuI+9HgP4v/buLKaJrw0D+CMSEJFFjIIboNUCAgakLggJRKqokSAqbhfEGBPjAhcuiUuIpt4QNUaNRsXtH0FQEo2FAm4XKltEDG5IIIgbixd2AyKLpee7MJ2PSqeUtWV8f4mJtp2H98w0HKdzOu//b9mk1+vR2tqKw4cPw9XVFXV1dbwfl/+dYbgLDetxC6n09HQ8fPjQ5Pv073GMGTMGzc3NSE9Px6tXrxAdHW2yIay5GgBAq9XC1dWVq+Hq1at4+PAh7t27Z1FGVVUVFAoFHjx4wN1BR61WQ6PR8O7PhoYGo/+EGMZjOCbV1dXYs2cPkpKSeN/nTU1NvSZ/vV6P5ORk6HQ6rFmzBrW1tSgtLeUdy6hgjQt7o1F1dTULDw/n/q1UKtnz58/Z2bNn2b59+1h2djbT6XRMo9HwrkD8O0OlUrGSkhKWlpbGduzYwQoLCxljjL1588ZoOXN/Mh4/fsxaW1tZYmIi7xL5vjLy8/NZWVkZk8lkvHf64Ms4efIkO3ToEDt37pzZnlKmMpRKJSsuLubqKCgoYO/fv++1vLuvjBcvXrAzZ86wlJQUbtGHuVWVlh6XgWyfnJzMbt68yaqqqlhDQwO3EtHScaSlpbHt27ezgoIC1tnZyZ48eWL2awuVlZUsISGBXbx40ei7Ut++fWN37txhZ86cYYmJiSw9PZ23n1bPjJ6v6fm+1mq1LDAwkL1+/brfGd3d3Uyv17PNmzczPz8/9vHjxwHV0dnZyUpKSlh4eHiv73Ca276zs5PJZDK2du1a1traanbRSl81dHV1scrKSiaRSEzWYC5DpVKx8vJydvPmTZacnMzkcjnvIhhzdfSsv7i4mH369KlfGYz9+Z0jk8nYqlWrWGpqKquvr+fdJ6MBfRRpIbVajcrKSoSGhsLDwwNOTk7w8fHBtGnT0NjYiKdPnyI6OhqTJk3ivQhuKmPmzJnw9fXFz58/IZfLERUVBZFIxHutoK+M+/fvIy4uDjExMbwfq5jLUCqVyM/Px7Zt2xAREcHbj4svw9vbG1++fEF5eTmio6O5XlCWZnh7e3Njyc/Px9atWxEZGYmpU6f267jMmDEDTU1NUCgUiIqKMuoG3N99+uDBA67rdX+29/HxQXNzM0pKSrBu3Tp4eXnB3t7e5PuDbxyGY5KbmwupVIrg4GCzY9m/fz8YYxg7diyKioqg0Wjg4+ODyZMnIygoCOHh4UhMTERYWBjvtbW/Mwxnvz1bnTg6OiImJoa315q5DENPMRcXF4SFhSEmJmZAdbS3t6O2thZSqdRkuyC+7Z2cnBAVFYXly5fD3d3d7KKVvmro6upCTU0NoqKiuDP7vjK0Wi18fX3h5uaG6dOnIyQkBKtWrYKfnx9vK5i+9qehllmzZmHixIn9ynBwcICXlxckEgk2bNiAlStX8maMFjSxWcjZ2RlPnjzB+/fvERsbyz3u5uaGhQsXIj8/H1OnTjXbaJIvw9XVFRKJBIWFhfDy8hpURkFBATw9Pc3ekLevjLy8PHh6evI2VrRkfygUikHvj6Goo7CwcNiPS181yOVyrga+X6KWHNe+xlFfX4+amhrs3LkT8+bNg1arxdu3b7medSKRCN+/f0dWVhbCwsJM1mIq482bNygvLwfwp3VPY2MjsrKyEBsbO+CM5uZmlJaWIikpyWSPMksyWltbUVxcjPXr1/dq5Glue51Ohzlz5kCj0SAzM3NQ+0Kr1eLFixfYuHGjyWai5jIYY5g9e/aQHJOmpiZkZ2cPKMPw3lAqlcjJyYFEIjE72Y8GNLFZyN7eHgsWLEBGRgauX7+OyZMncxfv7ezscOvWLcyZM8doCe1AMubOnTuojIyMDIhEIqtnDMX+sJU6+sqwZBxDUUNf+8Ld3R2+vr6YPn06RCIRgoODMX78ePz48QMVFRWoq6vD8ePHERgYyF2z60/Gq1evUFdXh2PHjiEgIGBQGUePHsX8+fN5G7NaknH48GHuLLQ/279+/Zobx2D3xZEjR3hrsDRjqI7JQDMM743U1FTMmzePN2M0ocUj/dTW1oaMjAxkZWXBxcUF0dHR+P79O2pra/Ho0SPK+IczbKEGA9ZjcUVDQwM+fPiAK1eu4NevXxbnCCXDFmoQWoato4ltAPR6PZRKJe7fv4/6+npIJBKEhob2a2ksZQgzwxZq6KnnLzF/f39cuHABUqn0n8ywhRqElmGzhn99irANRSsHyhBmhi3UYPDs2bNB39hWKBm2UIPQMmwNnbER8g/Q6/Xo6urCuHHj/vkMW6hBaBm2hiY2QgghgkI3QSaEECIoNLERQggRFJrYCCGECApNbIQQQgSFJjZCCCGCQhMbISMoJSUFAQEBqKioMPl8RUUFAgICkJKSMsKVESIctNyfkBGkUqmwZs0aODs7Qy6XGzWEbG9vR3x8PNra2qBQKODh4WHFSgkZveiMjZAR5OHhAZlMhm/fvuHUqVNGz50+fRpfv36FTCazyqTW1tY24j+TkOFAExshI0wqlSI+Ph7Z2dkoLS0FALx8+RK3b9/G2rVrje7X19HRgQsXLmD16tUIDg7GokWLsHv3btTU1Bhl/v79GxcvXsSWLVuwdOlSBAUFYdmyZZDJZNBqtUav/fTpE/z8/JCeng65XI74+HgEBwfj5MmTwz94QkYAfRRJiBW0tLQgLi4OY8aMwd27d7F161bodDooFAq4uLgA+NM4MikpCVVVVUhISEBAQAC0Wi1ycnKgVquRnZ3N9apraWmBVCrFihUrIBKJMG7cOLx9+xZ5eXnw8/NDTk4O7O3tAfyZ2FavXg1/f3/8+PEDW7ZswbRp0+Dq6oqVK1dabZ8QMmSsd5tKQv5tRUVFTCwWs8WLFzOxWMyKioqMnr98+TLz9/dnZWVlRo+r1WoWERHBtm/fzj3W3d3NOjo6ev2MzMxMJhaL2dOnT7nH6urqmFgsZkFBQezLly9DPCpCrI8+iiTESiIjI7Fp0yao1Wps2rQJkZGRRs/n5uZCLBZDLBZDpVJxf/R6PZYsWYKXL19Cp9MB+NOM1NHREQDQ3d2NlpYWqFQqrmnku3fvev18qVQKHx+fYR4lISPP3toFEPIvCwkJwd27dxESEmL0OGMMnz9/Rnd3N293ZuDPR5CGhSa5ubn477//UFNTw014Bn9fZwMAX1/fwQ+AEBtEExshNogxBsYYAgMDceDAAd7XTZgwAQCQl5eHgwcPIjQ0FKmpqfD09ISjoyM6Ojqwa9cu6PX6Xts6OTkNW/2EWBNNbITYIDs7O3h7e0Oj0SA8PJzrdMxHLpfD2dkZt27dgoODA/d4dXX1cJdKiM2ha2yE2Kj4+Hg0Njbi9u3bJp//+fMn9/exY8fCzs7O6MyMMYZLly4Ne52E2Bo6YyPERu3YsQNlZWU4ceIEiouLsWjRIowfPx7Nzc0oKSmBu7s7rl27BgCIjY3Fs2fPsG3bNsTFxaGrqwuPHj3C79+/rTwKQkYeTWyE2CgHBwfcuHEDmZmZyMvLw/nz5wEAU6ZMQUhICBISErjXrlu3Du3t7cjMzERaWhrc3NwglUqxd+9eREREWGsIhFgFfUGbEEKIoNA1NkIIIYJCExshhBBBoYmNEEKIoNDERgghRFBoYiOEECIoNLERQggRFJrYCCGECApNbIQQQgSFJjZCCCGCQhMbIYQQQfkfqGcq1U7yDiQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#gca = get current axis\n",
    "ax = plt.gca()\n",
    "ax.set_ylim([0,1000])\n",
    "\n",
    "#Change size of plot labels\n",
    "ax.xaxis.set_tick_params(labelsize=12)\n",
    "ax.yaxis.set_tick_params(labelsize=12)\n",
    "\n",
    "#Set the axis labels\n",
    "plt.xlabel('Years', fontsize=18)\n",
    "plt.ylabel('Number Of Shots Made', fontsize=18)\n",
    "\n",
    "#Plot using MatPlotLib\n",
    "league_averages.plot(kind='line',x='Year',y='3P',ax=ax,marker = \"o\")\n",
    "\n",
    "#Set the title, legend\n",
    "plt.title('Number of Three-Pointers Made', fontsize=24)\n",
    "plt.legend(['Three-Pointers Made Per Year'], loc='upper right', fontsize=15)\n",
    "\n",
    "\n",
    "#Set range for ticks\n",
    "plt.xticks(np.arange(0,19), league_averages[\"Year\"])\n",
    "plt.xticks(rotation=60)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again, looking at these raw numbers allow us to get a good general feel for the data that there is an increasing trend, but it is more important to look at percentages, as raw numbers do not account for a counfounding variable that if overall there are more shots attempted in the league then of course we will see an increase in three pointers attempted."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ethical Considerations & Privacy\n",
    "One ethical consideration we can consider is that the data should be blinded. What that means is that the data should be based purely off of what we are analyzing, which in our proposed experiment is the shot distance, rather than taking into consideration inconsequential factors such as race, height, and weight. For example, one thing that we considered was whether we wanted to include the WNBA in our analysis, in order to get a more holistic view on the trends in basketball. However, we decided against this in favor of the time constraints for the project, as adding such a league with wildly different averages would have added a downtrend overall in the raw numbers analysis. Other than these, we cannot foresee any other big ethical roadblocks, as the analysis is simply made on observed data that would otherwise occur whether our analysis happens or not (i.e we are not instigating events in order for us to get data points)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion & Discussion\n",
    "   After analyzing all our data and graphs, we found that the average NBA shot distance has changed over time, and specifically that the number of attempts of three-pointers have increased tremendously. \n",
    "   \n",
    "   \n",
    "   In our first analysis, we analyzed the FGA percentage and average shot distance for the winning NBA teams from seasons 2000-2001 to 2018-2019. For the FGA percentages, we sorted them into seperate distances and calculated the FGA percentage for each type of distance. By doing so, we were able to assert whether or not the FGA percentages change when categorized by distance. For the average shot distance, we gathered all the winning teams for each season and put it in a new dataframe. After plotting both plots, we found that each winning team's average distance significantly increased by 3 ft. We also found that NBA winning teams have shifted their field goal attempts to further distances and decreased their likelihood of shooting at shorter distances.\n",
    "   \n",
    "   \n",
    "   From our second analysis where this question was broken down specifically to shots made, we found that the percentage of 3P shots made has not significantly increased. We cleaned the data set and plotted the percentages for each type of shot for each season versus time. We used a linear model to fit our data and eventually merged all three graphs into one graph to show the relationship between the percentage of 2P, midrange, and 3P shots made. P-tests were used to analyze the trend of shots made over time; we even found a significant increase in the percentage of 2P and midrange shots made. The conclusion from this portion of our data analysis strikes us as surprising since the number of 3P attempts has significantly increased over the years.\n",
    "\n",
    "\n",
    "   Lastly, in our third analysis, we discovered several relationships between the percentages and raw amount of shots taken. From our analysis, we once again found that the percentage of three-pointers made versus three-pointers attempted stayed relatively the same. This simply just means there wasn’t a sudden increase in ability to make three-pointers. However, we did find a dramatic increase in the percentage of three-pointers attempted vs. all shots attempted. Looking at the percentage in 2000, and the percentage in 2019, we can that the percentage of three-pointers attempted nearly doubled across this span of time. It is important to note that this is the percentage, and not raw numbers, as in general we can see the number of three-point shots increased, but this uptrend persists even when looking at ratios. This means the increase is not confounded with the fact that the number of shots attempted simply increased over time, but that there is actually a significant increase in the percentage of three-pointers attempted.\n",
    "   \n",
    "   \n",
    "   Some limitations of our analysis are that our data has only come from the past two decades, whereas the NBA three-point line has existed for forty years. This means our analysis is simply going to be seeing the trends as time increases ever since the introduction of the three-point line (i.e every year is just one year longer that the rule has been instated) while we believe the analysis would have had even more alarming results had we had access to data 5 years before and after the introduction of the rule. \n",
    "   \n",
    "   In conclusion, the average NBA shot distance has increased over the last two decades, and more specifically, the percentage of three-point shots attempted and made has increased over the last two decades.\n"
   ]
  }
 ],
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