FinalProjectGroup062-Fa21.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - Effectiveness of Height and Weight on a Basketball Player's Success"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Permissions\n",
    "\n",
    "Place an `X` in the appropriate bracket below to specify if you would like your group's project to be made available to the public. (Note that student names will be included (but PIDs will be scraped from any groups who include their PIDs).\n",
    "\n",
    "* [X] YES - make available\n",
    "* [  ] NO - keep private"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, we are trying to find if height or weight plays a role in a basketball player's success in the NBA based on their EFF (efficiency) score made up of points, rebounds, assists, steals, blocks, turnovers and shot attempts. We used a public data set on basketball players' stats per season in order to calculate their EFF and analyze the correlations to their height and weight. We used different statistical analyses to look at which factor has a statisically significant impact on the EFF. Our results reveal that neither height or weight have a statistically significant effect on their EFF which suggests that there is no correlation between height or weight and being successful in the NBA."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "- Elise Ngo\n",
    "- Ellie Kanda\n",
    "- Hayley Yamamoto\n",
    "- Zongze Chen\n",
    "- Kevin Wong"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='research_question'></a>\n",
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is height or weight more influential in successful NBA players based on their EFF (efficiency) score made up of the player's points made, rebounds, assists, steals, blocks, missed field goals, missed free throws, turnovers, and games played?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='background'></a>\n",
    "\n",
    "## Background & Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the most common questions tall people receive is “Do you play basketball?”, but is height really what makes a “successful” basketball player? We also recognize that weight could also affect a person’s athletic ability like their vertical jump and agility, so we were also interested in looking at how weight may play a role in NBA players’ success. While weight may play a role, we are only looking at the sheer mass of a player rather than their body fat percentage or muscle mass, which are the factors that may be more correlated with agility and performance. Our team seeks to answer the question of whether physical attributes are influential in making a “successful” basketball player by comparing the correlation of height and weight with their EFF (efficiency scores).\n",
    "\n",
    "Prior work indicates that the many basketball players often “possess high strength, power and agility while maintaining a fairly lean body composition” (Ranson 1). In Ranson’s article “Physiologic Profile of Basketball Athletes” (2017), he studies the structural and functional demands on basketball players such as energy demands, body composition, strength, power, and agility to find the ideal physiologic profile of a successful basketball player. Ranson found that although strength, power, and agility might predict success, “Ideal physique and physiology are not sufficient for excellence in basketball.” In a related article, “Is Height important in Basketball”, Nall asserts that the height of a basketball player gives them an advantage in shot accuracy and grabbing a rebound. However in the end of the article she admits that for “What a basketball player gains in leverage thanks to his height, he may lose on other aspects including speed and agility”, and thus we must not immediately assume that height is the most influential factor in basketball “success”.\n",
    "\n",
    "In a separate article, Scott Kaufman explains the largest study to date on this topic where psychologists, Jerad Moxley and Tyler Towne, researched whether performance in the NBA is predicted by physical makeup and athleticism. They looked at many variables, “including player position (guard, forward, center), age at the beginning of the next NBA season, college performance (win shares)*, college quality, height, agility, no-step vertical leap, arm span, and weight” (2014). They concluded that “the only variables that predicted NBA success were youth, college performance, and college quality” (2014). The researchers brought up a good point that their sample of over 300 NBA players is an elite sample when it comes to physical makeup and athleticism, so obviously there are certain physical and athletic advantages they have over the rest of the population which helped them get into the NBA in the first place. They ended up factoring out physical characteristics like height, weight, and wingspan as a reason for success since these characteristics are pretty similar across most NBA players. Since an average NBA is taller, heavier and more muscular, and has a longer wingspan than the average man, these characteristics didn’t stand out in Moxley and Towne’s study.\n",
    "\n",
    "All three of these articles offer interesting insight about what makes a basketball player great. While both Ranson’s and Nall’s articles believe that physical characteristics such as height and weight play a large role in a player’s ability to perform in the league, they are not sufficient for predicting excellence and leaning one way or another in both height and weight can have some tradeoffs in what the body is capable of doing on the court. Moxley and Towne’s study mention that physical characteristics play a large role in even making it to the NBA, so they are all around similar heights and weights once they get to the league. Because of this, height and weight aren’t good ways to determine efficiency because they are too similar among all the players. These findings report a somewhat consistent result about what makes an NBA player the most successful, but we believe that there is more that needs to be explored about this topic.\n",
    "\n",
    "References:\n",
    "- 1) Physiologic Profile of Basketball Athletes (Ranson) -https://www.gssiweb.org/sports-science-exchange/article/physiologic-profile-of-basketball-athletes\n",
    "- 2) What Predicts NBA Success (Kaufman) https://blogs.scientificamerican.com/beautiful-minds/what-predicts-nba-success/\n",
    "- 3) Is Height Important in Basketball? (Nall) (https://www.livestrong.com/article/363066-is-height-important-in-basketball/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We hypothesize that height will be the most common factor between successful NBA players based on their EFF score because height can be important when it comes to being able to score a goal or defend the ball. Additionally, the average height of players in the NBA is 6’6”, so we can infer that how tall a basketball player is more influential in their success than weight."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Dataset 1:\n",
    "- Name: Basketball Players Stats per Season - 49 Leagues \n",
    "- Link: https://www.kaggle.com/jacobbaruch/basketball-players-stats-per-season-49-leagues \n",
    "- Number of observations: 14582\n",
    "- Description: This dataset contains basketball statistics as well as player details, like height, weight, birth date, nationality, and high school, from the seasons 1999 - 2020 for 49 leagues."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "sns.set_context('talk')\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import patsy\n",
    "import statsmodels.api as sm\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import ttest_ind, chisquare, normaltest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We received our data through Kaggle, and it was already in a clean and tidy format. Each observation was already split into columns as well. So for our data cleaning, we only need to drop the columns that we will not need for our EDA. First, we downloaded the csv file and uploaded it onto Github to access it for our project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <td>147.0</td>\n",
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       "      <td>Vince Carter</td>\n",
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       "      <td>1696</td>\n",
       "      <td>95</td>\n",
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       "      <td>Jan 26, 1977</td>\n",
       "      <td>6-6</td>\n",
       "      <td>198.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>100.0</td>\n",
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       "      <td>NBA</td>\n",
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       "      <td>Regular_Season</td>\n",
       "      <td>Karl Malone</td>\n",
       "      <td>UTA</td>\n",
       "      <td>82</td>\n",
       "      <td>2947.0</td>\n",
       "      <td>752</td>\n",
       "      <td>1476</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>Jul 24, 1963</td>\n",
       "      <td>6-9</td>\n",
       "      <td>206.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>Summerfield High School</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Utah Jazz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NBA</td>\n",
       "      <td>1999 - 2000</td>\n",
       "      <td>Regular_Season</td>\n",
       "      <td>Allen Iverson</td>\n",
       "      <td>PHI</td>\n",
       "      <td>70</td>\n",
       "      <td>2853.0</td>\n",
       "      <td>729</td>\n",
       "      <td>1733</td>\n",
       "      <td>89</td>\n",
       "      <td>...</td>\n",
       "      <td>Jun 7, 1975</td>\n",
       "      <td>6-0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>Bethel High School</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>NBA</td>\n",
       "      <td>1999 - 2000</td>\n",
       "      <td>Regular_Season</td>\n",
       "      <td>Gary Payton</td>\n",
       "      <td>SEA</td>\n",
       "      <td>82</td>\n",
       "      <td>3425.0</td>\n",
       "      <td>747</td>\n",
       "      <td>1666</td>\n",
       "      <td>177</td>\n",
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       "      <td>Jul 23, 1968</td>\n",
       "      <td>6-4</td>\n",
       "      <td>193.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>Skyline High School</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Seattle SuperSonics</td>\n",
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       "      <th>53944</th>\n",
       "      <td>Ukrainian-Superleague</td>\n",
       "      <td>2019 - 2020</td>\n",
       "      <td>International</td>\n",
       "      <td>Kyrylo Meshheryakov</td>\n",
       "      <td>MYK</td>\n",
       "      <td>15</td>\n",
       "      <td>127.0</td>\n",
       "      <td>7</td>\n",
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       "      <td>Aug 17, 1995</td>\n",
       "      <td>6-6</td>\n",
       "      <td>198.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53945</th>\n",
       "      <td>Ukrainian-Superleague</td>\n",
       "      <td>2019 - 2020</td>\n",
       "      <td>International</td>\n",
       "      <td>Yaroslav Kadygrob</td>\n",
       "      <td>ODE</td>\n",
       "      <td>10</td>\n",
       "      <td>81.7</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>Oct 28, 1991</td>\n",
       "      <td>6-3</td>\n",
       "      <td>191.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>53946</th>\n",
       "      <td>Ukrainian-Superleague</td>\n",
       "      <td>2019 - 2020</td>\n",
       "      <td>International</td>\n",
       "      <td>Ernesto Tkachuk</td>\n",
       "      <td>ODE</td>\n",
       "      <td>16</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>Sep 17, 1994</td>\n",
       "      <td>6-2</td>\n",
       "      <td>188.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53947</th>\n",
       "      <td>Ukrainian-Superleague</td>\n",
       "      <td>2019 - 2020</td>\n",
       "      <td>International</td>\n",
       "      <td>Andrij Shapovalov</td>\n",
       "      <td>KHAR</td>\n",
       "      <td>12</td>\n",
       "      <td>59.2</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>Nov 10, 1993</td>\n",
       "      <td>6-2</td>\n",
       "      <td>188.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53948</th>\n",
       "      <td>Ukrainian-Superleague</td>\n",
       "      <td>2019 - 2020</td>\n",
       "      <td>International</td>\n",
       "      <td>Dmitriy Lypovtsev</td>\n",
       "      <td>KHAR</td>\n",
       "      <td>5</td>\n",
       "      <td>86.3</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>Oct 10, 1986</td>\n",
       "      <td>6-8</td>\n",
       "      <td>203.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "                      League       Season           Stage  \\\n",
       "0                        NBA  1999 - 2000  Regular_Season   \n",
       "1                        NBA  1999 - 2000  Regular_Season   \n",
       "2                        NBA  1999 - 2000  Regular_Season   \n",
       "3                        NBA  1999 - 2000  Regular_Season   \n",
       "4                        NBA  1999 - 2000  Regular_Season   \n",
       "...                      ...          ...             ...   \n",
       "53944  Ukrainian-Superleague  2019 - 2020   International   \n",
       "53945  Ukrainian-Superleague  2019 - 2020   International   \n",
       "53946  Ukrainian-Superleague  2019 - 2020   International   \n",
       "53947  Ukrainian-Superleague  2019 - 2020   International   \n",
       "53948  Ukrainian-Superleague  2019 - 2020   International   \n",
       "\n",
       "                    Player  Team  GP     MIN  FGM   FGA  3PM  ...  \\\n",
       "0         Shaquille O'Neal   LAL  79  3163.0  956  1665    0  ...   \n",
       "1             Vince Carter   TOR  82  3126.0  788  1696   95  ...   \n",
       "2              Karl Malone   UTA  82  2947.0  752  1476    2  ...   \n",
       "3            Allen Iverson   PHI  70  2853.0  729  1733   89  ...   \n",
       "4              Gary Payton   SEA  82  3425.0  747  1666  177  ...   \n",
       "...                    ...   ...  ..     ...  ...   ...  ...  ...   \n",
       "53944  Kyrylo Meshheryakov   MYK  15   127.0    7    28    2  ...   \n",
       "53945    Yaroslav Kadygrob   ODE  10    81.7    5    16    4  ...   \n",
       "53946      Ernesto Tkachuk   ODE  16   124.7    1    15    0  ...   \n",
       "53947    Andrij Shapovalov  KHAR  12    59.2    0     8    0  ...   \n",
       "53948    Dmitriy Lypovtsev  KHAR   5    86.3    1    13    0  ...   \n",
       "\n",
       "         birth_date  height  height_cm  weight  weight_kg    nationality  \\\n",
       "0       Mar 6, 1972     7-1      216.0   325.0      147.0  United States   \n",
       "1      Jan 26, 1977     6-6      198.0   220.0      100.0  United States   \n",
       "2      Jul 24, 1963     6-9      206.0   265.0      120.0  United States   \n",
       "3       Jun 7, 1975     6-0      183.0   165.0       75.0  United States   \n",
       "4      Jul 23, 1968     6-4      193.0   180.0       82.0  United States   \n",
       "...             ...     ...        ...     ...        ...            ...   \n",
       "53944  Aug 17, 1995     6-6      198.0   182.0       83.0        Ukraine   \n",
       "53945  Oct 28, 1991     6-3      191.0   187.0       85.0        Ukraine   \n",
       "53946  Sep 17, 1994     6-2      188.0   200.0       91.0        Ukraine   \n",
       "53947  Nov 10, 1993     6-2      188.0   171.0       78.0        Ukraine   \n",
       "53948  Oct 10, 1986     6-8      203.0   220.0      100.0        Ukraine   \n",
       "\n",
       "                       high_school  draft_round  draft_pick  \\\n",
       "0      Robert G. Cole High School           1.0         1.0   \n",
       "1             Mainland High School          1.0         5.0   \n",
       "2          Summerfield High School          1.0        13.0   \n",
       "3              Bethel High School           1.0         1.0   \n",
       "4              Skyline High School          1.0         2.0   \n",
       "...                            ...          ...         ...   \n",
       "53944                          NaN          NaN         NaN   \n",
       "53945                          NaN          NaN         NaN   \n",
       "53946                          NaN          NaN         NaN   \n",
       "53947                          NaN          NaN         NaN   \n",
       "53948                          NaN          NaN         NaN   \n",
       "\n",
       "                  draft_team  \n",
       "0              Orlando Magic  \n",
       "1      Golden State Warriors  \n",
       "2                  Utah Jazz  \n",
       "3        Philadelphia Sixers  \n",
       "4        Seattle SuperSonics  \n",
       "...                      ...  \n",
       "53944                    NaN  \n",
       "53945                    NaN  \n",
       "53946                    NaN  \n",
       "53947                    NaN  \n",
       "53948                    NaN  \n",
       "\n",
       "[53949 rows x 34 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import csv file\n",
    "stats_by_season = pd.read_csv('players_stats_by_season_full_details.csv')\n",
    "stats_by_season"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're looking at whether height or weight contributes more to the success of an NBA player. To quantify the success of a NBA player, we're going to calculate their efficiency (EFF) which includes: points made, rebounds, assists, steals, blocks, missed field goals, missed free throws, turnovers, and games played. \n",
    "\n",
    "We decided to look at their height in centimeters and their weight and kilograms. So we're removing the columns that aren't necessary to calculating the EFF and aren't their height in centimeters and weight in kilograms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>League</th>\n",
       "      <th>Player</th>\n",
       "      <th>GP</th>\n",
       "      <th>MIN</th>\n",
       "      <th>FGM</th>\n",
       "      <th>FGA</th>\n",
       "      <th>3PM</th>\n",
       "      <th>3PA</th>\n",
       "      <th>FTM</th>\n",
       "      <th>FTA</th>\n",
       "      <th>...</th>\n",
       "      <th>PF</th>\n",
       "      <th>ORB</th>\n",
       "      <th>DRB</th>\n",
       "      <th>REB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>PTS</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NBA</td>\n",
       "      <td>Shaquille O'Neal</td>\n",
       "      <td>79</td>\n",
       "      <td>3163.0</td>\n",
       "      <td>956</td>\n",
       "      <td>1665</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>432</td>\n",
       "      <td>824</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>336</td>\n",
       "      <td>742</td>\n",
       "      <td>1078</td>\n",
       "      <td>299</td>\n",
       "      <td>36</td>\n",
       "      <td>239</td>\n",
       "      <td>2344</td>\n",
       "      <td>216.0</td>\n",
       "      <td>147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NBA</td>\n",
       "      <td>Vince Carter</td>\n",
       "      <td>82</td>\n",
       "      <td>3126.0</td>\n",
       "      <td>788</td>\n",
       "      <td>1696</td>\n",
       "      <td>95</td>\n",
       "      <td>236</td>\n",
       "      <td>436</td>\n",
       "      <td>551</td>\n",
       "      <td>...</td>\n",
       "      <td>263</td>\n",
       "      <td>150</td>\n",
       "      <td>326</td>\n",
       "      <td>476</td>\n",
       "      <td>322</td>\n",
       "      <td>110</td>\n",
       "      <td>92</td>\n",
       "      <td>2107</td>\n",
       "      <td>198.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NBA</td>\n",
       "      <td>Karl Malone</td>\n",
       "      <td>82</td>\n",
       "      <td>2947.0</td>\n",
       "      <td>752</td>\n",
       "      <td>1476</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>589</td>\n",
       "      <td>739</td>\n",
       "      <td>...</td>\n",
       "      <td>229</td>\n",
       "      <td>169</td>\n",
       "      <td>610</td>\n",
       "      <td>779</td>\n",
       "      <td>304</td>\n",
       "      <td>79</td>\n",
       "      <td>71</td>\n",
       "      <td>2095</td>\n",
       "      <td>206.0</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NBA</td>\n",
       "      <td>Allen Iverson</td>\n",
       "      <td>70</td>\n",
       "      <td>2853.0</td>\n",
       "      <td>729</td>\n",
       "      <td>1733</td>\n",
       "      <td>89</td>\n",
       "      <td>261</td>\n",
       "      <td>442</td>\n",
       "      <td>620</td>\n",
       "      <td>...</td>\n",
       "      <td>162</td>\n",
       "      <td>71</td>\n",
       "      <td>196</td>\n",
       "      <td>267</td>\n",
       "      <td>328</td>\n",
       "      <td>144</td>\n",
       "      <td>5</td>\n",
       "      <td>1989</td>\n",
       "      <td>183.0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NBA</td>\n",
       "      <td>Gary Payton</td>\n",
       "      <td>82</td>\n",
       "      <td>3425.0</td>\n",
       "      <td>747</td>\n",
       "      <td>1666</td>\n",
       "      <td>177</td>\n",
       "      <td>520</td>\n",
       "      <td>311</td>\n",
       "      <td>423</td>\n",
       "      <td>...</td>\n",
       "      <td>178</td>\n",
       "      <td>100</td>\n",
       "      <td>429</td>\n",
       "      <td>529</td>\n",
       "      <td>732</td>\n",
       "      <td>153</td>\n",
       "      <td>18</td>\n",
       "      <td>1982</td>\n",
       "      <td>193.0</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53944</th>\n",
       "      <td>Ukrainian-Superleague</td>\n",
       "      <td>Kyrylo Meshheryakov</td>\n",
       "      <td>15</td>\n",
       "      <td>127.0</td>\n",
       "      <td>7</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>198.0</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53945</th>\n",
       "      <td>Ukrainian-Superleague</td>\n",
       "      <td>Yaroslav Kadygrob</td>\n",
       "      <td>10</td>\n",
       "      <td>81.7</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>191.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53946</th>\n",
       "      <td>Ukrainian-Superleague</td>\n",
       "      <td>Ernesto Tkachuk</td>\n",
       "      <td>16</td>\n",
       "      <td>124.7</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>188.0</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53947</th>\n",
       "      <td>Ukrainian-Superleague</td>\n",
       "      <td>Andrij Shapovalov</td>\n",
       "      <td>12</td>\n",
       "      <td>59.2</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>188.0</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53948</th>\n",
       "      <td>Ukrainian-Superleague</td>\n",
       "      <td>Dmitriy Lypovtsev</td>\n",
       "      <td>5</td>\n",
       "      <td>86.3</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>203.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53949 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      League               Player  GP     MIN  FGM   FGA  3PM  \\\n",
       "0                        NBA     Shaquille O'Neal  79  3163.0  956  1665    0   \n",
       "1                        NBA         Vince Carter  82  3126.0  788  1696   95   \n",
       "2                        NBA          Karl Malone  82  2947.0  752  1476    2   \n",
       "3                        NBA        Allen Iverson  70  2853.0  729  1733   89   \n",
       "4                        NBA          Gary Payton  82  3425.0  747  1666  177   \n",
       "...                      ...                  ...  ..     ...  ...   ...  ...   \n",
       "53944  Ukrainian-Superleague  Kyrylo Meshheryakov  15   127.0    7    28    2   \n",
       "53945  Ukrainian-Superleague    Yaroslav Kadygrob  10    81.7    5    16    4   \n",
       "53946  Ukrainian-Superleague      Ernesto Tkachuk  16   124.7    1    15    0   \n",
       "53947  Ukrainian-Superleague    Andrij Shapovalov  12    59.2    0     8    0   \n",
       "53948  Ukrainian-Superleague    Dmitriy Lypovtsev   5    86.3    1    13    0   \n",
       "\n",
       "       3PA  FTM  FTA  ...   PF  ORB  DRB   REB  AST  STL  BLK   PTS  \\\n",
       "0        1  432  824  ...  255  336  742  1078  299   36  239  2344   \n",
       "1      236  436  551  ...  263  150  326   476  322  110   92  2107   \n",
       "2        8  589  739  ...  229  169  610   779  304   79   71  2095   \n",
       "3      261  442  620  ...  162   71  196   267  328  144    5  1989   \n",
       "4      520  311  423  ...  178  100  429   529  732  153   18  1982   \n",
       "...    ...  ...  ...  ...  ...  ...  ...   ...  ...  ...  ...   ...   \n",
       "53944   13    3    4  ...   27    4   14    18    8    1    3    19   \n",
       "53945   14    1    3  ...    2    2    4     6    3    0    0    15   \n",
       "53946   11    7   14  ...   12    0   15    15   10    6    1     9   \n",
       "53947    7    3    6  ...    6    1    4     5    3    1    0     3   \n",
       "53948    9    0    0  ...   11    2   12    14    4    2    1     2   \n",
       "\n",
       "       height_cm  weight_kg  \n",
       "0          216.0      147.0  \n",
       "1          198.0      100.0  \n",
       "2          206.0      120.0  \n",
       "3          183.0       75.0  \n",
       "4          193.0       82.0  \n",
       "...          ...        ...  \n",
       "53944      198.0       83.0  \n",
       "53945      191.0       85.0  \n",
       "53946      188.0       91.0  \n",
       "53947      188.0       78.0  \n",
       "53948      203.0      100.0  \n",
       "\n",
       "[53949 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop columns we don't need\n",
    "stats_by_season = stats_by_season.drop(['birth_year', 'birth_month', 'birth_date',\n",
    "                           'nationality','high_school','height','weight',\n",
    "                           'draft_round','draft_team','draft_pick','Stage','Team','Season'],axis=1)\n",
    "stats_by_season"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset also contains the stats of players in International leagues, but we're only looking at players in the NBA. Therefore, we removed all the stats of players who aren't in the NBA and then dropped the 'League' column. Since the remaining players should all be in the NBA, we no longer needed that column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>GP</th>\n",
       "      <th>MIN</th>\n",
       "      <th>FGM</th>\n",
       "      <th>FGA</th>\n",
       "      <th>3PM</th>\n",
       "      <th>3PA</th>\n",
       "      <th>FTM</th>\n",
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       "      <th>TOV</th>\n",
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       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
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       "      <th>PTS</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shaquille O'Neal</td>\n",
       "      <td>79</td>\n",
       "      <td>3163.0</td>\n",
       "      <td>956</td>\n",
       "      <td>1665</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>432</td>\n",
       "      <td>824</td>\n",
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       "      <td>255</td>\n",
       "      <td>336</td>\n",
       "      <td>742</td>\n",
       "      <td>1078</td>\n",
       "      <td>299</td>\n",
       "      <td>36</td>\n",
       "      <td>239</td>\n",
       "      <td>2344</td>\n",
       "      <td>216.0</td>\n",
       "      <td>147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Vince Carter</td>\n",
       "      <td>82</td>\n",
       "      <td>3126.0</td>\n",
       "      <td>788</td>\n",
       "      <td>1696</td>\n",
       "      <td>95</td>\n",
       "      <td>236</td>\n",
       "      <td>436</td>\n",
       "      <td>551</td>\n",
       "      <td>178</td>\n",
       "      <td>263</td>\n",
       "      <td>150</td>\n",
       "      <td>326</td>\n",
       "      <td>476</td>\n",
       "      <td>322</td>\n",
       "      <td>110</td>\n",
       "      <td>92</td>\n",
       "      <td>2107</td>\n",
       "      <td>198.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Karl Malone</td>\n",
       "      <td>82</td>\n",
       "      <td>2947.0</td>\n",
       "      <td>752</td>\n",
       "      <td>1476</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>589</td>\n",
       "      <td>739</td>\n",
       "      <td>231</td>\n",
       "      <td>229</td>\n",
       "      <td>169</td>\n",
       "      <td>610</td>\n",
       "      <td>779</td>\n",
       "      <td>304</td>\n",
       "      <td>79</td>\n",
       "      <td>71</td>\n",
       "      <td>2095</td>\n",
       "      <td>206.0</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Allen Iverson</td>\n",
       "      <td>70</td>\n",
       "      <td>2853.0</td>\n",
       "      <td>729</td>\n",
       "      <td>1733</td>\n",
       "      <td>89</td>\n",
       "      <td>261</td>\n",
       "      <td>442</td>\n",
       "      <td>620</td>\n",
       "      <td>230</td>\n",
       "      <td>162</td>\n",
       "      <td>71</td>\n",
       "      <td>196</td>\n",
       "      <td>267</td>\n",
       "      <td>328</td>\n",
       "      <td>144</td>\n",
       "      <td>5</td>\n",
       "      <td>1989</td>\n",
       "      <td>183.0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gary Payton</td>\n",
       "      <td>82</td>\n",
       "      <td>3425.0</td>\n",
       "      <td>747</td>\n",
       "      <td>1666</td>\n",
       "      <td>177</td>\n",
       "      <td>520</td>\n",
       "      <td>311</td>\n",
       "      <td>423</td>\n",
       "      <td>224</td>\n",
       "      <td>178</td>\n",
       "      <td>100</td>\n",
       "      <td>429</td>\n",
       "      <td>529</td>\n",
       "      <td>732</td>\n",
       "      <td>153</td>\n",
       "      <td>18</td>\n",
       "      <td>1982</td>\n",
       "      <td>193.0</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7628</th>\n",
       "      <td>Wesley Iwundu</td>\n",
       "      <td>5</td>\n",
       "      <td>75.8</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>201.0</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7629</th>\n",
       "      <td>Semi Ojeleye</td>\n",
       "      <td>13</td>\n",
       "      <td>122.2</td>\n",
       "      <td>7</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>201.0</td>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7630</th>\n",
       "      <td>J.R. Smith</td>\n",
       "      <td>10</td>\n",
       "      <td>74.9</td>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>198.0</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7631</th>\n",
       "      <td>Juwan Morgan</td>\n",
       "      <td>7</td>\n",
       "      <td>86.5</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>203.0</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7632</th>\n",
       "      <td>Matisse Thybulle</td>\n",
       "      <td>4</td>\n",
       "      <td>75.3</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>196.0</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7633 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Player  GP     MIN  FGM   FGA  3PM  3PA  FTM  FTA  TOV   PF  \\\n",
       "0     Shaquille O'Neal  79  3163.0  956  1665    0    1  432  824  223  255   \n",
       "1         Vince Carter  82  3126.0  788  1696   95  236  436  551  178  263   \n",
       "2          Karl Malone  82  2947.0  752  1476    2    8  589  739  231  229   \n",
       "3        Allen Iverson  70  2853.0  729  1733   89  261  442  620  230  162   \n",
       "4          Gary Payton  82  3425.0  747  1666  177  520  311  423  224  178   \n",
       "...                ...  ..     ...  ...   ...  ...  ...  ...  ...  ...  ...   \n",
       "7628     Wesley Iwundu   5    75.8    6    20    4    7    5    8    3    5   \n",
       "7629      Semi Ojeleye  13   122.2    7    28    5   23    2    2    1    9   \n",
       "7630        J.R. Smith  10    74.9    7    26    6   22    0    0    7   11   \n",
       "7631      Juwan Morgan   7    86.5    3    12    2   10    2    6    1   11   \n",
       "7632  Matisse Thybulle   4    75.3    3     7    1    4    0    0    2    6   \n",
       "\n",
       "      ORB  DRB   REB  AST  STL  BLK   PTS  height_cm  weight_kg  \n",
       "0     336  742  1078  299   36  239  2344      216.0      147.0  \n",
       "1     150  326   476  322  110   92  2107      198.0      100.0  \n",
       "2     169  610   779  304   79   71  2095      206.0      120.0  \n",
       "3      71  196   267  328  144    5  1989      183.0       75.0  \n",
       "4     100  429   529  732  153   18  1982      193.0       82.0  \n",
       "...   ...  ...   ...  ...  ...  ...   ...        ...        ...  \n",
       "7628    1   10    11    4    3    2    21      201.0       88.0  \n",
       "7629    2   10    12    1    3    0    21      201.0      109.0  \n",
       "7630    0    3     3    3    2    0    20      198.0      102.0  \n",
       "7631    6   15    21    5    2    0    10      203.0      105.0  \n",
       "7632    1    6     7    2    3    1     7      196.0       91.0  \n",
       "\n",
       "[7633 rows x 20 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop rows where the league isn't the NBA\n",
    "stats_by_season = stats_by_season[stats_by_season['League']=='NBA'].reset_index()\n",
    "#Drop league column\n",
    "stats_by_season = stats_by_season.drop(['League','index'],axis=1)\n",
    "stats_by_season"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, we checked all columns for any null values and looked at the data types to make sure all the observations were useable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Player        object\n",
       "GP             int64\n",
       "MIN          float64\n",
       "FGM            int64\n",
       "FGA            int64\n",
       "3PM            int64\n",
       "3PA            int64\n",
       "FTM            int64\n",
       "FTA            int64\n",
       "TOV            int64\n",
       "PF             int64\n",
       "ORB            int64\n",
       "DRB            int64\n",
       "REB            int64\n",
       "AST            int64\n",
       "STL            int64\n",
       "BLK            int64\n",
       "PTS            int64\n",
       "height_cm    float64\n",
       "weight_kg    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for null values\n",
    "print(stats_by_season.isnull().values.any())\n",
    "#Check all data types are ints or floats (except players)\n",
    "stats_by_season.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Include cells that describe the steps in your data analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tony Parker         False\n",
       "Tim Duncan          False\n",
       "Dirk Nowitzki       False\n",
       "LeBron James        False\n",
       "Vince Carter        False\n",
       "                    ...  \n",
       "Isaiah Whitehead     True\n",
       "Bimbo Coles          True\n",
       "Julian Wright        True\n",
       "Mario West           True\n",
       "Juwan Morgan         True\n",
       "Name: Player, Length: 1119, dtype: bool"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check if there are repeated values in the 'Player' column\n",
    "(stats_by_season['Player'].value_counts()==1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that there are a number of repeated values in the Player Column, and that there are 1119 unique players in this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>index</th>\n",
       "      <th>Player</th>\n",
       "      <th>EFF</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6213</td>\n",
       "      <td>Russell Westbrook</td>\n",
       "      <td>33.600000</td>\n",
       "      <td>191.0</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1220</td>\n",
       "      <td>Kevin Garnett</td>\n",
       "      <td>33.500000</td>\n",
       "      <td>211.0</td>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7079</td>\n",
       "      <td>Nikola Jokic</td>\n",
       "      <td>31.928571</td>\n",
       "      <td>213.0</td>\n",
       "      <td>113.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>207</td>\n",
       "      <td>Shaquille O'Neal</td>\n",
       "      <td>31.478261</td>\n",
       "      <td>216.0</td>\n",
       "      <td>147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>904</td>\n",
       "      <td>Kevin Garnett</td>\n",
       "      <td>31.333333</td>\n",
       "      <td>211.0</td>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7628</th>\n",
       "      <td>3057</td>\n",
       "      <td>Tony Allen</td>\n",
       "      <td>1.066667</td>\n",
       "      <td>193.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7629</th>\n",
       "      <td>1672</td>\n",
       "      <td>Darvin Ham</td>\n",
       "      <td>1.047619</td>\n",
       "      <td>201.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7630</th>\n",
       "      <td>7069</td>\n",
       "      <td>Zach Lofton</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>193.0</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7631</th>\n",
       "      <td>7067</td>\n",
       "      <td>Tyler Ulis</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>178.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7632</th>\n",
       "      <td>7068</td>\n",
       "      <td>John Holland</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>196.0</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7633 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index             Player        EFF  height_cm  weight_kg\n",
       "0      6213  Russell Westbrook  33.600000      191.0       91.0\n",
       "1      1220      Kevin Garnett  33.500000      211.0      109.0\n",
       "2      7079       Nikola Jokic  31.928571      213.0      113.0\n",
       "3       207   Shaquille O'Neal  31.478261      216.0      147.0\n",
       "4       904      Kevin Garnett  31.333333      211.0      109.0\n",
       "...     ...                ...        ...        ...        ...\n",
       "7628   3057         Tony Allen   1.066667      193.0       97.0\n",
       "7629   1672         Darvin Ham   1.047619      201.0      100.0\n",
       "7630   7069        Zach Lofton   0.000000      193.0       82.0\n",
       "7631   7067         Tyler Ulis   0.000000      178.0       68.0\n",
       "7632   7068       John Holland   0.000000      196.0       93.0\n",
       "\n",
       "[7633 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate each player's EFF in each season and store them into a new column \"EFF\"\n",
    "#EFF = (PTS + REB + AST + STL + BLK − FGM − FTM - TOV) / GP\n",
    "stats_by_season['EFF'] = (stats_by_season['PTS'] + stats_by_season['REB'] + stats_by_season['AST'] + stats_by_season['STL'] + stats_by_season['BLK']\n",
    "                         - stats_by_season['FGM'] - stats_by_season['FTM'] - stats_by_season['TOV']) / stats_by_season['GP']\n",
    "#Keep only the 'Player','height_cm','weight_kg','EFF' columns\n",
    "stats_by_season = stats_by_season[['Player','EFF','height_cm','weight_kg']]\n",
    "\n",
    "#Sort the dataframe in an descending order of EFF\n",
    "stats_by_season = stats_by_season.sort_values(by = ['EFF'], ascending = False).reset_index()\n",
    "\n",
    "stats_by_season"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the columns that we kept from our data cleaning (excluding height and weight), we will calculate the EFF. We are essentially taking the sum of all the positive contributions done by the player and from that, subtracting all the negative errors or missed opportunities of the player. After computing this value, we created a subset dataframe made up of the values that we wanted to analyze.\n",
    "\n",
    "EFF = ( points + rebounds + assists + steals + blocks - missed field goals - missed free throws - turnovers ) / games played"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There were multiple entries of the same players due to the fact that they've played multiple seasons in the NBA. We decided that the most fair way to determine a player's EFF was to take the average of a player's EFF score (if there were multiple)."
   ]
  },
  {
   "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>Player</th>\n",
       "      <th>EFF</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LeBron James</td>\n",
       "      <td>25.968845</td>\n",
       "      <td>203.0</td>\n",
       "      <td>113.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anthony Davis</td>\n",
       "      <td>25.750773</td>\n",
       "      <td>208.0</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Luka Doncic</td>\n",
       "      <td>25.650046</td>\n",
       "      <td>201.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nikola Jokic</td>\n",
       "      <td>23.876559</td>\n",
       "      <td>213.0</td>\n",
       "      <td>113.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Karl-Anthony Towns</td>\n",
       "      <td>23.868245</td>\n",
       "      <td>213.0</td>\n",
       "      <td>112.0</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>1114</th>\n",
       "      <td>Rodney Buford</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>196.0</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1115</th>\n",
       "      <td>Acie Law</td>\n",
       "      <td>1.428571</td>\n",
       "      <td>191.0</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1116</th>\n",
       "      <td>Mario West</td>\n",
       "      <td>1.390625</td>\n",
       "      <td>196.0</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1117</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>196.0</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1118</th>\n",
       "      <td>Zach Lofton</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>193.0</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1119 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Player        EFF  height_cm  weight_kg\n",
       "0           LeBron James  25.968845      203.0      113.0\n",
       "1          Anthony Davis  25.750773      208.0      115.0\n",
       "2            Luka Doncic  25.650046      201.0       99.0\n",
       "3           Nikola Jokic  23.876559      213.0      113.0\n",
       "4     Karl-Anthony Towns  23.868245      213.0      112.0\n",
       "...                  ...        ...        ...        ...\n",
       "1114       Rodney Buford   1.800000      196.0       86.0\n",
       "1115            Acie Law   1.428571      191.0       92.0\n",
       "1116          Mario West   1.390625      196.0       95.0\n",
       "1117        John Holland   0.000000      196.0       93.0\n",
       "1118         Zach Lofton   0.000000      193.0       82.0\n",
       "\n",
       "[1119 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate the average EFF of each player of all seasons\n",
    "stats_average = stats_by_season.groupby('Player').mean().sort_values(by = ['EFF'], ascending = False).reset_index()\n",
    "stats_average = stats_average.drop(['index'], axis = 1)\n",
    "stats_average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='EFF', ylabel='Count'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zVVVV1erntVgkmy3W6+2io6MktW7bYBUVbW3yyMexz63XP35rrp2D4+2Bu3betDXVztFXx0eNgapPkhy/af54jYXj67c5kdJXk/10FwNuT4K88847euWVV3TPPfe02K5Pnz5avXq13n33Xa8K9ERCQoIefvhh/eEPf9Brr72m3/72tzpx4oSmTZumTz/9VJJUU1Mj6dr0Djez2WzO9UA4sUi6Ut+gmJhot/8sVi6FQPBwe+Tzt7/9TUOHDnUeYTSnd+/eGj58uLZu3erzOX2ysrKUlZXl/D47O1ujR4/Wfffdp5KSEr366qvOdc0ddbXlaMxul2prL3u9nePdRWu2DUYpKQm6Wt/Q5Ltxx1FAQ32DJDXbzsGxzl07b9qaaufo69XrfQ1UfY627aKtzQ4EcbBYpMWzRurbb//PzSP+V7i9flsSKX012c/k5PgWj37cHvkcO3ZMQ4YM8ejJBg0apM8++8zj4toiJSVFw4cPdx75JCUlSVKTRzi1tbVNHhEB4cIxEKTZfwwEQZBxGz61tbVKTk726MFuvfVWox9vNTQ0OP/vONfT1LmdysrKJs8FAQACw234xMXF6cKFCx49WE1NTZP3ffOHb7/9VgcOHHAOve7SpYt69+6tLVu2uITSwYMHVV1drdzcXCN1AQDcc3vOJyMjQ/v379ejjz7q9sH279+vjIwMnxR2o6efflrdunXT3XffrcTERJ08eVKrV6/W999/rzlz5jjbFRcXq6CgQHPmzNGUKVNUXV2tJUuWqG/fvho3bpzP6wIAtI7b8Bk7dqwWL16s3bt3t3j3gj179ujAgQOaN2+eTwuUpF69emnr1q16/fXXdfnyZSUlJWngwIF68sknnXMKSdemdigrK1NpaakKCwsVFxennJwczZ07V1FRUT6vCwDQOm7DZ+rUqXrjjTdUVFSkgoICTZ482eWWNl9++aXeeustrV27Vt27d9fUqVN9XmRhYaEKCws9ajtixAiNGDHC5zUAAHzHbfh06NBB5eXleuKJJ7Rq1SqVl5crLi5O8fHxunTpkr777jvZ7Xalp6dr1apVat++vYm6AQAhzKPb66SlpWnz5s168803tWPHDlVVVencuXOKi4tTdna2cnNzNXnyZHXo0MHf9QIAwoDH93Zr37698vPzlZ+f7896AAARgDkGAADGET4AAOMIHwCAcYQPAMC4Vk0mByC0OKZeSElxP9vwDz/W62KY390ZgUf4ABHCm6kXAH8jfIAI4ph6ofkGpipBpOOcDwDAOMIHAGAc4QMAMI7wAQAYR/gAAIwjfAAAxhE+AADjCB8AgHGEDwDAOMIHAGAc4QMAMI7wAQAYR/gAAIwjfAAAxhE+AADjCB8AgHGEDwDAOMIHAGAc4QMAMI7wAQAYR/gAAIwjfAAAxkUHugBPHDx4UJs3b9Ynn3yis2fPymazqU+fPpo5c6Z69erlbJefn6+PPvqo0fZ5eXkqKSkxWXJISbTFqn1MSLwUAISJkPiL88Ybb6impkaPPPKIevTooXPnzmnNmjV68MEHtW7dOvXr18/Ztnv37lq8eLHL9h07djRccWhpHxOtZ5btk93efBurRVo0a6S5ogCEtZAIn9///vdKTk52WTZ8+HCNGTNGr7zyikpLS53LO3To4BJG8IzdLrWQPS0GEwB4KyTO+dwcPJKUmJiotLQ0nT17NgAVAQDaIiSOfJpy/vx5VVVV6Wc/+5nL8lOnTmnAgAG6dOmSUlNTNWHCBD3++ONq165dq5/LYpFstlivt4uOjpLUum1Ni4q2tnjkY3XTzmKxXGsXbfXJ47Wmral2jr5GXe9roOrzx2Narn+12WJD6vXbVpHSV5P9tFhaXh+S4WO327VgwQI1NDSooKDAubx///7Ky8vTHXfcobq6Ou3evVvLli3TsWPHtGLFigBWDIQGi6Qr9Q2KuWEASkwzg1Gu1F+VvYHPY9E6IRk+L7zwgnbv3q3nn39ePXr0cC4vKipyaTdq1Ch16tRJZWVlOnz4sLKzs1v1fHa7VFt72evtHO8uWrOtSSkpCbpa39DyOZ/rX5tr5zgKaKhvaLGdp4/Xmram2jn6evV6XwNVn7+eu120Vc8s2ydrlGs/b2SxSItnjdS33/6fmwpDQ6j8rraVyX4mJ8e3ePQTEud8blRSUqK1a9dq/vz5mjRpktv2EyZMkCQdOXLEv4UBYcQxAKXZf14c8CTaYpWSkuD2X2KYf+QFVyF15LN06VKVlZVp7ty5mj59ukfbNDRce9dmtYZczgJhwZOh/I4jKUSOkAmf5cuXa+XKlZo9e7Yee+wxj7fbvHmzJKlv377+Kg2AG+6G8rv9fBFhJyTCZ+3atSotLdWoUaM0dOhQl4/QYmJilJWVpcOHD6u8vFy5ubnq2rWr6urqtGfPHm3YsEHjxo1T//79A9cBAICLkAif9957z/nV8X+Hrl27au/evUpJSZEkLVu2TBcuXJDValV6errmzZun/Px84zUDAJoXEuGzbt06t23S0tJUXl5uoBoAQFtxFh4AYFxIHPkACG+Oi1tTUhLctv3hx3pdDPPrcSIB4QMgKDgubmVIdmQgfAB4zZsjFW8wJDtyED4AWsWTIxXmgUJzCB8ArcY8UGgtRrsBAIwjfAAAxhE+AADjCB8AgHGEDwDAOMIHAGAc4QMAMI7wAQAYR/gAAIwjfAAAxnF7HQBhK9EWq/Yx7v/MXam/KnsD9wIyifABEDJaczdtT6dp+PHH+rYXCI8RPgBCiid305b+e0dtpmkIToQPgJDjNlDEHbWDHQMOAADGceQTxjw92QoApvGXKYy1j4lmpkkAQYnwCXPMNAm0zDGCLiYm2u0ouh9+rNfF2stuH9PTTx08fbxwRPgAiHjtoq363cp/qP5KQ7NtHEOyPeHJpw7ePF44InwAQB6MoPPyUwKGeLeM0W4AAOMIHwCAcYQPAMA4wgcAYBzhAwAwjvABABgXdkOtL126pJKSEm3fvl0XL15URkaGnnrqKY0ZMybQpfkMt80BIos/LloN9IWwYfcXbMaMGfr8889VXFys1NRUbdy4UTNmzFBZWZlGjgyPC7q4bQ4QWfxx0WqgL4QNq/DZt2+fDhw4oOXLl2vs2LGSpMGDB+vMmTNatGhRQMLHYrV4PPHVlfqrahcd5VFbbpsDmNWaiex8+Xj+uGg1kBfChlX47Nq1SwkJCS4fsVksFk2cOFELFizQiRMnlJGRYbSmdtFRXk18xRENELw8mcjOm99RXz9eKLHY7eHzHnnKlCmyWCz6y1/+4rL8008/1UMPPaSSkhLl5eV5/bht+RFZLBZd/sGz6Xlj20d71DbS2oVCjfxsgq9dKNToj3ae/r3y9G+TN4/Z1HM0J6yOfGpqatS9e/dGy202m3N9a7T0A/REbHvPf8yeto20doF87mBvF8jnDvZ2gXzuQLXz5u+VPx7TU2E31LqlH5I/foAAAO+FVfgkJSU1eXRTW1sr6b9HQACAwAqr8MnIyNAXX3yhhgbXOTkqKyslSZmZmYEoCwBwk7AKn7Fjx+rixYvau3evy/JNmzYpPT3d+Eg3AEDTwmrAwciRIzVo0CDNnz9fNTU1Sk1N1aZNm/Txxx9r5cqVgS4PAHBdWA21lqTvvvtOL730knbs2OFye52cnJxAlwYAuC7swgcAEPzC6pwPACA0ED4AAOMIHwCAcYQPAMA4wgcAYFxYXecTLCJhNlVJqqio0PTp05tct23bNvXo0cNwRb5x9uxZrVmzRseOHdPx48dVV1en1157TYMGDWrUdv/+/Vq6dKmOHz+uuLg4jR07VsXFxUpMTAxA5d7xtJ/5+fn66KOPGm2fl5enkpISU+W2ycGDB7V582Z98sknOnv2rGw2m/r06aOZM2eqV69eLm1DeZ962s9g2KeEjx9EwmyqNyouLtaAAQNclqWmpgaomrY7ffq0tm7dqqysLA0ePLjRHTMcKioqVFhYqDFjxqioqEjffPONlixZosrKSq1fv15Wa3B/sOBpPyWpe/fuWrx4scuyjh07+rtEn3njjTdUU1OjRx55RD169NC5c+e0Zs0aPfjgg1q3bp369esnKfT3qaf9lIJgn9rhU++//749MzPTvnPnTueyhoYG+9SpU+3jxo0LYGW+9+GHH9ozMzPtu3btCnQpPnX16lXn/3ft2mXPzMy0f/jhh43aPfDAA/bx48e7tP/ggw/smZmZ9q1btxqptS087ecvf/lL+89//nOTpfncuXPnGi2rra21Z2dn22fMmOFcFur71NN+BsM+De4YD0EtzaZ68uRJnThxIoDVwROevLutrq7W0aNHNX78eJf2w4YNU+fOnbVjxw5/lugTwf4u3peSk5MbLUtMTFRaWprOnj0rKTz2qSf9DBaR8+ozpKqqShkZGY1+sR2ftzrusB1OFi5cqKysLPXv319PPPGEPvvss0CX5HeO/dizZ89G6zIzM1VVVWW6JL86deqUBgwYoKysLOXm5mrlypW6cuVKoMtqk/Pnz6uqqsq5D8N1n97cT4dA71PO+fiYv2ZTDUYJCQl6+OGHNXDgQCUlJemLL75QeXm5pk2bptdff119+/YNdIl+49iPTc0RZbPZ9PnnnxuuyH/69++vvLw83XHHHaqrq9Pu3bu1bNkyHTt2TCtWrAh0ea1it9u1YMECNTQ0qKCgQFJ47tOm+ikFxz4lfPwgUmZTzcrKUlZWlvP77OxsjR49Wvfdd59KSkr06quvBq44Q5rbn+G0n4uKily+HzVqlDp16qSysjIdPnxY2dnZgSmsDV544QXt3r1bzz//fKNRmeG0T5vrZzDsUz5287FIn001JSVFw4cP16effhroUvwqKSlJUtNHsrW1tWG/nydMmCBJOnLkSEDraI2SkhKtXbtW8+fP16RJk5zLw22fNtfP5pjep4SPjzGbqhr1PRw5Pj9v6jxAZWVlk+cNwoljH4faoIWlS5eqrKxMc+fObXSNWjjt05b62RzT+zS0XjkhINJnU/3222914MABl+sJwlGXLl3Uu3dvbdmyxSVsDx48qOrqauXm5gawOv/bvHmzJIXUeb3ly5dr5cqVmj17th577LFG68Nln7rrZ3NM71PO+fhYJM2m+vTTT6tbt266++67lZiYqJMnT2r16tX6/vvvNWfOnECX1ybbt2+XJB09elSSdOjQIV24cEGxsbHOC4WLi4tVUFCgOXPmaMqUKaqurtaSJUvUt29fjRs3LmC1e8NdPw8fPqzy8nLl5uaqa9euqqur0549e7RhwwaNGzdO/fv3D2T5Hlu7dq1KS0s1atQoDR061OWjpZiYGOe5y1Dfp570M1j2KZPJ+UGkzKZaXl6urVu36quvvtLly5eVlJSkgQMH6sknnwz5jxdvvuWKQ9euXV2Oav/+97+rtLTUeSuWnJwczZ07N2TOD7jr5+nTp/WnP/1Jx48f14ULF2S1WpWenq4JEyYoPz9fUVFRhituneZuJyOF1z71pJ/Bsk8JHwCAcZzzAQAYR/gAAIwjfAAAxhE+AADjCB8AgHGEDwDAOMIHAGAcdzgAgkBFRUWL9+CKiopy3tK/uQtDpWt3nSgsLJTU8gWHw4cP1yuvvNKGioG2IXyAIHLfffdpxIgRjZbffLPHu+66S7/61a8atbtxigvp2i1VnnvuuUbtfvKTn7SxUqBtCB8giGRlZWn8+PFu23Xu3NmjdtHR0R61A0zjnA8AwDiOfIAgcvnyZZ0/f77R8piYGMXHxzu/r6+vb9TOarU6J0S7UVOPZ7PZQuamoAhP3FgUCALuBhz8z//8j1atWiWp+QEHnTp10v79+53ftzTgYNu2bY2mjwZM4sgHCCJTpkxpct6YW2+91eX7vn37qqioyGVZTExMo+3at2+vsrKyRstvu+22thUKtBHhAwSRtLQ0DR061G27jh07etQuKirKo3aAaQw4AAAYR/gAAIwjfAAAxnHOBwgin3/+uTZv3tzkupycHMXFxRmuCPAPwgcIIu+++67efffdJtft3LmT8EHY4DofAIBxnPMBABhH+AAAjCN8AADGET4AAOMIHwCAcYQPAMA4wgcAYBzhAwAwjvABABhH+AAAjPt/9YFvWAKd0/oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#histogram of average EFF\n",
    "sns.histplot(stats_average['EFF'], bins = 30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, there are no severe outliers in both diagrams. Therefore, no transformation is needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='height_cm', ylabel='Count'>"
      ]
     },
     "execution_count": 10,
     "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": [
    "#histogram of players' height\n",
    "sns.histplot(stats_average['height_cm'], bins = 20, color = 'g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='weight_kg', ylabel='Count'>"
      ]
     },
     "execution_count": 11,
     "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": [
    "#histogram of players' weight\n",
    "sns.histplot(stats_average['weight_kg'], bins = 20, color = 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2 = pd.plotting.scatter_matrix(stats_average)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='height_cm', ylabel='EFF'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8UL1Bbbkxxvr9pWkKZ+ujB/uXzS2IKz5DUUBdS6QxAfpcU0+vXoDsVJNk/F4/i8rq9og41GvvVeKhlTNQWpCEohw7PjxYo/jahdk26HQMtNqB/e2O5DVCTTJtyOTR8TyPhx9+GBzH4Qc/+AEAwOl0AgDsdnvE8+12u/h3wsggwHJwdvmwY+9ZbP5/n2Pnvmo4u3yyCzBFUaiKktl0ts4FKk7BwIG6LwBYjBo8fPss2Qryh38wC1ajVmY8wP7j6qeE/cfrJT9yng+eiC7Wmc3zPIqinKoKs22q2VgUFTRMoeOjKAqHTjREVSQOn2N3TwCPvHIQLMdJ6nhYLpjU0OUJYNaETNVY0qwJmURBeAAZMieUJ554Art27cKjjz6K0aNHS/6m9ONN5EcdCs8n5uMcyf7RwULJrbP9HflKb62WQWFWdBdSV5c3LndMf91XzscPAHlpZkW3lLvLG3EfvV6DqtroBs7j8cvGXuSQ+/4qZbPFIhnvdEb+DoSYz5naDrjcftjMWozOsUNDUTAaNDG5przegBjrMBi0KD9ahxnjM/CNK4twrKoVDa1uTC5Ow3eXjsPfPq5C+dE6LJmeh5/dPhP/vV0+lsRE+c33V1bfSF4jUlIsiqeUIWFQNm7ciO3bt+OnP/0pvvWtb4mPOxwOAJA9iXR0dMieXAjDk3jjFgPVD+Vi76vm4+cCbNx9NwY7JTiRepFotSDpGjom11ToHDMMDZqmMG9ytmwdz4++MxUdnV4wNKUq+6IUSxqI1OjLkUE3KM888wy2bt2KtWvX4tZbb5X8TYidnDp1CldccYXkbydPnsTUqVMv2TgJA0eiwd+BKo5L9L60hkFVvUvRx1+UZRMLF2M1dAPZSGyggv2xBNxnlmbiZZWTz8zSTMl7YlkOk8emYfXGjxTjLhtXzwfLcqLRvnJiFhZMjm60hXn4n1cOoCQvCcl2A8qP1uOVHRX46cpLq4M23BnUGMqzzz6LLVu24IEHHsAdd9wR8ffMzEyUlZXhnXfekdSclJeXo7GxEVdfffWlHC5hgIi10js8VXGgWrcmel8fy6v6+P1sYqeIgWosFav+V7SmYaHEWgti0NKqisSasLfEshyOnG5R3XQcOd0CNuTvsSoSB3ge/zhwHr9aNUciifOrVXPwjwPn+0UH7XLRFRu0E8r27duxefNmLFy4EHPnzpUUM+p0OowfPx4A8OCDD+IHP/gB1qxZgxtvvBGNjY148sknMXnyZCxdunSQRk/oTy7GrTNQrVvjva/BoMWuz2pEH39jezd8PhY6HdPn4z9WhyXTRkliKrGOpb9TgikKMRVahp4KYzkFxdMCOB7XFE1TOBsllnSuzoWvTu6Tl4klHqLVMjhd48ScSVmKrrSz9R0oHeVI+BR4ObnSBs2g7NmzR/x/4b8FcnJysHv3bgDAnDlzsHXrVmzevBmrVq2C2WzGkiVLsHbtWjDMyLb2lwv94dYZ7L4Ygo9/+RUFoCgKze0eMXaQmWzG8isKUF3XCUYhAyka/W04KWrg9L9irQWJxzUVz6YjnkWcpimkJZmwdtPHiieqJ++/UrGQT40Ayw2ZlgKXikEzKK+//nrMz50/fz7mz58/gKMhDDYDFQ9JlHh3lizLYfq4DJyu7VCMHUwbly5xySRCfxlOnh+YYL/fz8YdcI/lPcW66QiwfFyLOEVROHq6OYorrRlLpo2KOsZwujyBASm8HcoMmToUwuVNqFtn1fX9Fw9JhET0q1iWQ4Dl8fQbh2VjKEEjw1+0QekveB6i/hdNA9deUYhV35yIa68oBE1fZJZcqXotSHjAPVZiiSXF2xeG53mcb1A/UdU0dsW9mYmlfmiwWgoMJIOe5UUgCAgukKWzg5XeQq3HpS5ES0R6RYgdqMVQhNjBQLvnYq2l0FAUNv5oPrRaBvuP1ePMBSfyMqzYsm4x/AH2IrLkoFoLoukVg4yXaLEkDUNh7xH1gsnwuBDH8SjKsQEHlV+3MDv+k1osBbIjsSslMSiEIQfPB3eOgxEXuRj9Ko2GFmMooVAUJcZQQl+nv+X243XTUTSFZqcnIs359feDac7WLBtCdR9jHXNoC+Burx8MTYPlOJj02ouSphfurRRLorWauONCfj8bU/FmvJ9RLCoD/dGVcqhBXF6Ey4ZYUjcTTWH2+1nMGJcp/ru53YMvz7ejub2vUnr6uAz4AtyAye3H66YT0pyV+rMIac6JSPnTDA0ewOenmvHmv07j81PN4HsfDyeRlFqGoaHRMJIkh0SzBQciLZvngVkTsgasK+VQhZxQCCMeYed+sro9QgYkfLd8sZXpzk6fYlA+02FGVb0L//Ny/xfQxeumo2lg37E61ZNY+bE6LJqWi9MXXHFlKjFaDU7XdeCxVw+K7/Pg8Ub8/v1KbLhtBsZk28H6Awml1KopESSaLTgQadlAULttKCWaXAoGrR/KUID0Qxma9Of8JtJ3I1oPkC3rFkIT9rMxmXRodvXIVnKHXvfaeyewfF6hqEWVmWJGWVEK3v3kLFYuH59QbxGtlsFHR+rx4tvHFJ9z53VlopvObjeCYWhs/n+fq2ZjLZmZh5uuKsZ/PrYbLMdhQmEKku0GtHX04PjZVjC0fD+UAEXj1R0V+PoVyu9TQyHunjNKSgRCC+WiLBt4Tj7LS64vjNJc9qeWl6vTiwCPfjVUg41aPxRiUIhBGXL05/zyDI0T1e2Kp4bS/GRQrHThiKdZlYDFoseO8nN4VaW50/pbp4MC8OQf/i07Fp2GTqiALt7mXXa7ETQNvPPJWWx/p0LxmrXfm4aOLi+OnmmNSDTISDLhbx9XBbPZQuJJZrMO/z7VApbjFeecoSmMHeXAfz62O67GXQGKxt1PRL8mePJJbBFXEvWMl/Dv8EC1qB4M1AwKiaEQRiyJtoRNRHqF43hc6C3ooyhI0oYpKvhYZoo5wpiEjiU9yZRQAV2om07utYFINx3HAbPLsqFhaNlrNAyNiaNTwfOQFGsKcSEh0YACJGNmGAaZKWbVOc9Nt+DA8ejZWKGfi8GgjclFZzBo45KKEaA1DAIUjV2f1WDb345h12c1CFA0aE3/pPXGKgMz3CExFMKIJR4ZEDm/ejyV6UJBX3dPQFZe/d9fNuHIABXQCbGDirNtEjeT8NrvfnJWNnagYyg8cvdccBxEd+Dk4jR875pS0DRAU8FEglO1Tjzzx8NiPOSLk834v10n8cBNUzFtXIbEUPE8J75PikKEmyzAcvj8ZDMCQsBf5jk8H5mNxTA0qhs6Va8539glCdLHunjHI+pJUIcYFMKI5mJbwsajCjx7QhbsFr2sJtTa701Di0zfEMlYEyigE9AyNBbPGCX72g+tnAEdQ0csijzHo7snoFgvwnE8/CyH8iP1+NWqObKGqiDThlBHh8/HoqahC3MmZinW45xv6MTcSdnic+R6m4SfqFiWQ36mVfWavAxLQoWj0bLdRmJF+0BBDAphxJKIDMjFwHHSSvnQHfRvfv8Znl69QHRBye2wEymgE/D3Ln7xLIoBno8wJsI1/739ALZuWISaRpeqcGJNowupY9MkhYKzJmTAaNQo1uN4PAEUZdtUe5uU5idJPpeeHj/mTcpBst2oek28cQ9B1DOaKy0RUc/LERJDIYxYQmVAlOIEicqAhKPVMjhQ0YAZ4zPwyF3zsGRWHsqKUrBkVh4euWseZozPwBenm3HDkmI8ctc8iUz6I3fNw7xJ2QkV0PW9dnTl4NCYRExqw8cbkJthVZWTyUq1SK4LGnGH6niLchzw+TnVOIucXeV4dWmbRGxxuCtNLv4U7kojKENOKIQRjYYCHrtnHgIsHxEn0DBUwjIg4QiBaTW1YY8ngOxUC37xYjm+Nisfo3MdqG/uwi9eLMeGW+XdUrG+drwV4rGoDfv8wXiHWtvdI6ebkR4S99FqGXT71N1OgQCHs/UdqnGWA8elagSCgoHaWOSuiZZZNZCutMsRYlAII54uj19STCgElX/6/ZlIsej75TU4jo8awC7NT8LHn9di05qF2H+8Tztr05qFKD9Wh1Hp1oR89YkUY4aqDSsFuQuybKht7lJ1Tbk6vZK4j1bLgPf1Ld7hhhUAePDQMLTqIh5uAAWDPW9yNn6+rVx2fl2dXtA0FVfB5EC50i5XSB0KqUMZcvTn/LIUhVd2nMDXryiMqKOIpZgw1voBrZZBhyeAN/7+peprnW/qlFSQt3X04Mvz7dhw6wyYjRrkJJsScnvFU4wpzG+bq0ecG7kCxDuvm4AuTwA/2vgvxfs+vXoB7EaNOGaLRY+2Tq+qjP+YHDsoisKpC1LjK8zFAzdF1uSEz6/ceL9zdQlsJi2q6iIr+9UKJlmaxl2Pq9S3rF8EJs4ixJG8RpDCRgWIQRma9Nf8arUMTtQ4YTJqoKGZiEr5AMei2xOQLSYUdrkHKhpjkgUxmXQ4fKoZVotO8bUyk814+R3lCvJbl5XComfg9Qbifq/xFGMK89vp9qlWno/JsePjI3V48S2VCvzryzB/YpbEoDQ6PaqKARtXz4dRrxHnwutnwdAUWI6HXsvg3U/O4rbl46EJMfTCZ+kLRMZeQgtDC7Pt6sYhrGAyXpWBWBnJa4SaQSEuL8KIhaYp5KRbcbZOebdclG2P+HEoLc7ROu3lZdlVXyvDYVbNmKqq68BXxqbC643/vSaiRxUtM2zr+kVxt90FgEMnGlXjI9X1Lui1DFYsHgOjXov9x+pxvrETeRlWzCrLworFY1Bd34FxIYY+ls6KG1fPx8GK+OTrE4k/DXcGsmqfpC4QRiwURYHrzQBSygxiOS4ivTXeJk1A8LQrvJbcdc/88TA4RMqRCH9/+o3DyEoxA4i/Ul4gngrxWHvKTy9NV33NyAr8oGLAnIlZstlscyZmodvLIi/LjrYOL+57cg8OVjTCH+BwsKIR9z25B20dXuRnSQ19LJ0VPz/ZDG+UhIBwpeiLFQMdTiSiGh0v5IRCGLHQNIXPTjSqZgZ9dqIRS2fli9ck2g9FeC2l60rykrDvqPp9vzjdjKyZeRfxjoPEsusMzfJSCsqfrXPhysk50DB0zOq9fj+LWRMy4Qtwiiex3DQzAB67D9UoFkzetrxUYuhj6awoFEyqvadw45CoQvFwI9FTd7yQEwphRKPR0GKW0h92VuLDgzX4w85KPPxCOeZNzoY2TKsp0X4ogHpVfrLdgPONnYp/B4ALjV0IBNR/1In0DpFDyPJSO0kUZNkAnsfPvj8TWg0tOeFpNfK9Qvx+FgVZdtWTmMWkQ1VtB+ZMysLPt5Xji5PN8Ac4fHGyGT/fVo45k7JwprYDGo2010lRjvpJojDbhsIsK+ZNylas9ZEzDgPRD2WokcipOxHICYUwYvH7WUwZmy6bpSQsbk+vXiBZYBJ1gYRW5cvtjts6ejBzQobqfdUq5eNJhY3FR87zwOyyTNitesU03NL8JPh9ARRkWvHc2kXYd6wO1Q2dmDkhA/ffOBU6hoqom9FqGfEkplZjMnFMKv7495OKJ5RvLx4rOaHE2lkRPBKQoBmYfihDhYvpQhovxKAQRjRfRPG7f3G6GfMnZomPJeoCEaryK6rkBRp3fHoWs8uy8dp7lXG3mo3VXRFPIzEgqDisptNVkpcEDU3jXEMn/ueVvjqeA8cb8fr7lbJNwWiaQlWtS7XGxOfnUNfsVk1QqG91IyvZKJ2j3pOEWsOqRCRogPjFQIcTlzLxgKQNk7ThIUd/zW+8fUIEEumHAkRvADU6246z9Qo1Eir3jVZj8vy6RdBQiLmRmN1uBEUBhyqb4AtwqrUghdl2vPJuRcx1PKHpvUr3zUwygdHQshlbwnt68v4rkWbTR6RQq/U60TCUmAKsFENJJAU4EYbSGtHfqdGkDkUBYlCGJv1Zh5LoDymRJk2xLvzx3DfW9zB7YhZOnGuLqZFYsMEWhXMNnfjzh6cU62JuuroYDS1uGOOs44nWsfH7Xx+PvUfrVJt73X7teFVBRrlGWMIGwt3jl5yOhNf+28dVsBi1ERuIgWCorRGJdCFVgtShEC5LLiaDJ14XSDx+at7PxnzfUHeF0q7bH+Dg6QmoBsLDK9oBHo2t6m6numY38jOtqlXv4XU8Wi2DMzVO1fuereuA0CRT6T0pSfkLbr0jVa0Rbj2O4zFzQoZqhplOQ4+IFOB4icVd2B9ni0E9oTQ0NOCll17C8ePHUVlZie7ubrz22muYNWuW5Hm33HILDhw4EHH9smXLsHHjxoRfn5xQhib93VP+YnqMx0qi7rVoCCeUY1Wtirvu/1g4BsfOtOCVHScUF+iVy0uxdFY+urt9khPK2k0fK/aL37h6PgAKqzf+S/U5KRa95JTQ5PJi7aaPwfEcls8tRFaaBfXNXdix9yxoKujOcnZ58cG+asX3NHF0Cq6cKD05UjQtuvUa2tyi+y0z2Sy69fw8VNsEb1m3SFKBP1AMxTXiYlojhzJkTyjV1dXYsWMHxo8fj9mzZ2P37t2Kzy0oKMDjjz8ueSwpKWmgh0gY5lyqDJ7wNrzRaiBirVb2+1kxG0tp152ZYsL75V2qgfDIRmI8jp5WVxL+4lQLHBad+By5ZlnhdTxCAeLNXyvG3Ik5aOnoBs8DeRkWLJ9XhL1Ha3HkdDMWTsuNqx8KAHAU0BMIQENHhtV7AgHwtAGHjqmfEg+daIgwVJcLlyLxYFANyowZM1BeXg4A2LVrl6pBMRgMmDJlyiUaGWEkcSl+SOFteEP1qb6r7WvDG2B5sBQVU/qvAMdB1Z21df0isZhQKQVYp6HDFlEKDE1HVRI26rWiJH8oQrOs6jppbQ3P88hMMQer/ingzIUOUVYl1WHCjNJMNLS64fWp90N5fr00GysWWXy/nxMr5dWKNRdMHjkyKokwkMZ0UA0K3Y8l/wRCNAZ6V6plaNx0VTG0WiZCn+qmq4qh1zCyWV5q1cqxxGb2H2/AzAmZeOXdCvxq1RxZscWVy8dL3j/P85g8Ng0/6nVnhXeYFOIuOi2DJqdym+Tp4zIi6njG5jpQVefCY6/1qSofrGjE73dWYsOtMzAmNwnlx+ri7ocSKosvBw8eZWNSVNsPjxQZlaHKsAnKnz17FjNmzIDb7UZubi6uv/563HnnndBqtQnfk6L6fJ3xoOmtrk7k2pEERQV3qjzP9++xeZjOb2e3H03tHsWF1GLS4X9eOSC7gP/3ywfwwobFcIS9Z5qmcLbe1fvfiIhJcFwwKH+urkMUWzxzwQm3xw+zUYvMFDNWLB6Dc/UdmDYuHTzfN79fRHF5HTndjOmlmeJY5HqbAIDZrBM/f5oGmpw9qrIqo5ZbYDXro/ZDWTa3QFz8aZoC3L6oY8lJM6s2OctOscBk0vXvBy/DcP0Ox4KacMCwMCjTpk3DsmXLUFRUhO7ubuzatQubNm3C8ePH8dxzzw328C47AiyHLk8A+4/Xo6rOhaJsG2ZNyILFqImQr7hcoCjAF2DVF9IMC761YDSmlKTLLqL7j9dj6ex8iXHmeR5F2Tbcumwc5k7MwZnaoLEYV5AsxiQKsm3ISrWgo7MHXp8XZqMWNrMOLMfD2ekFTQH5WXbR+PfdHKouL6Y3c8rZ6VNsGpZqM0ruS1G0RFZF7poztR2YODrYYlfpOXoNLZt1FG0sXi8HZ6dPMSstOyX65zgQm6TLhSFTh7Jr1y7cc889sllecmzcuBFbt27FH/7wB0yfPj2h1yRZXvGjmjXVTyJzw3F+LRY9PjvZDJaLVBQWFrNkqx7dXhaPvRpZ+CjELBZMyY4s5tMwaHZ6wPFAjy8gurMMOg1oCshMNcHn59DZ7Ys4oYzOdcDj9cNm0sGoZdDT4xcLG+taulWbZz23diF8ARZ//PtJxcLGm64uRobDiK6uoOa+3W7E+aYusb5F7ppvLx4Lq0mH7e8cV3xOeD8Uoc+KMBa5+pbvLi0BxyGuhmDiHMfZ/yYaw/E7HCtqWV7Ddjt5/fXXAwA+//zzQR3H5calEpkbKsQqxkhRFDJTzKry9Sl2Y4QxEf7+9BuHMWlsmqx/n+MBrZaGw6pHdb0L//z3BVTXu+Cw6qHV0vAHePBcMCjt7PSirsWNL8+3o67FDWenF34/B46DRGwR6JOloShIhB8pKjimzyob0dHlxTXz8kUX0pfn29Hc7gFFUbhmXj4uhAleBgIsGlrdqtd09fhQVesUnxM+j9fMy0d1fUfEnNc0dor1LeFCn3MmZaGtswefVTaqvqdDJxoi7itsku56Yg9efPsYdh08jxffPoa7ntiD6qaufpV3H+kMC5eXHFzvroEE9i8dl1JkbrCJR4wRCH4fj4Qs0OGB5pK8JOw7Hl2+PlRXDAhWhLu9LNo6vHj01U8k17/+fiV+ctsMWA263tiFR/F0lGI3SH4rFEXhbIjmllwA+2ydC9PGZUS06g11MxVl2yVGMBDgkJdpU200NjbXgYCVR3tncPfusOjFU5dATro1om9JVqpFtcHWM2sW4HzjhTjTp6NvkpT0vwiRxGRQ7rzzTtxxxx2iK4rjOFRXVyM7Oxt6vX5AB6jE22+/DQCYPHnyoLz+5chQ7m7Xn13oEukdEQhwqGlQrgU5eb4d5xvU5evluh9qtQw4bwCPKpxsHn31ILasWwSvNyBpJBaesbVx9Xw4LH2/VZ4PVpWbjBrFADZDUfAFOFUByfxMq8SghDY1U1r4N/34q2AoCmaDtrdjY5skI87j9YPujWUIcBwvNtiSS04IsBz+/WUTlswYhVaXV7VSPvT7cTltki4FMRmUjz/+GN/4xjfEf3d0dGDZsmXYvn075syZc1ED2LlzJwDg6NGjAICDBw+ivb0dRqMRCxYswKFDh7Bt2zZcffXVyMnJQXd3Nz788EP89a9/xdKlSzFt2rSLen1C7PRHd7v+bj8a70kiFoQdq1I2ltyOleN4zJqQAa+C7MeD3/0KMpJN+PBgjeLrys0dTVNRG3PtO1aHgmx71EZiWfOKxOt4Hhid48CJ6jbF08f4gmR8Wd2mGmC/0NiJNJtBMl61RmMBlsPZug4Uj0pSPXXlZegkJxShwZZacsL5hk7MLsvCwy/sU65vWbcoojHaUN0kDUcSdnn1Vyz/gQcekPx78+bNAICcnBzs3r0baWlpAIBNmzahvb0dNE2jsLAQGzZswC233NIvYyDExsVoYw3Ewi+cJEKl1cuP1uOVHRWy0uqxGDNhx6pWHS63Y/X7WeRn2XH3E7tlDdGTf/g3tqxbBK2Ghl+miZbS3HEcH7Ux1/nGLkwoSlXN2Oro9IpuYiCYzeTxBlRPHwWZNuRmWKP2LQlHrdEYAOh1DDjwUU5dCyNcXktmjgJNUXB2BeNEwokqxW5EWVEqppXQUXvKHwyrlL+cWgBfCgY9hvLll1+q/j0/Px/btm27RKMhRCMRkbmBaj8a4Hn848B5xcVu5fLxYIC4+oQIi5haLcP5us6ILBehsZTaKeHQiQY8evc8bHju05jnzu9nkZ9pDc6jQvV3XoYFZoMGT79xGBzP4dorpO4goUhRItFPUahpdKmKODq7euDuCWDOpCz84sVyfG1WPkbnOlDf3IVfvFiO+2+cioZWN9LtBsl4hUZjShRk2fDpF+qFjeVH67F8boHkvmkOk+qJqrQgGVW16qeN8Er5y6UF8KUiprThcePG4Te/+Q2uvfZaAEB7ezvmzJmDl19++aJdXoMJSRtOjHhF5mKRdQ/tqRHL/MbSd0OnoTEuLwlN7R4EOD4i5Ta8T4hw324fh1O1TuWgco4DJp3UF6/Xa/DPz+tgt+oVr3N1erFgag66vSzO1DrR2e2H1aTF6ByHqq5YqBy8XIrtndeV4dOjdfD6AhJ3kNkYvPfeo7XQ6zSY37szF8QhWzp68Oz/+wJHTjdH1FxoGBpPr1kAf4DD5yebMKcsW9Tloigg1W5C+bE6TC1Oj+hbwlI07npit6I45P/+6hq88NYRdPcEIk6BGUkm/O3jKpiNWvzwujJ0dvYACCYntHZ5o6Ywd7p9eOj5vYpFoHLtCuROusL36KffT0xAdCSvEf0iDhme3qf0GGHkE4821kAFPWmaQlqSCX/+8JSqOybA8WA0FCx6nWzwl6MohH+L/b3+djVJkvCMe46LTcokmMnEo83Vg6paF4pybBidwwMRo+hDr6Fx3YIicFxkhfh1C4qgYSikOYxwWPSK7qCOLi9omgJF03B2+bC/oh5nLnSgbHQKbrqqGHUtbnj9rCTIfb7BhSSrAdPHZcjqck0fl4HObh+okBMKAGgoYOPq+dBqGLEmZlxBMpZfUQR/gEUgEGznazFrFU+BXW5/RPC8prFTko4ceo2QwjxtXAZuW14qMYCCMGX5sTrZ0wbPcXG3Oe7POOBIIuYTil6vB8P0hSK7u7thMBhk03YpisJnn33WvyMdAMgJZeBJRNY9lvk1GLQ4UtWKgEohoZahMSbXgXP1Ul0pYfe54dYZyM+wQq+hxIXBZNJh5/5qfHneqbh7LslziFLwAlotg4+O1uPYGWWZ+YXTcqHTMvjv7fEVhfIMjRPV7Yrvc3xBMniex4nqdsXTWml+ErQMjao6qZbYrcvGYe6kHBw4Xo/qhl5jOyELe4/WwmrSY9q4dJytU56/wmwb9AwlOaHQGgZNHR5wHCKactE0kG43guUR4b6SjrevIRgQLGxsdnlxtq4Dm/50GF+blS+ePj7YX437bwymMNstWrR1eGX11Px+FjaTDqw/sgtkLMW68RQ/juQ14qI7NiYS/H799dfjvuZSQwzKwJNI18RY5je074aSK23j6vnQaRm8tuOEYnX1bctLYTNqReNgMumw67MaFGTbFLsUVtd1YvG0XIlBCXV5KS2SRdl23P/UPxVdQeGuP2H+OjwB1ervZx9cCD/LqlaR33R1MYw6Df7z8d0SY5Kfacfjrx+MWKDX3zIDeRlWcOCjzp/dpIU7RGeLZxicqFbpHlmQDE9PAG/8/csIZWZBzPI7V5dIKtoFl9ehE42K7rfppRmwGHSoqutQNIBF2XbwAalBEVyySj1fhE6bim4xmQSQkbxGkBbAChCDcmmIt/1orCeUXZ/VYPs7FYrB6vW3TgdNUaILS1aviqYwY1w6XK6grz7RGIqw8AuLpNKifrCiQTnOodVEuP6EE9MrO04ozsX6W6YBVHBBfu7Ph/Hz789GWrIZzW1u/OrlfbhnRfB90hTw6KuHAARFHLesXYzyY3WYU5YNj88PDUMjwHIw6rQoP1aHKybn4PQFp6qcTHD+MuByecTPpbXLi9UbPwIoDmtvno7MVAsaWrrwm/89BPBBQ+/3c6BoKErFNLS4MXVsmsTQV9V3wGrSB7tChl3j97Po8viRYjfg1V4DKC/pUgqLXiM2BIulgVlZUYqo5qz02a5cPl41DjiS3GTEoChADMqlId6uibGeUEL7h8u5pr5Sko4xoxyqp5gn778SOalmMfgby4lATg8qNElAafHNTTPDF+DkXUEU0NHlxfj8JIn7yGTS4cV3jqtmTd2zYjLGjHIA4GE26HCqpl1cbMeOSoK7xwcKFE6eb8eWvxwBAFx7RSHmTMxCsi3otg6/huM4tLl6YLfq8cBTynPx5P1XYlS6RfysrFYD3t17FmPzHEh3mCPu2+R04/R5J+ZPzUVLrz5ZePdFmgIcVgNsRo3EoHT7WPEauflLc5hQeb4NVrNW8XTZ6fZLDGAsyRQMRUHDUPCqfLY6DY3SUY6IU7ar09uvGmFDgYsOyr/44otYtGgRRo8e3a8DI1wexNs1UVB81WoZxd2cUEhoVKn0thq0ol6VUnX1F6ebkZXcJzGu1TI49O8LUbv+LZ2VH1Egl5ZkUpUGeWHDYlRWt2GjkisoPymi3iGWNNycdDPMBg2cXV60uXoigvI0HZQ3yU23iNdkpVmQlWpBs7M7KIHfOx4/GzQkNA1kp1rwyRH19N7w+eN5Hl8pSYfXz6LN1RNxX52GwYwJmeB5Hj6Wle2+6GNZMDTCKvABjuejyssU5dhxqkb5dFmUbZfIr4cmUyh9bpsf/Cp6fKzqZ/vk/VdGLLIBlourTmokEJMQ1lNPPYWKigrx3x0dHbjiiiuGReCdMDTgOQ4Mz2H+pCysvGYc5k/KAsNzkh8URdNgKQo791Xjub8cwUdH6sFSlKw4X3ChdcDZ6cPPtu6ViAX+bOteODt9sJr1YnX1lrWLkeow4swFJ1IdRmxZuxi3LhuHC41dCIQVGkYrzJPTgxJa3yoZIpbj0O31RxgToG9R6vYGwITJ//v9LGaWZiq2BdAwNEalWcHQNJraPfj5tr1wdXkxOtcBV5cXP9+2F03tHjA0jVHpVvE+KVY9OI6HLyBvsH0BFizHw97bt+SRu+Zhyaw8lBWlYMmsPDxy1zzMmZgVMX9ebwAWo068b6g4pHBfg1YjW9wped8BTiJmyTAMPL3yMmrzx8u450Kfw3IcAmHv+QuVzy3Acjj8ZRPqmrtUn3PkdHNE1muXJyDWSU0uToNWQ2NycRp+tWoO/nHg/IgTUgUSLGzkOA4tLS3w+XzRn0wghKB04oi3+FFwTaktHk+vXoBrryhAS4cX9z4pjeEIEh/jC1MkO+FYTgSF2bbI1NNeaRBAvgBxQmFKVEmSz040YvncQtG/L6ChgJ/dPhOPvHIgInj+0MqZMBk0cLa60dzejU1rFsqmy+ZnWuGwmMWi1MxUM/wKxkSciwCLycUpSLLrQFGURMRRaAHs8QQk82cwaOB2eeHs9GHz/x3GbdeMx5VTclDX1IVfvLgX990wFak2FkzIJkFOHBKQCr/SNHAoyvz9u7IJ6UnGqHO8dFZ+yH2DIplqnK1zYewoBwDl4tKaxi5JUSpFAVW1TtXC0bP1HRI32UiASPUShgTxyuJrtQwOnVCX2Th0ogEmg05V4iPZZoyQUBFOBHIS6BqGxszSTFmJlKIcm7ibD92RPnLXPMwYnyGefOTuCwRPPnIhTZ7jUJhpw7MPLkJqUu8pK8mIZx9chMJMG2iahsvtldSL/PPfF3DmQgdABdv0drq9oGka+ekWvLBhMTJTTDF9LmwAMBu0stL5ZoMWRTnSBVGjYeBnOdAUsPnHi+DnOHz8eS38HIfNP14Emgq6v3io37f3nfd9XgEOF6LNX1MXOrulxjic8NNlLNIrhdk22C06xc92zsQsFGbbEC6SmZpkUt3wpCeZFGMRw5VBl14hEBItfgxdoOV2jSkOk9i7XOm++47VYcm0UZJTgYYCHrtnHgIsLwZ2Jxen4XvXlELDUMG4T9i67/ezmDUhEzaLXlEcMivVLFsdLkizFGbbIpprAcG6jjN1HXjkFalhfP29Sjy0cgZKC5KQlWLB2TpXhPEUTmKF2TbwvS5Gh90Ihom+kNE0BZbl0NbhxWOvfSLb1thq0EEXEuvieR4BloNep8G9T+6WyLXc++RurL91BujeYtJo4pChC7TXG0BRjl2cv9BU4+9q++bPZlFv7xt+uoxFemV6aQYYmoKf5RVPG6X5SWGbDF7VBSq4yZZMG6U63uHGRRkUUilP6A8SUXwVXFNqC3RehhWfn2xSve/5xq6IuAUAdHn88gWIt89EikW+ZQPHQXFH+uQf/o0XfrJYVS4+K8Ui6/7wsXyEMRHu+8grB/H8+kXg+KDYolyVviC2GHrvWHI7eQ7gaF5saxw+x0Iarlbbl/Hm87EwG3T4/GQzNq1ZKKYjlxYkie63UelWURxSTntMGG8ofj+L2WVZGF+YHJFqnJlixg1LxsJm1oNjeTH9ORyl02Us+nQBlc/26TcO4/n14QrUlOgCVSLcTTYSiNmgvPzyy9ixYwcAIBAIgKIoPP3003A4HBHPpSgKzz//fL8NkjCySUTx1e9nMXtCFvKzrIoLdLJNH5OwIhu+SPB8hDEBet1v2+Xl66OdsliOQ8Cv3u88S6bfuVBvo7bTbWxzo6q2Q1UhOSi2WKg0vRib64BRr4HHGxDdRjRN4UxIZ0U5uZMztR2YMS5dvA/DUGhxdmNOWRZomsKR0y0439CJvEwr5pRlY05ZFlqc3TAaNLj5a8WYOzEnwujsPVobIQ4JADQo+P0cvD6vJHvM2ekFTQE0RYGmePzs9pmKmwG502W0LEQNQ2FvtBP0cekJmueDLlAcVJzyCDfZSCBmg1JRUSHJ9AKU2++SkwshHi5G8VV9geYwuywbX1Y7JQVpoXpfs8uyJO6uUMOglCor536LdsoqK0yBj2WjaoSF17cwDI3q3sZcSoKHPE+BpilRITkUIXheXdcJuZ9lmsMEChSa2t1ocwUNb3qSGTx4cByPgiwbTtf2yZ0I7qufb9sryp2ELtAURSMr1YKqug48KuOi+8nKYLV6IMBh7sRsWaMzd2I2zjd2Su6r1TLwcZxqqjHHcdAxFPLTgsbh4IkGsfZjRqmyeCmgrk9HazVxn6B5Hpg1IQvb31F3pY2kgDxAChtJYeMQId7ix9ACRCUpk6dXL4DdosXJmsjFTcPQ+MnKGRiTbZdoO4UXTMpVTluMWon2mDAeQWJGzhB95+oSaDU0TvZqhMndV04jLPSEErqbFyra9x6tRXFeElLtRrS4PNDQTEShYIBjkWozwmbUoqfHL6oNOzt96PL4FHWvLEYdPL4A9h+vV6ymn16agXS7QZResdkMaOv0iX1h5KRMtqxbBJ2Gxpm6Dvzm9wex+saviNX0G//0b6z93gyMzrZDx1CisbdY9Gjr9IrvUa5oMdVmRLJVj64urzh3DEODZbmIzLl4SFQ+KMByOHGuPebv9HDhklbKezwetLS0YNSooR9sIgZlaCHI4gs7S7XiR0GShON5zJ2Yg/3HQxbEXnFDmqJwxeQciX5VKHLS+bFUvIdXRQuwFIVXFHSv2lw9aO/sgcNqULxvR6c3QiMMACiNBl5/QLGi3aTXwONl0ezqhoZmImT6AxyLNJtJlIsJNSjRdK+c7h4YdRqVanoPirLs4pjtdiPe3XsWlefa8Y0ri6DV0NDrGHh9LPwBDn/7uAoTCpMxc0IWmp1upDnMKD9WJzmhCI9b9IxoCGw2I1o6PDhd26GolTYmx45UuxGdXYlXpysZoUTlg4KV8rG3ehgOXLRBKSsrw+OPP47ly5cDALq6uvDggw9i9erVKCkpkTz3b3/7G9avX48TJ5S1h4YKxKAMTRwOIyiKQleXV9ElYDLp8EVVKzQ0HZHZpGFo/OS2GTDoGVTXu7Dtrdh3lkCw/8jdTygboS3rFkHDR/6N1jCoqg9mWoUveD9ZORM5KWbc86SyCKGcywsIulyaeivaw40FTQPpSSb0eAPw+AKK2lhGnQYOsw7d3T7RoDQ5PVGFH3UaRvW10xwmGLW0aFCSkkzYsfcsppakQ8swEUbIz7LwelkY9AzqW7vx+GsywpS3zkBWigkZSSbxN2azGVDf1h1VADPNYUDlOWfMIo6hn52P5SPGK8jXX6x80OWi5RVTDCUQCEjah/r9fvzzn//Ebbfd1j8jJBBC4PlgUFPth+f3syjMsuFumV2jUGOyZd0i7Dl0AYByUD7c9y10XoxW3xLaRlYcE8vhw4M1iv1ZvnN1iWpHxy9ON2P+xCzJPQ0GLTx+DloN3WssPJLsJo/XD5blodUwcLl98Pq8MBu1sJl1YDleDFhbDLRkEaBpClW1HaqFd26PH4yZFl9btp8Mx0tiMz5fAHPKstHq8oDj/LKSLulJJvj8wX4rm9YsjOgPL2SChbcArmnsjNrf3qjXiNXpcirGQhfPUELl9uVka9LtRnABNi75oHCGuxGJFVKHQhiWMAyNfZ/XRq0xWTQ9V1ZAUljEw7PHggttMACrZITC28gCQUN0uka9Mtrl7sGCqTmKC6Kr0xux89PrNXB7vWJ2U8SCRwUXW5qmoNUGF/4WZzCYrGEoOKx6eLx+yKjXIDPFjLWbPgYPDquuK0N2ugV1TV343bvHxFRYjuPR1uHF469/IgnK3//UHqy/ZQYsBh00mr4l2ufrTR8OBIPn4ZlhvgALluXR2uHBhMIU2YZgEwpT0OLsRqq9Lz2b43hkpVpUG6rddHUxztZ2YMXiMbKpxSsWj5GtTmd5oKldRSPMZgSF+BrLXa4Qg0K4pPTX0T80+0mJ841dWDIjT7U/fFayWTIOIYV5zsQsxZNEuBECYhOHfG7tQry394Ti7vk7V5fI3peiEUUU0QiO46HptRodXT5xIU11mKChaXAcwnb8wcK61d+ZGlQBbncH06izrHh+3RKcqmlHVa0TBdl2nG9UOUlkSN0fRqMW7o4eODt9ir3fU20s0pOMEQ3BQp8zLi8JoV0sOY5HY6tbtb99XbMbRdl2tHf2KBrfgiy7ZLyC3L5a9t3G1fORYtGLMZXL5bSRCMSgEC4JQre78iP1/SLjzbKcWGOiRF6GBT0+NkpqsUVyTbCALhN2q3zFu3xVtFQcUinduKquAysWjYFBr43IqlqxeAzqWrpg79WMEuB5XhRFVDpJbFw9HxaDFh5vAO6egGyNhsWohbTNMIWSvCSYDVpwPB9hhPIyrDDoNGh2dkc5SXiQGtICmKaD/WfKj9QrFkOOzrGj2xtQfU5+plVS9MfzPDJSzPj8ZJOiXtn00gzR+CrpiKXYDZLU6qCET6OqK1JJY40QCTEoQ4jhFriLdbzxCj/GQk+PH7PLsvHae5WKwfP5U3LQ1SsgGU/th1DxLlfFLV8V3ScOKZxs5LKbinIcuNDYqSg3Er57FsZy6ESjeJI4VdOOprZu2K168SRxvqETJflJYo1GuNiij2XB8dJYB8/zyEgyo9nZDXdPQD7uYtQhPcmEyvPqJwmWDV34EbX3+/nGTpgNGvE54aKTQn/4zKQ+vTGKouDp8av2t6cpCh5vQNQRCz11bf7xIpy+0I5ubwBWo1YyD1oNjXmTsxU3EB2d3kGpaB9u6wEQR0/5r3/965gwYQKAYGrw5s2bccMNN6CgoEDy3GPHjuG9994jWV5xEE+v6qFAvOONlnIZnrob6/wKWVXhsiQahsZDK2dgQkEy3t93TuwPH0vth1Bz4PUHFNOR5TorarUMKmucyE63QMswEamwAZaFTsvgPx9Tyx5bKOkmCATrL87Uu8DQlGKmVZLFAJbjomZ5pTmM6Ojw9GZ5Aa0uLzq7fTDqtdBqaBi0DHr8QQPo8fphM+nR7fVHzazKTDKiszNY+2Ey6dDa5cPZOuX03qJsOxiaQrfXD6NeC483pL6l9996rQaptr6aEr1eAy/Lq/a3F9KcNTSt2NWR5TjkplnFBluJNlSLxsWsEUN9PbjoLC8AePfdd/Huu+9KHvvTn/4k+1xSKR87A7F7H0gSkZlPRPgxFrgAi6IsG55ftwgna9qDO22DBsWjkqBlKPA8D00Mu89QgrEQIxiaVpS85zgu4gfl97MYOyoJZ1QqxAsybWAVPssAy6H8aD2umZ0vedzrDSDdYYKr26uY5UUzFABaVZbEYqAlJxSapsFzgM2kB8vx+OSLWokBtJn04DkeHW6vatwi/CTBcTw4Tt3lVZRtB0NRwfoWKrJS3qjTgKFpSZ8VjYZGl88v6orJxaBGLbfAYTaIpy65eUhzmCQZq0D0fihy2XcDxXBbD8KJ6YRy4MCBuG88c+bMhAZ0KRkKJ5R4d++DTbzjFSrPdx08r3jPq2bmSSrP45lfYTd38EQjqupcKMq2YUZpcDenYai4d59aLQNPgJNNRxau2bJuIYyayJ7yngAftX7luT8dxrHqVvzg69JYCMsCS2bm4YfXlYntiAHAbNbB1R1Ae2ePYrvcZKsBFA3Ut7mhoRloGEp0twVYHgGORVayGXazDi5XD+x2IzQaGs5OL07XKqsIjM2xo6unr1JeiFtQFJBqDyo5Ty1OR3aKSTxJmEw6HD7VDKtFBw3NwGrSSjTCAlxwznJSrbLyLMJrj862wxBS32KzGXGwshGZKabeTLaQsThM8Hj9aGrzYEyuXZwHuWr6rGQzbCateKJK5PsZC4muEcNhPbjoE8pAGYeGhga89NJLOH78OCorK9Hd3Y3XXnsNs2bNinjup59+imeeeQaVlZUwm8246qqr8OCDD8JmUxcVHMoM5O59IEhkvKHCj0ppuOFZU7G0AAaUd3Pb3wnu5oqybeLuU62FbejuMzQdWemafcfqsWTaKMnYjEYtdpefU72u/Fgd7vr2ZLHqPDwW0trhiVTC1TCg6IAYH4lIw2VZUDQFf4BDktUALcPA3ePrfS8UbGY9/Cwb0SGR53l4/RwefUVB8feVYB1PaJ8V6eT39lnp9kV0vMzLskPDUIoaYTQVHG+4MRG+Q8JrW4x9yxNFAUU5Dvh8wUVdLpNtTK4D/kAwCUNJeyzVZpSoSyciTDpQsY1LtR4MZGxmUIPy1dXV2LFjB8aPH4/Zs2dj9+7dss/bv38/Vq1ahcWLF+NHP/oRmpqa8OSTT+LkyZP43//9X0lnt+FEIrLtg0miMvMzx2eg4mybokCjIPwonDZ27qtGVZ0LhVnqvuNoTbm2rl+Es7UuMVAuV4dyrs6Fr4bUlAjpyMI1cn035CTvKYrC+ZDr5F6rJC8Jrm6fbA/3tKRg1lR4JhFFBZtLqWWqpdk46LUMvP7gohfuQmIoCloNg/Asr/JjdaLi7/7j9ThzwYm8DCu2rF0cVPw9Vof5k3PR5VHuzJqZYpYErDmOh0mnETXCwhd+v5+F1aTDx1+o1xCVH6vDtfP61JEDARZahkJHbyabUhKBn+Widq4Mdf3FI0za35mK4Qz0ejDQ4wcG2aDMmDED5eXlAIBdu3YpGpTf/OY3GDt2LJ5++mnReKSlpeH222/Hzp07sWzZsks25v4kkd3RYJLoeLUMjcUzRsnGMR5aOQM6hgbP8QnHZtRUgRdNzwUHXrEOpTushS3LcphRmoGMXtdKeHrvDUvGorG1O1LyPsBiemkGrGat4mtlJJlR29opn4kVYMHyfMRBQDA+ar04Nv14ASiKQW2LG4+FyL4cPN6I379fiQ23BV1IoR0QKSpoDDQ0jfue2iPbPCvZpgcP9e8ehWB8w9sbigq6QngxhVmuFsRi1IknJoaBrPuvprFLEoulKAocz4vFm7JV+zyPLrdf/UTl9oJNlnarjKUfCihqwGMbA7keXKrYzKBu7WM5WTQ2NuLo0aO47rrrJM+fN28eMjIy8MEHHwzkEAcUYXekkWnwBKjLtg8GiY7Xz3KqDaL8LC+eNoT0XqG9K8vJtwAWdnNqbVnP1rmQkWKGs9OHn23diz/srMSHB2vwh52V+NnWvXB2+jA6rIVtT48fJXnJaOvw4t7f7MHL71bgw4M1ePndCtz7mz1o6/CiJC854iThdvtQPCoJzk4ffr5tL1xdXozOdcDV5cXPtwVfC+DFtrdujx/1LW64PX6x7a3fz8JsDu84yIu91OVa3wZYDtUNLvgCHHb3yr4smZWHsqIULJmVh1+tmoPdB2vgC3CSBZrjOBRk2cQgd8Q1h2pgMUnHYjYGx24OSbsFAIbpS6LW6RiwPIcmpwe/fGkv8tIt+MaVo5GXbsEvX9qLJqcHHM9h0fRcrP3eNDy/bomkTfDz65Zg7femYVSGRXRvBV8jmETQ1uHF/U/tQYvTg9G5DrQ4Pbj/qeDnwnG8pLWxpBVyL5kplggXXWg/lDuvK8NVM/Nw53VleH7dIlGjK97vZyIM5HoQb4vtRBky8vW7du3CPffcExFD+fjjj3HHHXfgd7/7Ha644grJNXfccQfq6urw3nvvJfSa0fSilBCkJgKBi1/oAyyHymonfr19f8Tu6OEfzMK4PIfiF2wwiHe8FAXs3FetKtD4k9umo9XVg6OnWyPcRRlJJvzt4ypMHJ2CpbPzRZkLigI+q2yCV0UVOMWmh9Wkx482/ktVkDE71SS5b0tHT9SgfKrdENYHBKhr6cbBEw2YU5YtVpWbjVqMznGg/Fgdvjo1F81ODzgeEQFjmgKSrAakJxslCx5NU3j2z1/Itr7Vaxn87eMq3Py1Elxo6oLVrFVUG+50+zF3YhYCAU78/pYfrYOl9xq5QL5Zr4XdoofPz4KmabGuQ4iHcFzQ1ZZsN4hj1mhonG/shMvtRZrDHHFNs9MNm1mPZJseldVORXHI0dl2WE19hkujodHQ1o29R+oUEwTmTcqBhqFkWyELgqFF2TY4rDoobcaF+B3P85LvxM591Th6plUx/Tz8+5noGjEQ60Esv8FV15dJxq+GVssoZvIO+cJGp9MJALDb7RF/s9vtEU2/hhsahsa4fAde2LAY+4/3+TZnTciCxagZUsYEiH+8FEWhKopf2OX2g+egKpFyvq5T/KEDwQK6gmw77pKRphdcQVs3LMInn9eKVdBy8ZBDJxpw3fzR4n0pikJ5FHHI8qP1+MaVRZLYAUVRcLl7VKvKQQUVfpWKBFPshogCOp7nMWtCJtKSjDDqteB5vjdewoKiKNywZCzsZh3oTBt8/oBianGy1RjxXkZl2gDwsnUzfpaFQRtsV+wPcNBqqYhiQn+Ag0EXuYTQNAWbSQ+O5yOusZmCrj63h1UVh8zLkKog8DyPVqdH1Z3lD7DgQUdthUxRNAD5z1cQJpXcnqLA81BNP3d1eiXfz0QZiPUglt/g2TpX/4z/oq6+hChZxIupeeH5xFJ/B0K+ngJw5cQsLJjcJzrn7hoari45Yh2vVsugMEvdL2w1aVGYlYpTtU7F08a0cRkSOXutlsHBigb1GMrxBqQlmTC5OE0xHlJR1QavNyCmp1qtBpxvDGqEKWWl1TR1IRDgJOm9JpMO6UlmVJ5vl80weuDGoOZWNLkRe4CDy9WDUMbmJiHAcuABnAop1hs7KgkWgw4sB+gYGu5u9ToUny+Azk6vWNho0DI41+BSjLsUZtqAXuHEJ35/UCJl8sBv92Dd92bAYtSB4zjxt2Cx6GHSaSP6rITGZoqy7ahv7VI1vs3t3bAZNWLjLqvVgMwUM9yeoKsx3FABgEmvxUefX1CVUQluBgoVf7tyGVBaLYPJY9Nk08+FzcvTqxdIvp8Xu0b053oQy2+wIMum2i4ilJQUi2z3T2AYGBShZ71wUgmlo6ND9uQyXBkqsZJYiWW8sWTRjMtPEiVS1H6woSE/QRVYLavqbJ0LN11VgnP1LkW13FnjMyXvw+9nkZ9pVb1vXoZFRssL8PgCYoaR3K57dI49qtxIVrJZcl+tlgFHAe0dPeA4SLKbBDn4jCQTKBqq7XFpGhKJFCD42kLcJVwqJtgPZTx4cPD6A4pSJn6WBdDnmqJpCj6WjVqAmJFsxonqNsX03nF5SZLYjJBQ0NTuUTRUFqMWRTl2pDiMioWYHZ1eWXeXkAF1srodLrcfNrMWo3Ps0PSunINR/Nhf68HFtNiOlyFvUMaOHQsAOHXqVEQM5eTJk5g6depgDIsQB9GyaAw6Bv/894WoPUiWzsqX1LfMnJABk1Gj6CajQYHlOFEtVy6NdFSGBaE+lJ4eP+ZNykFRrl2xP3tWsiUiKE/TNFxur7jrzkg2iQu0syv4uFbDwGzQqrqlwqu49XoNOj0B1X4oAZZHaK1beAYZAPgDHDRmqbvkTK0TNy8tgZZh0NTuhscbEGMdNy8tQVWtE4XZdoxKt8q6r0alW9Hd4wfQ505jGBpVIRLyQgGihqGQZDNgxeIxaHJ2Byvao6T3OtCXFEBRNPwsF8VQlSIzxYzTF5zYtGah2LK4tCBJIiAZXqBI0TRaO70IcDxqmrrE75HDaoCGppCZbMTZ2ijpvGHp50ONWDLZ+iOcPuQNSmZmJsrKyvDOO+/gtttuEzO9ysvL0djYiKuvvnqQR0iIRmgWjVxzIvS6kdS40OyW/NvvZzE6x4ET1W2KMYnxBcloaHWrCgq2OD0Yk2OX7s6ieVFl/i6ILXoDrGKnQoamRHkUpSZY4RlIWi0Nzs2r9kMBggZWMFZyKbXhA6dpCmNHJcHT4wdHSxcSjufBsTzGjkoCTVG40NSFx18/KHvCK8q2RyxEoQWI4WhoGlnJFjS2dYmfi9moFbW8QtN7QyVdGAYSQxWu/7Vi8RicrXMhM9mIOWVZoOlISZc5ZVlwdnnhCMtS4yigob1b0d2abDcMq/R+OaL9BvurDmXQI747d+7Ezp07cfjwYQDAwYMHsXPnTvzrX/8Sn/Pggw+isrISa9asQXl5Od566y2sXbsWkydPxtKlSwdr6IQ44DkODM9h/qQsrLxmHOZPygLDc+A5Dn4/i6IcdddlYbYtUuokRAI9PO21/Eg9unsCyEwxo6ndI5tq2tTuQWaKOULOPODv+3E1t3vw5fl2NLf3+cIDfg5ardS1xPNB15Xb40ebqyeicNHt8YOiggbCYdXDbtFJ/l+rpUHTiJC0oChalGR/+IW9+OJkM/wBDl+cbMbDLwTTcAEeGg2lmlLL0DRC61A4jgMTJf5IU0H3lXDCS3UYceaCE6kOIzatWYjzjS74WVaykPp8AWiZaPcNpu9qNTTo3jlzdnrh9vhBUxS0GhqZKZawgsmgoQrV/3rrX2dw5HQL6F5dsMJsO+wWA2pb3Lj7id14+Z3elO93KnD3E7tR2+JGRrJZMsfB7xGr6m7t7glg3qSsYZPer4Tab7C/GPS04fCe9AI5OTmSQsePPvoImzdvFqVXlixZgrVr115UDGUoaHkRgrAUjbtUdLDCNYzCNaPCNa4CHAt3dwCF2TZ82ptqGq4cHEw1zUaSRSdRy3W6fThd26G4Yx2TYxf7swtYrQa4uv2ob+tSTN3NTbWixxcQpVdCTzAcx4GhaSRZdZKgfFKSERea3aLib3hK9bufnMV3l46DhqHE9ymfUpsNh1mHri6v2FO+vXcRV1LmtRh1aGhzo7snIAblhQLEV9+vwLrvzYBRz2BcfhKczr6gfFdPQLxveCJE8L7B4s/O7mA1fbgul1BNb9TRYlDebNah2xfsKaOWauwLcLj7id2KaeJb1i2C3dQX7DeZdNi5vxqv7FBWR1+5vBRLZ+ejstqZcE/5kYSaltegG5TBhBiUoQNF06hu7sL/vHwgQpr8p9+P/MEKMuntnR5Fg5LuMKPF2Q2PlxVdNpJF6Jbgglg8KkmUM7dY9Gh0erB640eKi9LG1fOR4TCKgogA4HAY0dTuESXkwxfSYIzEgMb2bnCcTB0KDaQ7TLAYteJYgKAo4oETDbCpGU5PAOlJRlXZ9s5uH4pHOSTikK0dPWjprYuJkMWngDSHEX6Ww+kLTozJTYqoKTl9oR2jcxxIcxhEg2K3G9Hh9uFsnUtxzouyg5tAQZ5FyZhZTVrJ5+J0+/HpkVqVzUEO9h2rw4lz7Yr1TOMKkvD1uX1ZXiaTDi++cxwfHqxRzOpbMjMPd3x9PDw9AQR4xOQyGslrBDEoChCDMrSgNQx8LI99x+pQ3dCJ/EwrZpdlQ8dQ4MIKxAwGLVweP1pcHkVl2TSbCaAhFsPJLUJzJ2Yj1W6QpL3uKD+Hk709VOQWpZI8B5bNKYgwKK0dXpxv7FRcSEfn2HHsbKuY2RT69/tvnIrS/GQ4LDrJ98pi0aPZ5VU1nMlWI4w6RrVoMtVhhNWoRWdnn0FpdnrEfihyPUlsJh16/Cw4llftL5KTahZPeElJRjQ5e2IqQBSMmZyCcprDCIdVh/ZeV6PDYURldTs8XlaxH4pRz0Cv0wSD8QpzVV3XiWVzC9De3g2gt//N0XocUylaLBudgvkTsyRpxNHEFUfyGtEv/VAIhIGEommca+iMcCm89l6lrNaQ8IVWE01Md5jR2uFBXoZNsbdJa4cHyda+bCKOC3bwUyuyrK7rjAjA8jzAgY/Sf92KliiZTUlWqdxJIMAhNCQhl8GlZWiwPB+l77wBCNPl4jmIMYm6ZrfEBWfUacBxgEmnFfuLSDLXJP1FpJ9ltALE1g4P0pOMqmnOHM8j9AYURSHFbsRr751QTHO+dVkpNAwNT1swCSH8swOAaePSJQkDfj+L2ROyYLeotXxOjkgtJ8hDTijkhDIkiLcPRGyuqQXQaWnc88Qexec8t3YhHGYd3O7gDlurZdDt41SLLMfmOGDSSfuh2GwGnKxxQsPQirtui0kLtne3H34iCLqlvCgtSBF3z0BfTEdwpcn1ATHptGB5Hvc/9U9wPIflc6VS9DQVdNNlJZvhcnnEE0qby4tmZ9AFFy69IrjgWI5Dj185c82gZcQ+K0Cfy0uIoYRX9gvuLK8/IJ4u5U4SqTYjMpKN4gnFZjPgYGUT8jKtipX97Z09SLOZ4v7sosbv1i8CEyZMSk4o5IRCGKIk2gfi0IlGsSpargDxfIMLba4e1crpfcfrsSysS2I0dd/wIksgeEJJTzKhod2tuOs26U3BoLxMl0IhsylcxZimKTA0DU1vurxcHxCapnC6xilK0fPoW8SXzyvC3qO1+OxEI75xZVHIePlgqrFRq2gsWI4HTdFwe3rAcf6IzLVgtb1GsrgIdTQGnQY8AINBA72WgUZDo8fL9kq18BKh19w0i8SYhc6pANubxlzd4FLsiFmUZUdXT+SJNfSz27h6PuxarcR9FfW7dzz43Qv0ipgOpPz7cIcYFMKgk0gfiFhcUz5fUJYlWgvg0LVAq2VwKM4iyyC84jUCFIWoEvPhBgUINspSl4PXIjfdguJRSeABNLV3S4LnV07Oxdn6jrAal6ARcHv84Di/bAW+QasBx/PwBRRcU71xrdDsY4oKGjgKFPwshz2f1UgMp4YJpkeD5cWGYGyvyKSGocHQdG/1PcLcikED+OgrCjpdvU25/l3ZpCrH89mJRiybUyDeNdbv3oIp2aiuj3TJDpfWvJcKYlAIg04ifSD8fhYzxmXiVK1TsbCxeFQS0pJMWN2rNhy+CAk71vBFPN4iyyBSF4DcrjvA8tijWulthSWs6I6mg31AmpwebP6/wxI9rV+8uBf33RCMjzgserjcyk2tSvKSJBXighEQKvA9XjoiKA8A/pBkiLG5Dkk7X+HvoScJhgme3U7Vd+A3vz+I1Td+BVPGpqOhpQv3/3Y31n4v2FpYQ1OgwIBHsGAx9HSkY/pcSn33pVF+rE5dp+tYHQqz7aqyORea3ZL7xvrd8/g5Vfn359ctQqTJ7WMguyQOJYhBIQw6iWoN+VlOLGyU6wRZkGlDbXOnqlvsyOkWiQaTUGSplkYaXmQJ9C3Qwq5bzoXEcTxWLBoDg4IkyZlaJ9ImZITdN3g6oSlg848Xibv5jGQTpo/PxOkL7ej2BmAx6MRTjFzw3GLUQavpix0ILi8hKO/2+MVTjdmgg1GnAc8FXVNpDhOo3g6PoacNHjx4merwHj8Hg47GpjWL4O7xBeMxKSZsWrMIzU43evwc9FoaLS6Pqj5Z+DwwNIV5k7Px823lshuIjk4vctMtqqfWbk8gIsAe7bs3b1IWPk2wNe+l6JI4lCAGhTAkiFdrSKtlUHOuDXMmZSm6s2oaXUi2GaLK4ofGAPx+FjNLM1FRpdyyeGZpZsTCEXTBMXC5POA4f6RrigasSVr0+IKxA4ncCIKSJKNzHAg/6QhNtMbmBt1ZByoaJIv62NwkdHv94HgOZpNWMWDN8RwMBr2oQUZRVDCu0R2AVit9TY7ng9L0Jg14Hqiq78ATr0vVhu97ajfW3RKMW0j7wtAIsCxSbMGgdLihSrEZEeiVoYmmTxbaZjkQYDF5bBr++PeTilleN11dDCCY+ad0ag3P2Irpu8fQCbXmDbDcJemSOJQgWV4JZHk5HEZQFBWz3DOhD7Wjf3A3Bxw80YCzdeqFYyaTDvXtHqzd9LFiBteT918Ji1GLyvPtcWX90BoGVfWuiC6TQsvioixbRF2MxaJHpycQoS0m1Ek8cNNUlBWmqFbK67UMLEaNWNMh3NfjY3G6VrlCfEyOHQGWj5CiF2s0bpuBgkwbUuwGtLW5YbcbwTAU2l0+sbhQrlpd6NF++kI7xuQmRYxZeNxh1oo1OSkpZrS7vGIVvFztitUUbAHc3tmjmBGXZDUg1aEXs7y0WgYnapzISbcoGs26pi7kpFvxh52V+PoVhbJuxe9cXQK7UaP43ZMrWtQwFD46Uo8X31ZuUHXndWWSE4rdboSzy4cfPvZhzJmLwwW1LK9B1/IaTlA0DZaisHNfNZ77yxF8dKQeLEWBiqGV8eWOMHcfHanHy+9Vys6doDW0dHY+7v6PSapaQxzH4+jpZswYnyHbAnjG+AzUNnehx6eu0+SX+bELLYvl2r0KLYvDYRga3V6/6IILHc+vVs3BvqP1AABXt09W68vV7RMbOUnmjQK8fk5sRhWhp9XggtfPSaTo5VoAsxwfJuJIRe0XDwSvSXUY0ebqgdmoRVaqGWajFm2uHqQ6jOju8UGj6fsMOY4DzwMebwDOzsjeLB5vADwP0L1y+0CkXpogtx9shBWEpikU5TjQ0NqNe36zGwePN8If4HDweCPu+c1uNLR2oyjHgfMNnVixeAySbAZoegt4Qt2KNY2uCB02ILrWXLyteSkK2H88uptMbizDGeLyihGKpi+742t/MRBzx/M8GJpWzeBKdRhw6ERDXBlbQhqpWpdHOX85RQE1jZ2YMylLthfHg9+dDp6HasYUy0ndPECw9W2Ls1u1ONPd40NTm0fstRLu2rtmXj7O1DqRau+Lz4TrQqbYjWKqseT1GVp0TbU4WTHu47Dq4fH6YTXpw2T+KbA8JxYthicn+HrdbxwXdE09+/8OY/WNX8HM8ZloaOnCL1/ai3u/PRWpNiP4kN07RVHgOE40mnKuyNuWj8foHDt6egsXw9WlNTSN0blJst8FASWPQ7wu2Vi6JMq5yYY7ZGsdIwGeV83yCFy2jsPoxDp38ZwAOY7H5LFpqqcPh8UQd8aWcJS/YclYJNkMcHv8qG8JVpEn2Qy4YclYUIhUBeY4HlmpFrHHx7iCZGhoCuMKkrFpzULUNneCC1kg5VSM/YHgzjwUiqKRYjdK2tqGnpgeffUgzAYtcjOscHb68MuX9iIv3YJvXDkaeekW/PKlvXB2+pCfaZPtF27oDcofqGjAHz6oxIGKBtAU1VsvEnRNCjUwZqMWDqse5t5MNA1Ngws7+VAUhUCAQ5LVgGSbAUzvKYFhKCTbDEiyGuAPcGA5DklWHTatWQSzSQuX2wuzSYtNaxYhyRp0tbEha7tOx+BMrRNzJmXh59vKJarLP99WjjmTstDkdEPDUGhq9+C+J/fgYEXvKaaiEfc9GVSX1jIUEpGZD5V/33DrdNyzYjI23Dodz69bFKEzBwQ3PEXDXPI+EcgJJQYSLbwjxD53V07ORlWdK65TTLQuep9VBgsfPzxYozi+8IwtjuMxa3wm2jp7FNvpzhyfGbEQ+HwsPD1+scGW5LreBlsABWenT7FLYarNiPCgPMty2He8XjVT7VSNE/mZNnGBbmp3SxboZqcbLMdFNO+iaQpn65ULBcfk2MGAEmXmDTqNeIoRZOY1jNRNx/MctBoGCMj/DhiKgl7LoMfPin3nQ+F4Xuw7HyoVQ1EUMlPM+POHpxRbKN+yrBS+kCZccqeYUctLYTYoL3vR03uDmWhVtS4U5dgwOkcqEdM3D8CsCVnY/s7Ad0kcSpATSgzEWvykFKi6nIl17nyB4GlFbhcudwKkaSpqF72zdS5MKEiBhqFBUZDcl6KCP2q5jC0qin+fkvnV8DyP9CSTeF04fffrlo2FNLd3w89yEYYqEODA0JSYqSYZZ28HyVSHATQNcYE+c6ED//z3BZy50CFZoNmw2I/Pz+HRVw6C4zlce0UhVn1zIq69ohAcH3zc6+d603WDb7iqtgOHTjSiqrYDAMDQNCiKkrjPWJbvNQbKUBQFg7ZvUe/o8qG+xY2Orr52AEadVlKIyXEcGlrdEree8LkILZTbXB6cq3OpnmLO1HZEuBWB6DE+wW171xN7sO2tY9h18Dy2vXUMdz2xB9VNXbKnaItRg599fya0Glry3dNq+txkIw1yQomBRArvCEFimbtp49JxoKJBtWgt/AQY62cCnsdj98xDgOUl2UTfu6YUGoYKZpCFfGx6vQbuTq9q6mmqzQirVS8xRBoNjUBPX+GgnDAhAORl2HD/b/dIux/+Ntj9sNPtBevoa6cLBE8o08dloMWlrAmVbAtK11fVdeDRVw/KxlkEyfhQyo/ViXIt+4/X48wFJ/IyrNiydjH2Hq3FqZp2lBWloL0r2M9eTnolI8kUljYsXSTDJWYEePBi3UxJXpJYMNnS0SNW/4cG+30+FnmZNpytU+5TEyy81IqnGLkTyrcXj42Yh1hifNHctnKFjRqGRkGmFc+tXSQqaM+ckIH7b5wqq6A9EiAnlBhIJMuDECSWuRtfmAKeh1i0Fr6znDc5OyJuEetnErrLlYtbhEPTtKRgUq4TpJ/lJFpUwnVAMND882174eryYnSuA64uL36+bS86OoO7b7Xuhyl2U8SmhGU5UCEuFdn3wAdPQGpxFj/LShZoAMhMMSM/0457n9yDl9/t7W74bgXufXIP8jPtKB6VFAykKyx8vkCfkGTfPAT/X61rIhA80SXbg3GWTo8PbZ096PT4kGwzINluAMdLExSEoPzTbxyWfY9Pv3EY/gAHd49P9YRS3+pGIOz9RDMWLIADFY1xZ2wFWA7nGjpx9xO7sb13Hrb3zsO5hs4RmR1K6lBirENR2sUodWwj9BFt7kZn2dDR7ccbf/9SUkwo9KRQqh+I5TPhKOBEtXIdSml+MqgQN5XNZsCBE42qDa06u3yYUZoh6axosxlQ39aNQycaFXuvXDEpB+cbOyNOERqGxk9uC/bzGJ1tE7sJAtIOkkr1LcWjkrD/eD0qzrYpN5bKT8I3rixCe3t3b8dGoMnZo6rE/Pz6RWBZTrXnTFAV2CQqJCclmdDs7InaNVGvpYMKADQNd49PLPI0G3TgOA4GnQZ2i068r9BZ8csofWpmTsgS61Dkih+/vXgs0mx6UYZGq2Wi1pisur4MXh+HV9+rUHzOVTPzsPKaceJ9L9c6FGJQ4ihsjKfwjiAlWuHYiRonfIFIlV9h4ddpaJSOcigWpJ2pdaKz2w+rSYvROQ7xvh2eAH608V+KP+qnVy+QGKpYOkEmW41ItkhbAFssepyqdaLHyyk2gCrMtmHVox/GLKUv3LfR6RFbAMsV6910dQnON7hgt+oUF/7QxlKCQXnnk7Oq3Q3HFyZj1oQsnKxRNshjcuzITDGjrS148khKMuFvn1Shsve+cg2rxhUkYcGUXLHIM7xIUTAoNrNW7ARpMumw67MaFGTbFN+jq8sPhqGQ21v8KCd/U9PQiUlFKaJigF6vwcvvVWLXwfOK392rZuZh7qRs/NdL+xSfE17Y6HAYsXNfNba9FXsx5HCByNf3EzzHgQGwdHa+pFL+8jXJsSPM3fxJWfjqlGwxi4bnAVqrQVqSCWs3fayYAvzk/VeqJD3IZ94kohzMcbykoZUcNBWuhBv8d6rdpNoA6rblpZg5PgPXxiGlDwTrW1YsHtMrU+KUyJSsWDwGFxpdKM5LwpcqigDTxqXD4/GLjwuqwEKwP7RxlxDs7+zyw+tXLwzduHp+RPZYNM2tklHBWpDaFrdihtnobLsk2B8qBqr0HktGJYGiKbSpyN+MHZUkSXOONRZXmGWVSOWEIl/YeHnWoRCDkgA8H/QBD7edxVCFoigc7U0BVpIdP3K6GUumjZJeJ+fyOghsfycYTC0eZU+4DiUa4c+jKKCqrgM3Ly2RlQa5eWkJWpwefHXaKFVxw/CDLsfxGJ2bhI7eNGY5+foxuUnwRlEEeHr1AomqMs8Hg/1C4679x9r6XHRlWfB4/chMMuPjL2qjysFnhMjB+3yBqJpbpfnJ8AW4qFL0VFg6ndCnRkk5+unVC2DUa9DUrtK50maUJA7EKkzKs3xchY2Xax0KcXmRjo2XBEF19UBFY4Tqqk5L4+X3KuHu8Su6SSxGrcRHDUTv8rh1/SLsr2jAi2puh+vLJP3CLRY92jq9UbsJJlv1kp7yZrMenT0B2QZQGoYONoDKtuN3fzuG5fMKI1xMgrhhiqVPwBEADAYtuv0sKqvbVXvRf/JFLV5+V9nHv3J5KZbOykd3tw92uxEUBbR1eFHd2InHX5fRCLtlBvIzrPjoi1qcqnEqfi5moxZ3fH286P4L19wKj4/4WRZ1zW74/Cw++aJOOeZTkITrrhwtutLCYyjhrr+/fVyFby8eC62GxuqNHyl+J4KdK02S+Fes8VE1t224yzuWGMqWdQuhGYbLL3F5EQaVaGmZRdk2zJqQAW+AU5RR0WtoyW4uloLJ/ccbMG9SNl5WKS6Tq0MJRSn9N5K+BlByp4RHXzmIFzYsxrK58srH18zLR32LG46wfigaDQ1Pl1esX5HrVZ+faUWSzRCcawXJ/fCTGEVRYMGLWWdy9x2VYcGM8RlITzYpfi46TZiwZq/mlpBJJe1VrwNDUSjKtuNcvUtVBbq6rhPhe12NhsYNS8aK/VpC+7fcsGQsbGY9Pjp8IeqJKrRzJSCtgpeLxQnGQs1tK4dQhxLrqWYkQAwKYcCJJYc/P8uOu2X6egsumy3rFkUsXDFJik/Nwc9un4n/3i7/ow6vQxGIVocSjtAAKsByoGlE9HVnOQ7+AIu2Tq+iO6Yo2x6x86NpGi63V7UCv9PtRUmeQ7WxlFwPl1anR7xvuCttQmEKWpweZCSbVd1Mz4d9LhRFQUMDbd0+cBxk4xjpDhPyMqz4UiXYP21cOny+vtOo389i9vgsBDhOtoWy0NflQlOXOA9ycarwBltSYquCj9XVrWFo0VDFcqoZCRCXF3F5DSixpmWm2A149NVDqs+5MsQ1Fct9hSyaYC/wvuy8wmwbZpTK/6jDs6qUXFMZDqPE5WW3G7Hlr0eQk2bG3Ik54o7fbAzudL+sbkNRrh2rN36kmOW1cfX8CJeXxaKHq9sv9qrXMJREbDHAschMMsNk0KCqvgMamkGPLyC6gww6DQIci6xki5geHZSvp9Hi9KDyfLvYCTI73YK6pi68+n4F7rthKkrzk7D/eAOOnmlVdHlNHJ0i+VxsNgM63H5VGf/S/GT4AyzuffKfinMRnn2n1TJgQeFMXYeiS3F0th11rV1ocykbbblsQVWXV4LipeFrxEjq2EjShhUgBmXgiTUtc+woB7b85Yjqc+KNoYT7qGP5UVssehz6silqHcq0kvSwGIoOZ+pc0DA0OB4Raa00HRR/fPeTc6p1FEKcI/S+7V0+sDyv2AeEoSjotAw63N6ITLDRuQ54vH7YzHpQgT6DQtNBN1hNYyfG5Cahqd0t6UN/+kI7RmVY0dHphdPtUzQOrk4vFkzJFj+XpCQjLjS7oxpkl9uHdz85q2iowufCYtHD6fbLnmL7Pu9FoGngPx9TroF5fv0iMGHGIdr3KJF6kZG8Rgz7GMr+/ftx6623yv7tvffew+jRoy/xiAixEktaZmG2DTazTvU5chkx8UqKx7Iz5Dge+Vl2tHcGFwK5GEp+lj1iLG63DxnJZpyoblPcHY8vSI4aNwiHpmloNQxqZYL9QoptUZYNLMvD7+cUBS15lodOy4hzEOysyGFUuhUcz0f0oR+VbgXLckhPNuH98mpFKZPvXF0SNhcUaho7Rc2t8HTka+bl40JjJ/IybaqtBzpCmowBwfiJ4FKUI8ByKD9Whylj01RbDxysaIg46RLh1/5jWBgUgQcffBAzZsyQPJabmztIoyHEQixpmdNLM0D1/nc8yqyhwdT+8lELdSjOTp+iYUi1GSMMisGgRWtXpKsFCC5Kz/zxMJ5fv0j1vtPHRb5HigLYKMF+YWcu9CAJN1aCoKU2xKAAPBxmA5qd3XD3BGTTkdMcJpy+0K7aZrmupQv2UQ7xcZblkJ9lh8+vnI6cbDVCw1BR05zDYzPnGyINbig1jV2YNT5TDNyH1+zcsGQsTlS1SXbXMcfiLrJeZCS5vNQYVgalsLAQU6ZMGexhEOIklpMEgIQyYuLNvIkGTVNR6x02rp4fceTXahkcOqGs9zQuLwkebyDqIhour0dRlLgzV8pcKj9Wh3kTs6MaQY05pDsmz4PlOTQ5VWo27AbkZlhxX2+sQ24uwgtOfT4WOoZGQ4sXj732iegmO1jRiN/vrMSGW2egIEsvdjOUS2AIsBy+ON2M+ROzQu4bQF6mVfWzG5VhgU7LwOX2KdbshLceGGjhVyFdvvxIfUS6/EgMyg8rg0IYnsR6khCek4i0TSy7PoNBC4ahwbKcJPAdCk1TOHSiUVX5+LMTjVgWUswHBBfoC71FlHKL5MSxqaLBUTIM4VX7AucbOlUzuDgOYqtjHhxWXVcmBth/9+4x0QiGZizRNB2Tgatt7lR1IYUXnNI0BQ682JNErm/JbctL4fVxuHXZOEkCg5CyvPdoLc7VufDVyX2nArfbhzll2Xj9vUrFU+yCKblguehGMpRYCxsTOVUEWO6y6/I6LILyQgwlJSUFTqcTRqMR06dPx3333YeysrKE75totbtGE1QVDVctJUSHotDbO51XPEUI88uy/SNr4/WzcPcEUH40KCGen2nFnInZMBuCDaNCoWkKO/aehcNqUFyUOjq9WDa3QLJrpSjg/X3V8PoCslleLc5u7Pn3BXT3BBQL88xGLe75j0mS+9I0sO94I6xmrWIGl92kx2eVjUhLMmHsqMgA+6madjS3d+P6BaPBsrw4v3/95ym8uuOErAHkuGAxZGlBMnQ6prcFcLd431SHCR6vHyeq2nBNyFzQNIV9xxvE8comNbj9GJvrQJurRz6BgQo22ioe5ZB8/h5fAF+ed+Lx1yILMTfcOgOTxqSivtUdtbAxN80imeMAy6Gy2olfb98f8Xk//INZGJfnUFS1VkKjYdDS4cEPH1UubHxhw2I4LOqxw6GIVstEtCkQGBYGpaKiAm+99RZmzpwJh8OBM2fOYNu2bWhqasLvf/97TJ48OaH7EoMyNElkfpUMlbe3yvzRVyMFG39y2wyMy0+SGBWKAupauqMKSmanmiKMnavbj+b2bsUsL72WAcfzvbGF+ojYQnO7B9PHpUfc19nlg6vbq3id3axHY3s3LAYttFomIsvL72fh7vGjJC8p2NK3d343/d9hMc05XB1579Fa1LV04+arS+D2+KHVMhGv7fezMOo0SLbrxTEzDI3aFndUcU29jsGJc8oJDKUFyXDIJGq4vX70eLmIbDeDnobNpMeb/zqNV3ecUPyerFxeiusXjJHI0ABBo9LlCWD/8T7X1KwJWbAYNXEbEyD4Hd6x9yy2vXlU8Tmrri/D0tn5w04LcNgbFDmam5vx9a9/HaWlpXjllVcSugdJGx6axDO/apIuPMchQNF4dUeFYgrryuXjJSmhWi2Dj47WxyXXIsAzjGKW1+rvTMWEwhScqY1sgiXI1xdl28EHApJ7arUMvAEe1Sqy9/kZVjAMjWangjHrDbBrqOAGSpBeOVnjhNsTULxvil0Pu1mPqjqXooJyUbYNGqrP5SikOatJ3qc7zPD6WaxWMdpyNTm0hkFVfXAscieUkjwHtvf2dVFiycw8rPrGBEnKd/h890fwPCnJhOf+cgS7Dqiny4enwg8H1NKGh22Hl7S0NFxxxRX44osvBnsohEEitC3ri28H27K++HZfW1adQYOqWqdq29gztU4YDH1yJ7G0Fj5XF9nu2WDQotvrV24A9cfDQUHEVxWytXqbYIU3aQoW9PGq13GAGGCXa+7V5PSA4znodH335vlgp8dHX1VoAfzqQZiNOkmP9snFadBqaEwuTsOvVs3B7kM18LMc9HqN5L5AMEvu4Rf2SppcPfzCXjh7G439u1K9YdVnJxoj5sLH8qhuCErFjCtIhoamMK4gGZvWLER1gwvdXhZFOZGdKUMpzLapLuB+PwuvN3DRmViXqzjksA7Kh8tmEy4vokm6bN2wCKOitI0tyrZLOgMmmvUjZHkpBbD//WVT9DqKo/W4ZrY0KK/R0Nh3tF41mH+qph35mbboel9WvXhfmgb2RWkBvO9YPYpybKppw2dqOzCzNCPkvpSk46Vc7Up+pg1slIX0QrNbktVnMGhxrrFTVSqmub0bC6bm4uV3KhQLG2eOV9du6y94Hpg1IQvbVXTkRmKX12F7QmlubsbevXtJGvEIhKKCC1P4DjUUrZaJ3pb1eAMoQDWTieU4ANLiR6G1MEVBctKgKJWaGJ6Htle8MMlmgNvjR31LUBgxyWbAFZOywUSRxq9p6orwTfM8cL4xmOX1yF3zJKeER+6ahzkTs6DXMXC5e6LqfYXemqJoZPS2AL7vqT04WNEIf4DDwYpG3PdUsAVwZrIJdktkckLo/GWlRApm1jR2qrbhvdDowqQxqapzEX6SYBga6UkmNDk9siefJqcH6UkmaBkKj90zD4/efYVkrh69+wo8ds+8S7qDFsQhw2Mw4enyI4lhcUL58Y9/jFGjRmHChAmw2WyoqqrCiy++iJ6eHqxZs2awh0foJ4R4yM591aiqc6EwSzlnP5aCNJ+fU60NEVwrS2dJm1ppKAqP3T0PAY4XYwCTi9PwvWtKoWEo2ZoYrzeA2eOz0KbSt2TW+Cz87m/HFYOweRmWCEPl97OYUZqBAMcr9lHJSDLBqNeIel9yhY2ZSWb4/dJ+KAVZNry244TiSeK25ePFehGl09EXp5uRldwnlslxPLJSLfjzh6cU7/vtxWORnmxSLWQNP0nQdDDLS/3kY4XFqEGXx68oBppi0Ue83kBxOYpDDguDUlJSgh07duD3v/89PB4PHA4HZs6cibvuugvFxcWDPTxCPxBN4j48Zz8m11S2DR9/Xqv6nHBZd4GunvgXJYpG1BqIKWPScPhUc8S1GobG7LLsiPqYnh4/inIceHVHheJCOu7aJAQCXNTCxnAjeLa2Q9WdVVXrhMNiUFfvbexCICD9XBpb3eIJRc4ANrS6ke4wqqtAI/TcCAiSLmrjvdDYCZNeG3FPoNcNuj2obK187u1/+rvwdqgzbLO8+gOS5TV0SESgL9o1Lz20GJ8mkLGVyFgEleJoNRBmgxarwmoTNAyNh3o1ubiwVGmtlkFljRPeAKdoLPQaGjnpFtz/1L/A8VxETQlNBV87M8mEzs4eUW34bL1Ltu2ycO8n778Seh2DM7XKMSi9hsa4EPVevV6DJpcXf/7wlGJm3bcXj0WaTQ+fn4tLBbq2tVt1vBtXz8fR0y1Doo/7SF4jRmSWF2HkEFM8pKIhIqYiSLoo+ah5lsfM0kzFOgK5BluJjoWm6Zjca3aLDs+vW4Tbrx2PJTPzcPu14/H8ukWyxiR4XwqpSSbVOEZmqhmHTjTh5q8VY8vaxUh1GHHmghOpDiO2rF2Mm79WjM9ONEpiKDwfbKssuLPCY0WCO0uroVVfOz/LHqG51djqVs2sa2h1B2uGOA4Mz+GrU3Jwx7UT8NUpOWB4TtYVFAhwYptopfn9/GQzvD51N9K5+sgMPUL/MSxcXoSRTaICfbFIumhoOq4GW4mOheM4UXpFiQvNbrCB4CK6ZNooiQwMp5DJSlFUTAtpUa4NXi+He5+Unqxef78SP7ltBliOk7imAAo1DerNqHgOOHC8QfW1D52QqvfyPB9TZh3P82LM7EhVK1xuP2xmLUbn2GVjZjzP43yD+vyeb+jE3EnZvfMm37lyJKbqDiWIQSEMOhcj0BfNR81zHPLTpBphaq6VRMfi9QZQlGNXLaoLzVxS0hKLeH8hC6nSInm+oROzJmTibhk3nVCrsmXdQslJguf5qG2XM5NN2FlerTq+s3UuLAjR3KIoChwX6Z4TxiLoivEU0NrpRYDjUdPUJSYROKwGaGgKKVZ9RMysKMcGHFQeS2G2DYVZVsyblI2vX1EoG28aiam6QwliUAiDTn8I9Kn9TTA6V07MwoLJ6oHRRMcSvC5TvX99AjUQwkI6p0f5JLFoei72HVPv6bHvWD2WTBsVcpJA1LbLz69fJBrXWHf8NE3hsxhcf/On5qKhvVvxFJNkM0ia7wrZbmp1HdNLMwAeWDxjlKyRfGjlDOgYWta1SOgfSAyFMCSIFg/pj5z9WKugEx2LBgi60RSuC929abUM9HqNaq2NMOZZEzLFZlR/2FmJDw/W4A87K/HwC+WYNzkbeZk2VEfpFXK+sUtSwElRwKET6u6sgxUNmDcpC/MmZcvWwMyblC1rXGuiuP54UOjuUVc69ngDccfMtFSwqPIRhd4xj7xyEH6WuLsGEnJCIQwJQuMhicjXD9RYztQ60dnth9UUVA1WG0ss7rVE+mNwnHpx5tb1i5AfpVdIXoZFIohIURSqokjMnK1zYf6U7Lh2/H4/G9X1N3F0SlRjJiflHy1mpmEo7D2ifl/SfXFgIQaFMGQQXFNLZwczhLq6vIOcs8+jzdWDqloXinJsGJ3DA1A/Kam51+KttQFia1G7/3gD5k/JxWsqvUJml2VJ4jY8H1usyONT3/GH13X4/Sxmlqq7/rJTLXi//JzqayvVB6nFzGit5pJ0XyQoQ1xehCEHz2NQW6WGik5ueysoOrntrT7RSYqO/rORc69F0x4LyBjOWLPOdJrgiUHOHSScJELheYgSM3JoGBrzJmVFzfKST+dWd/2xgdhEHKPFzMLnd6C7LxKiQwwKgRBGIgt/NBKtb4l1kWQDLIqybHHVuESNSTB0TMYsvK4j1PW36voyXDUzD6uuL8Pz6xYhP80Cvy8Qd31QLITqsCndl2R5DSzE5UUghBCLiykRP3yi9S3xZp0xQMw1LtFiEhzLXXQ6t1JmnXCKibU+KFYEIxm+IQgN3F/G4iADDjEoBEIIiS780bgYd0y8i2SsNS6AekzCz2HA0rlDTzFVdU6xsLEoWz3xIZb3c7kJMg4liEEhEEIYKD/8xdTaJJp1Fu/45Bj4HT+P1o6+xIei7OiJD4B6Z8XLTZBxKEHEIYk45JBjsOc3mjjklnULoUngZ6OU5SUszvlpkVleodcGeB4HTzSKi++MUvV0YyXind/ga6Nfd/yqc6GQ8dY3FuWWz0OFwf4ODyRq4pDEoBCDMuQY7Pm9mIU/lnvHuzgnuvgqkej89le/dSAxRef+noeBZLC/wwMJMSgKEIMyNBkK83uxu/Joi288i3Mii68agz2/Wi2Dj47U48W345OZ7+95GEgGe44HEiJfTyDEiSCtPn9SFlZeMw7zJ2UpSquHQtE0WIrCR0fq8fJ7lfjoSD1YioqoXYlVBiY03VhJZl4u3XgoE2viQ+iilWjaNeHSQoLyBIIK8bh2EqmEj4aw+KrJzA+36u9EEh8GKvuO0L8Qg0Ig9BPRCiITaT/LcTxmTsiAT0VmXqehh1X1dyIZb6QKfnhAXF4EQj8wUC4Zv59FQZZdVRyyIKxr4nAgXkVnUgU/PCAnFAKhHxgol4xWy2DfUfXK/fCuicOBRAoQSRX80IcYFAKhHxgolwxNxyYzH9o1cbgQbwEiqYIf+hCDQiD0A/3RdVKOyyF2EM+ckCr4oQ2JoRAI/cRAdJ0ksQN5Yk27JlxaSGEjKWwccgzn+b3kMiUJVO4P5/kdLozkOSaV8goQgzI0GQnz258yJUD/GqqRML9DnZE8x2oGZdjEUNxuNzZu3IidO3fC5XJhzJgxuOeee7B48eLBHhqBEEEsFfDxGBwhdrBwag6Y6aPEXieX73aQMBQZNgbl3nvvRUVFBR588EHk5ubizTffxL333outW7diwYIFgz08AiEmBLXc8iP1canlCtftOVw7pFV2CZc3w8Ll9a9//QurVq3Cs88+i6uuugoAwPM8br75ZjidTrz//vsJ3Ze4vIYmI3V+L0ayfSioDRNiZyTP8bAXh/zHP/4Bq9UqcW9RFIVvfvObqKqqwunTpwdxdARCbCTaq34getwTCAPBsHB5nTp1CmPGjAEdpthaUlICADh58iTGjBkT930pqm8nEQ8aTVA+I5FrCdEZifNLUcDOfdWqFe8HTzRg6ex8SVwk0evUGInzO9QYyXOslv0+LE4oTqcTdrs94nHhMafTeYlHRCDEB0VRqIoizXK2zgUq7Nea6HUEwmAwLE4oAFR/MIn+mHg+MR/nSPaPDgVG4vxqtQwKs6JXvHd1eSVZX4lep8ZInN+hxkie45QUi+IpZVicUBwOh+wppKOjAwBkTy8EwlAi0Yp3UilPGE4MC4MyZswYnDlzBlxYJsvJkycBAMXFxYMxLAIhLhKVZhkISRcCYSAYFmnD//znP/HDH/4Qzz33HJYsWSI+/t3vfhetra3YuXNnQvclacNDk5E8v4lWvJNK+eHFSJ7jYV8pv2DBAsyaNQs//elP4XQ6kZubi7feegufffYZtmzZMtjDIxBiJlG1XKKySxgODIsTCgB0dXXht7/9LT744AOJ9EroiSVeyAllaELmd2Ah8zvwjOQ5JuKQChCDMjQh8zuwkPkdeEbyHA/7SnkCgUAgDH0u6xMKz/MJ+aCFpJrLd+YGFjK/AwuZ34FnJM8xRSnX/l3WBoVAIBAI/QdxeREIBAKhXyAGhUAgEAj9AjEoBAKBQOgXiEEhEAgEQr9ADAqBQCAQ+gViUAgEAoHQLxCDQiAQCIR+gRgUAoFAIPQLxKAQCAQCoV8gBoVAIBAI/QIxKAQCgUDoF4hBIRAIBEK/MCw6Ng4kDQ0NeOmll3D8+HFUVlaiu7sbr732GmbNmhXx3K6uLrzwwgt4//330dDQAKvVivHjx+Opp56Cw+EQn+d2u7Fx40bs3LlT0gxs8eLFl/CdDQ1inV+fz4dXX30Vb731Fmpra2GxWFBWVoZ77rkHEydOlDyXzG8f5eXlePvtt3H48GE0NDTAbrdj0qRJuO+++1BSUiJ57qeffopnnnkGlZWVMJvNuOqqq/Dggw/CZrNJnkfmt49Y5rerqwuvvfYa9u7di6qqKng8HowaNQrf+ta3cPPNN0On00nuOZLn97I/oVRXV2PHjh0wmUyYPXu24vO6urpwyy234O9//zt++MMf4uWXX8Z//dd/oaCgAH6/X/Lce++9F++88w4eeOABvPDCCxgzZgzuvfde/Otf/xrotzPkiHV+f/GLX+C3v/0tlixZgueffx4PPfQQ6uvrcfPNN+P06dOS55L57eONN95AXV0dVq5ciRdffBEbNmxAXV0dVqxYgc8//1x83v79+7Fq1SpkZmZi69atWL9+PXbv3o1Vq1aBC+tJT+a3j1jmt66uDq+99homTJiAX//619iyZQsWLlyIJ598Ej/60Y8i7jmi55e/zGFZVvzvf/zjH3xxcTG/b9++iOf96le/4q+88kq+vb1d9X7//Oc/+eLiYv7vf/+7+BjHcfxNN93EL126tN/GPVyIZX59Ph8/YcIE/sEHH5Q8Xl1dzRcXF/ObNm0SHyPzK6WlpSXisY6ODn769On8vffeKz72H//xH/x1110n+Tw++eQTvri4mN+xY4f4GJlfKbHMr9vt5t1ud8TzNm/ezBcXF/OVlZXiYyN9fi/7EwpNR58Cj8eDv/zlL7jhhhskri05/vGPf8BqtUqOrxRF4Zvf/CaqqqoidtsjnVjml6ZpUBQFq9UqedxisQCAxGVA5ldKSkpKxGM2mw35+floaGgAADQ2NuLo0aO47rrrJJ/HvHnzkJGRgQ8++EB8jMyvlFjm12QywWQyRTxPcNUKzwNG/vxe9gYlFo4dOwaPx4OMjAysWbMGU6dOxcSJE3HLLbfg8OHDkueeOnUKY8aMiVhIBX/ryZMnL9m4hwsMw+B73/se3nzzTezatQtdXV2oqanBL3/5S6SmpuL6668Xn0vmNzptbW04deoUxo4dC6BvToR/h1JcXIxTp06J/ybzG53w+VVi3759oCgKY8aMER8b6fN72QflY6GpqQkA8Nhjj2HmzJl45pln4PV68dxzz+G2227D//3f/2HcuHEAAKfTiYKCgoh72O128e+ESNavXw+LxYL77rtP9Onn5eXhtddeQ0ZGhvg8Mr/q8DyPhx9+GBzH4Qc/+AGAvjkR5igUu92OiooK8d9kftWRm185jhw5gtdffx3XXXcdcnJyxMdH+vySE0oMCAtcRkYGnn32WcyfPx9XXXUVXnrpJTAMg5deeknyfKV+y9H+djnzwgsv4KWXXsLq1avx+uuvY9OmTUhKSsIdd9yBCxcuSJ5L5leZJ554Art27cJ//dd/YfTo0ZK/KfYBD3uczK8yavMrUF1djbvuugtFRUV4+OGHI/4+kueXGJQYEOImc+fOBcMw4uOpqakYP368ZIfncDhkdxkdHR0A5HeJlztnzpzBxo0b8aMf/QirVq3CzJkz8bWvfQ2/+93v0NnZieeee058LplfZTZu3Ijt27fjpz/9Kb71rW+JjwvfX6V5C50zMr/KKM1vKDU1Nbj11lths9nw8ssvi3FAgZE+v8SgxEBxcbHi33iel/hDx4wZgzNnzkSkYgq+UbV7Xa5UVlaC53mUlZVJHrdarcjPz8eZM2fEx8j8yvPMM89g69atWLt2LW699VbJ3wRff2isRODkyZOSWACZX3nU5ldAMCZ6vR6vvPKKbEB/pM8vMSgxkJGRgcmTJ+PTTz8Fy7Li483NzaioqMCkSZPEx6666iq4XC7s3r1bco+33noLhYWFkgAdIUh6ejoA4OjRo5LHnU4nzp07J4mhkPmN5Nlnn8WWLVvwwAMP4I477oj4e2ZmJsrKyvDOO+9IFrLy8nI0Njbi6quvFh8j8xtJtPkFgNraWtx2222gaRqvvvqq5DsbykifXxKUB7Bz504AfQvawYMH0d7eDqPRiAULFgAA1q1bh5UrV+Luu+/Gd77zHXg8Hjz//PNgGAarVq0S77VgwQLMmjULP/3pT+F0OpGbm4u33noLn332GbZs2XLp39wQINr8fuUrX8GECRPw9NNPo7u7G1OnTkV7ezteeukleDwe3HLLLeK9yPxK2b59OzZv3oyFCxdi7ty5kmJGnU6H8ePHAwAefPBB/OAHP8CaNWtw4403orGxEU8++SQmT56MpUuXiteQ+ZUSy/y2trbitttuQ2trKx555BE0NjaisbFRfF5eXh6Sk5MBjPz5pXie5wd7EINNuESFQE5OjmQnsX//fjz99NOoqKgAwzCYMWMG1qxZE3F9V1cXfvvb3+KDDz6QSCssWbJkQN/HUCWW+XW5XNi2bRt27dqF+vp6WK1WlJaW4u6778bUqVMl15H57eOWW27BgQMHZP8W/v396KOPsHnzZlF6ZcmSJVi7dm2E357Mbx+xzO/+/fsV3WAA8Oijj0piLiN5folBIRAIBEK/QGIoBAKBQOgXiEEhEAgEQr9ADAqBQCAQ+gViUAgEAoHQLxCDQiAQCIR+gRgUAoFAIPQLxKAQRjx//etfUVJSgv379/frfW+55RYsWrQo4esHalwEwmBBDAqBMEzYvHkzdu3aNdjDIBAUIQaFQEiQ3/3ud6KszKXg2WefJQaFMKQhWl4EQoKEtiYmEAjkhEK4jOA4Dr/73e+wZMkSlJWV4Wtf+xrefPPNiOft3bsXt99+O6ZPn46JEyfi2muvxRtvvBHxPKUYygcffIBvfOMbmDhxIr761a/i2Wefxd69e1FSUoK//vWvcY/rwoULoh7am2++iZKSEvF/8VJRUYH7778fc+fORVlZGRYsWIA1a9bg/Pnz4nNKSkqwYcMGlJeX48Ybb8TkyZMxf/58bNu2DUCwd8dDDz2EOXPmYPLkyfjhD38oEUMkXL6QEwrhsmHjxo3o6enBjTfeCJ1OhzfeeAMbNmxAXl4epk2bBgD405/+hF/84heYMmUK/vM//xNGoxF79+7FL3/5S5w/fx7r169XfY333nsPa9asQV5eHu69914wDIO33norQq48nnElJyfjiSeewLp16zB9+nTccMMNCb3/PXv24L777oPJZMKKFSuQn5+P5uZmfPLJJzh58iTy8vLE51ZUVGDPnj244YYbcN111+H999/HU089Bb1ej7feegs5OTm49957cf78ebz++utYv349XnnllYTGRRhB8ATCCOcvf/kLX1xczF933XW81+sVH29oaOAnTJjAr169mud5nm9sbOTLysr4NWvWRNzj17/+NT9u3Di+urpafOx73/sev3DhQvHffr+fv+KKK/g5c+bwTqdTfLyrq4tftGgRX1xczP/lL3+Je1wCxcXF/Pr16xOag+7ubn7WrFn87Nmz+YaGhoi/sywreZ2SkhL+888/Fx/zer38vHnz+JKSEv7Xv/615NpHHnmELy4u5s+cOZPQ2AgjB+LyIlw23HzzzZK4R0ZGBgoLC3Hu3DkAQVeVz+fDihUr0NbWJvnfokWLwHEcysvLFe9//PhxNDU14Zvf/KZEEt5sNuOmm25KeFz9wSeffIL29nZ8//vfl23+FNp1FACmTJmCyZMni//W6XSYOHEieJ6X9KcBgOnTpwMI9lInXN4QlxfhsmHUqFERjzkcDtTW1gKA2Gp45cqVivdoaWlR/NuFCxcAAIWFhRF/k3ss1nH1B4JxEhpuRUNuTIKRzM3NlTxus9kAyPesJ1xeEINCuGwI34WHw/e2Bnr88cfFtsThyC204df397j6A2FsFEXF9HyGYeL+W6LvnzByIAaFQOiloKAAAJCUlIS5c+fGfb2wcz979mzE3+Qeu5QUFRUBCAbb582bN6hjIYxcSAyFQOjlmmuugU6nw+bNm9HT0xPx987OTvh8PsXry8rKkJaWhjfffBMdHR3i4263G3/84x8venwmkylht9K8efOQlJSEl19+GU1NTRF/J6cLQn9ATigEQi+ZmZn45S9/iZ/97GdYtmwZvvGNbyAnJwdtbW04efIkdu3ahR07dkTEEAQ0Gg3Wr1+PBx98EN/+9rexYsUKMAyDN998Ew6HAxcuXIjZ5STHlClTUF5ejm3btiE7OxsURWH58uUxXWs0GvE///M/eOCBB3DttdeKacNtbW345JNPsHLlyhHR05wwuBCDQiCE8B//8R8oKCjA9u3b8ac//QmdnZ1wOBwoLCzEAw88gLS0NNXrr732WjAMg+effx6bNm1CamoqVqxYgZKSEtx7773Q6/UJj+0Xv/gFfvWrX2Hr1q1wu90AELNBAYDFixfjf//3f7F161b8+c9/htvtRmpqKqZNm5ZQkSSBEA7Fk7MugTDgbN++HY8//jj+9Kc/YcqUKYM9HAJhQCAxFAKhH/H5fGBZVvKY2+3GH/7wBzgcjpjTdgmE4QhxeREI/UhNTQ3uvPNOLF++HLm5uWhubsabb76JCxcu4Je//GW/C0o6nU74/X7V5xgMBlit1n59XQJBDmJQCIR+JDk5GVOmTME777yD1tZWaDQaFBcX48c//jGWLVvW769333334cCBA6rP+eY3v4nHHnus31+bQAiHxFAIhGHMsWPH4HK5VJ+Tnp6OMWPGXKIRES5niEEhEAgEQr9AgvIEAoFA6BeIQSEQCARCv0AMCoFAIBD6BWJQCAQCgdAvEINCIBAIhH7h/wPIEqwfYNoOJwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#lineplot of height as the independent variable and EFF as the dependent variable\n",
    "sns.scatterplot(x='height_cm', y = 'EFF', data = stats_average, estimator = 'mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='weight_kg', ylabel='EFF'>"
      ]
     },
     "execution_count": 14,
     "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": [
    "#lineplot of weight as the independent variable and EFF as the dependent variable\n",
    "sns.scatterplot(x='weight_kg', y = 'EFF', data = stats_average, estimator = 'mean')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observed that both height and weight do not have linear relationship, but to further this conclusion, we ran a linear regression to see what the r coefficient would show us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f99c4455a00>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 921.6x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#scatterplot of height as independent and EFF as dependent variable\n",
    "sns.lmplot(\n",
    "    x = \"height_cm\",\n",
    "    y = \"EFF\",\n",
    "    data = stats_average,\n",
    "    scatter_kws = {'color':'g'},\n",
    "    line_kws = {'color':'g'},\n",
    "    aspect = 1.6,\n",
    "    size = 8,\n",
    "    #fit_reg = True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f9558cee5e0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 921.6x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#scatterplot of weight as independent and EFF as dependent variable\n",
    "sns.lmplot(\n",
    "    x = \"weight_kg\",\n",
    "    y = \"EFF\",\n",
    "    data = stats_average,\n",
    "    scatter_kws = {'color':'r'},\n",
    "    line_kws = {'color':'r'},\n",
    "    aspect = 1.6,\n",
    "    size = 8,\n",
    "    #fit_reg = True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    EFF   R-squared:                       0.006\n",
      "Model:                            OLS   Adj. R-squared:                  0.005\n",
      "Method:                 Least Squares   F-statistic:                     6.745\n",
      "Date:                Thu, 09 Dec 2021   Prob (F-statistic):            0.00952\n",
      "Time:                        18:10:56   Log-Likelihood:                -3152.0\n",
      "No. Observations:                1119   AIC:                             6308.\n",
      "Df Residuals:                    1117   BIC:                             6318.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      2.9993      2.707      1.108      0.268      -2.311       8.310\n",
      "height_cm      0.0350      0.013      2.597      0.010       0.009       0.061\n",
      "==============================================================================\n",
      "Omnibus:                      118.986   Durbin-Watson:                   0.013\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              160.656\n",
      "Skew:                           0.832   Prob(JB):                     1.30e-35\n",
      "Kurtosis:                       3.823   Cond. No.                     4.49e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 4.49e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.00952198293457168"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=stats_average[['EFF', 'height_cm']]\n",
    "df.columns = ['EFF','height_cm']\n",
    "outcome, predictors = patsy.dmatrices('EFF ~ height_cm',df)\n",
    "\n",
    "mod_log = sm.OLS(outcome, predictors)\n",
    "\n",
    "fit_res = mod_log.fit()\n",
    "\n",
    "print(fit_res.summary())\n",
    "fit_res.pvalues[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the regression results we observed that the p value is 0.010, which is lower than the critical value. It means there is no significant chance that the linear regression model describes the relationship between players' height and EFF better than the null hypothesis, and therefore the null hypothesis cannot be rejected.\n",
    "\n",
    "On the other hand, the r value of height in relation to EFF is 0.0774, which shows that the linear regression model describes the data poorly, and therefore isn't the best to describe the relationship between players' height and their EFF."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    EFF   R-squared:                       0.010\n",
      "Model:                            OLS   Adj. R-squared:                  0.009\n",
      "Method:                 Least Squares   F-statistic:                     11.47\n",
      "Date:                Thu, 09 Dec 2021   Prob (F-statistic):           0.000733\n",
      "Time:                        18:11:46   Log-Likelihood:                -3149.6\n",
      "No. Observations:                1119   AIC:                             6303.\n",
      "Df Residuals:                    1117   BIC:                             6313.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      6.7180      0.983      6.834      0.000       4.789       8.647\n",
      "weight_kg      0.0331      0.010      3.386      0.001       0.014       0.052\n",
      "==============================================================================\n",
      "Omnibus:                      115.044   Durbin-Watson:                   0.020\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              152.986\n",
      "Skew:                           0.820   Prob(JB):                     6.02e-34\n",
      "Kurtosis:                       3.768   Cond. No.                         819.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.0007332691939644296"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=stats_average[['EFF', 'weight_kg']]\n",
    "df.columns = ['EFF','weight_kg']\n",
    "outcome, predictors = patsy.dmatrices('EFF ~ weight_kg',df)\n",
    "\n",
    "mod_log = sm.OLS(outcome, predictors)\n",
    "\n",
    "fit_res = mod_log.fit()\n",
    "\n",
    "print(fit_res.summary())\n",
    "fit_res.pvalues[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the regression results we observed that the p value is 0.001, which is significantly lower than the critical value. It means there is no significant chance that the linear regression model describes the relationship between players' weight and EFF better than the null hypothesis, and therefore the null hypothesis cannot be rejected.\n",
    "\n",
    "On the other hand, the r value of height in relation to EFF is 0.1000, which shows that the linear regression model describes the data poorly, and therefore isn't the best to describe the relationship between players' weight and their EFF."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We conclude that neither height nor weight have a linear relationship with the success (the EFF) of a basketball player. Therefore, we can not use the linear regression model to compare the impact of height and weight on players' EFF."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our research consists of data found in the dataset \"Basketball Players Stats per Season - 49 Leagues\", which had been scrapped using publically available data found on sport statistic websites. Additionally, NBA player and game statistics are publically available on the NBA website as well. Therefore, we do not anticipate any ethical or privacy concerns from the data we used because the players consented and have a clear understanding of how their personal data is being used. The players' game statistics also do not violate any of their privacies since they do not contain personal information and are made up of performance data. We do not anticipate any biases from our dataset because the data is collected by human scorekeepers present during each game and also an optical tracking system where games are captured by cameras that extract data through computer vision algorithms. Furthermore, there are no biases that would affect the data in terms of exclusion, besides players that may have started after the date that the data was collected which is why our research focuses on the players that were active during the NBA Seasons 1999-2020."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Through cleaning and applying appropriate manipulations of the data, we found that we were unable to determine the success of a basketball player through just their height and weight. By using the EFF formula as a metric to measure the success of a basketball player through specific stats that were included in the datasets, we were able to create models that displayed the effect of both height and weight in the determination of the score. However, because the results that we received from the model suggests that although both of them have an effect, neither of them actually had an significant impact on the score. What we could have done differently would be creating a linear regression model that had a formula that contained each of the variables necessary to construct the EFF score itself, and compare each of the p values that we got from the resulting model's summary. In this way, not only would we be able to calculate the effectiveness of every variable relative to others, we would be able to find an optimal combination of variables that had the greatest effect on the model itself. In conclusion, given the height and weight of a basketball player, it is not possible to find the probablity of success based solely on those values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Team Contributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Elise: Data Cleaning, Overview, Ethics & Privacy\n",
    "- Ellie: Background, EDA, Hypothesis\n",
    "- Hayley: Background, EDA\n",
    "- Zongze: EDA\n",
    "- Kevin: Data Cleaning, Conclusion & Discussion, EDA"
   ]
  }
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