{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Homework 3: Prediction and Classification\n",
    "\n",
    "Due: Thursday, October 16, 2014 11:59 PM\n",
    "\n",
    "<a href=https://raw.githubusercontent.com/cs109/2014/master/homework/HW3.ipynb download=HW3.ipynb> Download this assignment</a>\n",
    "\n",
    "- [Problem 1: Sabermetrics](#Problem-1:-Sabermetrics)\n",
    "- [Problem-2: $K$-Nearest-Neighbors and Cross Validation](#Problem-2)\n",
    "- [Problem 3: The Curse and Blessing of Higher Dimensions](#Problem-3:)\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "In this assignment you will be using regression and classification to explore different data sets.  \n",
    "\n",
    "**First**: You will use data from before 2002 in the [Sean Lahman's Baseball Database](http://seanlahman.com/baseball-archive/statistics) to create a metric for picking baseball players using linear regression. This is same database we used in Homework 1. This database contains the \"complete batting and pitching statistics from 1871 to 2013, plus fielding statistics, standings, team stats, managerial records, post-season data, and more\". [Documentation provided here](http://seanlahman.com/files/database/readme2012.txt).\n",
    "\n",
    "![\"Sabermetrics Science\"](http://saberseminar.com/wp-content/uploads/2012/01/saber-web.jpg)\n",
    "http://saberseminar.com/wp-content/uploads/2012/01/saber-web.jpg\n",
    "\n",
    "**Second**: You will use the famous [iris](http://en.wikipedia.org/wiki/Iris_flower_data_set) data set to perform a $k$-neareast neighbor classification using cross validation.  While it was introduced in 1936, it is still [one of the most popular](http://archive.ics.uci.edu/ml/) example data sets in the machine learning community. Wikipedia describes the data set as follows: \"The data set consists of 50 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals, in centimetres.\" Here is an illustration what the four features measure:\n",
    "\n",
    "![\"iris data features\"](http://sebastianraschka.com/Images/2014_python_lda/iris_petal_sepal.png)\n",
    "http://sebastianraschka.com/Images/2014_python_lda/iris_petal_sepal.png\n",
    "\n",
    "**Third**: You will investigate the influence of higher dimensional spaces on the classification using another standard data set in machine learning called the The [digits data set](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html).  This data set is similar to the MNIST data set discussed in the lecture. The main difference is, that each digit is represented by an 8x8 pixel image patch, which is considerably smaller than the 28x28 pixels from MNIST. In addition, the gray values are restricted to 16 different values (4 bit), instead of 256 (8 bit) for MNIST. \n",
    "\n",
    "**Finally**: In preparation for Homework 4, we want you to read through the following articles related to predicting the 2014 Senate Midterm Elections. \n",
    "\n",
    "* [Nate Silver's Methodology at while at NYT](http://fivethirtyeight.blogs.nytimes.com/methodology/)\n",
    "* [How The FiveThirtyEight Senate Forecast Model Works](http://fivethirtyeight.com/features/how-the-fivethirtyeight-senate-forecast-model-works/)\n",
    "* [Pollster Ratings v4.0: Methodology](http://fivethirtyeight.com/features/pollster-ratings-v40-methodology/)\n",
    "* [Pollster Ratings v4.0: Results](http://fivethirtyeight.com/features/pollster-ratings-v40-results/)\n",
    "* [Nate Silver versus Sam Wang](http://www.washingtonpost.com/blogs/plum-line/wp/2014/09/17/nate-silver-versus-sam-wang/)\n",
    "* [More Nate Silver versus Sam Wang](http://www.dailykos.com/story/2014/09/09/1328288/-Get-Ready-To-Rumbllllle-Battle-Of-The-Nerds-Nate-Silver-VS-Sam-Wang)\n",
    "* [Nate Silver explains critisims of Sam Wang](http://politicalwire.com/archives/2014/10/02/nate_silver_rebuts_sam_wang.html)\n",
    "* [Background on the feud between Nate Silver and Sam Wang](http://talkingpointsmemo.com/dc/nate-silver-sam-wang-feud)\n",
    "* [Are there swing voters?]( http://www.stat.columbia.edu/~gelman/research/unpublished/swing_voters.pdf)\n",
    "\n",
    "\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Python modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ko/anaconda/lib/python3.6/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "/Users/ko/anaconda/lib/python3.6/site-packages/sklearn/grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# special IPython command to prepare the notebook for matplotlib\n",
    "%matplotlib inline \n",
    "\n",
    "import os\n",
    "from IPython import display\n",
    "\n",
    "import requests \n",
    "#import StringIO # not working in python 3\n",
    "import zipfile\n",
    "import numpy as np\n",
    "import pandas as pd # pandas\n",
    "import matplotlib.pyplot as plt # module for plotting \n",
    "\n",
    "# If this module is not already installed, you may need to install it. \n",
    "# You can do this by typing 'pip install seaborn' in the command line\n",
    "import seaborn as sns \n",
    "\n",
    "import sklearn\n",
    "import sklearn.datasets\n",
    "import sklearn.cross_validation\n",
    "import sklearn.decomposition\n",
    "import sklearn.grid_search\n",
    "import sklearn.neighbors\n",
    "import sklearn.metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 1: Sabermetrics\n",
    "\n",
    "Using data preceding the 2002 season pick 10 offensive players keeping the payroll under $20 million (assign each player the median salary). Predict how many games this team would win in a 162 game season.  \n",
    "\n",
    "In this problem we will be returning to the [Sean Lahman's Baseball Database](http://seanlahman.com/baseball-archive/statistics) that we used in Homework 1.  From this database, we will be extract five data sets containing information such as yearly stats and standing, batting statistics, fielding statistics, player names, player salaries and biographical information. You will explore the data in this database from before 2002 and create a metric for picking players. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Problem 1(a) \n",
    "\n",
    "Load in [these CSV files](http://seanlahman.com/files/database/lahman-csv_2014-02-14.zip) from the [Sean Lahman's Baseball Database](http://seanlahman.com/baseball-archive/statistics). For this assignment, we will use the 'Teams.csv', 'Batting.csv', 'Salaries.csv', 'Fielding.csv', 'Master.csv' tables. Read these tables into separate pandas DataFrames with the following names. \n",
    "\n",
    "CSV file name | Name of pandas DataFrame\n",
    ":---: | :---: \n",
    "Teams.csv | teams\n",
    "Batting.csv | players\n",
    "Salaries.csv | salaries\n",
    "Fielding.csv | fielding\n",
    "Master.csv | master"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file succesfully downloaded\n",
      "['.DS_Store', 'baseballdatabank-2017.1', 'baseballdatabank-2017.1.zip', 'exprs_GSE5859.csv', 'lahman', 'lahman-csv_2014-02-14.zip', 'sampleinfo_GSE5859.csv']\n"
     ]
    }
   ],
   "source": [
    "### Your code here ###\n",
    "\n",
    "url = \"http://seanlahman.com/files/database/baseballdatabank-2017.1.zip\"\n",
    "\n",
    "r = requests.get(url, stream=True)\n",
    "\n",
    "# Total size in bytes.\n",
    "total_size = int(r.headers['Content-Length'])\n",
    "\n",
    "with open(\"data/baseballdatabank-2017.1.zip\", 'wb') as fd:\n",
    "    for chunk in r.iter_content(chunk_size=256):\n",
    "        fd.write(chunk)\n",
    "        \n",
    "print(f\"file succesfully downloaded\")\n",
    "print(os.listdir(\"data\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['AllstarFull.csv', 'Appearances.csv', 'AwardsManagers.csv', 'AwardsPlayers.csv', 'AwardsShareManagers.csv', 'AwardsSharePlayers.csv', 'Batting.csv', 'BattingPost.csv', 'CollegePlaying.csv', 'Fielding.csv', 'FieldingOF.csv', 'FieldingOFsplit.csv', 'FieldingPost.csv', 'HallOfFame.csv', 'HomeGames.csv', 'Managers.csv', 'ManagersHalf.csv', 'Master.csv', 'Parks.csv', 'Pitching.csv', 'PitchingPost.csv', 'readme2014.txt', 'Salaries.csv', 'Schools.csv', 'SeriesPost.csv', 'Teams.csv', 'TeamsFranchises.csv', 'TeamsHalf.csv']\n"
     ]
    }
   ],
   "source": [
    "z = zipfile.ZipFile(\"data/baseballdatabank-2017.1.zip\")\n",
    "# extract into the data dir\n",
    "z.extractall(\"data\")\n",
    "z.close()\n",
    "data_dir = \"data/baseballdatabank-2017.1/core/\"\n",
    "print(os.listdir(data_dir))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CSV file name | Name of pandas DataFrame\n",
    ":---: | :---: \n",
    "Teams.csv | teams\n",
    "Batting.csv | players\n",
    "Salaries.csv | salaries\n",
    "Fielding.csv | fielding\n",
    "Master.csv | master"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "read_files = ['Teams.csv', 'Batting.csv', 'Salaries.csv', 'Fielding.csv', 'Master.csv']\n",
    "teams, players, salaries, fielding, master = (pd.read_csv(data_dir + f) for f in read_files)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Problem 1(b)\n",
    "\n",
    "Calculate the median salary for each player and create a pandas DataFrame called `medianSalaries` with four columns: (1) the player ID, (2) the first name of the player, (3) the last name of the player and (4) the median salary of the player. Show the head of the `medianSalaries` DataFrame.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yearID</th>\n",
       "      <th>teamID</th>\n",
       "      <th>lgID</th>\n",
       "      <th>playerID</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1985</td>\n",
       "      <td>ATL</td>\n",
       "      <td>NL</td>\n",
       "      <td>barkele01</td>\n",
       "      <td>870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1985</td>\n",
       "      <td>ATL</td>\n",
       "      <td>NL</td>\n",
       "      <td>bedrost01</td>\n",
       "      <td>550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1985</td>\n",
       "      <td>ATL</td>\n",
       "      <td>NL</td>\n",
       "      <td>benedbr01</td>\n",
       "      <td>545000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1985</td>\n",
       "      <td>ATL</td>\n",
       "      <td>NL</td>\n",
       "      <td>campri01</td>\n",
       "      <td>633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1985</td>\n",
       "      <td>ATL</td>\n",
       "      <td>NL</td>\n",
       "      <td>ceronri01</td>\n",
       "      <td>625000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   yearID teamID lgID   playerID  salary\n",
       "0    1985    ATL   NL  barkele01  870000\n",
       "1    1985    ATL   NL  bedrost01  550000\n",
       "2    1985    ATL   NL  benedbr01  545000\n",
       "3    1985    ATL   NL   campri01  633333\n",
       "4    1985    ATL   NL  ceronri01  625000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Your code here ###\n",
    "salaries.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aardsda01</td>\n",
       "      <td>David</td>\n",
       "      <td>Aardsma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>Hank</td>\n",
       "      <td>Aaron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>aaronto01</td>\n",
       "      <td>Tommie</td>\n",
       "      <td>Aaron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>aasedo01</td>\n",
       "      <td>Don</td>\n",
       "      <td>Aase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>abadan01</td>\n",
       "      <td>Andy</td>\n",
       "      <td>Abad</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID nameFirst nameLast\n",
       "0  aardsda01     David  Aardsma\n",
       "1  aaronha01      Hank    Aaron\n",
       "2  aaronto01    Tommie    Aaron\n",
       "3   aasedo01       Don     Aase\n",
       "4   abadan01      Andy     Abad"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master[[\"playerID\", \"nameFirst\", \"nameLast\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First to group the salaries by median:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5155, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>yearID</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aardsda01</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>419000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aasedo01</td>\n",
       "      <td>1987.5</td>\n",
       "      <td>612500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abadan01</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>327000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>abadfe01</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>525900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>abbotje01</td>\n",
       "      <td>1999.5</td>\n",
       "      <td>255000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID  yearID    salary\n",
       "0  aardsda01  2009.0  419000.0\n",
       "1   aasedo01  1987.5  612500.0\n",
       "2   abadan01  2006.0  327000.0\n",
       "3   abadfe01  2014.0  525900.0\n",
       "4  abbotje01  1999.5  255000.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medianSalaries = salaries.groupby(\"playerID\").median().reset_index()\n",
    "print(medianSalaries.shape)\n",
    "medianSalaries.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now to add in the first and last name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5147, 5)\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>yearID</th>\n",
       "      <th>salary</th>\n",
       "      <th>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aardsda01</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>419000.0</td>\n",
       "      <td>David</td>\n",
       "      <td>Aardsma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aasedo01</td>\n",
       "      <td>1987.5</td>\n",
       "      <td>612500.0</td>\n",
       "      <td>Don</td>\n",
       "      <td>Aase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abadan01</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>327000.0</td>\n",
       "      <td>Andy</td>\n",
       "      <td>Abad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>abadfe01</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>525900.0</td>\n",
       "      <td>Fernando</td>\n",
       "      <td>Abad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>abbotje01</td>\n",
       "      <td>1999.5</td>\n",
       "      <td>255000.0</td>\n",
       "      <td>Jeff</td>\n",
       "      <td>Abbott</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID  yearID    salary nameFirst nameLast\n",
       "0  aardsda01  2009.0  419000.0     David  Aardsma\n",
       "1   aasedo01  1987.5  612500.0       Don     Aase\n",
       "2   abadan01  2006.0  327000.0      Andy     Abad\n",
       "3   abadfe01  2014.0  525900.0  Fernando     Abad\n",
       "4  abbotje01  1999.5  255000.0      Jeff   Abbott"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medianSalaries = medianSalaries.merge(master[[\"playerID\", \"nameFirst\", \"nameLast\"]], on=\"playerID\")\n",
    "print(medianSalaries.shape)\n",
    "medianSalaries.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "medianSalaries.drop([\"yearID\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5147, 4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>salary</th>\n",
       "      <th>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aardsda01</td>\n",
       "      <td>419000.0</td>\n",
       "      <td>David</td>\n",
       "      <td>Aardsma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aasedo01</td>\n",
       "      <td>612500.0</td>\n",
       "      <td>Don</td>\n",
       "      <td>Aase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abadan01</td>\n",
       "      <td>327000.0</td>\n",
       "      <td>Andy</td>\n",
       "      <td>Abad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>abadfe01</td>\n",
       "      <td>525900.0</td>\n",
       "      <td>Fernando</td>\n",
       "      <td>Abad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>abbotje01</td>\n",
       "      <td>255000.0</td>\n",
       "      <td>Jeff</td>\n",
       "      <td>Abbott</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID    salary nameFirst nameLast\n",
       "0  aardsda01  419000.0     David  Aardsma\n",
       "1   aasedo01  612500.0       Don     Aase\n",
       "2   abadan01  327000.0      Andy     Abad\n",
       "3   abadfe01  525900.0  Fernando     Abad\n",
       "4  abbotje01  255000.0      Jeff   Abbott"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(medianSalaries.shape)\n",
    "medianSalaries.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Problem 1(c)\n",
    "\n",
    "Now, consider only team/season combinations in which the teams played 162 Games. Exclude all data from before 1947. Compute the per plate appearance rates for singles, doubles, triples, HR, and BB. Create a new pandas DataFrame called `stats` that has the teamID, yearID, wins and these rates.\n",
    "\n",
    "**Hint**: Singles are hits that are not doubles, triples, nor HR. Plate appearances are base on balls plus at bats."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "looking at the [readme file](http://seanlahman.com/files/database/readme2016.txt) for the data needed for this q: \n",
    "\n",
    "teams table:\n",
    " - \"G\" is games played\n",
    " - \"W\" is wins\n",
    " - \"yearID\" is the year of the game\n",
    " - \"H\" is all hits\n",
    " - \"2B\" is doubles\n",
    " - \"3B\" is triples\n",
    " - \"HR\" is homeruns\n",
    " - \"BB\" is base on balls\n",
    " - \"AB\" is at bats\n",
    "\n",
    "So first, getting a df for all teams which played 162 games from 1947 onwards:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1068, 48)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yearID</th>\n",
       "      <th>lgID</th>\n",
       "      <th>teamID</th>\n",
       "      <th>franchID</th>\n",
       "      <th>divID</th>\n",
       "      <th>Rank</th>\n",
       "      <th>G</th>\n",
       "      <th>Ghome</th>\n",
       "      <th>W</th>\n",
       "      <th>L</th>\n",
       "      <th>...</th>\n",
       "      <th>DP</th>\n",
       "      <th>FP</th>\n",
       "      <th>name</th>\n",
       "      <th>park</th>\n",
       "      <th>attendance</th>\n",
       "      <th>BPF</th>\n",
       "      <th>PPF</th>\n",
       "      <th>teamIDBR</th>\n",
       "      <th>teamIDlahman45</th>\n",
       "      <th>teamIDretro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1366</th>\n",
       "      <td>1961</td>\n",
       "      <td>AL</td>\n",
       "      <td>KC1</td>\n",
       "      <td>OAK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>162</td>\n",
       "      <td>80.0</td>\n",
       "      <td>61</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>160.0</td>\n",
       "      <td>0.972</td>\n",
       "      <td>Kansas City Athletics</td>\n",
       "      <td>Municipal Stadium I</td>\n",
       "      <td>683817.0</td>\n",
       "      <td>101</td>\n",
       "      <td>103</td>\n",
       "      <td>KCA</td>\n",
       "      <td>KC1</td>\n",
       "      <td>KC1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1367</th>\n",
       "      <td>1961</td>\n",
       "      <td>AL</td>\n",
       "      <td>LAA</td>\n",
       "      <td>ANA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>162</td>\n",
       "      <td>82.0</td>\n",
       "      <td>70</td>\n",
       "      <td>91</td>\n",
       "      <td>...</td>\n",
       "      <td>154.0</td>\n",
       "      <td>0.969</td>\n",
       "      <td>Los Angeles Angels</td>\n",
       "      <td>Wrigley Field (LA)</td>\n",
       "      <td>603510.0</td>\n",
       "      <td>111</td>\n",
       "      <td>112</td>\n",
       "      <td>LAA</td>\n",
       "      <td>LAA</td>\n",
       "      <td>LAA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1377</th>\n",
       "      <td>1962</td>\n",
       "      <td>AL</td>\n",
       "      <td>BAL</td>\n",
       "      <td>BAL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>162</td>\n",
       "      <td>82.0</td>\n",
       "      <td>77</td>\n",
       "      <td>85</td>\n",
       "      <td>...</td>\n",
       "      <td>152.0</td>\n",
       "      <td>0.980</td>\n",
       "      <td>Baltimore Orioles</td>\n",
       "      <td>Memorial Stadium</td>\n",
       "      <td>790254.0</td>\n",
       "      <td>94</td>\n",
       "      <td>93</td>\n",
       "      <td>BAL</td>\n",
       "      <td>BAL</td>\n",
       "      <td>BAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1379</th>\n",
       "      <td>1962</td>\n",
       "      <td>AL</td>\n",
       "      <td>CHA</td>\n",
       "      <td>CHW</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>85</td>\n",
       "      <td>77</td>\n",
       "      <td>...</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0.982</td>\n",
       "      <td>Chicago White Sox</td>\n",
       "      <td>Comiskey Park</td>\n",
       "      <td>1131562.0</td>\n",
       "      <td>100</td>\n",
       "      <td>99</td>\n",
       "      <td>CHW</td>\n",
       "      <td>CHA</td>\n",
       "      <td>CHA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1380</th>\n",
       "      <td>1962</td>\n",
       "      <td>NL</td>\n",
       "      <td>CHN</td>\n",
       "      <td>CHC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>59</td>\n",
       "      <td>103</td>\n",
       "      <td>...</td>\n",
       "      <td>171.0</td>\n",
       "      <td>0.977</td>\n",
       "      <td>Chicago Cubs</td>\n",
       "      <td>Wrigley Field</td>\n",
       "      <td>609802.0</td>\n",
       "      <td>104</td>\n",
       "      <td>105</td>\n",
       "      <td>CHC</td>\n",
       "      <td>CHN</td>\n",
       "      <td>CHN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      yearID lgID teamID franchID divID  Rank    G  Ghome   W    L  \\\n",
       "1366    1961   AL    KC1      OAK   NaN     9  162   80.0  61  100   \n",
       "1367    1961   AL    LAA      ANA   NaN     8  162   82.0  70   91   \n",
       "1377    1962   AL    BAL      BAL   NaN     7  162   82.0  77   85   \n",
       "1379    1962   AL    CHA      CHW   NaN     5  162   81.0  85   77   \n",
       "1380    1962   NL    CHN      CHC   NaN     9  162   81.0  59  103   \n",
       "\n",
       "         ...          DP     FP                   name                 park  \\\n",
       "1366     ...       160.0  0.972  Kansas City Athletics  Municipal Stadium I   \n",
       "1367     ...       154.0  0.969     Los Angeles Angels   Wrigley Field (LA)   \n",
       "1377     ...       152.0  0.980      Baltimore Orioles     Memorial Stadium   \n",
       "1379     ...       153.0  0.982      Chicago White Sox        Comiskey Park   \n",
       "1380     ...       171.0  0.977           Chicago Cubs        Wrigley Field   \n",
       "\n",
       "      attendance  BPF  PPF  teamIDBR  teamIDlahman45  teamIDretro  \n",
       "1366    683817.0  101  103       KCA             KC1          KC1  \n",
       "1367    603510.0  111  112       LAA             LAA          LAA  \n",
       "1377    790254.0   94   93       BAL             BAL          BAL  \n",
       "1379   1131562.0  100   99       CHW             CHA          CHA  \n",
       "1380    609802.0  104  105       CHC             CHN          CHN  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Your code here ###\n",
    "stats = teams[teams[\"yearID\"] >= 1947]\n",
    "stats = stats[stats[\"G\"]==162]\n",
    "print(stats.shape)\n",
    "stats.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now to drop all the un-needed columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>teamID</th>\n",
       "      <th>yearID</th>\n",
       "      <th>W</th>\n",
       "      <th>H</th>\n",
       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
       "      <th>HR</th>\n",
       "      <th>BB</th>\n",
       "      <th>AB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1366</th>\n",
       "      <td>KC1</td>\n",
       "      <td>1961</td>\n",
       "      <td>61</td>\n",
       "      <td>1342</td>\n",
       "      <td>216</td>\n",
       "      <td>47</td>\n",
       "      <td>90</td>\n",
       "      <td>580</td>\n",
       "      <td>5423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1367</th>\n",
       "      <td>LAA</td>\n",
       "      <td>1961</td>\n",
       "      <td>70</td>\n",
       "      <td>1331</td>\n",
       "      <td>218</td>\n",
       "      <td>22</td>\n",
       "      <td>189</td>\n",
       "      <td>681</td>\n",
       "      <td>5424</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     teamID  yearID   W     H   2B  3B   HR   BB    AB\n",
       "1366    KC1    1961  61  1342  216  47   90  580  5423\n",
       "1367    LAA    1961  70  1331  218  22  189  681  5424"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cols = [\"teamID\", \"yearID\", \"W\", \"H\", \"2B\", \"3B\", \"HR\", \"BB\", \"AB\"]\n",
    "stats = stats[data_cols]\n",
    "stats.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>teamID</th>\n",
       "      <th>yearID</th>\n",
       "      <th>W</th>\n",
       "      <th>H</th>\n",
       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
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       "      <th>BB</th>\n",
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       "      <th>singles</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>1366</th>\n",
       "      <td>KC1</td>\n",
       "      <td>1961</td>\n",
       "      <td>61</td>\n",
       "      <td>1342</td>\n",
       "      <td>216</td>\n",
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       "      <td>580</td>\n",
       "      <td>5423</td>\n",
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       "      <td>6003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1367</th>\n",
       "      <td>LAA</td>\n",
       "      <td>1961</td>\n",
       "      <td>70</td>\n",
       "      <td>1331</td>\n",
       "      <td>218</td>\n",
       "      <td>22</td>\n",
       "      <td>189</td>\n",
       "      <td>681</td>\n",
       "      <td>5424</td>\n",
       "      <td>902</td>\n",
       "      <td>6105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1377</th>\n",
       "      <td>BAL</td>\n",
       "      <td>1962</td>\n",
       "      <td>77</td>\n",
       "      <td>1363</td>\n",
       "      <td>225</td>\n",
       "      <td>34</td>\n",
       "      <td>156</td>\n",
       "      <td>516</td>\n",
       "      <td>5491</td>\n",
       "      <td>948</td>\n",
       "      <td>6007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1379</th>\n",
       "      <td>CHA</td>\n",
       "      <td>1962</td>\n",
       "      <td>85</td>\n",
       "      <td>1415</td>\n",
       "      <td>250</td>\n",
       "      <td>56</td>\n",
       "      <td>92</td>\n",
       "      <td>620</td>\n",
       "      <td>5514</td>\n",
       "      <td>1017</td>\n",
       "      <td>6134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1380</th>\n",
       "      <td>CHN</td>\n",
       "      <td>1962</td>\n",
       "      <td>59</td>\n",
       "      <td>1398</td>\n",
       "      <td>196</td>\n",
       "      <td>56</td>\n",
       "      <td>126</td>\n",
       "      <td>504</td>\n",
       "      <td>5534</td>\n",
       "      <td>1020</td>\n",
       "      <td>6038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     teamID  yearID   W     H   2B  3B   HR   BB    AB  singles  plates\n",
       "1366    KC1    1961  61  1342  216  47   90  580  5423      989    6003\n",
       "1367    LAA    1961  70  1331  218  22  189  681  5424      902    6105\n",
       "1377    BAL    1962  77  1363  225  34  156  516  5491      948    6007\n",
       "1379    CHA    1962  85  1415  250  56   92  620  5514     1017    6134\n",
       "1380    CHN    1962  59  1398  196  56  126  504  5534     1020    6038"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats[\"singles\"] = stats[\"H\"] - stats[\"2B\"] - stats[\"3B\"] - stats[\"HR\"]\n",
    "stats[\"plates\"] = stats[\"BB\"] + stats[\"AB\"]\n",
    "stats.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the per plate appearance rates for singles, doubles, triples, HR, and BB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>teamID</th>\n",
       "      <th>yearID</th>\n",
       "      <th>W</th>\n",
       "      <th>H</th>\n",
       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
       "      <th>HR</th>\n",
       "      <th>BB</th>\n",
       "      <th>AB</th>\n",
       "      <th>singles</th>\n",
       "      <th>plates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1366</th>\n",
       "      <td>KC1</td>\n",
       "      <td>1961</td>\n",
       "      <td>61</td>\n",
       "      <td>1342</td>\n",
       "      <td>0.035982</td>\n",
       "      <td>0.007829</td>\n",
       "      <td>0.014993</td>\n",
       "      <td>0.096618</td>\n",
       "      <td>5423</td>\n",
       "      <td>0.164751</td>\n",
       "      <td>6003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1367</th>\n",
       "      <td>LAA</td>\n",
       "      <td>1961</td>\n",
       "      <td>70</td>\n",
       "      <td>1331</td>\n",
       "      <td>0.035708</td>\n",
       "      <td>0.003604</td>\n",
       "      <td>0.030958</td>\n",
       "      <td>0.111548</td>\n",
       "      <td>5424</td>\n",
       "      <td>0.147748</td>\n",
       "      <td>6105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1377</th>\n",
       "      <td>BAL</td>\n",
       "      <td>1962</td>\n",
       "      <td>77</td>\n",
       "      <td>1363</td>\n",
       "      <td>0.037456</td>\n",
       "      <td>0.005660</td>\n",
       "      <td>0.025970</td>\n",
       "      <td>0.085900</td>\n",
       "      <td>5491</td>\n",
       "      <td>0.157816</td>\n",
       "      <td>6007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1379</th>\n",
       "      <td>CHA</td>\n",
       "      <td>1962</td>\n",
       "      <td>85</td>\n",
       "      <td>1415</td>\n",
       "      <td>0.040756</td>\n",
       "      <td>0.009129</td>\n",
       "      <td>0.014998</td>\n",
       "      <td>0.101076</td>\n",
       "      <td>5514</td>\n",
       "      <td>0.165797</td>\n",
       "      <td>6134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1380</th>\n",
       "      <td>CHN</td>\n",
       "      <td>1962</td>\n",
       "      <td>59</td>\n",
       "      <td>1398</td>\n",
       "      <td>0.032461</td>\n",
       "      <td>0.009275</td>\n",
       "      <td>0.020868</td>\n",
       "      <td>0.083471</td>\n",
       "      <td>5534</td>\n",
       "      <td>0.168930</td>\n",
       "      <td>6038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     teamID  yearID   W     H        2B        3B        HR        BB    AB  \\\n",
       "1366    KC1    1961  61  1342  0.035982  0.007829  0.014993  0.096618  5423   \n",
       "1367    LAA    1961  70  1331  0.035708  0.003604  0.030958  0.111548  5424   \n",
       "1377    BAL    1962  77  1363  0.037456  0.005660  0.025970  0.085900  5491   \n",
       "1379    CHA    1962  85  1415  0.040756  0.009129  0.014998  0.101076  5514   \n",
       "1380    CHN    1962  59  1398  0.032461  0.009275  0.020868  0.083471  5534   \n",
       "\n",
       "       singles  plates  \n",
       "1366  0.164751    6003  \n",
       "1367  0.147748    6105  \n",
       "1377  0.157816    6007  \n",
       "1379  0.165797    6134  \n",
       "1380  0.168930    6038  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in [\"singles\",\"2B\",\"3B\",\"HR\",\"BB\"]:\n",
    "    stats[col] = stats[col] / stats[\"plates\"]\n",
    "stats.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the final stats is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>teamID</th>\n",
       "      <th>yearID</th>\n",
       "      <th>W</th>\n",
       "      <th>singles</th>\n",
       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
       "      <th>HR</th>\n",
       "      <th>BB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1366</th>\n",
       "      <td>KC1</td>\n",
       "      <td>1961</td>\n",
       "      <td>61</td>\n",
       "      <td>0.164751</td>\n",
       "      <td>0.035982</td>\n",
       "      <td>0.007829</td>\n",
       "      <td>0.014993</td>\n",
       "      <td>0.096618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1367</th>\n",
       "      <td>LAA</td>\n",
       "      <td>1961</td>\n",
       "      <td>70</td>\n",
       "      <td>0.147748</td>\n",
       "      <td>0.035708</td>\n",
       "      <td>0.003604</td>\n",
       "      <td>0.030958</td>\n",
       "      <td>0.111548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1377</th>\n",
       "      <td>BAL</td>\n",
       "      <td>1962</td>\n",
       "      <td>77</td>\n",
       "      <td>0.157816</td>\n",
       "      <td>0.037456</td>\n",
       "      <td>0.005660</td>\n",
       "      <td>0.025970</td>\n",
       "      <td>0.085900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1379</th>\n",
       "      <td>CHA</td>\n",
       "      <td>1962</td>\n",
       "      <td>85</td>\n",
       "      <td>0.165797</td>\n",
       "      <td>0.040756</td>\n",
       "      <td>0.009129</td>\n",
       "      <td>0.014998</td>\n",
       "      <td>0.101076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1380</th>\n",
       "      <td>CHN</td>\n",
       "      <td>1962</td>\n",
       "      <td>59</td>\n",
       "      <td>0.168930</td>\n",
       "      <td>0.032461</td>\n",
       "      <td>0.009275</td>\n",
       "      <td>0.020868</td>\n",
       "      <td>0.083471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     teamID  yearID   W   singles        2B        3B        HR        BB\n",
       "1366    KC1    1961  61  0.164751  0.035982  0.007829  0.014993  0.096618\n",
       "1367    LAA    1961  70  0.147748  0.035708  0.003604  0.030958  0.111548\n",
       "1377    BAL    1962  77  0.157816  0.037456  0.005660  0.025970  0.085900\n",
       "1379    CHA    1962  85  0.165797  0.040756  0.009129  0.014998  0.101076\n",
       "1380    CHN    1962  59  0.168930  0.032461  0.009275  0.020868  0.083471"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats = stats[[\"teamID\", \"yearID\", \"W\", \"singles\",\"2B\",\"3B\",\"HR\",\"BB\"]]\n",
    "stats.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Problem 1(d)\n",
    "\n",
    "Is there a noticeable time trend in the rates computed computed in Problem 1(c)? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x140dd53c8>"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5afD3tbsmJoq5yCfi2mYHd9+6kNieP/6KuyiDaDS76Jk2Jop9oZucrqJS0jE3XUTXtHFg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z9/+8RyCdduVAFYaLYMFPNKIIfafaCE4QmY7ig+PCoOvz24uex+REGILtFH9yrsw75i8wbIYSYY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CFeA+DJ2L6nAFwnhNgrhKjAd3s81uc9PxRC3BH8/RYAD4+99YQQsg1M2qK+D8CbhBCPwp8A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UX2WsyQ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\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BiH4GslexRKAx0Ws/smwv63eB0WUNfOuFy3jp4g0AgObna+/sWt4AJPNBNxN/+NneLoc0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\nKBSKjKMMtUKhUGSc/x/hk7+YZB0MqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x140dd5518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Your code here ###\n",
    "sns.pairplot(data=stats, x_vars=\"yearID\", \n",
    "             y_vars=[\"W\", \"singles\", \"2B\", \"3B\", \"HR\", \"BB\"], size=5, kind=\"reg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the pairplots It looks like the singles and third base hits are decreasing over time while the second base hits are going up. Home runs interesting enough have a sudden shift upwards - probably linked to some kind of rule change, like allowing heavier bats or a more hittable ball."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Problem 1(e) \n",
    "\n",
    "Using the `stats` DataFrame from Problem 1(c), adjust the singles per PA rates so that the average across teams for each year is 0. Do the same for the doubles, triples, HR, and BB rates. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>W</th>\n",
       "      <th>singles</th>\n",
       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
       "      <th>HR</th>\n",
       "      <th>BB</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yearID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <td>65.500000</td>\n",
       "      <td>0.156249</td>\n",
       "      <td>0.035845</td>\n",
       "      <td>0.005717</td>\n",
       "      <td>0.022975</td>\n",
       "      <td>0.104083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>78.454545</td>\n",
       "      <td>0.165632</td>\n",
       "      <td>0.035853</td>\n",
       "      <td>0.006777</td>\n",
       "      <td>0.023811</td>\n",
       "      <td>0.088590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>78.142857</td>\n",
       "      <td>0.162467</td>\n",
       "      <td>0.034020</td>\n",
       "      <td>0.006896</td>\n",
       "      <td>0.021254</td>\n",
       "      <td>0.080336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>81.727273</td>\n",
       "      <td>0.167251</td>\n",
       "      <td>0.036336</td>\n",
       "      <td>0.006748</td>\n",
       "      <td>0.021548</td>\n",
       "      <td>0.079152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965</th>\n",
       "      <td>82.000000</td>\n",
       "      <td>0.160042</td>\n",
       "      <td>0.035539</td>\n",
       "      <td>0.006534</td>\n",
       "      <td>0.022693</td>\n",
       "      <td>0.085745</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                W   singles        2B        3B        HR        BB\n",
       "yearID                                                             \n",
       "1961    65.500000  0.156249  0.035845  0.005717  0.022975  0.104083\n",
       "1962    78.454545  0.165632  0.035853  0.006777  0.023811  0.088590\n",
       "1963    78.142857  0.162467  0.034020  0.006896  0.021254  0.080336\n",
       "1964    81.727273  0.167251  0.036336  0.006748  0.021548  0.079152\n",
       "1965    82.000000  0.160042  0.035539  0.006534  0.022693  0.085745"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Your code here ###\n",
    "# to get the mean for each year:\n",
    "stats_mean_by_year = stats.groupby(\"yearID\").mean()\n",
    "stats_mean_by_year.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I tried sklearns preprocessing, but that doesn't work in this case, so we need our own function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>teamID</th>\n",
       "      <th>yearID</th>\n",
       "      <th>W</th>\n",
       "      <th>singles</th>\n",
       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
       "      <th>HR</th>\n",
       "      <th>BB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1366</th>\n",
       "      <td>KC1</td>\n",
       "      <td>1961</td>\n",
       "      <td>61</td>\n",
       "      <td>0.008502</td>\n",
       "      <td>0.000137</td>\n",
       "      <td>0.002113</td>\n",
       "      <td>-0.007983</td>\n",
       "      <td>-0.007465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1367</th>\n",
       "      <td>LAA</td>\n",
       "      <td>1961</td>\n",
       "      <td>70</td>\n",
       "      <td>-0.008502</td>\n",
       "      <td>-0.000137</td>\n",
       "      <td>-0.002113</td>\n",
       "      <td>0.007983</td>\n",
       "      <td>0.007465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1377</th>\n",
       "      <td>BAL</td>\n",
       "      <td>1962</td>\n",
       "      <td>77</td>\n",
       "      <td>-0.007816</td>\n",
       "      <td>0.001604</td>\n",
       "      <td>-0.001117</td>\n",
       "      <td>0.002158</td>\n",
       "      <td>-0.002690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1379</th>\n",
       "      <td>CHA</td>\n",
       "      <td>1962</td>\n",
       "      <td>85</td>\n",
       "      <td>0.000165</td>\n",
       "      <td>0.004904</td>\n",
       "      <td>0.002352</td>\n",
       "      <td>-0.008813</td>\n",
       "      <td>0.012486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1380</th>\n",
       "      <td>CHN</td>\n",
       "      <td>1962</td>\n",
       "      <td>59</td>\n",
       "      <td>0.003298</td>\n",
       "      <td>-0.003391</td>\n",
       "      <td>0.002497</td>\n",
       "      <td>-0.002944</td>\n",
       "      <td>-0.005119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     teamID  yearID   W   singles        2B        3B        HR        BB\n",
       "1366    KC1    1961  61  0.008502  0.000137  0.002113 -0.007983 -0.007465\n",
       "1367    LAA    1961  70 -0.008502 -0.000137 -0.002113  0.007983  0.007465\n",
       "1377    BAL    1962  77 -0.007816  0.001604 -0.001117  0.002158 -0.002690\n",
       "1379    CHA    1962  85  0.000165  0.004904  0.002352 -0.008813  0.012486\n",
       "1380    CHN    1962  59  0.003298 -0.003391  0.002497 -0.002944 -0.005119"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def meanNormalizeRates(df):\n",
    "        subRates = df[[\"singles\",\"2B\",\"3B\",\"HR\",\"BB\"]]\n",
    "        df[[\"singles\",\"2B\",\"3B\",\"HR\",\"BB\"]] = subRates - subRates.mean(axis=0)\n",
    "        return df\n",
    "\n",
    "statsNorm = stats.groupby('yearID').apply(meanNormalizeRates)\n",
    "statsNorm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem 1(f)\n",
    "\n",
    "Build a simple linear regression model to predict the number of wins from the average adjusted singles, double, triples, HR, and BB rates. To decide which of these terms to include fit the model to data from 2002 and compute the average squared residuals from predictions to years past 2002. Use the fitted model to define a new sabermetric summary: offensive predicted wins (OPW). Hint: the new summary should be a linear combination of one to five of the five rates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(684,) (384,) (684,) (384,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "83.049337451741621"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Your code here ###\n",
    "from sklearn import linear_model\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# the test train datasets\n",
    "mask = statsNorm[\"yearID\"]<=2002\n",
    "xtrain = statsNorm[[\"singles\",\"2B\",\"3B\",\"HR\",\"BB\"]][mask]\n",
    "xtest = statsNorm[[\"singles\",\"2B\",\"3B\",\"HR\",\"BB\"]][~mask]\n",
    "ytrain = statsNorm[\"W\"][mask]\n",
    "ytest = statsNorm[\"W\"][~mask]\n",
    "print(ytrain.shape,ytest.shape, regr.predict(xtrain).shape, regr.predict(xtest).shape)\n",
    "\n",
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(xtrain, ytrain)\n",
    "\n",
    "mean_squared_error(ytest, regr.predict(xtest))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taking a look to see how far the predictions are from the actual:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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19uXUEIeDLmj7IZQs4l9trVMtTNy2/T/LpdGhSLdWgSHd94V3v4uyLYwZj7Moqtik7jUT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\nrENc1M3MOsRF3cysQ/4F2sQf5aaiumEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14b5c7c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(ytest, regr.predict(xtest))\n",
    "sns.regplot(ytrain, regr.predict(xtrain));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x14b99a630>]"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sR97NY6KQR8VJ+trcqQrm5yv4/vIFwAcCtJPxOFVEhRXgeUTWI18kCI2+VakU\nMD1VxMxMGZU+v3d+3l5XAOgGHiorSR3OnN5VRVpl2DOGyX6V/f8FjuZfA/AGgPcBPKacrwBIZ38y\npF7v7HZJtkQU3W5C06yvb6Nf4J2s2eQIrka3UZtsyr/ltRs7aDd7KHcDbDfa2F66CyyuoHn2FNxB\nKMut11uo1RKDlVnOVGeAXDdAu9NHs9nDWm0b7XYf3SCAE1E0mz0Umj1ZXqvZQxABO6SLWi2pVz7s\nIt/pId8NEAUUOzscRQxqTWxutZL21Bro55OBFESBPLex0cJOv5Fcu9GA3+Odor7dhtsN0Gr20fYY\nmkWCkIZa29T2TXcGCLsB2p0AW/Xk+esbO6h32vLvOmnJtu00OqiwLoL1HbB+Gq5u1XbktbX1HQQl\nD/XtduqdAsDM/AxqtSaCraR8L+xhs9YAdcvyGNtuo3ThFnrdPpzFFew8fkrrEwDQbvbQdT3ssA5a\ngxaagx420ZBtbzZ7AKVotfvY3myi36mjUttGu13Gjt/FwHhH3WYXFQDdboBWs4cwyEae7qCBZr8H\nv91HTrQ9Lq/X6QMM6DZ7WF3flu9B1L/T6KLZ7IH6Lmq1JurNNrrdAMJO3W520Y+XBRubSV8KVjaB\nBaB+8y7CcpJaIoyCpB82uggDilqtia16C712T1sBNRtdgBC4zS4KYizstNHs9dDtDOCEETrtPlq0\ni26X90HRrkajq+Whr20kfXLb62CtsCPfe73eRs1rAv0+Gk1eVkiBdr2t9JVtkIFduT3YjO05hGDL\nUfpprQHP8eTfA5eB9lpoKt5b2/UuakjrhkqlgPo2L8vvJGO3u1UHjL7VaHbhOh3U654yRvX+okon\n6GjPG3ZtlszPV1CrNfel8PdL43wFwM+r1ervAvhbAEsAzi8sLHwrPv9t8IngyMRc3tl8mbNS+Uax\nNw7Al2zldz/A/NIKSBRpNMTS1nWstO5by7CJmrtElE80iib+d8j2hykaZ4iBVt2EhEFfHlNGcWtn\nCW+uvKEtYaXr5YieRmZQlerFoC6XV9sPcG3rSmbyOFgS12W51XmWwC8bjcMoNfLzWJ5t4exNTyTC\ngAftFdzbuYMnPro5dB9YRHb7jiqFnTbyzU7yfWzXseSHfI9D6DxBPQkPjixvHPWum9s38M6Dt4eW\nzWNBzLoJztpSYUlFjsZZm0FRVrpFDYKjTO5AByAVQ0MaO/DOvw8EyVgreaXMJIBJ7Y16pIylCcNg\nNdAOoXH7hi57AAAgAElEQVSOIzPnfmS/yP4GgP9hYWHhX4Ej+P8MwASAv1pYWMgBuArg7w6ninYx\nPWUioUyUzpSVylc10FJG5UAilKU8Pl6+8zMtWdowCWkI6hIgUNIkq8VleONwIUkd1FuGdBB1ExKT\nC2VgeO3eqyARBWEMz4Bz4dz1Mju2QK2n+XxKqcHZK2l8Bw00Bg24O7fw9MypdJFaeovsrIfq8dSg\nNZUmpXAcxQg6xBBKGZUG4LSrLb/meu2ybJ1sZxgCnhc/gnG3uziRWJaR9LHLtwAAd393Si3eKoTB\nruyNfjCIJ6ick+cgRjPP2CYThsvvfB/FnTYefPtJzPkz2jMBpHZSS87HR1XFLG0yitJn8mTyWLM8\no27WiFgjWaCjAgPD/uC99w5IEMJdvi2PFXdR9oSQdKZZxrRJ33VcqUNsOZaCcACTsNtrDID6bnJu\nNv13VLIvZV+tVjdgN8D+/sGqM7qkXLRoCLTbyL3+S0yRDew8PpeZs4PSiA9a8I4hLPUOpToS3+PG\nFwENQGP3z0TZK+UJK/8wA61p0NI8VkwFoO9Jatso5dm3lT1fiQiqikZD9jFl4IQRJmrbiE7r79Pq\nARVm5EkxkT2lGjoDgM+c+qx0heSNUpE91ZVCfFTzlx7iHSKpBUpBWi3tnFxx2QbvYJAoe8SKyHdE\nQ9LXa2AjyC5X1pnJ96gi6Uxk7/pAkPaoSdWHUpTqLRQabXQGTTB3MlVHc4/k5Nlcj6u9VI43xUAr\nxBkyTqhRTxuyV1fTBNANxoayJ7GBXA1yKnpFzdtmlNTJDEyu9FJ1tnjjUEuU/kHSVPzL5/90b/ce\ngpzYoCoYHgRXNi/CWecBOLNxIE5W4FDIMrxxKNMU/NBBapGADkBj9z+h7Pc8eTBgsX49SdY0hMYx\nN2QeJWWwyEFf79VxeePSrkqfgWHu1gPM3XqA3NItffKx3HtrZ9GaLE5Ll0Ap/F+9jtNvvK+176nK\nU1rd1QmdMaSVK6Oax4utzeKdC0+cM9fv4sz7l0G266lrbEICxUsj1BG1LdeRk+HZklk+mNUVNI3s\n+cQhA3Es/YJQvrq5s72MxmBHKtF+0Et5rwEC6AyZsCxulhKw0OReNSbACSOcur0Kd8Drq1FxzO41\np9M4FG6o9pVkDFe3rmG9vYblxh20BgnfTQhB8f6qdIE2JzACYlf2SpoH2yZBOt2VMU72oCPEd5rI\nTchd4x6mnPh89g4hoIzhfuMe7jtlPB0fA7J9vyOqbGnoAFEYl0WZwbHvHdkLpwYz9FyV3VKuLjeX\nsdJewdewBxqn1UDh6kU4pRDU90BBUfbL+nNB4MaPvrR+Cc1mDwEd4DcmPgvv0kX4pIegVNB8wBmY\nzJZIOm2tPjY7yU5vGy8u/ShNfakIjVGQdpujdcaki6fYzEEMtogaaM38HoZiDS0rOdEWgeLKW01Q\nNw/SaIBNC2rDgm4Frh0oq6cgHeFrihPYMlxqnEuqjEQBqsDAaBsiuAB8J5dZJmEMzaCJqxtXsNla\nghtPGEHY14P7aKLss2gcozpJnifRNSiVUFFF4jPLayiubCDf6eHB559J79ym0DjynNY39LYLl937\nzXt4+8GbeC52oQ7XIuBsSZYzeXMZtFeHE0agzw0AZXMX7VnylTEtJ5Y1IFEFfhZAsN9UDnvdTOWw\n5MQie4FUHJl/BDICc7fINMoSGidyklmbGDTOXmZtgG9YIdCeRPZqJxEDxWKgNblMsSoZbqBNFFH+\no4/grq8l29wxlvLj5UFV+rM3uxvwqlfh1Ldw+qbFGK1w4czR6xPaEtBlvTPNd1qdUJNLfCehS/h1\nhr+1hcZR30k4ZG9d3dBscLpDvjMJlTKN8m2Rsa5tkjf7lGGQ3I3GCWkIGlNWriXMP0H2OvctyhiE\nPWtwH99cJ05PkJ/GVH5Ke7aGbGmUsinI1NmKsndias7rW5C9bcLmFZFjggi7iDzH728M9PTZzYHq\nyZIEL87degC/eg2mmMp+WC4mm5/9KBlrd5Mk3mWs7PckYlnpSJTMUHS5f/puwQrcG4d3ohDJYHMo\nTdE4uXYP7vWqNVDFlLuNO5JasdI40pPBkgjNUGSCizajTFUZhD2JJOXkF0TyPtty1owcpIyChaE9\nx3ds9BX1joiBgKx52u2jgmn516P42QwqfJRtlshe2WTGpigoVd+OVdlnbdy9pezuZF08iX6lpGxm\nJmq33Odakb1xQEXZUHjtDMpvEA3AwPu1WBXqwX/pe4jy9yDsa4ZOSeOAyj11z5TOJhG/NK3sAabt\nl+uo1M0QW4lpR9INqel2A/qEKeo9bIWboqM66X117TSO7uCgngPsY9cswyab3U0tLxfAt3wUuZ9G\n2UzlKOQEK3v+8QKWKFXxElWjnY2TDmmYIHuiIPuIGiiMZ99zby3Bu3hh1zp1w65UOMMyZqY/tpFW\nAfGEwNhQZO8vLuKZd6/B7/TA4mRPgqeljKU4dQIChxFM36/Bj6kZBoZrW1fw4fp52JKYMZbYMRj0\n12OjcbJEnczU36oizN1YRGUtSX6lT4BpZc+oTkPYaJyslcYryy+jHe+2NBzZK5G/Bo1ju8+K7M36\naLTF7sh+EPUBQuA7nkxRbTPQmqtS8XcQ9jRjd2LPpSDx5EQIkfMbUcqQPynVs4lSvh4FkBmBC6SR\nvTWxnwGkHAtnn6JM1Fw2UahPKjmdD2csDXy4gdZO44iy9FV5Bo1jHO+GXfxk6Yf4/o0kx/9y4w7+\n8daL+OW9V3i5Y2S/RxG5bUSIPFUVpjJQLO6XkULjIArlsp4wg7OnDDSO8HXW1kajdaTRyj4A7bdY\nFFms3IZ54+Tv8OjZmfsbYA4BZVR6RlAb7UH4Djun7qzh3AfX4+cA9xrL8flkKS1OqsieppC9xbA4\nAo2T+MMrKylKkbtzB/M3k7S2zDDQpmgTRiG+NaHMvmNZ1icjfGBO36/hyfM3Mi6Clh5B5FpnYg9X\nS9mOddvC4TSOLSWAhuxpAEY4PSnUxOkb92ROf2b0OYBPHBqyVw20ituxULQcgBCt3sO2nSS7IXum\n9EN5iKU4fP4cY1UbRsmKXWyWMgTZsyjSbUm+B9JqwouzopqoXoC6gSVFgnr9qPY7dVyadBOQJErr\nxLt8jZH9HkWgPjVFrM3SP4j6IFEEv9tXrkuUPQsD6XdNTG8cMJm7hDEGb5DNCZ+6vYryxk5C1YgB\nYKNxDN41YpGFE0wre1PaU9wAW9pqSAWUoHCWVvYg8MI0d5m8S/PqmMZR2rKbgTazuuq2hCZih7F0\nFzSDkWgtCAa6BwVlcjVCKEVkUTrWjJOxuMTBqTv2beXkq1Dpj5iPFgBgN85eTvgGQiSGZ0zu0iVM\nrWwgNSnEkiB7H0QZsmevxXse2JSmiuyDnjLBJsK/fai0V0z2shCl7VwxtmanQF0HhDIZtKh7IOmd\nKGLcbfep96sIe209xkG00fhubhhJupFFSn/OEBZFuiea68J/43U89X4MaJRz84v38ezbV0D6vWzX\n7BENtDbpWzZykXmFktJGKuuw5eR648SdRiS/IooiUifhQRTgiQs34fcGuP2Vz4L6HkIawRdcYBjo\nNI6bDCZV+VNQeP0AYT4dDOGGFOUVvsSNcsorNQy+bhihtNUAptMKNxV8EiPAYTSOsA8QysAcoilm\nxtLKnu9Ulfbxl6jPRBzx8lfmXg91I519b9WM1YvFQEuVst1BCDEI5Hljs44Xl36IrfAMnhPPUmgc\nh5p7B4hrhiiJIQpELrXVdxhPWELZW5G9wtnL4CRFKI3gaqscwF1ZwezGKtqzk9q9QgZ0AAaicfaA\neGf2iE8CJumVftRPpQTn96n0BpFomljKcwVKzrmyn2x0a/F1w2gcitM3eAK4+nIVLw1qMgeNfDum\nvSqKOCBiUWLrMVe+apK8KIIaIWuugFVkX1nnLrdOo4GgbK/31OIySkEX/XJRHrO10dZ/zAlkEA1Q\nN/ZGKG1sg8zvgE3uLzfOfuXEI/tfO/0CAI5GbK5QAR3IfN3CeBaxSHE/i+Rg4QZaHR2p/KbwMDDF\n0TahVo5Tpv09v3gfZ68tI7+u+6Hz1MG2Rho0jhkFKFAngaLsVRrH4EIJSW3aoHpIyFVS/Ejpuy6q\nEIU6sh9hO0gh6g5PTA28iotzwySjpkT2pvI2JjCGxEBLImrfbCZLnzNYVwKpeqsGxRjdJsrejuyf\nm/6Ufl65jNIoReMkEbTQjif3CGcEZyjfq1OGycqDRZGdswdT/OdJUraFfRITS+Sn8SGJhlEc2XSP\nHK9i0hY2g4gm/ZRSINT7XfohkW6gNfql7V5GGcJuB6eW11Ju0sWtbZQ3G9r7zDZC62WreYGAhEau\nrG7x2ANKMXftNvw3f5XdniOSE6vsxYCZLM7ibPlsrJhjVKt8APXle4MQT793DbnNuhZUJXlfk7PX\nDJ5UcoCmuIGNp0XalVPUo9nWm2JB9ryJERgD/E4PUysbqPz8FZD1deWCJKqRxROL5GOtEafpHXo4\nco9/77LTE6JQXy7vAdlrRrhQ4WEFsg/CRNkIdGyuTIw4CERUTiI8yMeC7DOUBGFpd9fd6i1Xk27a\nSCrki5MLKHtl7bxah4iGOspW2lhsqP0ioS84WcViI2r6I2XSDlJpU90bR+HTRZtUzl7ST0q9fREk\nlfNTb9QZ4qmmpw83lL2B7GVAoqLs87fuIPfzf4JXz86ryAwQwkIdlNlAIANDpbqI6Xs1zCheRrw8\nTvOq9JR9QrMhe13ZM0ZR3G5hfmkFj19cghvSYyJxTrCylxZzzwOBw/lly/d47d6r8vfk2hbfb/PS\ndW0pKyMBI2rwq0nHj4Yge1fd0EI5Tgxkn9TdSPXAqNWPV3ibPPnhImZvr4KBwb17J7kgRjAk9vBg\nYAkCYQymOycPqtJTSvPlr4DyuoEOjOFufSm5P35GaasBJ8zIrzOCgdameNxBmLBIwiiX4pmZ/p4U\nIzQPiOOH77/wHNaffyJVH1VREmY3jKZEVWTxb5ESwxsEmL63rrXN6Q9krnTZlzSvFh3ZO5RKCmJ+\nMYlzkLYXYdRkLOU6K5Tx+2vvyjYl9+veT7Y9ExijEiARlcZBut4iIjb0vRTdJ+r6s9svackAAd0b\nzgntyv7G5jV8VLsgKVmu7MXEE+cEWqvh6fd0/3nRl03OHobXVHGnJaNrk4dToNOO22aAlrj/uYqN\nzk7jpKVP01HtAiR6/UCfGLsHz5G/Fzm5yl4gOsfhfY8pCCcDzQnkYCZNEp3O7w0we3tVniMsmYUZ\no/D7AbyuPnObjxMf0yEOJja2ZeoGvSJ6x4lYZFWSjEZphaQONCOHCGNMW7EQ0/XSggxV10qzDgSG\nG1wUwd/cwtlryzh35TYiWxBThq5nNmSvrCo4xRYP8EBw0fzk1tNn+HFl8gVixakge+kB4rrSdqIH\nbSUueYRlhO7HIgkilU4Rdp7YGD639ACnltcx9SDZWMTtBxKVJsFJSrlMz6zKXRjTIhWyQPYMgMXm\noooW3KQp+wikq/iea54ycR01bxxRh+QWT9A4uXSYvxNF6IZdrLbTfZ3vDRxHSBsxCEJBX6nxHcIS\nZB9JQ7QEYp2OrpSVrSAZ5ci+NRtz4MYGM+cu38bpG/c0F1HGWGpiSipNtTYDGStWhQ4TokXlxn3M\nV0Ci1xsk/aO59xTHB5GTq+xFZ3Z9zjbu4rkCQCIH3f0r6XQTGzva9apyoQQobrfw1PkbmFwdkgI3\nroJDXJxaXrdfY+EubXQDYzoK5LymMthVZR4GHNnHbmUqfy/bg2RAC4WlulamjJmM6XwmjeDEaCff\n6lq3pcv0flF51ChN43hBmAQMCaUat49qnkYqjcTk+yEKsudXGasU2X7I623JraTIdxLxZGi9xFed\nufqwESs7Jwg59QfdM0qtQ4qzj7KSkSXIXrjmary6RbLyMJU3d+AvKjtKyXksoXe48V7cy+uXd3Io\nuHneRsnZuylnEkeJgDWFMSrflxlwZsYHUCeZbGxBhaaIyZuFHOjI72LZTQwA5qM8np95nrfDoCSF\nqBOChuwtsQRi7KjFaN5i8X9eN0H7pe3EM4e0Hq6yP7HeOHL56TjgFkrIQZWVs8LReHpRjuLFQ0hq\nwBBGQYnizw+eX6VxNtkYwia2sHYpVr9kyyGbr7yjlBullT3A0bjNz54oBlrbxOdIrja+nhkDNAoR\nKorORuNker8YExNvXzLJuUEIko/LEMheKFclSlrzOFGUNVdQCh1lBggB2qbghDGwYEh6BYVPFtsT\nslnunUFdR9uqXSLXQQjAT3zEmf4+Ad23XdZjCGoU9xDRLGK/Jr4wKVdRThObDbC5NI1DQcEoi8sm\ncpI6e20Z3ekJFJt9EJGORHh5OY7m7mw+13xGxCLk4nvdQQaXLigrZQ9huSE4Y3jQXsFSeAdF6CIi\n5Rnl3jiCXuOcfXoFkuv0IL5Gj1pW6Eo7AWi0rc1Au9XbRG1tA93PJBvTd8NkBUVpBNbtwu8lz/J7\nAyCOVGYzSdrphyEnFtmLJTURyB67G9wESqUwovqYjjCgnAXlvvZUWdMOXz/Ezxqm7MMQ/i9/EftW\ni0JtNI7uTsiYieyV9obpzc5tk4pUmyqylxutUDx+8aaWnlmLLQgjDdWOxHkr90oRKR6QTA4LlU8l\ntTM5e+lea3wjxUtDTXXBHKJ4Fim8M3Hx2ES8oRplYIF9wPO6xfepE6qi8LRr4/q5g0BfPSi2j6SI\nKPVdbP3WpHGAGH0PGbJZQUCR72n5gDQDrYiCNZB5USJQPdDOZsTP2gMa4ONUfBeTGzfjA6i6YvJ9\nec3VzSuwiUichzgDp67s05Jr92U7Q8sG4oCB7C0xE6qsd9ax2alp+w9rDiEffoDS668j306exR0R\ngPCLL4BZ9n04Sjm5yj423LieLzn7ZCPrWAwFKgOplMNmCDcAdKcnAAhkz30hqKpkR4iAc5zsvXVz\nOw2QbtewD6SvY8zizaMqMRUtK1GETkS1YCnlZtnZhfug3JADAChDvqkbjVRkz6LIMK6m6ywpobvL\nfEchqfCU+wQij11b/+Cpfx+fqjybpLsQ+X2EEpARq0z7eDyVQcLdaEa0+DUVt1t47OJSHJXpYGHm\nc5gpzMTIPiP3PpJiNRot1IOqkmcJZB/w5xZ4xsUkijq51Fu8Ae+qqbysH185G9M4u/S7rFUVdR1k\nJ0KzuF4OLdtyjUqhmjYhRfG6RpyGBCeRoHFUZZ/TrrFRjHJHs9h2JDj/LBon3+rKNgaW4Cf+nD0A\nGCD16bSV8vpaatUmlD3zHn6K4xOr7IWV33F9PggURcDAed/n3ryM2VsP5D32tMNJjg8hnakJcYr7\nmjuKwQ4JulFRjmN0cmeYg1UY4fbOrVQ9UjWLIh3ZKxy3c/9eKuBKVfaMIR2sAsVPO64vjUKpoM1c\n7ARMKnvquSA02j39g/CuufwRyNoaLt97F2vNFf1SxfWSgKHklYFIiTUQ5y2Bc9qgNzh3QUMxQsCE\n6167h0Kzg8m1LTjEBSEEBTdO4zxE2S83l7HZ3bBvp+gYCi1+l2Igo1iQ9TXfk7OWjti15ZRXvXFY\n7K3FPWaMIauhebuiIpTi5nbC2Uv/e3XzEoMi6pcLUMGBRPbqqkk+d8gqz3jHah9LQJagcZRyc3nt\nGhP0EJZkSZXBbgpnzyzvvtDqyAlzkIXsLRPmxnPnrNdKUVMyG4neTN0iHREs8QpHLSdS2Uc0AqHc\nF5e7XpKUK115ixs/1P1Ebf7A1DCwAEmnI4wHWVHCE6YNE3N5PQwlbXRrWNq5qV9v1ZtRGmlQClKr\nwbt8SZuA4hsA8HZS0BTPqDpcJAZIBYmYRiiWeCQEeZ8PIEXB2jbqUI2kte46Lqx9gH9aelFfPRlB\naIQgVqrxe5fKPlauymrGpHFURSm/LyEpuoGBwJHgk/BB3dbjHUy5UPtQU6BM1MvVkb1ApDzeggCF\noqxPvtnRjdam0iJ2G5NUyIJmjCk8E92r3y8L2TsR1XO2SBqHIXG91AO2gmJe1k97jm11McyzyVT2\nmpFWR+0ajeO6fGIZguxFKnM5EQkaR/Gw03zllckrythRzezTjJBd7XO6t5Xu8WMOUSKM2f4Y2Y8k\nIeN54x3iAI4jc82oCqW03Ur42Vi+MP25VFk2RMIIQdErSo6YEQKqOdCnO3xqib3XyAnGMFecx2+c\n/k3pAQFK05x9GIL09QRYgO5CKj1yUhGlFmSorHZSkyFjnGclQJj3ubePoqht6XwTIzlFL+zBiaiW\n1gBQlKagJ0D4qkG4pIW6N47M+8MMXtpYqYnAFyasmXrT4TGxd6wDJwyRv3UHuwkNdQM1SBrZC+Eu\ngwTIc2R/pnoXj19c0qix9AbcgJ3GAfxuH2JvYQIGx/L9nIii0GjzOIUMxwTTk0TbvETQOMZEEhRy\nqXvkpGtOOFCUnFEHkz93I6M/x20FdFuI4/mgrqv0aTODa5Ly2Zws1FXu3M0VAMCgVODti6ue5YmV\nCvxyhru7AjZbn2hWYkdUV0OEAMwdI/uRJKQhwPhmDszhhInu1UBR3tzBdH4GgxJXnJ7jYcopp8qy\nGceY42AyN4mSW+CGPDC+kbg4b9HkmmFur4o+rv9kroJThVnZuTbPvwrW03NzI6JAjCxNW4P4ywkj\n3G7cSrteGkFFADTeP3U94zx05Htg8cBjuyj7xG8mrktEQSKKWluN/A3lVSRW9ggjiXbEt5ReUiqN\now6sSOHsoSoEuwOoK88SlOotkKCPQalguTKRvuI3jWAQU0SmsoupKxFJHVMQNtGW+bFySq3QYnny\n/A2U17eUlBXpjpXr9vHYpVt44sLiUGQPAO1TFailUCT7FvNFXxrZa8+00Jf8gOIcYQLwQZxTR2SP\nDSn8dg/T99ZTBlpNIXoeqOtgrbMW35cGIg5xODVpegqxZCKZ2OTu1FHOUxyPYfeIQ3piZA5/RspO\no94TuwBP36/paUGUl6GtWsbIfnThgSmMR9A5TowMExqHsWS5pAWaWEPN0wOEuk6MpmLUQqBPCRZl\nriIuc5k/qhQ8oXj4A+7c/RDhR+8n5SLOmGjhI5nijeBEEQbRIM1zIqGbhGKw5oBXnugGIULfi98J\npFskoAedKBUBT+BG4/rytMvNoJm8N4HcmeDh47frc2O72AXJdL30egOcu3w7aY/hoy55bguyZ8ou\nXU7sYksZkwowS7qasg9Tnjjxa+LlhhGnWvLZyl6zszgJ7ZAlpXozsdUQpDaf8Tvc+8MNQk3pqSL6\nQWe6IioBAFqaDmKQ9qIvqRNAoozTnH0kDb3GeIrBQZjnys0JI5w9fwOnlteR36hDS9ehJiF0eZ8T\nicVsFKNDXE7lqraaOEeUOevwySZR91kTYxayj4Yoe4Bv7HLqzhrmF1e042Jy12IzCJGb2D9MOZHK\nPqQhCOUzOzwevm0LqnKIk7gV6vO6FBvXKA1ujMScvUHjWOSgyp4whoIXc73KoFN9fTlnHikGTqXO\nYDJqVISlp5G9wssKpTNk4wk35G5zUc6XHh2Osiy3GrwZzwIqihc0DpB4SxCNxknQFPNz0DZtUVwp\nAciEdlLCUHsHGq9scvaask9iDXqT6dWevMchepBMFIA5JOVplRi4I8DzQIbslGYi+yzOPqk3pEJ0\n4KQmMc/IsinqbS3L1Sd6yihE6g4Ss/byWsfB1qefBFOUXMq/Xp5Qkb3Rliit7MV3cgaDOFVIXB+V\nxnG8oeOIMIqZi9dQWa/r7Y438THfaOS7fJKXyl6Z6IyIZlWo68AhjjUBnHqPGKeFVrISt03sAMCO\nQdEDJ1bZc3TrOC4fXCAc5Zm8HiHSSElIeks+seEHv0C7EYzwqwljYA73yFHPp0T5gPvi4xhQcEXY\niLKcNbptGPQxCMQuU7oxKIqXhoLOSHGJxRJHKUrHHZrvXcl0yJU9k8h+rjgnrxPpDMQznTDkFAEY\n57ENpd1rivQCnJaRhmTfA0Akxy8UEbMob97m+Drhsqkoe2baJgjgKgZaABiUi5mKEeCTkxr+TiIe\n+u8a31fm9wkjIJcDGeJ2q30zJdYh83qm5G4iAKtMauf1/C3iPduHdUIlJHYVGRBF5P/iMghaZ2fR\nfDz5tqLfW/c9sO3uhMTmIZS9mkcKcX4ls38ACY2TJX53gGK9gfnF+ykaJ2BBKq889eK+JdM46ytC\nEkU4Xb2Lwraee57FzEHkDwFwjCV2JcPbSKbrVts2LAbnCOVEKnvKhDeOC+a4HJUoRkBp1FGWeAQE\njjEIVB6OqigiXgSQCADjFE6kGhht+kFT9u5QbxybOCDIx4ZZ7V6m/mR44+4v8E+LP4lTMxsTQU6g\np3jgGag9/NKX0f+Dfw+R72anSLBImPNAPZcr+1i5qHlmGqdncO8FntbXiWjMXSfGYhPZP2ivYHH7\netrTwvcVSk6PLLVumRgv1+USmSExwLnEvBiuoLDifyPX2UXZOxqNw5hAesbAF8g+iMB8H84QRKp+\nMj6B2qlEvepxO/0c+l/5Le2cLX+Lmc5BlqMYugEe3SrTexNHm1Cp46DsT2jARr6r1L4HyYrFfJsi\npiLMcYOvE9KkHkGY5IUi+iTleLmhyp5vQ+qAECJzE3FkDyxt38Q7q29r10eeq61szb2mJ1frmNjc\nQXldzz3PHI7sh9l2tFTIRv4d+V5cXT8ch+xrPbGwsOAD+H8APAMgAvBnAEIAfw0+5C4B+PNqtbrH\nCIXRJIwC+J0+WHkO8LjvNIkUlBKLZjQ1/gZ4hxavXR30DARBuYR8fQN9xJy9dqtF8WgfM+mkzdMz\ncsOEYeLGHXeYdMMOoqAPGvTRJ0Z2PTCJ2DORveuD5DjCSZawuyv7KOdJzwhB46jKHjFXCvDNISrr\ndbBzZzlFQxMXUHXw3uqtoASXTwQCmXl+YmyH4k0Ul59KM8vigDfPkZSSmBRsvLUrgT+/JnKd9ApA\nLd5xsKNsTC5WEV4K2fOJlTAGeP5QZE8V33YTaWffw3cyI8TBTPk0/D/4NoKtdZxbfIDlnr4vrK3t\nSVnUMLoAACAASURBVHt0Cq8X6jtYqSvfufIZfO3x38MHd743tG6iPNsGKkACOATF6CpR2CQM4l3a\nYn8jzWPFzWwHEGfGdB2derIYz+U51wFIEpgm0o+7xJVpUVShnsvTjjjcA6r+xDyYa9/ZTE0rYtsb\nGNCVPYa06yhlv0/9DwF41Wr16wD+ewD/E4C/BPAX1Wr1m+Da8E8Op4ppoc0dEEoRTk4CMYrmUaMm\njaMYfOK8MNo2hory0aL3CNA5Oy+5XUoy0Lwqnn3mrn36cdx/4Tn5t7aTlSKuNjGlH7b59Fms5gNu\nq4goQiNjHwMD9T1OV8hMkMbAi1EKI8q53XU9QsHZI6FxnGLCdVsNohqyN5UbMCjmJWcvc5X7Pre/\nUF3ZCy7WFCbaraZwUNCnepxQJt+xsImEnjP0u1LXQS/qoxt2wBhDSLmBNhUdzZicYJnvD+fsoaxA\nhTfOLhOuNKTGgOCrn/5DfPP5/wBT+SnNpuN3+6Cem+k5Im0m8fNaQVNOtASO9oq/8dTvYyJX0QBI\n4nqpl0uYkifJABEC6UrOPoqSfZ0HATfCM8YNqwrgcnx/6LeRcTaqrcxxsiksx+EZchVkz1cHxBo1\nK1bJLObs4TjYfnzeXhdGNXrKUdKBSNpO5emPCdnvV9lfB+AtLCw4ACYBBAC+DODV+PxLAP7w4NWz\nC2lw5UAnp6SyJ0puFLms1gZlrOyVDqUOCs2AQgj6M5Oyo7+19hYaobmPpFEnR6WEDNuAMjpEpxfP\nAYCJ2jZcBbXaED5zCDYHvN1OGHL3U0Wubl7BUuMWqOsquW4MJSI6u8ofjhAeHvle/K4SZe/mS8oV\naeafo1dm0DjK5Oo6ABimVzaQq/K9QuHGBsiYwjGzk5oiBlLK00GcV5U9o3CkeYYf58reQlNodQS2\nult4c+V1LG7fsBtoobhd+n6cnM8uEY3w9gO+S5F49rBUy+I8V0zKQT/J9ioP9QboTxQzlSSVLqz8\n79agpdAvhgODaKPl/WiUGuFHknKYqDT/mxrKPozkWCtsboM8uM/bYEagu77dNibORzQGcHr9stou\nvGpUzp47eRDk2z1Mr+iZbMMYlAkaZ5h4vQC+ktnSj/PXa4ZiVcGfJBoHQAucwrkGYA7AHwP4vWq1\nKtrWBLDrBoszMyV4Q12a7NJcZFgDUHnqLObOzqA3WcQEfExM5DAVFVHcyCFkISanyyhu805W9vOY\nmiqitFMEiX1/USmiGP92yznkYnQyUSlgaraCiXIBPeKDThSwjU1UirwsWvTRqxRQKueRo+KeItCK\njVDTZZBaB95sBZVKAT5hKIp7T1VQFN4ojoNKpYCnzq+hXC5jaoobaMvNPAJX98MtVwoohAGK/T4c\n30Wx5AFPzKFfKWI6zrFTmiwg3yigQBkqlQLKRV8+FwDmT0+CeR6KpTxIs4NKpYBiv69dY5PiqQk4\nYYRC0YfneAhdH7Nn5rCywe+bmCzAHXhaOZXJAtpODhO+h1wxh2LRB60UUexywxmdLKE4oHCjAaam\nish7eeD0NErlAtqMYW5uAvfLOYRFHxOVAorlfCprIgB4xENUKaIQv1Pqe/j1pz6PpfoSChMFuf/A\nRCmP2akyploRGiii2PNRnC5gwivKekc5X3sGmyyi2O9j4LVBchRF+MBEAa7jIa+0NSj6cAq8/dPz\nU5g7PZn5TjtkB/AjFH3er4rtDoolD0Vmv56W85icKqBfzGFqqoz5+dh9suKjXymg2NXvG5yZBpod\n6/Mnpkr8O5RyqFQKKNXrOL3TRFT0MTVVRNfl7wUATp+dQaFYQWWyiCguyyvl+X2lHPIic6nrwsv7\nmJkpobJTQKngIxeGoJ6LSqWAiTx/L6XpMv8ePqfuikUfpbyPuTuLKBd8EN9FeTL5FqfmplBaz2e+\nR+p7mKqUsDZ7DsX7y7x9k0UUy3nkLJPnRDmPiQrDZKGIYtOHE9ez5OTRFbjJ50q9WPQx8/jTcNkK\nvvK538ebk23QOHOlrT7P3F3l98b3T/kuupUCKpU8nCBEseOjMDUBsrMl6ym/4z5kv/fuV9n/VwD+\nsVqt/rcLCwtPAvhnAGrIXQVA9j5isdTrnd0uscr98iTWv/AsQuajttVBq9lHF1003A52wi463QEi\nFqHVDtDt8k7phAPs7HTR7UcgPX6s1Q2A+Hzf8xDFv5utPuqtPnKdAbrdAJ3OAGGewYvPB5tN9B/U\n0Wn35T3o9OWzur0Q+PqvY3FjBazZg9/pYzo+14gYnG6iUErvVtHtBnDCADs73BjY7QToGrtitdp9\nRL0QpBsgpG3UgxaWFs6gslZHvqu0hxEUd9pobbdRbnRBlGfVNtuA66LTDZBnDPkLS+hUSuh2g1R6\nZ1W2+yG8QYhOpw+XROgGATp9ItvbbPXhDZJ3DQA7jS6a7Q66fhd9EBS6AVq9QLa92Q+R6/QR0hCN\nRg85lyLc7qLTD0ByOazXGmg2Ouh2AzTbA0z1AklZNM6eQmWtHnOuFK2CBxaXG4UMn8ktwC2VsNa/\nJN9Np9VDc7uDnR2Kdpt/16jdR9NLvlvACPxe0oZW/L63WQvd+Hi7F2DGcbS2dlp9dF1e1+3mAN5W\nWzuvSrebcOztzgBON0ALXe169Vt0OgNE9RacTh/NZg+1WpwDPYrQavVSz9kiDs4SkjrOCEGzxdva\nafGy5t+5CrFerZ95Eju9HXnfRr2LfAtotfrJGHE9NJs9VLoBqHjfPgMNKTY2m2g2e5hqDxB1AzAn\nRLPZw0SrB3QDNDoDtIIICLooFXx0uwHcsI+tXohuuw/CGFrtPipxuTuNPtqdgRxzprBeiLY3wNbp\nHNafewJ+t49mZ4BKL0jGpCLtVg/tjoNmwN9Z3+mBdvpwXBddxZ24WOR1y5NJvDB1DhGbQ7O5hWav\nF3+/7LTYQnpbLTTzeTQaDO2Af9uoF8EV47QTJt9xjzI/X0Gt1tyXwt8vjVMHIHb62AJPHn1+YWHh\nW/GxbwN4bZ9l7yqh76B3apLnjPc4T+0oQVUAuDeNytmLprou7r/wHDaePafx5/rSVCwHY9c4w/CT\nb3XxxEc3tZwY2tKeENB8LqERlHtDg7MXOXw0t1ArjePIZbgTRrhSvwrTn5w6DgZx5GOu09dStMaV\njP/lN02ubmFylaMNqriWtWanpLGXOQRweAShyi27xQmtvqZhjDGGiPGAKrl7lmunuiRt5boAcSCy\nKNqCjaKch43nHpMUHEWkezdIWpno/K3C2cs0t65yA9L0m/h7oASeMUIwSO1wpHQE1x3ZAJfsKcCs\nx5Oqx99RLdd1E6pFkSx/cKZ4HhFGlQC++Bkzs+h89jPyb0lLqhSGsO8S5W8C6Lt+6bYgJ6Kx6ywB\ndV3pZz8o5bnhP77fHGNscjLbrx8xRUl4PXtTZTTPnoofm76nO1VGe6bCqyvddLm9KJOiKcSBca67\nK41jiogHyTLQDvMAO0rZr7L/3wB8aWFh4TVwVP/fAfhzAP96YWHhTXCU/3eHU8W0RDFf7TmezI2j\neuOIbCI2rwjiuOhPlNA4N6t3Js0Qxf8VfKBpPBLiK1sUqs8yOVsGYK7IjTtBPoetp06n66V6Ilhb\nndgVnEjZqEG1NTgkUfbdfjqdgSUCUuSrVyMEO6cqUmkIw7Uw0MoQ91wOOTcHT7ohmgZanitd87NX\nOXvPTYx5sv7cA0P62Ysdk/yi/FainpI/ZrqSZnHgDCEkxdmLyVmmuTW+q2nYdOI0tIGyryhzHJyZ\nOMvPS/5XsY8QDPXw0d6R4OwNi4TWL4UiZTp4AWANzhGK1XZc9GvCgEmmK3viuAY4itumVoXo4EUo\naHcQwr97F2BJVtLEtTfpq9RzpcGeOQ738AoGAONl9SaK2H5sDvdfeA6kXNHsW1lCDM8o2zhd+8yT\nfKIkDkRvkx5rWd8qjmdg+fzIyl5kx5x6sInT1+9CzY2jgcGTxNlXq9UWgD+1nPr9g1VnNBGWf1d4\nPbi8EzEaJZojhezjzqu5QKUVPL9IIEA7speXqW5W2sRhBvQQzBfn8dTk0/h3hR10ZyrIt7oS1QN6\nZ7B54zBHV142X2rmOAjiXEB+p2/NShk/LPkpULfnAejLZ6WeQwgih8AXiimXx9cf+10ADNdRs+RL\niQ2LGa6X1HNV3MP/iVExiXMfCWT/zSe/hZ1rW6j1buvxEKIs410QIrazUY5RFvvZR3Lwhi4BUypu\nluP7XCGqe5UyhyDn5/F7T3wLraCJD9be554dQsmRdJRrlkhvHDNhnWpAZ3zaROxXblQwXWZspEx9\nDicxwJbqTcy9V4WydQ6PQVFiEyT4sICgpExHUk7F64uYnGmlUmA7UbItYeSJCF4K5nuIXAIaBXHg\nIl9Bbj3DJ1LXcdE4MwM3jFBZr6MYADnXRzvQqV9iOkNkODfwpiTeOMJV15ZChRECNjWNwTe+DJTL\nILcvy3P3X3gOk6tbqKzrLHXjzAwaZ05hbomnVJ/Y2AGb504KAB/fcjSeMNfLY5UwHhxid3k4rpFZ\nkS8LNYQtFHjWrKp26vhf4Vsvcm4ME20VYV4bjzOfeBKtmOhP9RemltwqjBBQh8gNGxJkr3sjiOCP\nfKeXkb8fWlsFwlEjBFUUaLqqyrGcy/FEVOIbGIOmWr+GjhPBDaJMbxy5ShDfJgq5so9pHOnhQRyw\n2VlEnouGWK47ellq3c2c/QBHs2qKYwAIPN17wwx28XxuMNf0FyHoR4P4O8g1SXKB446M3KikcQwf\nb6VthDK5LaEt26QpzLFPNuo3BYC8a2S1dBx9dSrfof5ugaS1Wt8ARaHZ0ZR9vtXVVqFUWYlQh7vG\nslDk9DHaFrs71p88jUExz+NQLOrKRPZZbQcAKBksxeRsQ+1MBM5N8KAydULoT5TQPJ3eTpB6burZ\n2/06btRv8Odo+uFkuV4eq0QspnFEPmvf5+hR26mIGGg5Fifdec3fIARPTT6NyBFLUjqUP+S36KsI\nM4EUEZUweHNZLaX8wJKvhU84TrLJslD26oCLufUo56HQaIvKpMuy/KEiZlVhqEo1UhS02EkoqZ/+\njHbQxjracIMw2Q5SjViOXTnVKtLKJEdfVM+N4xAXrYVP485vfw6t+el0uzXOntMYxFBuhDGp7FVk\nj4xJAwByuXTUJHMd9GNaJ9nYQ6VxCFBI32eW3Z6dVNIlpJNvyXpTKv3sTcVk3QPBsrmILFM5njOV\nPXFAlMhgqRQtK96kbyhlMsZdDpU6PR7vEJYge7WPcfDCIGgdgwZU3W4dYqVbCNLgzQrKRMQuSW8p\nZKZQEWWo+xWb751v7MK/oRCTAmQOwZ3GHaUKhr3lGOREKnvhY+6KF+jyKDgRDSiYZVXhig9GXNXP\nHdbfXz77FTw39SkERASGMAyL5osrI39aB6FRn/SAjAfEpz+Nzrk0p4+YXxb7bgoFZ/MTn559UiIX\nm8HO5geuGaudhAdQdw/iSDRunKHQbAomjHOi52IfZM1A6yQTR/jZz2Pwh38ElErQDLQ04TtTedyV\nyYa56QnczOIImmRXpNPT6E2WERofKsjrCjCXK8EU6nBkDyhUB2OJ/UEE4pnGXkUZrHzhWawtPJXY\nQ1IGWhPZxzuEGe+g92ufx9ZTp9GrJPXMjCA1uPycY6wejQju5H0PoXFIYhejYPB6gb4MikW209MD\noKjryLab9VbjSMJ8zkpt8ooa/XtIgJqg9/Rj6bFAHSdhDaAr+28/+8d4dvYzWPr6F7DxbLJfhvje\ny7/5vCxDe45azzGNM7pIzp6onH2U7GzEADhEz2EijGElc496/TwAzBa5shXKYJTAI7XTOEZ/i3I+\nek8/if5vJXlNTATixvdHn3o+zfkjQfY5oewteUqEkpg4lXTCyGrES9c/cnXUlVyrIF+CJJ/NCEtR\nQVkJQ7ZJt0gw7HoJ2nFjAy1jAHjiMYdYNl3J8OzhfLWgG1QaJ8muOPjUp7DyhWeTqM9Y2rOTuPsb\nn5Z/+54NobsS2SdlMzkHiolU2E5kvZQBLqi2JII2vTuSWnZioNU/XDQ9je0nTqeovCxvLlXyXlI/\nR0wENiWk0ThiNSludFCUVBdNtmU0RDXQqvWJtESERtuUbzMo5bNTiRg0jgxuswhx0jmrXNtYcxwN\n2av0UdEvYibPqUSdluTXh8U8+uVCape4R8FAeyKVvRj4wn2MeD4fbOrSz+DsxScOpvSsgfJ6VTHE\ngyfaq7KXFC7DuYq+b2XvU8+CnUq2NzORjKrMbIqUc/aORPbMiuzjQVVOfHBt6Rls6I9qnL2T0Duq\nt0pyOL3SMcskfCtD9ZlZ3k/ES6MexijokM2wHWWQmzSOvF6jcRKeVqAsMwqZOQSBkvDK9XzF2yi+\nxk28cWTxjElzsJj0N58+q93XneToe/OZs8lm7xmcvU7jKLYL453LPmP0AdtkLsrcfnwO24/PwXfU\nVQyR9JcpRAUl4qdC58gN7EVCNcvWn7JfahM0p3FkvncD/JS8hMoclAtWZF/wSlrSOYc4aXdjtS1G\nnxD3pOrrkkxkr63OUxHh8fHY7qQ9Z4zs9ye/efpL+OZT38SZUjygxIsMdP9nG10RTCeKUMsdo1I6\nAp0Jt6kRMkMSx5UThkMIvnTuS/jqud9JymQwBqW5nFSW0DYaiAgaR7hEigGn1jseeMqEZvW7tgxq\nk0+1pZ2N5MYQXKnRU6fAJiczyxT5RYB4YFg8OwgBmGa8ipU9jThvSxAbgo1lcZbfMkESFm8g5ASQ\n2nf6ShkJXVe+bwA4VTiFXz/32/itx7+iX6dMJAL99ibLWPr6F+Q13ekJLH3t89h5LEkNTZWJbW3h\nSeXdpDl7grQ3jlzBmJPoECPl1tNnUX/itMbZC8qLOC7CvC+N4OY7SfHhDpH0paSiLH2XWjl7QeNQ\n623zpXn8zmPfAAAMioVU2z8/+3lM56e1fuASV7obW/MDyXTFyT29Jx5LXTaMxhFuval71La5TqpB\nKjgZI/s9SCU3ic/Nfy556Z7YFCOSNIPJUfamK4ieeBKOnyxf1U+moSkRdCOQ/ZANPqQ4StAKZfAc\nDwunPqtdMoyzV5eKjoV35L7+jlx2Ss5eQ978d7+ctNGaJna3zkqSCE6VxmGOo3nIhF/5KoLf+Ua6\n/FhUP+lUPVRuXfOaihVgxPdHlblJzDprKFGpOxJkrz6fUCbfq1iip5B9yoziypUUAOTdPD41uwDf\n07l9aDQOrEK9dLCVRLygCAp55bidxkll8lQNpVo7kkmAWa5hriPTaQNIPH0IwfKXF/D/t3fuMZIc\n933/dve8d3peu7N7e7e3e3u3e3Uv8e7IIymROvJI8SlaoiTrQUm0Iwl5QoBjxIgTK3ICAzaCwJYS\n2IHjRH4wdhzEiCLZsgBFRAxbkRQLkkUrsV5N0ZL4uiNv73bvdnZnZufV+aNf1d1VPT09szt9s/UB\nFtiZ7q7+TXX1r371q1/96tpRSgG6fpDbPaZLMnS73fINoh1zTqHnifjqKrKzRytjYnWltGrL6w2R\nPDBlyChTc3Cy5GQ/9S5eNH6ioajp/XW3V476RmG6R9l75zJYowxXG2S8X64J2oBEebvJLansfZiL\nXyR6YwSPsr9xbBHd02eYEzLGw6EekPlgWidOoJXLuBs/D1dkgv+wuczLLZ/J68cPuxoza4IXHsve\njsaxQ+QcuSVIePncCmqzJVfEgCOLn57itezdm7pI5v3tJHOUpZLgNN4ubdl73AuuGHvPENgQqOts\niGHuNUpDT7S7OhIqimTj0Aw2FsxMhfQkqim7LwGZ17Umuy17SZJsw8KQ3Oqw4HPjeOkyrDlr8run\n69wEXlLP2Z7RVwcsy54uX5bQyhtuKe8WkrS12tG7TAPAvImrPNd3VBbJoGRujaLhkqEnwHVFNt04\nThpr362p7y6/6TzqZiSW6xyqHSTkBGqzxjn1EiOdgLmoqqcYKYuvri5AlmVXEAJgWPa0j52ud+/G\n7Jabj5589k7OAoAsUe3oFltBGytsn2+n4wzNJcnlh7deJrrHpvPfu8L0zIfxpuOP4pVzK2hNZfrs\nQQlAkm0Fxssxw9zwWzImoGRJhp4zLSBG6J7lsy+kiziUX3BcALLzwh9WF3GicgqLhSW0cxmsrSww\nlQzLZ+hSxJTP0V61CyN9rL39HPVbHl9+AlNJd7iokWdesRVDj3INGQVSlj0lozVfoZvbxllRTN6h\ns86boKViqXVFwcbinPGdrttZL30piu068I4eEp4hvGyk5zDbW2fW6EiyN7bsPOc8Zc9yK9BKgaew\nJWpxme+5eUIhvd/rkoSGajyX9FYDXlzPjKvs3ZPfNDrLjcOgYyr5HuU+0WEs0rNGV/1Cm3sJmbnp\nB72oKiEnsHb0IF46v4rEzCwO5Rdc53aLRUiQ0MpnsbE4h61qCYqk+FN9KBJyCSeQg+ezt+4JeIwl\n78Y58Fj2YqeqITB7d1dz81j21jHXg6MUrsu/a56TT6m237BRVnH9iHu450KSHQXGU/YuM8lUpjAm\nBTfPnUH7bsPH31PzuHx6Ge2se2ivK4aFu1pedRScFWUky1gsLOKu+bvd/m3WSxTg07X/t1ywVicp\nGy+F9VLTCrOcqdj15MWKBvJOWjkhknCvfbA6r17XWBFtbh7h99mz01MbZXrWOciyuQG9/1oa67e+\ndH4Vr9x2DJKsuEdckIz5BVlG6+FH0Tp7zuvm506+sZQ9LzWz67d0e+j2nH1iXcc4lr0TLSNha8ZI\nPssa4ZXSlKUs+TtU42t/NI7TyTjuNe9aAQDYqpbwshmKaGHVQ7K5A12R0eqa2TNDWLus0Y9CWfaK\nZKRP6WTTUOQkSOWEq900ji2jfdtZrC84eekV2b9Jii6bu3TB+rkenz31HLaOLaFeVn1uUC/0BK2w\n7IdAspaN687yZJdbxTjL/JoT1siJFKFfdnpyzS+EhHrJaCCtKX94p67rbsveMwHarVQAc+s2CRKa\nxSmP31Gyw+pkSfEtqtIVamOGAGsM4HhXPeF7rM2rg5QTLw7aDrtTZNvCM4b1jlVKT9DakUg9Z/6F\n6Sd1LVJxW8jeGHtrDsLacJy7uYh5TSebRiufNdxHrok6ydmEQjbi0n2Jtzzta+PwLHbyWebciXfD\nHKZIlGUveyf1LWXvW7HtdAKtfBYvn1vB1ZVDvrKLtLIPEyFieSmt29HRKFRH3kknsTlXxtVjB10G\nCwBcObmEbjqFm/PT6MoylXcoWAEyN2X37C3gcrmZ37vW2iSS6M3Pu36rLCn+NRGeHPbe9Tr058bi\nAl47ueSWleXGoWUbk9odz0zBqLFC2VxqTHa1H6Ybh3b5eCw4+/+wQy5JwrXlg9iuFJCeZ3cKrnI9\ni0lY/nzfgqme0wgdyx72uZasdFmldBmlTBnVrGPNsKKUdElCN5UwNhmXHJ+9Zc3JkuwKw/RZ2ly/\nsWMNdlMJvHjhBLpJxZ5IyygZlxvHVlw9Y1GVEWfvt+yNzs0q2t25+RbPSMBMpoJqZgbYvMp14xwr\nr+Ll2kt2lksjrM/tr4V33YIEV+/prDyVoOs6Ng7PYuMwY5Ec3EohMHmaFc7IqXO/ZW91AmZUGWf/\n1NncHK7WX0M+qfJHFrK/XVqNTnftFOVY9jv5LK4d83cugDFZ++rdJ9GuNZHabtl1HS4TpN+VRz9L\nOmaeFdHGCuFlunECVrd7ffYpmZGfiNGxy57w4HEwGcreduNQ2XE8rhln4o56ee0smeAaFqzl1Cx0\nyXCz1CsFpFluEs8Ere2/ZiyOsu7pXtxk+AKtbxzL3jrXUYj0fRJyArfP3gEAaFFlseR/8fbjdmI0\n2+VC+daDFJKvU7Q6IcXt2rLj/s3fW0qX3T576/f0OkbmTFvZ+190R/W4XTbe745WjuNo4ajjyuNY\n9tXsLM5Wz+EzPzAStkpKwt1eJNktK6PRSNT9/aGdbrzPl4e1QKdv6KWnjH5+cFmScVv1PACg5e0g\n7ZsEzCvIkn28p+vYUbNI1xquXZuCMCx7040TRf95Ju4V6rk67wJ1uuRfnKcw9rr1Kutgn71f2fcY\nHRctW7/nsltMhhvHdH80Og1844qxq7xvQs/8yIx68fj32cvG+8gQwjKhy21njEbSzBsuH/eMvyWz\n+0XTZadx2xO0lFK1GzhnlNJHOECWnUk022fvuL/oiSefZc9z41jKr8teJVrOeJQ9FWePHt+No3Oe\n104+67felKSRN8lSmh6f/eUzy7i6uuCLzfeOKLyZVSRJ8s/PDDL55h25mdTL7kgSZzGY17K3BWWW\n610cRNM9vMiVxf21v0Ny8tk7+9auN9exbcqdqjeZZXlpK6AmaPvXm7+zca90pd04dmkel6yvbciy\nz5JPJNyuJ5ey95ThzTFkyOn/LZIs4+rqAlq5DDplf1TRXjAZyp4Kpat3qBSojIZKN/6maijaelnl\n2mC8l8UHx+d5rLQCWZKxWFhyNZIbB2dw7eg81lYWTFEZCppuhGZyMivyyJv1skeFwblX4zIaHmfR\nFgtn8RM7wZZPZu/1VjSPT9kbL0o5U3bXnRV73jU2oqaTWNF4rSdLtEZhyjfUVpQkYG3aDX80TrMw\nha1qyUjLQL8SinuClqkQvXXJGFmEwzm/UlnED9902k76Zu8p3KeDdeLfnc8Kw2U1N3UA3VOn0Tl5\nqr9Y1PWVnOkKtOpBkV1yb85V0Ekn7bzuNN7NUgAn5bH5YwLRoaPp6QShKK7307UQijXqgn8SWpL8\nm5TfeeieQFnoIgqpInJJdw4lVjSOJCvYqpbwyrkVsahqKBh5vb3WOhiKsDZbxpXTR7B2dJ65qMp7\nfiAcRXjvoYv4wMmfQjaRdTc0WcbmgWl7Ft9rPQDsoa0dmkhtXtJNJtBJp5hunG4ug+6RZbQv3OUU\nwlm0xcL22cP9UrB89ldOHbGtUis5l23Zm1b1Q0uP4KdPfxhPnXwa9xx8s38YbCn7XhuGJ46tPF3+\nbl2HDKNerKX19PmKnDDub1vI/JfNNaqTEx7LPsRoJuJSePpZlzIlPH36Q1CnjPQadgoA3qS4/2eo\nmQAAIABJREFUb3RhWfb+EdGp6dN49MjjxgfWe+OhR62VSCcyeP+Jp6n7yK7f20smUHn8KWwemIaX\nx5efwFLhiPvLEKGnNM1KES/dftxJE55Iujpj2lXC+m0SY4SoSIrLcHjgyAOYK7DnG1iklBR+cvW9\nuK16Nvi3jCnckmYifPZSgtNoqUq3ctN7wxIbxbzv3ChunKBhKEsJ+0SFv7OxFiVZ4XOAOYrpUpEc\nkoSXbl/l+uwlSOgS90petgA8y9z0uUsSezUudZ9GKY9GIQf16g071K+bshaduBVsKsHZAci27I0N\nTKyRCWuC1v4fOl4/cxTtbhtWCKHLsk8kgY5uuHFk57g3wsOrDGTv7k3eOmLUmd05SRIn7IkDI++N\nbiosazcx7qS47Y40P1ITt16ZXSPVEB2TTqeyNtuYPXKQJUg9TwfEeQ+Scor7LklS2AlaJ5MqACCh\nuKKl6DxG7VIJ3fwSLlevOPdhWPaKpLhG0JIkuX8z/O+ty5CQZN9cADsah17lPUjDGB3j725GAUPZ\ne8PvrP9ljluGtXelcdngPntmmBgYyoLCZUGa/68vVPHK2RVcPe7kTbFdVq5dqxRTyTF89iyFFLAA\nxgsdLRRkfdkWliyjdqBi+/7XF+ewOVe2c7/w6sa+nzVy6RppfenoFhraGtOho1UuolHKg4WSSJsp\njnt2O2BOrnqUgSS7JwDDTNYHPWMLlkvDhVlFVg4XxVT2XuvQ1wEyokp8iop2bXqOVTLmSIJ+R6iF\nUJJkpUF2nrW3w5Mh42DeWHGepRYmJeUkV9lPJfJcY8OWVddByicBAIey5nqXhHvklfBM0HaPrqAz\n5V4cxZqgdS1uu3AB+rR7ZBLUyVujCVpPsIyiMO1it5lcy97rxqEmGtmF0P+Ozo0TFpZlD1lGa8qz\nV2giAbS7TKvMUkZB0QMAmG4cLqZSbXabgZYgb9RSmqrinkd+Ep/72z82bt3HqrEs+V67bSg93opU\nrxsnwJ+dUJIA2kC368whSLJvib/X/SPDrRzaB+a497C/CzGKOzA1j7PVc/jalb/EjYXXkaw32es/\nzJGdldyLpzD8k8R8y96F53nmkjm8a/XdSCn+ndJY50OWjVSorltLeHDxYdRam3h+Q8P3rn/X+A2M\n9MIWq+XjeBGX+XKaHK8QzOZmMfPq1wAd0JUE12cvMTp1Vl3IsnuHKWl+Hljf8Z3n+o0uy950w1KG\nAMuy567t2EPGL8EIkGXFlanRxmXFOJEdLHhWK+0TDFb84ZQ9y33gvU/g9bk8dEa4l1EGw43DtOxD\n3QqAUy/NThNpxsRT0H0A42UoZZxt3IJyqABAW80jAUC5tgYJ/g07bHyx0HxlryTTANpAa8cuj/Us\nvZEWtCV4dXUB82dOcu/hfOkfXbEopktISApeXzQ6kARjZXfPdCcoLXMFrWeuwR/u6p7rYVn2rrbH\nUEr5lCcSiG5LpsIOchNJMFwargVbtnj+OpnLHYAsKb787zxKmbITQ++x7Fmhl6zfQnf0Mozd3dYX\nZ7GT929WQ1/HwhpNuNw4Ae4944TxuHEmQtkb/mQFSs+TCI0BL7qGt4KWPj/hiZV1WVRhlTU48dch\nr++ev8MYfn/vBd8xq8PoP2Ts39jWl+ZQefF1Z04DwfMSvBfCGw2iUsqkc+Ik4Nlvt5ufQkvNIbG2\nBujgpjboQTJ8vdZyAO/kKf0MMzkAW5DaHeiM/X3p3+Cdr7FeYnrRWhDcPY5Z57IisOjjZkix5bPn\nR+PwLXufq8e1X26INkfXh724y/xCkaF3gztdtrwO6VTWCInso+yZ70wiwLKH/11gr0MxrrmxMMuV\n0Yvbsmcoe2Y0jt/w3GsmQ9nDmDxU2t4Dfj8614LmfE0/xKRMZ3GUIHUHf2i8xuQaBgY0Bt7qz6Cy\nfeeFEPvGoSrecPe7sSynkEvm8Kd/+yfMxSJ2mdzO1fhdT668E7VWDeWMkyu9t3SEWU5tfhq9HxuK\npVkp+s4BnEiTfiM2AFCyeQBXjQ+cCV/r3t4JfPt3ScEdivNd+Gkwetm8qyyzrZYKB7ANvhvHVmie\nVPJ0WuPANhHCwHAptk4XrrUIkuIbeQ16v3TKSMbmDc0Ng664J2hZ7kuWe5SePA+1EtwXrunvLOj7\nsLJehtnZbbeZCGUvSzLamRSSTffKPdakK/dl5FhZ9PlpxfGf+/PRh3fjsA374SdwQi8AM91dPUUO\nfMmWi0ddn1nLwO0yOfe2XsZiusQc2vvKgYTt2RJ6P7oOAGgcqDLP6yUTWF+aR93M3R+kZA3L3hKI\n71Nn+uzhWPah6neA58ialKeZKx3GDyVH2cuS7Eo31q/N6JLsO8dt2YcYqcDIXip3upA9xo0uS/zd\nsxiwJrjT6Rykdn/LnonHjUMbcs4mO36XJsvnbp8zsGXvD52mU3vb19DROGNy40xENI4EyZd/xLsa\n0iKMG8f7wluk6dVy3m3UqHSxQZY5z2edTbD9hYPAnORjbQBthkHSmziEIcqiqtCL0qxyJAmQZazd\neRteve0okGTLqEPH5sIsmmaudO9vt5bhA4Ceolw3ARP1suTfdDtIIUedoC2kCua5wW6canYW3VTS\n6ZA597fyxLfM1dh2u+9n2YdcE/DqmWU0SnlsLxrx5/QIwh/t4/yvpgrcYxbZ0pzthg2G58ahR0d8\nPz193P2MvfMgbCOAh2L77CnLnpHhVBrwPdgNIln2hJAPAfiQ+TED4ByANwP4dzCeyrcBfFTTtAjd\n9eDIkowdNYfXTixi7vmXTcuVrez5sb48y9T5no5SoBXfkcIRLBSP4svXtL6yspT9+bk7MJtzOquo\nPX9YF8KNw3NIpXS8Xi5i8bnnAQCnZ85AgoRaq4YXN3/su+ax5SfwxRc+x7839ULMZKu41lgD4PfZ\n98Mqp5VNYyefgxrwkgRFTXXp+Zu0O1acdT6zfMk5j2XZMxVDgAJ969G34aXNH+PMzG0+GWRJxuvk\nMIpX1pEpG66rpJI0Jpd32syyrfvfnK9Ap9IZO2kN/BOodNsKk6JAkoxEaldOHUHBWqhku3Ek//aa\n1DM5Xiaot7exoC76jr350EXsdFtIrxxD44VvY6Nzs68sXnTffgPBVrl1PK2k7QRsYdpnUPiqZcz0\ne/ckxqhjr4lk2Wua9oymaZc0TbsE4JsAfgbAvwTwcU3TLsLwgD85Min7YFVkvVJAi87wx4rG4fxk\nOi82b1FVWknj9eOHsTOVwXbFmWg8WloZyif3BvPl98o6KEzLnlFWL5nA+vHD6GRSaJTy2JwrYzoz\ng9vnLnB3nZrNzeL07Fn0FNlexs+7950H7rb/H9iyN18sS1nzlbLOfU4A0Ok5G0/radr9xre8eda+\ncUE4Nw7LerSYybrr2NtZbU8XcfnMssviVhL+UYn90bpelrE5P+3kNXL57AMmaAf02fsie2Rq0trW\n/+4O7Pa5C7YhQ9fJkeJRnJw+BaTT6KyusveM7Uci4d5Rqo9lbx2n3bHDunFY0TjMa2Lgsx/KjUMI\nuQDgtKZp/wnAHQC+ZB76AoCHhpRtIJzVhJSvjuXG4VQ6b+LTbdmnsD1TxKtnV/y75uzxoolBh5s8\nrpw6gmvHDjH9mV6SiTR+fPcpXF1d8B1jTVoB/EVsPKxyuuZ2ddbwuF+nwUpSZpNK0QfMcoMVg/VZ\noiz7oJWU9ncD5Mbp57MHAJleQxJynigoNNJd+GCvv1XUdlnFdkW195YNI5P3mDs9SH850qzYf8W9\n6I0un+mzty37FPMa7/lhsC17b0fofVZj2rCEZtgJ2o8B+CXzf0nTNMtsqAFgh1FQlMs5JKL06CbV\nqmNdF9QsenoPuVwKqV4XmMqgMp1HNmu8LN1CBtWqim52G+p1/wrGElL2ubPVgr3Ksaf3oL5i/D83\nU8aLO8b/uam0fX6xmAVm8lDrxrFCJuuTz0JV/ff2nlduTUHdYa+ytM59+sL78I1Xv4HDxcP42itf\nM+SbLdpZ+Kz7FAs5X/mFy1k0O02XLDPTKqolFeX6FNRuhinXnF6yf6P3WKubhvqqcezAbBnqVeP/\nmYrKrAce5foUXrsGqIU01F4G5UIe1aqKmRmCNf0V/HDjh8ZvyGaRaOtodoyXvVzIY0t2ZKtMn0NN\nvobl8jKq00VgpgC028B0HqiqKK7l0G24szPOzKio5lW7XmZmVCSKU8huJpFXM6jOFFDNq/Y9Wt2W\n3QYsqlUVsiSjcMWQz3uMptLIQ+2YzymTQ69pLOYpl6fscwslFfVtY7K6Yspu0el17Dqn2ckkkF2e\ng7Q4i1Ixhxa1YrdUotpDKw0UTT8/5xlVdvJQW8b1lbLxLNLzZdTnyyikVRR3muhlk9BlGaqaMdqR\nN2GZCd2u6brItWSoV8z3KpnD/Uv3o1o0jn/wwnvx3JXncHHxItS0eY0l81wJai5tX1udKUC9Yfxf\nKhq/s3jVqdfZagGVrIrZrTI2JaNOZ2cL9vtt4X1O0728q91vJ3NQNzL29QDQSDn3BoB0IeusfAZQ\nnS3Z17ieQQSiXhtZ2RNCSgCIpml/bn5FO6NVADf6lbGxUe93CpdqVcXaWs3+vL3VQqfXQaHeQrfR\nhl7fwfpGHY2GUeGbmw2srdWwXq+jVvOnYJ3algHz3LW1GjIJ50FZ52/dbNv/K40WUub5N2820K61\n7GNyq2GX44V1b+956xtbzPPocyVkcVf5PlzeetU+9/q1bSTkHdd9bup1X/m1WhPJrFuWjfU6pto1\n3Ljh1I/vups73GPtrlM369edMjbTTayl/fXA48ZNo+6ub2yittVEDTv2vc6qd+P/vmSsyEy0G2h0\nmmh2zPug6ZPt9uI9QM/4nNzRIdUa6Cnb6KzVUNvcQa3pruPr17ehNGpUfW4ht9lEo9HG1lYT6+t1\nyI2a3fba3bbdviyurW0Z4aO1Juptd/neOrtxo2HfK9F25NlIbdt11mh07Xusr2+jS5XR7XWZ7URV\nM3hxvgq0e0huNlGjUg5vJOtYy5hltNtImfXdYrRVQ0bnWd5I17GWduoHO0nUajtoNNrQzd+8sVFH\nvsMuy2rXqppx1UW97dzjgWOPIdMq2ccVTOHO0kU0N4EmjO9smdfr6DScOthYp2RVjHZfqzXter1+\nbQvdTBLbtY593rU1512zrHrvc9rYcL8TV2/e9LW1jS23Xql1ekhTbWOdkm09vYW1VPh3gsZqe1EU\n/jBunPsA/Bn1+a8JIZfM/x8H8OUhyo6MHXPMdeOwY5tlJcH8nibBcfV0zp2Hrvr3+IxK1Ana0Kkd\nGLDikr0kZX70DmvSCog+Qdu1VjiGXKTTz82jZw1rUNrZ8cnLKs86hxfaZx3fyWd937HKYhFmdbac\noOrcE/oaxuXgcy9ECL3klcX6LoorkZXxNQi9YL5rmQx3gpb1Dlllp6l2HCXkuad3fd95n99Vc27P\nuc/4Ax+HkYAA+CH1+ecA/BIh5C8BpAB8ehjBBsXr0+P57F0723BWx/LDCOkVtE45vbmAjciHJJvI\nYrGwxD3ez+8btuOwGv2Z6m3IJDK4Y+5O3zkH84dQTBdxvEwC5XDlFY84QdvpM0HrzYfTr6PTs4Z/\nWWrU+55/pLiMSmYahVTRPeHIeF1ePbOMG4f821BeXLiEtJLGfQuXMJ2dwWF10XdOP0UKAAql7HXF\nPRCXJRkHpoLb3pmZN7gSkg2q7GmYcxrOB1smHmESBIbpLNp3vwmtB95i7jfQr6Pwd6jc3D8cjpVW\nMJWcwpmZNwAAuow1AXQ7mZs6gIRawuwj77O/i7QT14iJ7MbRNO1XPZ+fB3D/0BJFpJKZxnZ7m3uc\n3rzawpUxL0TPS6+gDWqTwy6aoF/I95Cn8HVz960o9wob2WPVRT6Zx3vJ+5nnJOQEnlx5F/MYd/Jt\nQEvPel8t6ylsNE9/ZZ+1Cu4r130Ll+z/7ThunRNnL8t2fnWa2dws3nfiAwCMzqOfzDz5XdE4jFj0\nR448jv/2/T+0Qwm95JI5vIc8hd//zu/5D1oTuTn+Go9+GVR950ew7N2x8CGul2X3pLsJ05pndKjM\nyV7O9YCZs/74e+3PXZZlTxkC+WTe3jPgK4yyRbqEIZnJVfFy7SX7swSOZU+5FVwKSk7Ykw68BuvN\njbNXBDWOKA3H+H0647vo8JTCoMNkJ/TSisbhWPZ9Qi9955sTq9ZiuLCJ5wIXVVFRL1EIchFZKPQm\nHN7NzkPge64ehdZ6y8OBFn7f0YdZnp0KO0JdDGMcuERBsEKVbMt+sMWEXljKnjc6qZdVpGv1sSl4\nmvE7kkbE0eIx8z/3JuILeSOP+umZMwDcPTDP9eDFGga78rok2JtyjAKvgbEnDWWEnVcY9wRfDHfo\nZZCvk5fWgoU+PY0OOYH2m+4FMEBqCTsuXw+VamMQghaFWdD7oeqc+Y+gOg5MhGbcIFjZ9+m4veUF\nPQdeO3bJP2IjimUQVM24fysp36Ad1GHV0CkzWSeVh8ypp+unV/DiXSeRT6q2y22pwB7p7TYTY9lP\nJafw0NIj+P63fgDAseyPVwgWC0uQS6sAgtw4Cqy5c+/Df+fqu9HpdVwWwdZMEal6EzXGAqNRowek\nBe6XMjgsYTbm6MdTJz4IIJp1Z+H12SshlX0Y+XtHnJcsdC4jM69OJ+XffMMqYxSWPTdBnkJb9oMb\nFsNM2nthKfJRGCKD+ux5sN4FujzLfZNNZPFe8n7bLZuQEmjr3iyKfIrpEt59/H2uTWh48f7vIU+h\n0+sgqSTx0NKjaHaavj1r94qJUfaA8RDpjI5WIrRMIgPLo8mb0AnaXCAhJ/wrSyUJ60vsybFhXwDv\n9cPMAYS9chSJ2FjD40GVjRON08eNo3vdOIOOIMLJ1bnnzbic+i7a2TRjG8PhlD1c1iBbnmSS8i+P\nwI0zaFvqq3ytPSQ8ixpZ8A6Nyo3Doqs7aTPo+9CKWpEVVy6lMHgVtjdbqgWtO2RJHpuiBybIjQN4\nGgpnaEq7a+iGP6jrwbIKCqnRhVxaWMM9yxKxPk9RydYsonQs3myWwOhfsqhYL40djRPQRMO4QcJc\nG0gqZSdb4/l6w+6f6oXnUqQp5pwt8nhunECG7cQl/uhJkiSfNT2qkWYUCqmC/T4enDKStvEmrmmS\nnM2ABmE3O6xRMVGWfRjrlH4odMOkLw1TzrHSKg7lD2E66w+7C+Jdx9+DrVYN/+vFZ7kvxlLhCB5a\nesSeI1guHkNayTDv1Tcah3H89rkLOJ1exeWr1/DVV414gUEt47AM2vCTHoUaFKc/yAStl0HOf/ux\nd6Cjd7l5g3bTjVPMTeP22TuQTqS5ln3g3sZBuXFC0C9SxnIxWqUGKfvdmnt61+q7sd3eRiFdxGPL\nT+B64xoO5g1lH8Zi5z3XgRiRK2o3mShlD8DJjcM5TDdYumFaOxixwuh4HFL9OWL6kU/mkU+yN8am\nsRorYMgc5V48ZEnGYnERa9c2nXvsUgMd1D2U8izc4rk3dOiQQljGPHibl7Cgt1VkEnWCNkxnJcv2\n/VsD7ILFK3cYhRumjfQwnGUfRb58SrW3U8wkMq53pUNnP+VAr5+JistnH09dP1nK3mWFhFgwQodQ\ndXNZXD69jFZusAUXURmVch0mD/6gKxejwHI9BeF1lYSdOB50hSLr96YjhuRFtuxDdFYu1w1H2Qcm\nH/PIFmYDGV7ZbMveHYaYUbK+c+xzQyjykVjZFJZBFxRtlxzBPYUbZ48ZVGHRyl6SJNs3G9eHxaKa\nq+Ku+TeinO5jfTIYxufdj8eWn8C1xpprhBIGn7IPkMsVWTXg9BN9fiUzDVI54dtsIzSR52dD1D+d\nImHAFa+A84zffuwdeHnrZZyqnI4sI2vOwnYTShLumn8jqjn2zmL9eHDxIbS6O9wFT8MS1Ikoo1b2\nMTXtJ0vZe946qY8lwZ2gjenD4nGicjLSdbtpjczmZl0bsoTFm3+Hu7PYgIuqvNDXHswfxGr5+EDX\nu2SJOkEbxs8bwXXjvodRL6VMub87igEtV5ZltVPvUNR2CAALZuz6bhG0kGq/TNBOVDQOJAnNguHW\naE2NL8QpDHvVoQQNnSX3rPQeSNMfryslSInnKBdWOjGYRSiN8OVk7XU8sAwBPvv+5YRfVDUMrDpu\nFoz5p62ZEKOiMe29CgQn8RuF6+hWUPYTZdkDwLXlg8YS5fnBrcr9Rr8kX+PAG40jc5Sdruv2jlgn\npk+hmq3iSuky5vMHQ93HlRBvTLsIhQn3jRRuyblHpOupjoTlj6/PVbB+27E9m+uKSlLhW++jmKC9\nFTwDE6XsJUjQFWN7t3QE/+aoiEMeDIug0Mw4NlBvNE5QJ5RL5nD/4Qfsz/ceuhj6PvTPHfpl38Xc\nOMO6cUb5XOmFSBY9XXc2Ou/DON+LIFfNKCZo4/L+BBEPc25ExFF5xZlBUgTvFZIkufyr3DzvQ8pL\nXz8Kn20UQk2QD5hz3nXpCJ5pm1qUFHaPYx7jzOkeNPE76gigYbPe7hbxeMNHRFgFf7Z6DuVMZVdW\nv4YlDn69YRKW7SZByv7BxYegplTct/CA97KBcG8aPR43TqjO1lxI1ZueZh/3sEDFmI/imTa77B3T\nrPIHUWy3zZxFMV3ExcXwI7BheeP8PSikCjg3ezv3nMSIO/s4jexpJs6N4/qf0xDPzp7H2dnz+JMX\nPrNXoo2NoIYXKhpkDNDK3huNs6AeHknkhivj6ZCWXTdpXN9NDVZOqEk9WUbr4UdDh10+uPgw/l/t\n6/hW7TsjGd3udHcCjw+SHiGXzOHJlXf5thTdTY5XCI5X/Jvt0IzcshfKPn6MM49HHNjNOPthoIfc\nuyXXKN043VQCr5xdGWj1NTBA/Q+6o5Sp5EdRdzsdvmUPxFexDcKoJ+iFG2cPGHTTjPOzdwAAzs2e\nH8n9z5tDRavcIOIwpxDXhSD0ROBu+Xld+9eOwLJrTWUG3ttA9oReLhWOADC2wRsE77MLs59wWE6b\nW/GdmuYtxoqnYhsEK2laNcK6kFuJibLsB23cR4rLOJg/hJSSwnOv/9XQ939D9SxI5eTQO+GMksBo\nnJi6cdS0av8fdlvCQaE7kcQIQu8iCuH8Cwn3H34ArW5r6PZjb3o+gg78wNQ8njrxQX7Gz5hasYOQ\nSWTw/hNPj8ydE9fRzsQqe8Nn3/+aUSvmOCn6fsQ1eimfchLF7UU2zrFN0DJy40RpP7yOelQdeJBM\ncVVsgxIUhz8oce3/JtaNs9+xojJYuevjjrVdHLA3PvtRR2NEkWGkO0phdD77fljbgS4Wlnb9XrcK\nce0AJ8qyp9nviv++hQdwvXkds9lbzw+5F24ct7Ifz2tAN9HRbh+4N8pekiScmbkNs1MHMJMZbF+H\nSSauyn6yLHuXGwfoHlqAnk6jfY4fYzupJOQE5nJzt2SnR7txditnj8uNMyafPW/f0lGxF/MwkiRh\nLjc3tpQTgvBMlrL3KoZsFu1LD0KfmxuPQAHEYULUm4cmLtA+4r1QxGPLjTPEHro081NGPqCSmave\njsa5BTv6iSCmTvvIbxIh5BcAvB1ACsBvAvgSgGdgTIt+G8BHNU3b00D2OCjQW4m0ksalww/GclL5\n0SOPo9FpMPOxjIKuZ9ONYalkpnHPoXsHusYbehmVO+buRClTxqK6ZJa1dz57gZ+JcuMQQi4BuAfA\nvQDuB3AYwCcBfFzTtIswvChPjkjG0AwTSrhfO4rFwhIOTM2PWwwfc1MHcKS4vGvlj3pB3Up5FZVM\nuJQGFqNKs5xUkjhROYlcMucqS5qsgfstQ1zDUaO2hkcB/A2AzwL4UwCfB3AHDOseAL4A4KGhpRMI\ndoneiC37KMp6t1YwjzLOXjA4cbXso7pxZgAsAfgJAMsAPgdA1jTN+pU1AMV+hZTLOSQGXHVIU62q\nvu9U1Rj2F9Qs8zhXltYU1J0Mt1xv+eVyrm/5QccLl7NodqS+5+0m47pvGHZbtkI7A7XR/3nzsK6x\n2kOlMjVwOfl2AuoVU4aZAqrF0fzmH7xsyFWa6t9Go2C/Y9nB3jGaSWx7Vr2USrtT7xZRy46q7K8D\n+L6maS0AGiGkCcOVY6ECuNGvkI2NesTbg5tMqVYzcnnc7NUHSra0sbFtXxt0nXXORrKOtQz/vH7J\nnmq1Jpqd/vfbLfYyGdWg7IVs19droZ43C1o+q4z19W2sYbBymp2m057W68i2RvObJUio1ZrIdnd2\npR4tmRPtRqTyJ7Xt2c9S2d6132fJF0XhRx07fgXAY4QQiRByEMAUgD8zffkA8DiAL0csWyDYdeIw\neblbW9k5E7TCjTMOdmttyLBEsuw1Tfs8IeQ+AF+H0WF8FMCPAHyKEJIC8D0Anx6ZlALBiDlROYXL\nW5dxvByc/nY32e0VtPs16GBcXFy4H9+9/h2crY4mseKoiRx6qWnazzO+vn8IWfYV4kUcL5lEBm89\n+hNjlcHVBkZohe/dBK1owzTLxaOxTk8y/rHsLhHP+XCBwMFl2e/CqxgHV5UgPojWMCAnKqcAAKTP\n7jcCQT8G3X8hdLkizl7AYGIToe0Wd83fjQsH7hRWk2Ck7MYErYizF9BMrMbazVVso1D04kWcLOI0\nByMmaAUsJlbZCwR7gZW7ZzZGW9qJ3DgCFsKNMyaE1TUZvHPl3Wh06iik+y4Y33N2PZ+9aMO3FELZ\nCwRDkFSSSCrxUvTCjSNgMbHjvLgmI7I4pBrZJWZz8cu1L9h7RpnKWUzQClgIy35MXJi7E7PZqq30\nBfuTJ46+Hc1OA1PJqZGXPUyOfMHkMbHKftBonL22ghJyAkdLK3t6T0H8mM4OlgM/DPaG45M7cBdE\nQLQGgWDCsLZZVGTxegscJtayFwj2K8fKx3By+rWxJnkTxA+h7AWCCSObzOLOA3ePWwxBzJjYcV7c\no3EEAoFgL5lYZS8QCAQCB6HsBQKBYB8wscp+NxOhCQQCsWjrVmNilb1AIBAIHISyFwgEgn2AUPYC\ngUCwD5hYZS9CLwUCgcBhYpW9QCAQCByEshcIBJEQ+fJvLSZY2Q/mxqlmjW3lxFZuAkFCKbUDAAAH\nIUlEQVQwM9kqAGBBXRizJIJBiJwbhxDyHIBN8+OPAPwKgGdgaNlvA/iopmm9YQXcKw6pC3jL4sMo\nZyrjFkUgiDUPLT2C1+uv4VBeKPtbiUjKnhCSASBpmnaJ+u5zAD6uadpfEEJ+C8CTAD47Ein3iEPC\nUhEI+pJSUjisLo5bDMGARLXszwLIEUKeNcv4GIA7AHzJPP4FAI/gFlP2AoFAMKlEVfZ1AL8G4LcB\nrMJQ7pKmaZajvAag7y7M5XIOiYQSUQSgWlV936mqsZenmskwj+8l475/P+IsX5xlA4R8wxJn+eIs\nGxBdvqjK/nkAL5jK/XlCyHUYlr2FCuBGv0I2NuoRb2/84LW1mu/7Wq0JAFBaDebxvYInX1yIs3xx\nlg0Q8g1LnOWLs2yAI18UhR819OQjAD4BAISQgwAKAJ4lhFwyjz8O4MsRyx4JYlGVQCAQOES17H8H\nwDOEkK/AiL75CIBrAD5FCEkB+B6AT49GRIFAIBAMSyRlr2laC8AHGIfuH04cgUAgEOwGYgWRQCAQ\n7AOEshcIBIJ9wMQqe7FTlUAgEDhMrLIXCAQCgYNQ9gKBQLAPmFhlL+LsBQKBwGFilb1AIBAIHISy\nFwgEgn2AUPYCgUCwD5hYZS989gKBQOAwscpeIBAIBA5C2QsEAsE+YGKVvVhBKxAIBA4Tq+wFAoFA\n4CCUvUAgEOwDJlbZi2gcgUAgcJhYZZ+Qom7CJRAIBJPHxCn7R448hnKmgvsOXxq3KAKBQBAbJs78\nPTA1j7cde3LcYggEAkGsmDjLXiAQCAR+hLIXCASCfYBQ9gKBQLAPEMpeIBAI9gFC2QsEAsE+YKho\nHELILIBvAngYQAfAMwB0AN8G8FFN03rDCigQCASC4Yls2RNCkgD+I4CG+dUnAXxc07SLACQAIv5R\nIBAIYsIwlv2vAfgtAL9gfr4DwJfM/78A4BEAnw0qoFzOIZFQIgtQraqRr90LhHzRibNsgJBvWOIs\nX5xlA6LLF0nZE0I+BGBN07QvEkIsZS9pmmYlpKkBKPa9eUKRotxfIBAIBIMR1bL/CACdEPIQgHMA\nfh/ALHVcBXBjSNkEAoFAMCIi+ew1TbtP07T7NU27BOBbAH4awBcIIZfMUx4H8OWRSCgQCASCoRll\nbpyfA/ApQkgKwPcAfHqEZQsEAoFgCCSxfZ9AIBBMPmJRlUAgEOwDhLIXCASCfYBQ9gKBQLAPuOU2\nLyGEyAB+E8BZADsA/q6maS+MVyqAEPIcgE3z448A/ArGnD6CEHI3gH+jadolQsgKSx5CyN8D8A9g\npLv4ZU3TPj8m+c4D+DyAH5iH/4OmaX80DvnM1eG/C+AIgDSAXwbwXcSk/jjyvYz41J8C4FMACIz6\n+ocAmohB/XFkSyImdUfJ2TcVzaDy3YqW/TsAZDRNexOAfw7gE2OWB4SQDIxFZZfMvw9jzOkjCCE/\nD+C3AWTMr3zyEEIOAPgZAPcCeBTAvyaEpMck3x0APknV4R+NUb6nAVw36+oxAP8e8ao/lnxxqr+3\nAYCmafcC+DgMwycu9ceSLU51FyoVTRT5bjnLHsCbAfxPANA07WuEkAtjlgcwRhk5QsizMOr0Y4iQ\nPmLE/C2AdwH4A/MzS54ugK9qmrYDYIcQ8gKA2wB8Y0zyEULIkzAsrJ8FcNeY5PvvcEKHJRiWU5zq\njydfLOpP07Q/JoRYVuYSjAWWDyEG9ceRLTZ1ZxImFc3AdXcrWvYFADepz11CyLg7rTqMB/QojGHh\nHyJC+ohRomna/wDQpr5iyeOtyz2TkyHf1wH8U03T7gPwQwD/alzyaZq2pWlajRCiwlCqH0eM6o8j\nX2zqz5SxQwj5zwB+A/z3YVz155UtNnVHp6Khvh5J3d2Kyn4TRjoGC1nTtM64hDF5HsB/0TRN1zTt\neQDXAcxRx+OQPoKeL7Dk8dblOOX8rKZp37T+B3AeY5SPEHIYwJ8D+ANN0/4rYlZ/DPliVX8AoGna\n3wFwHIaPPMuQY2zyeWR7NkZ19xEADxNC/gLBqWgGlu9WVPZfBfBWACCEvBHA34xXHADGA/oEABBC\nDsLodZ+NWfqIv2bI83UAFwkhGUJIEcBJGBNA4+CLhJC7zP/fAmNyaizyEULmADwL4J9pmva75tex\nqT+OfHGqv5+iEiTWYXSUfxWH+uPI9pm41N0AqWgGlm/c7o8ofBZGz/d/YPgrPzxmeQDgdwA8Qwj5\nCowZ848AuIZ4pY/wpbPQNK1LCPl1GI1HBvAvNE1rjkm+fwTgNwghbQCvAfj7mqZtjkm+jwEoA/hF\nQsgvmt/9YwC/HpP6Y8n3TwD825jU32cA/B4h5H/DiHT5WRh1Fof2x5LtZcSn7bEYybsr0iUIBALB\nPuBWdOMIBAKBYECEshcIBIJ9gFD2AoFAsA8Qyl4gEAj2AULZCwQCwT5AKHuBQCDYBwhlLxAIBPuA\n/w/y2POJBkkxFQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14b99a668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.fill_between(x=range(384), y1=ytest, y2=regr.predict(xtest), interpolate=True)\n",
    "plt.plot(range(384), ytest, label=\"Actual Y test values\", alpha=0.4, color=\"g\", lw=2, ls=\"-\")\n",
    "plt.plot(range(384), regr.predict(xtest), label=\"Predicted Y test values\", alpha=0.3, color=\"r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x14a1edf28>]"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+noKf9BjGPMadg9voxtZ3EfMY/VAKEXejGmcduSQoHfECAAdBDQ/q99P+APPu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ql0pM81NKchCxBwbjy5EpkEJKurGygvg/xiaZ4CW7GyTsSi9Z7LaO4e3iAlcFpMYQywampshnRg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\n42ZQv8mIiR+QvDq9y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iUoRbR/bpXXf+pxOgu+D3pvSl166Fgicap5FgpWITj13ISyXcay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\nHDlOEXKlniNHjhynCP8fty6IQvsc1u4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14a03e630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.fill_between(x=range(ytrain.shape[0]), y1=ytrain, y2=regr.predict(xtrain), \n",
    "                 interpolate=True)\n",
    "plt.plot(range(ytrain.shape[0]), ytrain, label=\"Actual Y train values\", alpha=0.3, color=\"g\")\n",
    "plt.plot(range(ytrain.shape[0]), regr.predict(xtrain), label=\"Predicted Y train values\", alpha=0.3, color=\"r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Your answer here: ** the mse is 83\n",
    "The predictions have less varibality than actual - they are on trend, so to speak from looking at their plots, but the actual values spike higher and lower than the predicted."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Problem 1(g)\n",
    "\n",
    "Now we will create a similar database for individual players. Consider only player/year combinations in which the player had at least 500 plate appearances. Consider only the years we considered for the calculations above (after 1947 and seasons with 162 games). For each player/year compute singles, doubles, triples, HR, BB per plate appearance rates. Create a new pandas DataFrame called `playerstats` that has the playerID, yearID and the rates of these stats.  Remove the average for each year as for these rates as done in Problem 1(e). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 624,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(102816, 22) (603, 7)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>yearID</th>\n",
       "      <th>1B</th>\n",
       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
       "      <th>HR</th>\n",
       "      <th>BB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41336</th>\n",
       "      <td>brutobi01</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.176560</td>\n",
       "      <td>0.022831</td>\n",
       "      <td>0.007610</td>\n",
       "      <td>0.025875</td>\n",
       "      <td>0.092846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41386</th>\n",
       "      <td>colavro01</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.132184</td>\n",
       "      <td>0.043103</td>\n",
       "      <td>0.002874</td>\n",
       "      <td>0.064655</td>\n",
       "      <td>0.162356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41661</th>\n",
       "      <td>marisro01</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.114035</td>\n",
       "      <td>0.023392</td>\n",
       "      <td>0.005848</td>\n",
       "      <td>0.089181</td>\n",
       "      <td>0.137427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41778</th>\n",
       "      <td>richabo01</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.213873</td>\n",
       "      <td>0.024566</td>\n",
       "      <td>0.007225</td>\n",
       "      <td>0.004335</td>\n",
       "      <td>0.043353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41785</th>\n",
       "      <td>robinbr01</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.195804</td>\n",
       "      <td>0.053147</td>\n",
       "      <td>0.009790</td>\n",
       "      <td>0.009790</td>\n",
       "      <td>0.065734</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        playerID  yearID        1B        2B        3B        HR        BB\n",
       "41336  brutobi01    1961  0.176560  0.022831  0.007610  0.025875  0.092846\n",
       "41386  colavro01    1961  0.132184  0.043103  0.002874  0.064655  0.162356\n",
       "41661  marisro01    1961  0.114035  0.023392  0.005848  0.089181  0.137427\n",
       "41778  richabo01    1961  0.213873  0.024566  0.007225  0.004335  0.043353\n",
       "41785  robinbr01    1961  0.195804  0.053147  0.009790  0.009790  0.065734"
      ]
     },
     "execution_count": 624,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Your code here ###\n",
    "\n",
    "playerstats = players[players[\"yearID\"] >= 1947]\n",
    "playerstats = playerstats[playerstats[\"G\"]>=160]\n",
    "\n",
    "# adding in the two new columns\n",
    "playerstats[\"PA\"] = playerstats[\"AB\"] + playerstats[\"BB\"]\n",
    "playerstats[\"1B\"] = playerstats[\"H\"] - playerstats[\"2B\"] - playerstats[\"3B\"] - playerstats[\"HR\"]\n",
    "\n",
    "# convert numbers into rates per appearance\n",
    "for col in [\"1B\", \"2B\", \"3B\", \"HR\", \"BB\"]:\n",
    "    playerstats[col] = playerstats[col] / playerstats[\"PA\"]\n",
    "    \n",
    "# we only want these cols\n",
    "playerstats = playerstats[[\"playerID\", \"yearID\", \"1B\", \"2B\", \"3B\", \"HR\", \"BB\"]]\n",
    "\n",
    "print(players.shape, playerstats.shape)\n",
    "playerstats.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now to normalize per year:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 625,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(603, 7)"
      ]
     },
     "execution_count": 625,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def meanNormalizeRates(df):\n",
    "    \"\"\"takes in a DF and the columns to normalize\"\"\"\n",
    "    cols_to_norm = [\"1B\", \"2B\", \"3B\", \"HR\", \"BB\"]\n",
    "    tempDf = df[cols_to_norm]\n",
    "    df[cols_to_norm] = tempDf - tempDf.mean(axis=0)\n",
    "    return df\n",
    "\n",
    "playerstats = playerstats.groupby('yearID').apply(meanNormalizeRates)\n",
    "playerstats.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show the head of the `playerstats` DataFrame. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 626,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>yearID</th>\n",
       "      <th>1B</th>\n",
       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
       "      <th>HR</th>\n",
       "      <th>BB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41336</th>\n",
       "      <td>brutobi01</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.007998</td>\n",
       "      <td>-0.008939</td>\n",
       "      <td>-0.001184</td>\n",
       "      <td>-0.008974</td>\n",
       "      <td>-0.004180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41386</th>\n",
       "      <td>colavro01</td>\n",
       "      <td>1961</td>\n",
       "      <td>-0.036378</td>\n",
       "      <td>0.011334</td>\n",
       "      <td>-0.005921</td>\n",
       "      <td>0.029806</td>\n",
       "      <td>0.065330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41661</th>\n",
       "      <td>marisro01</td>\n",
       "      <td>1961</td>\n",
       "      <td>-0.054527</td>\n",
       "      <td>-0.008378</td>\n",
       "      <td>-0.002946</td>\n",
       "      <td>0.054332</td>\n",
       "      <td>0.040400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41778</th>\n",
       "      <td>richabo01</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.045310</td>\n",
       "      <td>-0.007203</td>\n",
       "      <td>-0.001569</td>\n",
       "      <td>-0.030514</td>\n",
       "      <td>-0.053674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41785</th>\n",
       "      <td>robinbr01</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.027242</td>\n",
       "      <td>0.021377</td>\n",
       "      <td>0.000996</td>\n",
       "      <td>-0.025059</td>\n",
       "      <td>-0.031292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        playerID  yearID        1B        2B        3B        HR        BB\n",
       "41336  brutobi01    1961  0.007998 -0.008939 -0.001184 -0.008974 -0.004180\n",
       "41386  colavro01    1961 -0.036378  0.011334 -0.005921  0.029806  0.065330\n",
       "41661  marisro01    1961 -0.054527 -0.008378 -0.002946  0.054332  0.040400\n",
       "41778  richabo01    1961  0.045310 -0.007203 -0.001569 -0.030514 -0.053674\n",
       "41785  robinbr01    1961  0.027242  0.021377  0.000996 -0.025059 -0.031292"
      ]
     },
     "execution_count": 626,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Your code here ###\n",
    "playerstats.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Problem 1(h)\n",
    "\n",
    "Using the `playerstats` DataFrame created in Problem 1(g), create a new DataFrame called `playerLS` containing the player's lifetime stats. This DataFrame should contain the playerID, the year the player's career started, the year the player's career ended and the player's lifetime average for each of the quantities (singles, doubles, triples, HR, BB). For simplicity we will simply compute the avaerage of the rates by year (a more correct way is to go back to the totals). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "playerstats from 1g has most of the info, and to get the career start, stop we can either search for the min and mix year for each player or just use the \"debut\" and \"finalGame\" columns from the master table.\n",
    "\n",
    "- debut          Date that player made first major league appearance\n",
    "- finalGame      Date that player made first major league appearance (blank if still active)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 627,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>debut</th>\n",
       "      <th>finalGame</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aardsda01</td>\n",
       "      <td>2004-04-06</td>\n",
       "      <td>2015-08-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>1954-04-13</td>\n",
       "      <td>1976-10-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID       debut   finalGame\n",
       "0  aardsda01  2004-04-06  2015-08-23\n",
       "1  aaronha01  1954-04-13  1976-10-03"
      ]
     },
     "execution_count": 627,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master[[\"playerID\", \"debut\", \"finalGame\"]].head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 628,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>1B</th>\n",
       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
       "      <th>HR</th>\n",
       "      <th>BB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>-0.007832</td>\n",
       "      <td>0.002895</td>\n",
       "      <td>-0.003433</td>\n",
       "      <td>0.024918</td>\n",
       "      <td>0.023050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>abreubo01</td>\n",
       "      <td>-0.014127</td>\n",
       "      <td>0.008774</td>\n",
       "      <td>-0.001982</td>\n",
       "      <td>-0.008251</td>\n",
       "      <td>0.052051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ageeto01</td>\n",
       "      <td>0.009557</td>\n",
       "      <td>0.003229</td>\n",
       "      <td>0.003496</td>\n",
       "      <td>0.001698</td>\n",
       "      <td>-0.027181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>allendi01</td>\n",
       "      <td>0.001701</td>\n",
       "      <td>0.007721</td>\n",
       "      <td>0.009833</td>\n",
       "      <td>0.003657</td>\n",
       "      <td>0.014888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>alomaro01</td>\n",
       "      <td>0.009168</td>\n",
       "      <td>0.010377</td>\n",
       "      <td>0.008724</td>\n",
       "      <td>-0.015226</td>\n",
       "      <td>-0.014299</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID        1B        2B        3B        HR        BB\n",
       "0  aaronha01 -0.007832  0.002895 -0.003433  0.024918  0.023050\n",
       "1  abreubo01 -0.014127  0.008774 -0.001982 -0.008251  0.052051\n",
       "2   ageeto01  0.009557  0.003229  0.003496  0.001698 -0.027181\n",
       "3  allendi01  0.001701  0.007721  0.009833  0.003657  0.014888\n",
       "4  alomaro01  0.009168  0.010377  0.008724 -0.015226 -0.014299"
      ]
     },
     "execution_count": 628,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Your code here ###\n",
    "playerLS = playerstats.groupby(\"playerID\").mean().drop(\"yearID\", axis=1).reset_index()\n",
    "playerLS.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 629,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>1B</th>\n",
       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
       "      <th>HR</th>\n",
       "      <th>BB</th>\n",
       "      <th>debut</th>\n",
       "      <th>finalGame</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>-0.007832</td>\n",
       "      <td>0.002895</td>\n",
       "      <td>-0.003433</td>\n",
       "      <td>0.024918</td>\n",
       "      <td>0.023050</td>\n",
       "      <td>1954-04-13</td>\n",
       "      <td>1976-10-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>abreubo01</td>\n",
       "      <td>-0.014127</td>\n",
       "      <td>0.008774</td>\n",
       "      <td>-0.001982</td>\n",
       "      <td>-0.008251</td>\n",
       "      <td>0.052051</td>\n",
       "      <td>1996-09-01</td>\n",
       "      <td>2014-09-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ageeto01</td>\n",
       "      <td>0.009557</td>\n",
       "      <td>0.003229</td>\n",
       "      <td>0.003496</td>\n",
       "      <td>0.001698</td>\n",
       "      <td>-0.027181</td>\n",
       "      <td>1962-09-14</td>\n",
       "      <td>1973-09-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>allendi01</td>\n",
       "      <td>0.001701</td>\n",
       "      <td>0.007721</td>\n",
       "      <td>0.009833</td>\n",
       "      <td>0.003657</td>\n",
       "      <td>0.014888</td>\n",
       "      <td>1963-09-03</td>\n",
       "      <td>1977-06-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>alomaro01</td>\n",
       "      <td>0.009168</td>\n",
       "      <td>0.010377</td>\n",
       "      <td>0.008724</td>\n",
       "      <td>-0.015226</td>\n",
       "      <td>-0.014299</td>\n",
       "      <td>1988-04-22</td>\n",
       "      <td>2004-09-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID        1B        2B        3B        HR        BB       debut  \\\n",
       "0  aaronha01 -0.007832  0.002895 -0.003433  0.024918  0.023050  1954-04-13   \n",
       "1  abreubo01 -0.014127  0.008774 -0.001982 -0.008251  0.052051  1996-09-01   \n",
       "2   ageeto01  0.009557  0.003229  0.003496  0.001698 -0.027181  1962-09-14   \n",
       "3  allendi01  0.001701  0.007721  0.009833  0.003657  0.014888  1963-09-03   \n",
       "4  alomaro01  0.009168  0.010377  0.008724 -0.015226 -0.014299  1988-04-22   \n",
       "\n",
       "    finalGame  \n",
       "0  1976-10-03  \n",
       "1  2014-09-28  \n",
       "2  1973-09-30  \n",
       "3  1977-06-19  \n",
       "4  2004-09-05  "
      ]
     },
     "execution_count": 629,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "playerLS = playerLS.merge(master[[\"playerID\", \"debut\", \"finalGame\"]], how=\"inner\", on=\"playerID\")\n",
    "playerLS.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show the head of the `playerLS` DataFrame. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Your code here ###"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Problem 1(i)\n",
    "\n",
    "Compute the OPW for each player based on the average rates in the `playerLS` DataFrame. You can interpret this summary statistic as the predicted wins for a team with 9 batters exactly like the player in question. Add this column to the playerLS DataFrame. Call this colum OPW."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "offensive predicted wins (OPW)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 630,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>1B</th>\n",
       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
       "      <th>HR</th>\n",
       "      <th>BB</th>\n",
       "      <th>debut</th>\n",
       "      <th>finalGame</th>\n",
       "      <th>OPW</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>-0.007832</td>\n",
       "      <td>0.002895</td>\n",
       "      <td>-0.003433</td>\n",
       "      <td>0.024918</td>\n",
       "      <td>0.023050</td>\n",
       "      <td>1954-04-13</td>\n",
       "      <td>1976-10-03</td>\n",
       "      <td>107.694862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>abreubo01</td>\n",
       "      <td>-0.014127</td>\n",
       "      <td>0.008774</td>\n",
       "      <td>-0.001982</td>\n",
       "      <td>-0.008251</td>\n",
       "      <td>0.052051</td>\n",
       "      <td>1996-09-01</td>\n",
       "      <td>2014-09-28</td>\n",
       "      <td>95.462674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ageeto01</td>\n",
       "      <td>0.009557</td>\n",
       "      <td>0.003229</td>\n",
       "      <td>0.003496</td>\n",
       "      <td>0.001698</td>\n",
       "      <td>-0.027181</td>\n",
       "      <td>1962-09-14</td>\n",
       "      <td>1973-09-30</td>\n",
       "      <td>79.155456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>allendi01</td>\n",
       "      <td>0.001701</td>\n",
       "      <td>0.007721</td>\n",
       "      <td>0.009833</td>\n",
       "      <td>0.003657</td>\n",
       "      <td>0.014888</td>\n",
       "      <td>1963-09-03</td>\n",
       "      <td>1977-06-19</td>\n",
       "      <td>107.425460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>alomaro01</td>\n",
       "      <td>0.009168</td>\n",
       "      <td>0.010377</td>\n",
       "      <td>0.008724</td>\n",
       "      <td>-0.015226</td>\n",
       "      <td>-0.014299</td>\n",
       "      <td>1988-04-22</td>\n",
       "      <td>2004-09-05</td>\n",
       "      <td>80.458396</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID        1B        2B        3B        HR        BB       debut  \\\n",
       "0  aaronha01 -0.007832  0.002895 -0.003433  0.024918  0.023050  1954-04-13   \n",
       "1  abreubo01 -0.014127  0.008774 -0.001982 -0.008251  0.052051  1996-09-01   \n",
       "2   ageeto01  0.009557  0.003229  0.003496  0.001698 -0.027181  1962-09-14   \n",
       "3  allendi01  0.001701  0.007721  0.009833  0.003657  0.014888  1963-09-03   \n",
       "4  alomaro01  0.009168  0.010377  0.008724 -0.015226 -0.014299  1988-04-22   \n",
       "\n",
       "    finalGame         OPW  \n",
       "0  1976-10-03  107.694862  \n",
       "1  2014-09-28   95.462674  \n",
       "2  1973-09-30   79.155456  \n",
       "3  1977-06-19  107.425460  \n",
       "4  2004-09-05   80.458396  "
      ]
     },
     "execution_count": 630,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "playerLS[\"OPW\"] = regr.predict(playerLS[[\"1B\", \"2B\", \"3B\", \"HR\", \"BB\"]])\n",
    "playerLS.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Problem 1(j)\n",
    "\n",
    "Add four columns to the `playerLS` DataFrame that contains the player's position (C, 1B, 2B, 3B, SS, LF, CF, RF, or OF), first name, last name and median salary. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fielding table contains the position 'POS', and we alread have the name and median salary in the df `medianSalaries`.\n",
    "\n",
    "First adding the median salaries to the playerLs df:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 631,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>1B</th>\n",
       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
       "      <th>HR</th>\n",
       "      <th>BB</th>\n",
       "      <th>debut</th>\n",
       "      <th>finalGame</th>\n",
       "      <th>OPW</th>\n",
       "      <th>salary</th>\n",
       "      <th>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>abreubo01</td>\n",
       "      <td>-0.014127</td>\n",
       "      <td>0.008774</td>\n",
       "      <td>-0.001982</td>\n",
       "      <td>-0.008251</td>\n",
       "      <td>0.052051</td>\n",
       "      <td>1996-09-01</td>\n",
       "      <td>2014-09-28</td>\n",
       "      <td>95.462674</td>\n",
       "      <td>9000000.0</td>\n",
       "      <td>Bobby</td>\n",
       "      <td>Abreu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alomaro01</td>\n",
       "      <td>0.009168</td>\n",
       "      <td>0.010377</td>\n",
       "      <td>0.008724</td>\n",
       "      <td>-0.015226</td>\n",
       "      <td>-0.014299</td>\n",
       "      <td>1988-04-22</td>\n",
       "      <td>2004-09-05</td>\n",
       "      <td>80.458396</td>\n",
       "      <td>5466667.0</td>\n",
       "      <td>Roberto</td>\n",
       "      <td>Alomar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>altuvjo01</td>\n",
       "      <td>0.055975</td>\n",
       "      <td>0.010345</td>\n",
       "      <td>0.002287</td>\n",
       "      <td>-0.012276</td>\n",
       "      <td>0.001718</td>\n",
       "      <td>2011-07-20</td>\n",
       "      <td>2016-10-02</td>\n",
       "      <td>109.362671</td>\n",
       "      <td>1250000.0</td>\n",
       "      <td>Jose</td>\n",
       "      <td>Altuve</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>anderga01</td>\n",
       "      <td>0.034407</td>\n",
       "      <td>0.002158</td>\n",
       "      <td>-0.002039</td>\n",
       "      <td>-0.014866</td>\n",
       "      <td>-0.076270</td>\n",
       "      <td>1994-07-27</td>\n",
       "      <td>2010-08-06</td>\n",
       "      <td>46.572173</td>\n",
       "      <td>3250000.0</td>\n",
       "      <td>Garret</td>\n",
       "      <td>Anderson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>andruel01</td>\n",
       "      <td>0.023382</td>\n",
       "      <td>0.001604</td>\n",
       "      <td>0.001352</td>\n",
       "      <td>-0.030660</td>\n",
       "      <td>-0.024045</td>\n",
       "      <td>2009-04-06</td>\n",
       "      <td>2016-10-02</td>\n",
       "      <td>57.419704</td>\n",
       "      <td>3837500.0</td>\n",
       "      <td>Elvis</td>\n",
       "      <td>Andrus</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID        1B        2B        3B        HR        BB       debut  \\\n",
       "0  abreubo01 -0.014127  0.008774 -0.001982 -0.008251  0.052051  1996-09-01   \n",
       "1  alomaro01  0.009168  0.010377  0.008724 -0.015226 -0.014299  1988-04-22   \n",
       "2  altuvjo01  0.055975  0.010345  0.002287 -0.012276  0.001718  2011-07-20   \n",
       "3  anderga01  0.034407  0.002158 -0.002039 -0.014866 -0.076270  1994-07-27   \n",
       "4  andruel01  0.023382  0.001604  0.001352 -0.030660 -0.024045  2009-04-06   \n",
       "\n",
       "    finalGame         OPW     salary nameFirst  nameLast  \n",
       "0  2014-09-28   95.462674  9000000.0     Bobby     Abreu  \n",
       "1  2004-09-05   80.458396  5466667.0   Roberto    Alomar  \n",
       "2  2016-10-02  109.362671  1250000.0      Jose    Altuve  \n",
       "3  2010-08-06   46.572173  3250000.0    Garret  Anderson  \n",
       "4  2016-10-02   57.419704  3837500.0     Elvis    Andrus  "
      ]
     },
     "execution_count": 631,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Your code here ###\n",
    "playerLS = playerLS.merge(medianSalaries, how=\"inner\", on=\"playerID\")\n",
    "playerLS.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now to add in the POS of each player from the fielding table. This is trickier than it looks as the fielding table contains the fielding position for each year, and the playerLS table has only one entry per player. So I'm going to go with the most frequently used fielding position for each player and add that to the playerLS table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 632,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(227, 13)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>1B</th>\n",
       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
       "      <th>HR</th>\n",
       "      <th>BB</th>\n",
       "      <th>debut</th>\n",
       "      <th>finalGame</th>\n",
       "      <th>OPW</th>\n",
       "      <th>salary</th>\n",
       "      <th>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "      <th>Pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>abreubo01</td>\n",
       "      <td>-0.014127</td>\n",
       "      <td>0.008774</td>\n",
       "      <td>-0.001982</td>\n",
       "      <td>-0.008251</td>\n",
       "      <td>0.052051</td>\n",
       "      <td>1996-09-01</td>\n",
       "      <td>2014-09-28</td>\n",
       "      <td>95.462674</td>\n",
       "      <td>9000000.0</td>\n",
       "      <td>Bobby</td>\n",
       "      <td>Abreu</td>\n",
       "      <td>OF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alomaro01</td>\n",
       "      <td>0.009168</td>\n",
       "      <td>0.010377</td>\n",
       "      <td>0.008724</td>\n",
       "      <td>-0.015226</td>\n",
       "      <td>-0.014299</td>\n",
       "      <td>1988-04-22</td>\n",
       "      <td>2004-09-05</td>\n",
       "      <td>80.458396</td>\n",
       "      <td>5466667.0</td>\n",
       "      <td>Roberto</td>\n",
       "      <td>Alomar</td>\n",
       "      <td>2B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>altuvjo01</td>\n",
       "      <td>0.055975</td>\n",
       "      <td>0.010345</td>\n",
       "      <td>0.002287</td>\n",
       "      <td>-0.012276</td>\n",
       "      <td>0.001718</td>\n",
       "      <td>2011-07-20</td>\n",
       "      <td>2016-10-02</td>\n",
       "      <td>109.362671</td>\n",
       "      <td>1250000.0</td>\n",
       "      <td>Jose</td>\n",
       "      <td>Altuve</td>\n",
       "      <td>2B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>anderga01</td>\n",
       "      <td>0.034407</td>\n",
       "      <td>0.002158</td>\n",
       "      <td>-0.002039</td>\n",
       "      <td>-0.014866</td>\n",
       "      <td>-0.076270</td>\n",
       "      <td>1994-07-27</td>\n",
       "      <td>2010-08-06</td>\n",
       "      <td>46.572173</td>\n",
       "      <td>3250000.0</td>\n",
       "      <td>Garret</td>\n",
       "      <td>Anderson</td>\n",
       "      <td>OF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>andruel01</td>\n",
       "      <td>0.023382</td>\n",
       "      <td>0.001604</td>\n",
       "      <td>0.001352</td>\n",
       "      <td>-0.030660</td>\n",
       "      <td>-0.024045</td>\n",
       "      <td>2009-04-06</td>\n",
       "      <td>2016-10-02</td>\n",
       "      <td>57.419704</td>\n",
       "      <td>3837500.0</td>\n",
       "      <td>Elvis</td>\n",
       "      <td>Andrus</td>\n",
       "      <td>SS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID        1B        2B        3B        HR        BB       debut  \\\n",
       "0  abreubo01 -0.014127  0.008774 -0.001982 -0.008251  0.052051  1996-09-01   \n",
       "1  alomaro01  0.009168  0.010377  0.008724 -0.015226 -0.014299  1988-04-22   \n",
       "2  altuvjo01  0.055975  0.010345  0.002287 -0.012276  0.001718  2011-07-20   \n",
       "3  anderga01  0.034407  0.002158 -0.002039 -0.014866 -0.076270  1994-07-27   \n",
       "4  andruel01  0.023382  0.001604  0.001352 -0.030660 -0.024045  2009-04-06   \n",
       "\n",
       "    finalGame         OPW     salary nameFirst  nameLast Pos  \n",
       "0  2014-09-28   95.462674  9000000.0     Bobby     Abreu  OF  \n",
       "1  2004-09-05   80.458396  5466667.0   Roberto    Alomar  2B  \n",
       "2  2016-10-02  109.362671  1250000.0      Jose    Altuve  2B  \n",
       "3  2010-08-06   46.572173  3250000.0    Garret  Anderson  OF  \n",
       "4  2016-10-02   57.419704  3837500.0     Elvis    Andrus  SS  "
      ]
     },
     "execution_count": 632,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "def findPosition(df):\n",
    "    \"\"\"takes in a df, and for each row returns a POS by selecting\n",
    "    the most frequent POS for that player from the fielding table\"\"\"\n",
    "    playerID = df[\"playerID\"]\n",
    "    positions = Counter(fielding[fielding[\"playerID\"]==playerID][\"POS\"])\n",
    "    return positions.most_common(1)[0][0]\n",
    "\n",
    "#apply the function to each row:\n",
    "playerLS[\"Pos\"] = playerLS.apply(findPosition, axis=1)\n",
    "print(playerLS.shape)\n",
    "playerLS.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show the head of the `playerLS` DataFrame. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Your code here ###"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Problem 1(k)\n",
    "\n",
    "Subset the `playerLS` DataFrame for players active in 2002 and 2003 and played at least three years. Plot and describe the relationship bewteen the median salary (in millions) and the predicted number of wins. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, converting to datetime:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 633,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(227, 13)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>1B</th>\n",
       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
       "      <th>HR</th>\n",
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       "      <th>Pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>abreubo01</td>\n",
       "      <td>-0.014127</td>\n",
       "      <td>0.008774</td>\n",
       "      <td>-0.001982</td>\n",
       "      <td>-0.008251</td>\n",
       "      <td>0.052051</td>\n",
       "      <td>1996-09-01</td>\n",
       "      <td>2014-09-28</td>\n",
       "      <td>95.462674</td>\n",
       "      <td>9000000.0</td>\n",
       "      <td>Bobby</td>\n",
       "      <td>Abreu</td>\n",
       "      <td>OF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alomaro01</td>\n",
       "      <td>0.009168</td>\n",
       "      <td>0.010377</td>\n",
       "      <td>0.008724</td>\n",
       "      <td>-0.015226</td>\n",
       "      <td>-0.014299</td>\n",
       "      <td>1988-04-22</td>\n",
       "      <td>2004-09-05</td>\n",
       "      <td>80.458396</td>\n",
       "      <td>5466667.0</td>\n",
       "      <td>Roberto</td>\n",
       "      <td>Alomar</td>\n",
       "      <td>2B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID        1B        2B        3B        HR        BB      debut  \\\n",
       "0  abreubo01 -0.014127  0.008774 -0.001982 -0.008251  0.052051 1996-09-01   \n",
       "1  alomaro01  0.009168  0.010377  0.008724 -0.015226 -0.014299 1988-04-22   \n",
       "\n",
       "   finalGame        OPW     salary nameFirst nameLast Pos  \n",
       "0 2014-09-28  95.462674  9000000.0     Bobby    Abreu  OF  \n",
       "1 2004-09-05  80.458396  5466667.0   Roberto   Alomar  2B  "
      ]
     },
     "execution_count": 633,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Your code here ###\n",
    "playerLS[\"debut\"] = pd.to_datetime(playerLS[\"debut\"])\n",
    "playerLS[\"finalGame\"] = pd.to_datetime(playerLS[\"finalGame\"])\n",
    "print(playerLS.shape)\n",
    "playerLS.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Building a mask to select:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 634,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(78, 13)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>1B</th>\n",
       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
       "      <th>HR</th>\n",
       "      <th>BB</th>\n",
       "      <th>debut</th>\n",
       "      <th>finalGame</th>\n",
       "      <th>OPW</th>\n",
       "      <th>salary</th>\n",
       "      <th>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "      <th>Pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>abreubo01</td>\n",
       "      <td>-0.014127</td>\n",
       "      <td>0.008774</td>\n",
       "      <td>-0.001982</td>\n",
       "      <td>-0.008251</td>\n",
       "      <td>0.052051</td>\n",
       "      <td>1996-09-01</td>\n",
       "      <td>2014-09-28</td>\n",
       "      <td>95.462674</td>\n",
       "      <td>9000000.0</td>\n",
       "      <td>Bobby</td>\n",
       "      <td>Abreu</td>\n",
       "      <td>OF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alomaro01</td>\n",
       "      <td>0.009168</td>\n",
       "      <td>0.010377</td>\n",
       "      <td>0.008724</td>\n",
       "      <td>-0.015226</td>\n",
       "      <td>-0.014299</td>\n",
       "      <td>1988-04-22</td>\n",
       "      <td>2004-09-05</td>\n",
       "      <td>80.458396</td>\n",
       "      <td>5466667.0</td>\n",
       "      <td>Roberto</td>\n",
       "      <td>Alomar</td>\n",
       "      <td>2B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>anderga01</td>\n",
       "      <td>0.034407</td>\n",
       "      <td>0.002158</td>\n",
       "      <td>-0.002039</td>\n",
       "      <td>-0.014866</td>\n",
       "      <td>-0.076270</td>\n",
       "      <td>1994-07-27</td>\n",
       "      <td>2010-08-06</td>\n",
       "      <td>46.572173</td>\n",
       "      <td>3250000.0</td>\n",
       "      <td>Garret</td>\n",
       "      <td>Anderson</td>\n",
       "      <td>OF</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID        1B        2B        3B        HR        BB      debut  \\\n",
       "0  abreubo01 -0.014127  0.008774 -0.001982 -0.008251  0.052051 1996-09-01   \n",
       "1  alomaro01  0.009168  0.010377  0.008724 -0.015226 -0.014299 1988-04-22   \n",
       "3  anderga01  0.034407  0.002158 -0.002039 -0.014866 -0.076270 1994-07-27   \n",
       "\n",
       "   finalGame        OPW     salary nameFirst  nameLast Pos  \n",
       "0 2014-09-28  95.462674  9000000.0     Bobby     Abreu  OF  \n",
       "1 2004-09-05  80.458396  5466667.0   Roberto    Alomar  2B  \n",
       "3 2010-08-06  46.572173  3250000.0    Garret  Anderson  OF  "
      ]
     },
     "execution_count": 634,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = (((playerLS[\"finalGame\"].dt.year - playerLS[\"debut\"].dt.year) >= 3) & \n",
    "        (playerLS[\"debut\"].dt.year <= 2002) & \n",
    "        (playerLS[\"finalGame\"].dt.year >= 2003))\n",
    "\n",
    "playeractive = playerLS[mask]\n",
    "print(playeractive.shape)\n",
    "playeractive.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 635,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ko/anaconda/lib/python3.6/site-packages/seaborn/linearmodels.py:267: RuntimeWarning: invalid value encountered in log\n",
      "  grid = np.c_[np.ones(len(grid)), np.log(grid)]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0, 250)"
      ]
     },
     "execution_count": 635,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Cv22tvcUYcynwNfwV1Y4Cn7LWlo0xvwh8EnCBz1tr72xnm1qpW3pUnTI5s1j9SjW/WFOG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\nkZD8/8Ad/EcsCxQcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x151ddc438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SlV0PJk8EHD6VYWi87QKJtS1fgDAMfwL4Y4AoiubCMFwmdYb8HPDvSIfz/j/N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\nkiSNgQVVkqQxsKBKkjQG/wcuK1U2bIp7PQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x151d70b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.log(playeractive[\"salary\"])\n",
    "sns.lmplot(\"salary\", \"OPW\", data=playeractive, logx=True)\n",
    "plt.ylim(0,250)\n",
    "sns.lmplot(\"salary\", \"OPW\", data=playeractive, hue=\"Pos\", size=6, fit_reg=True)\n",
    "plt.ylim(0,250)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "in the first chart, there is a lot of OPW variation at the same salary, with a steady inrease with salary. Though the guy on the far right seems vastly overpaid for his OPW. \n",
    "The second chart does a linear regression per position, where there is a lot more varition, though this dataset doesn't have enough points to really say."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Problem 1(l)\n",
    "Pick one players from one of each of these 10 position C, 1B, 2B, 3B, SS, LF, CF, RF, DH, or OF keeping the total median salary of all 10 players below 20 million. Report their averaged predicted wins and total salary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 812,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Salary: 19,225,000, Mean OPW: 85.99\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>1B</th>\n",
       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
       "      <th>HR</th>\n",
       "      <th>BB</th>\n",
       "      <th>debut</th>\n",
       "      <th>finalGame</th>\n",
       "      <th>OPW</th>\n",
       "      <th>salary</th>\n",
       "      <th>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "      <th>Pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>mcgrifr01</td>\n",
       "      <td>-0.045828</td>\n",
       "      <td>-0.004944</td>\n",
       "      <td>-0.002342</td>\n",
       "      <td>0.027440</td>\n",
       "      <td>0.078672</td>\n",
       "      <td>1986-05-17</td>\n",
       "      <td>2004-07-15</td>\n",
       "      <td>115.192971</td>\n",
       "      <td>4275000.0</td>\n",
       "      <td>Fred</td>\n",
       "      <td>McGriff</td>\n",
       "      <td>1B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>biggicr01</td>\n",
       "      <td>0.012406</td>\n",
       "      <td>0.006105</td>\n",
       "      <td>-0.000200</td>\n",
       "      <td>-0.015707</td>\n",
       "      <td>0.008167</td>\n",
       "      <td>1988-06-26</td>\n",
       "      <td>2007-09-30</td>\n",
       "      <td>81.420779</td>\n",
       "      <td>4000000.0</td>\n",
       "      <td>Craig</td>\n",
       "      <td>Biggio</td>\n",
       "      <td>2B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>booneaa01</td>\n",
       "      <td>-0.025670</td>\n",
       "      <td>0.004521</td>\n",
       "      <td>-0.001056</td>\n",
       "      <td>-0.006579</td>\n",
       "      <td>-0.012648</td>\n",
       "      <td>1997-06-20</td>\n",
       "      <td>2009-10-04</td>\n",
       "      <td>56.273163</td>\n",
       "      <td>925000.0</td>\n",
       "      <td>Aaron</td>\n",
       "      <td>Boone</td>\n",
       "      <td>3B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>rolliji01</td>\n",
       "      <td>-0.008713</td>\n",
       "      <td>-0.001946</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>-0.008171</td>\n",
       "      <td>-0.010932</td>\n",
       "      <td>2000-09-17</td>\n",
       "      <td>2016-06-08</td>\n",
       "      <td>73.728189</td>\n",
       "      <td>8000000.0</td>\n",
       "      <td>Jimmy</td>\n",
       "      <td>Rollins</td>\n",
       "      <td>SS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>gonzalu01</td>\n",
       "      <td>0.004170</td>\n",
       "      <td>0.003094</td>\n",
       "      <td>0.001470</td>\n",
       "      <td>0.012240</td>\n",
       "      <td>0.012480</td>\n",
       "      <td>1990-09-04</td>\n",
       "      <td>2008-09-28</td>\n",
       "      <td>103.358827</td>\n",
       "      <td>2025000.0</td>\n",
       "      <td>Luis</td>\n",
       "      <td>Gonzalez</td>\n",
       "      <td>OF</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      playerID        1B        2B        3B        HR        BB      debut  \\\n",
       "139  mcgrifr01 -0.045828 -0.004944 -0.002342  0.027440  0.078672 1986-05-17   \n",
       "22   biggicr01  0.012406  0.006105 -0.000200 -0.015707  0.008167 1988-06-26   \n",
       "27   booneaa01 -0.025670  0.004521 -0.001056 -0.006579 -0.012648 1997-06-20   \n",
       "176  rolliji01 -0.008713 -0.001946  0.010000 -0.008171 -0.010932 2000-09-17   \n",
       "89   gonzalu01  0.004170  0.003094  0.001470  0.012240  0.012480 1990-09-04   \n",
       "\n",
       "     finalGame         OPW     salary nameFirst  nameLast Pos  \n",
       "139 2004-07-15  115.192971  4275000.0      Fred   McGriff  1B  \n",
       "22  2007-09-30   81.420779  4000000.0     Craig    Biggio  2B  \n",
       "27  2009-10-04   56.273163   925000.0     Aaron     Boone  3B  \n",
       "176 2016-06-08   73.728189  8000000.0     Jimmy   Rollins  SS  \n",
       "89  2008-09-28  103.358827  2025000.0      Luis  Gonzalez  OF  "
      ]
     },
     "execution_count": 812,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Your code here ###\n",
    "columns = [\"playerID\", \"Pos\", \"salary\", \"OPW\"]\n",
    "\n",
    "salary_cap = 20*np.power(10, 6)\n",
    "\n",
    "def make_team(salary_cap=salary_cap):\n",
    "    df = pd.DataFrame()\n",
    "    for pos in [\"C\", \"1B\", \"2B\", \"3B\", \"SS\", \"LF\", \"CF\", \"RF\", \"DH\", \"OF\"]:\n",
    "        df_temp = playeractive[playeractive[\"Pos\"]==pos]\n",
    "        if df_temp.shape[0] > 0:\n",
    "            df = df.append(df_temp.sample())\n",
    "    return df\n",
    "\n",
    "scores = []\n",
    "best_team=make_team()\n",
    "              \n",
    "# going to make a bunch of random teams with the salary cap less then 20M\n",
    "for i in range(150):\n",
    "    df = make_team()\n",
    "    while df[\"salary\"].sum() >= salary_cap:\n",
    "        df = make_team()\n",
    "    score = df[\"OPW\"].mean()\n",
    "    scores.append(score)\n",
    "    if score >= max(scores):\n",
    "        best_team = df\n",
    "\n",
    "# The team with the highest mean OPW as per above:\n",
    "print(f\"Salary: {best_team['salary'].sum():,.0f}, Mean OPW: {best_team.OPW.mean():.2f}\")\n",
    "best_team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 813,
   "metadata": {},
   "outputs": [
    {
     "data": {
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1YYDxqTBraopM21nRaurKY6WZ0xcHTY5EpAtJ7mLFvXE2tp6tSaY/pkxlaR7uHAenLw7I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\nwoL+PyxLCklcsOgZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x158b8a9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(scores)\n",
    "plt.axvline(max(scores), color=\"r\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: redo this later, but good enough for now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Problem 1(m)\n",
    "What do these players outperform in? Singles, doubles, triples HR or BB?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "so we need to know which of `[\"1B\",\"2B\",\"3B\",\"HR\",\"BB\"]` contributes most to OPW. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 816,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 621.88427825, -340.92979757,  476.95997605,  834.65337277,\n",
       "        442.93047415])"
      ]
     },
     "execution_count": 816,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr2 = linear_model.LinearRegression()\n",
    "regr2.fit(best_team[[\"1B\",\"2B\",\"3B\",\"HR\",\"BB\"]], best_team[\"OPW\"])\n",
    "regr2.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 817,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>1B</th>\n",
       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
       "      <th>HR</th>\n",
       "      <th>BB</th>\n",
       "      <th>debut</th>\n",
       "      <th>finalGame</th>\n",
       "      <th>OPW</th>\n",
       "      <th>salary</th>\n",
       "      <th>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "      <th>Pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>mcgrifr01</td>\n",
       "      <td>-2849.959598</td>\n",
       "      <td>168.544770</td>\n",
       "      <td>-111.708611</td>\n",
       "      <td>2290.289714</td>\n",
       "      <td>3484.623079</td>\n",
       "      <td>1986-05-17</td>\n",
       "      <td>2004-07-15</td>\n",
       "      <td>115.192971</td>\n",
       "      <td>4275000.0</td>\n",
       "      <td>Fred</td>\n",
       "      <td>McGriff</td>\n",
       "      <td>1B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>biggicr01</td>\n",
       "      <td>771.524101</td>\n",
       "      <td>-208.147868</td>\n",
       "      <td>-9.523578</td>\n",
       "      <td>-1311.004953</td>\n",
       "      <td>361.722421</td>\n",
       "      <td>1988-06-26</td>\n",
       "      <td>2007-09-30</td>\n",
       "      <td>81.420779</td>\n",
       "      <td>4000000.0</td>\n",
       "      <td>Craig</td>\n",
       "      <td>Biggio</td>\n",
       "      <td>2B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>booneaa01</td>\n",
       "      <td>-1596.372187</td>\n",
       "      <td>-154.124887</td>\n",
       "      <td>-50.389977</td>\n",
       "      <td>-549.078708</td>\n",
       "      <td>-560.225737</td>\n",
       "      <td>1997-06-20</td>\n",
       "      <td>2009-10-04</td>\n",
       "      <td>56.273163</td>\n",
       "      <td>925000.0</td>\n",
       "      <td>Aaron</td>\n",
       "      <td>Boone</td>\n",
       "      <td>3B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>rolliji01</td>\n",
       "      <td>-541.837860</td>\n",
       "      <td>66.333591</td>\n",
       "      <td>476.965145</td>\n",
       "      <td>-681.958606</td>\n",
       "      <td>-484.191138</td>\n",
       "      <td>2000-09-17</td>\n",
       "      <td>2016-06-08</td>\n",
       "      <td>73.728189</td>\n",
       "      <td>8000000.0</td>\n",
       "      <td>Jimmy</td>\n",
       "      <td>Rollins</td>\n",
       "      <td>SS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>gonzalu01</td>\n",
       "      <td>259.351878</td>\n",
       "      <td>-105.476641</td>\n",
       "      <td>70.117467</td>\n",
       "      <td>1021.619694</td>\n",
       "      <td>552.762544</td>\n",
       "      <td>1990-09-04</td>\n",
       "      <td>2008-09-28</td>\n",
       "      <td>103.358827</td>\n",
       "      <td>2025000.0</td>\n",
       "      <td>Luis</td>\n",
       "      <td>Gonzalez</td>\n",
       "      <td>OF</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      playerID           1B          2B          3B           HR           BB  \\\n",
       "139  mcgrifr01 -2849.959598  168.544770 -111.708611  2290.289714  3484.623079   \n",
       "22   biggicr01   771.524101 -208.147868   -9.523578 -1311.004953   361.722421   \n",
       "27   booneaa01 -1596.372187 -154.124887  -50.389977  -549.078708  -560.225737   \n",
       "176  rolliji01  -541.837860   66.333591  476.965145  -681.958606  -484.191138   \n",
       "89   gonzalu01   259.351878 -105.476641   70.117467  1021.619694   552.762544   \n",
       "\n",
       "         debut  finalGame         OPW     salary nameFirst  nameLast Pos  \n",
       "139 1986-05-17 2004-07-15  115.192971  4275000.0      Fred   McGriff  1B  \n",
       "22  1988-06-26 2007-09-30   81.420779  4000000.0     Craig    Biggio  2B  \n",
       "27  1997-06-20 2009-10-04   56.273163   925000.0     Aaron     Boone  3B  \n",
       "176 2000-09-17 2016-06-08   73.728189  8000000.0     Jimmy   Rollins  SS  \n",
       "89  1990-09-04 2008-09-28  103.358827  2025000.0      Luis  Gonzalez  OF  "
      ]
     },
     "execution_count": 817,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = best_team.copy() \n",
    "df[[\"1B\",\"2B\",\"3B\",\"HR\",\"BB\"]] = df[[\"1B\",\"2B\",\"3B\",\"HR\",\"BB\"]] * regr2.coef_ * 100\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 818,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1B -791.4587330732895\n",
      "2B -46.574206918616504\n",
      "3B 75.0920892529091\n",
      "HR 153.9734283087909\n",
      "BB 670.938233712274\n"
     ]
    }
   ],
   "source": [
    "for pos in [\"1B\",\"2B\",\"3B\",\"HR\",\"BB\"]:\n",
    "    print(pos, df[pos].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Your answer here: ** BB seems to be contributing the most to OPW"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A better way would be to use a bigger data set to get the regression coef:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 819,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1B -685.2660211999464\n",
      "2B 69.3114948228255\n",
      "3B 172.04358802572847\n",
      "HR 157.48965503691235\n",
      "BB 778.5899413533468\n"
     ]
    }
   ],
   "source": [
    "regr3 = linear_model.LinearRegression()\n",
    "regr3.fit(playerLS[[\"1B\",\"2B\",\"3B\",\"HR\",\"BB\"]], playerLS[\"OPW\"])\n",
    "\n",
    "df = best_team.copy() \n",
    "df[[\"1B\",\"2B\",\"3B\",\"HR\",\"BB\"]] = df[[\"1B\",\"2B\",\"3B\",\"HR\",\"BB\"]] * regr3.coef_ * 100\n",
    "for pos in [\"1B\",\"2B\",\"3B\",\"HR\",\"BB\"]:\n",
    "    print(pos, df[pos].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discussion for Problem 1\n",
    "\n",
    "*Write a brief discussion of your conclusions to the questions and tasks above in 100 words or less.*\n",
    "\n",
    "---\n",
    "\n",
    "there was a lot of data wrangling into the shape we wanted for analysis. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 2\n",
    "\n",
    "## $k$-Nearest Neighbors and Cross Validation \n",
    "\n",
    "What is the optimal $k$ for predicting species using $k$-nearest neighbor classification \n",
    "on the four features provided by the iris dataset.\n",
    "\n",
    "In this problem you will get to know the famous iris data set, and use cross validation to select the optimal $k$ for a $k$-nearest neighbor classification. This problem set makes heavy use of the [sklearn](http://scikit-learn.org/stable/) library. In addition to Pandas, it is one of the most useful libraries for data scientists! After completing this homework assignment you will know all the basics to get started with your own machine learning projects in sklearn. \n",
    "\n",
    "Future lectures will give further background information on different classifiers and their specific strengths and weaknesses, but when you have the basics for sklearn down, changing the classifier will boil down to exchanging one to two lines of code.\n",
    "\n",
    "The data set is so popular, that sklearn provides an extra function to load it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((150, 4), (150,))"
      ]
     },
     "execution_count": 507,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load the iris data set\n",
    "iris = sklearn.datasets.load_iris()\n",
    "\n",
    "X = iris.data  \n",
    "Y = iris.target\n",
    "\n",
    "X.shape, Y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Problem 2(a) \n",
    "Split the data into a train and a test set. Use a random selection of 33% of the samples as test data. Sklearn provides the [`train_test_split`](http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.train_test_split.html) function for this purpose. Print the dimensions of all the train and test data sets you have created. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See [train_test_split docs](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split), version linked above is depracated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 970,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1203, 64), (594, 64), (1203,), (594,))"
      ]
     },
     "execution_count": 970,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Your code here ###\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.33)\n",
    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Problem 2(b)\n",
    "\n",
    "Examine the data further by looking at the projections to the first two principal components of the data. Use the [`TruncatedSVD`](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.TruncatedSVD.html) function for this purpose, and create a scatter plot. Use the colors on the scatter plot to represent the different classes in the target data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 975,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CSMaTjzmU6xwoh9uh29ezxhxrQDs9ReyBCk4JQiwFFwSTMmKZlNFTjNnllXny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P+7bMEynN6xcqW+5hCDHVMnxiVtaNYmfkbdmsXRRHahj5HuONt/cJJYcPSZ950J8sw3j7H30RZa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1223c9ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Your code here ###\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "\n",
    "svd = TruncatedSVD()\n",
    "\n",
    "x_train_centered = x_train-np.mean(x_train, axis=0)\n",
    "x_test_centered = x_test-np.mean(x_test, axis=0)\n",
    "\n",
    "x_pca = svd.fit_transform(x_train_centered)\n",
    "\n",
    "plt.scatter(x_pca[:,0], x_pca[:,1], c=y_train, cmap=plt.cm.Vega10, \n",
    "            s=(2+y_train)*20)\n",
    "plt.title(\"Principle component plot\")\n",
    "plt.colorbar()\n",
    "plt.xlabel(\"PC 1\"), plt.ylabel(\"PC 2\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Problem 2(c) \n",
    "\n",
    "In the lecture we discussed how to use cross validation to estimate the optimal value for $k$ (the number of nearest neighbors to base the classification on). Use ***ten fold cross validation*** to estimate the optimal value for $k$ for the iris data set. \n",
    "\n",
    "**Note**: For your convenience sklearn does not only include the [KNN classifier](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html), but also a [grid search function](http://scikit-learn.org/stable/modules/generated/sklearn.grid_search.GridSearchCV.html#sklearn.grid_search.GridSearchCV). The function is called grid search, because if you have to optimize more than one parameter, it is common practice to define a range of possible values for each parameter. An exhaustive search then runs over the complete grid defined by all the possible parameter combinations. This can get very computation heavy, but luckily our KNN classifier only requires tuning of a single parameter for this problem set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 976,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.98148148148148151"
      ]
     },
     "execution_count": 976,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Your code here ###\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "neigh = KNeighborsClassifier()\n",
    "neigh.fit(x_train_centered, y_train) \n",
    "np.mean(neigh.predict(x_test_centered)==y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pretty good accuracy right of the bat! now to find the optimal K using [GridSearchCV](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 977,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise',\n",
       "       estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform'),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid={'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 977,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "k_values = [k for k in range(1,25)]\n",
    "parameters = {'n_neighbors': k_values}\n",
    "clf = GridSearchCV(KNeighborsClassifier(), parameters, cv=10)\n",
    "clf.fit(x_train_centered, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Problem 2(d)\n",
    "\n",
    "Visualize the result by plotting the score results versus values for $k$. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 978,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FXvYcziu1vhePU2QcJSOnkPpxURUcXXUU6lXE4XCQ37EzlIQ6gG2DHm0TETld\npuGjVZM4WjWJY2D3YBN5enbhCRP95LA/o2xnumNN9h35FQCzPt1ySu8XFWkLTsOb5CyZmS84qmCr\nV+qDCTsnHCUz9/hkPT+9JZGV5zlp3waHWY/dB/MU6nWNv3MXAp9+FBrRp9GmjWQcOUJ9TcMqInLG\nDMMItQ706hgc7Cu/yMuOkol+tqflsOtgbujxvIpE2ILDAAdn2zs+vW6CK7LijnAGOKMicEZFkNzQ\nVe4ugYBJtruYzLzS0+sezS1i8Y6V5Fp30+WCfj/nIzhlCvUqdOGIUayb8Q8uLgzed+mZk817779D\n/1/9tpYrE5GqFg4d7uqyGEcEnVrVp1Or4IQ8Pn+AvYfdbE/L5p9LXyMq0IABbS4rCW4nzZJiSIqP\nqpbx8y0WIzRRD03jSm3768zeANis06v8fcujUK9C57VoyZL2Hbh4zbcARACsq5pnR0VEpGI2q4WW\nTWJp2SSWG9f8HYD3hp97X7z0vFUV83XqUmrZ/l0qfr+/lqoREZFziUK9ijl7X07GCcuX7thO6pIv\na60eERE5dyjUq1i3gVezPPn80HJjIHPxwlqrR0REzh0K9SoWERFBUafOpdZp1jYREakJCvVqYHZN\n4cS76M2+38ThA/sr3F9ERKQqKNSrQYcRo1jtdIaWe+Tl8d27/6vFikRE5FxQbY+0BQIBHnnkEbZu\n3YrdbufRRx+lefPmoe0ffPAB//nPf3C5XIwYMYLRo0cDMGLECJwlgdisWTOmTZtWXSVWm0bNzmNx\n+w5c+k1wpiIrYNmwvnaLEpGwVFXPy+u5+/BQbaG+cOFCPB4Pc+bMITU1lenTp/P888Ep9TIzM3n6\n6ad57733iI2N5ZZbbqFnz54kJSVhmiazZ8+urrJqjK9TV/jm+PSDju824PV6iYiIqMWqREQknFVb\n8/vatWvp06cPAF26dGHTpk2hbWlpabRt25b4+HgsFgsdO3Zkw4YNbNmyhcLCQiZMmMBNN91Eamrd\n7WAW3/cKDp2w3Hv3LtZ9saDW6hERqQuO/CZXrQY/Q7Vdqbvd7lAzOoDVasXn82Gz2WjevDnbt28n\nIyODmJgYVq1axfnnn4/D4WDixImMHj2a3bt3c/vtt7NgwQJstro38F3X/lexqmUrRuzcAUB9IHfZ\nVzB4aO0WJiJSTcItjOvi+VRbWjqdTvLzj8+iEwgEQuEcFxfHgw8+yOTJk4mPj6d9+/YkJCTQokUL\nmjdvjmEWjhyIAAAgAElEQVQYtGjRgvj4eNLT02ncuHF1lVltbDYbxZ26QEmoA0Ro1jYREalG1db8\nnpKSwtKlSwFITU2lTZs2oW0+n48ffviBN998kxkzZrBz505SUlKYO3cu06cHB70/fPgwbrebpKSk\n6iqx2pldU/CdsHz+D9+TtntXrdUjIiLhrdpCfcCAAdjtdsaOHcu0adN48MEHmT9/PnPmzAldsY8Y\nMYLx48czfvx4EhMTGTVqFHl5eYwbN44pU6bw2GOP1cmm92M6X3sdq+KOz9jTrSCfze/PrcWKREQk\nnBmmeZKZ3euA9PS82i7hpBaPGMJ1K5aFlucMvob+r7xZixWJiIS3BjNjgbp5T/xUJCWVP687aPCZ\navfTWduiN26guLi4lqoREZFwplCvZklXDmCfYYSW++zbx5pP5tdiRSIi4e1cfixOoV7NOvW+nG9b\nH+8kGA8UrlxeewWJiEjYqjTU09PTa6KOsGWxWPB0Lt0Er1nbRESkOlQa6jfeeCOTJk3i008/xev1\n1kRNYceScjEn3kVvu/kHdm7ZXGv1iIhIeKo01D/77DMmTZrE8uXLGTRoEH/5y1/YuHFjTdQWNlJG\njmZ5QmJouWNREdvnvV+LFYmISDg6pXvqF198MQ8//DCTJ09m0aJFTJ48mWuvvbZOj81ek+ITEsno\n1Dm0bABWNcGLiEgVq3Rkl5UrV/Lhhx+ycuVK+vbtyz//+U9SUlLYunUrt99+e2jUODk5X6cu8NXi\n0LJr4wYKCgqIjo6uxapERCScVHql/txzz3HppZfy+eef8+ijj5KSkgJA27ZtmTBhQrUXGC6a/GIQ\nO63W0PLlBw+y5kM1wYuISNWpNNRffPFFCgoKiIqK4vDhw8yYMYPCwkIAbrnlluquL2y0734pa9u0\nDS27gOJvV9VeQSIiEnYqDfV7772XI0eOABATE0MgEOC+++6r9sLCjWEYZUaXs323gTo+Sq+IiJxF\nKg31AwcOMGXKFCA4neqUKVPYu3dvtRcWjmyX9KDwhOWOWzezbeOGWqtHRETCS6WhbhgGW7duDS3v\n2LGjTs+cVpu6Db+WZUkNQssXejzs/lhDxoqISNWoNJ3vv/9+JkyYQMOGDQHIysri73//e7UXFo5c\nsXFkdegIixcBwUfbNLqciIhUlUpD/bLLLmPx4sX8+OOP2Gw2WrZsid1ur4nawpK3UxfMxYs4NsVL\n/MYNuPNycbpia7UuERGp+yqdT33nzp28+eabFBQUYJomgUCAtLQ03njjjZqq8aTO9vnUf2pr6jqS\nhgygbcmQuwXAx0/OoN9Nt9ZuYSIiUif8rPnUp0yZQmxsLJs3b+aiiy7i6NGjXHDBBVVa4LmkTeeu\nfNf2otByNOBbs7r2ChIRkbBRafN7IBDgrrvuwufz0a5dO8aOHcvYsWNrorawZBgGvs5dYNN3oXW2\nDesxTRPjhHnXRURETlelV+pRUVF4PB7OP/98vv/+e+x2O8XFxZUdJidh794T9wnLKdu2snnNt7VW\nj4iIhIdKQ33YsGHccccd9OvXj9dff53bbrst1BNezky3YcNZ1qhxaLmVz0fagk9qsSIREQkHlTa/\nX3zxxQwfPhyn08ns2bPZuHEjvXr1qonawlZMTAy5nTrDoYOhdXq0TUREfq5T6ijndDoBaNSoEQMG\nDNDMYlXA16kLJz52UH/jd2RnZdZaPSIiUvdVGuqtW7fm2WefZdmyZaxevTr0R36e1sOGs8nhCC33\nzjzKuvfm1mJFIiJS11Xa/J6dnc0333zDN998E1pnGAavvfZatRYW7lpe2I4lF7ajY+o6ACKBwLrV\nMHFS7RYmIiJ1VqWhPnv27Jqo45zk69QFSkIdIGLDBgKBABZLpQ0oIiIiZVQa6uPHjy/3+Wldqf98\njp69yHntv8SVLHff/iMbVyyjc5++tVqXiIjUTZWG+uTJk0N/9/l8LFq0iNhYjVNeFS4eMpRlzc7j\nmrR9ACQHAixf9Dko1EVE5AxUGurdu3cvtXzZZZcxevRo7r777mor6lzhcDjI79AJSkIdwLZBj7aJ\niMiZqTTUDxw4EPq7aZps376d7Ozsai3qXOLv0pXAgo9DjyE02rSRjCNHqN+gwUmPExER+alKZ2nr\n37//8Z0Ng8TERO6880769j07mojr2ixtP7Vv5w4CV/Ti4sICALzAu395jCvvuLN2CxMRkbPSyWZp\nq/RK/csvv8Tr9RIREYHX68Xr9WrwmSp0XstWLGnXnovXBp/9jwDM9WtrtygREamTKn126tNPP+Xa\na68F4ODBg1x99dUsXLiw2gs7l/g6dym1HPndBvx+fy1VIyIidVWloT5z5kxmzZoFQHJyMu+99x7P\nPPNMtRd2LnH27svRE5Yv3bGd1CWLa60eERGpmyoNda/XS/369UPL9erVo5Lb8HKaUn4xiOXNzw8t\nNwEyF6s1RERETk+l99S7devGPffcw9ChQwH45JNP6NKlSyVHyemw2+0UduwMe3aH1mnWNhEROV2V\nXqn/6U9/on379syZM4d3332XDh068Ic//KEmajunBLqkcOJd9Gbfb+Twgf21Vo+IiNQ9p9T87nA4\neOGFF/jjH/9Idna2OnFVg47XjuLbkiluAXrk5fHdu/+rxYpERKSuqTTUp06dypEjRwCIiYkhEAhw\n3333VXth55pGzc5jX/sOoWUrYNmwvvYKEhGROqfSUD9w4ABTpkwBwOl0MmXKFPbu3VvthZ2LfJ26\nllp2fLcBr9dbS9WIiEhdU2moG4bB1q1bQ8s7duzAZqu0f52cgfi+V3DohOXeu3ex7osFtVaPiIjU\nLZWm8/3338+ECRNo2LAhAFlZWTzxxBPVXti5qGv/q1jVshUjdu4AoD6Qu+wrGDy0dgsTEZE6odIr\n9csuu4zFixfzyCOP0L9/fxo0aMDtt99eE7Wdc2w2G8WdSj8uGKFZ20RE5BRVGur79u3j6aef5o47\n7uCFF16gT58+LFq0qCZqOyeZXVPwnbB8/g/fk7Z7V63VIyIidUeFof7FF18wceJERo8eTU5ODk88\n8QQNGjTgzjvvJDExsSZrPKd0vvY6VsXFhZa7FeSz+f25tViRiIjUFRXeU588eTKDBg1izpw5NG/e\nHAh2mpPqldSwIZs6dIIVy4Dgty6rHm0TEZFTUOGV+rx582jcuDHXX3891113Ha+++qoGnakhvk6d\nSy1Hb9xAcXFxLVUjIiJ1RYWh3qZNG+6//36WLl3KpEmT+Pbbb8nIyGDSpEl89dVXNVnjOSep/wD2\nndAq0mffPtZ8Mr8WKxIRkbqg0o5yVquVq666iueee46lS5fSs2dPnnrqqZqo7ZzVqU9fvm19QWg5\nHihcubz2ChIRkTqh0lA/UWJiIrfeeivz5s2rrnoEsFgseH/yaJtmbRMRkcqcVqhLzTG6XcKJd9Hb\nbt7Mrq1baq0eERE5+ynUz1IpI0ezPCEhtNyxqJDt8z+oxYpERORsp1A/S8UnJJLR8XgveAOw6dE2\nERE5iWoL9UAgwMMPP8yYMWMYP348e/bsKbX9gw8+YOjQoVx//fW88847p3TMucb3k/vqro0bKCgo\nqKVqRETkbFdtob5w4UI8Hg9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Dtm5N8Pgtm4lYtRJvr95lXtvTrz+R788NNVU7XvkPcSOH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\nzad6kE8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f2bc978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Your code here ###\n",
    "#plt.plot(clf.param_grid[\"n_neighbors\"], clf.cv_results_[\"mean_test_score\"], linewidth=2)\n",
    "plt.errorbar(clf.param_grid[\"n_neighbors\"], clf.cv_results_[\"mean_test_score\"], \n",
    "             clf.cv_results_[\"std_test_score\"], lw=3, elinewidth=2, ecolor=\"g\")\n",
    "plt.ylim(0.8,1.05)\n",
    "plt.title(\"K vs accuracy\")\n",
    "plt.xticks(k_values)\n",
    "plt.xlabel(\"K value\"), plt.ylabel(\"Accuracy\")\n",
    "\n",
    "# highlight the best value\n",
    "best_k = clf.best_params_[\"n_neighbors\"]\n",
    "plt.scatter(best_k, clf.best_score_, alpha=0.2, s=300, c=\"g\")\n",
    "plt.annotate(f\"Best k: {best_k}, Best score: {clf.best_score_}\", color=\"r\", \n",
    "             arrowprops=dict(facecolor='r', shrink=0.05), \n",
    "             xy=(best_k, clf.best_score_), xytext=(best_k+2, 0.88));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Verify that the grid search has indeed chosen the right parameter value for $k$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 979,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 1}"
      ]
     },
     "execution_count": 979,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Your code here ###\n",
    "clf.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Problem 2(e)\n",
    "\n",
    "Test the performance of our tuned KNN classifier on the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 980,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.98148148148148151"
      ]
     },
     "execution_count": 980,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Your code here ###\n",
    "# simple way to check:\n",
    "clf.best_estimator_.score(x_test_centered, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "looking at the cross_val score:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 981,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.96961102036050573, 0.01459413416300674)"
      ]
     },
     "execution_count": 981,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(clf.best_estimator_, x_test_centered, y_test, cv=5)\n",
    "scores.mean(), scores.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discussion for Problem 2\n",
    "\n",
    "*Write a brief discussion of your conclusions to the questions and tasks above in 100 words or less.*\n",
    "\n",
    "---\n",
    "\n",
    "We look at classifying the iris data set, and KNN did a pretty accurate job, with the ideal K =1. But why the diff b/w the score and the cross_val score?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 3: \n",
    "\n",
    "## The Curse and Blessing of Higher Dimensions\n",
    "\n",
    "In this problem we will investigate the influence of higher dimensional spaces on the classification. The data set is again one of the standard data sets from sklearn. The [digits data set](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) is similar to the MNIST data set discussed in the lecture. The main difference is, that each digit is represented by an 8x8 pixel image patch, which is considerably smaller than the 28x28 pixels from MNIST. In addition, the gray values are restricted to 16 different values (4 bit), instead of 256 (8 bit) for MNIST. \n",
    "\n",
    "First we again load our data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 864,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1797, 64), (1797,))"
      ]
     },
     "execution_count": 864,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits = sklearn.datasets.load_digits()\n",
    "\n",
    "X = digits.data  \n",
    "Y = digits.target\n",
    "\n",
    "X.shape, Y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Problem 3(a) \n",
    "\n",
    "Start with the same steps as in Problem 2. Split the data into train and test set. Use 33% of the samples as test data. Print the dimensions of all the train and test data sets you created. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 865,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1203, 64), (594, 64), (1203,), (594,))"
      ]
     },
     "execution_count": 865,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Your code here ###\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.33)\n",
    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Problem 3(b) \n",
    "\n",
    "Similar to Problem 2(b), create a scatter plot of the projections to the first two PCs.  Use the colors on the scatter plot to represent the different classes in the target data. How well can we separate the classes?\n",
    "\n",
    "**Hint**: Use a `Colormap` in matplotlib to represent the diferent classes in the target data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 982,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RXQe1Dg8PXjGmb9uWilb+vKWKg/Ud6FSFiVlxfO+iPEamnLg93cwRSTx0xVhe21VLeYsL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\nm631v41GLBZLu4spj938BalHTq1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/zbx583jrrbfQNI1FixYB8PjjjzNnzhw0TWPChAnk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f34ddd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Your code here ###\n",
    "\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "svd = TruncatedSVD()\n",
    "\n",
    "x_train = x_train-np.mean(x_train, axis=0)\n",
    "x_test = x_test-np.mean(x_test, axis=0)\n",
    "\n",
    "x_pca = svd.fit_transform(x_train-np.mean(x_train, axis=0))\n",
    "\n",
    "plt.scatter(x_pca[:,0], x_pca[:,1], c=y_train, cmap=plt.cm.Vega10)\n",
    "plt.colorbar()\n",
    "plt.title(\"Principle component plot\")\n",
    "plt.xlabel(\"PC 1\"), plt.ylabel(\"PC 2\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create individual scatter plots using only two classes at a time to explore which classes are most difficult to distinguish in terms of class separability.  You do not need to create scatter plots for all pairwise comparisons, but at least show one. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 983,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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v04NaCRMzYnkwSIn8MLWS+WOTeX1Hvc8fvkoB145N8UkV9EgSa8r17Kk3ogAu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EKMdsb76G+4fLpkP4/fj2/IF1/vPEvvJGoRfXEqIoSJEmgnI1uZuI398I+jTNW7V9vs5lPvg5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PGa14OYlgbbIhzsSBKmnqGu68qBA+jlpcjde+L9ZHaIQKteMzcH75zZOG7/VWPN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\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oaC/3lfNVweeM7ziRDEcH9np2N7m2ZHu7ln2MOGokSBNCCCGEaEUrV65kzJgxAAwaNIj1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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fe32048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "NUMS = 4 # make plots for digits from 0 to this number\n",
    "\n",
    "# setting up the number combinations\n",
    "from itertools import combinations\n",
    "combs = np.array([i for i in combinations(range(NUMS),2)])\n",
    "\n",
    "fig = plt.figure(figsize=(16,16))\n",
    "sns.despine(left=True, bottom=True)\n",
    "cmap = plt.cm.get_cmap('tab10')\n",
    "grid_size = (NUMS, NUMS)\n",
    "\n",
    "axes = [] # holds all the subplots\n",
    "i = 0 # the axis number\n",
    "\n",
    "for x, y in combs:\n",
    "    # first we need to seperate out the x and y digits\n",
    "    mask_x = np.ma.masked_where(y_train==x, y_train).mask\n",
    "    mask_y = np.ma.masked_where(y_train==y, y_train).mask\n",
    "    \n",
    "    axes.append(plt.subplot2grid(grid_size, (x,y)))\n",
    "    axes[i].set_title(f\"{x} vs {y}\", fontsize=18)\n",
    "    \n",
    "    # scatter plot for digit x\n",
    "    axes[i].scatter(x_pca[:,0][mask_x], x_pca[:,1][mask_x],  \n",
    "                    c=[cmap(i/10) for i in y_train[mask_x]], label=str(x))\n",
    "    # scatter plot for digit y\n",
    "    axes[i].scatter(x_pca[:,0][mask_y], x_pca[:,1][mask_y], \n",
    "                    c=[cmap(i/10) for i in y_train[mask_y]], label=str(y))\n",
    "\n",
    "    axes[i].legend(loc=\"lower right\", facecolor=\"grey\", frameon=True, framealpha=0.15)\n",
    "    i += 1\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Give a brief interpretation of the scatter plot. Which classes look like hard to distinguish? Do both feature dimensions contribute to the class separability? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Your answer here: ** When looking at just two numbers, some are clearly distinct from each other, like 3 and 4, while others like 2 and 3 have a lot of overlap. The However, even numbers which are clustered seperately have range into each others territory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Problem 3(c) \n",
    "\n",
    "Write a **ten-fold cross validation** to estimate the optimal value for $k$ for the digits data set. *However*, this time we are interested in the influence of the number of dimensions we project the data down as well. \n",
    "\n",
    "Extend the cross validation as done for the iris data set, to optimize $k$ for different dimensional projections of the data. Create a boxplot showing test scores for the optimal $k$ for each $d$-dimensional subspace with $d$ ranging from one to ten. The plot should have the scores on the y-axis and the different dimensions $d$ on the x-axis. You can use your favorite plot function for the boxplots. [Seaborn](http://web.stanford.edu/~mwaskom/software/seaborn/index.html) is worth having a look at though. It is a great library for statistical visualization and of course also comes with a [`boxplot`](http://web.stanford.edu/~mwaskom/software/seaborn/generated/seaborn.boxplot.html) function that has simple means for changing the labels on the x-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 984,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Your cross validation and evaluation code here ###\n",
    "\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "k_values = [k for k in range(1,10)]\n",
    "parameters = {'n_neighbors': k_values}\n",
    "\n",
    "clf = GridSearchCV(KNeighborsClassifier(), parameters, cv=5)\n",
    "\n",
    "# now to go through pca dimensions to find the best d\n",
    "\n",
    "dim, best_n, scores = [], [], []\n",
    "\n",
    "for d in range(1,11):\n",
    "    svd = TruncatedSVD(n_components=d)\n",
    "    pca = svd.fit_transform(x_train-np.mean(x_train, axis=0))\n",
    "\n",
    "    clf.fit(pca, y_train)\n",
    "    best_n.append(clf.best_params_[\"n_neighbors\"])\n",
    "    scores.append(clf.cv_results_[\"mean_test_score\"])\n",
    "    dim.append(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1087,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qTLptAAC9E4IZ1Orq6kvWVseuHRERMWrMYam2M2pMad8XAPByFUmSJKVssKXl+VI2B4ds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\nY6tUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12487c240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Your boxplot code here ### \n",
    "sns.set_style(\"darkgrid\")\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.boxplot(dim, scores)\n",
    "plt.xticks(range(len(dim)), [f\"Dim={dim}\\nK={n}\" for dim, n in zip(dim,best_n)]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "another look at the same thin as above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1091,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KmoUUZxrp7nQy3Vnkxg8mJ34wqa5ULGrfuLFJdF+fC+HD6VCrKH3TiBGjuOmmn/PnP/+R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\nogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122d6e080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(len(dim)), [np.mean(s) for s in scores], color=\"r\", lw=2, label=\"Mean Score\")\n",
    "plt.plot(range(len(dim)), [np.min(s) for s in scores], color=\"g\", label=\"Min Score\", alpha=0.5)\n",
    "plt.plot(range(len(dim)), [np.max(s) for s in scores], color=\"b\", label=\"Max Score\", alpha=0.5)\n",
    "plt.fill_between(range(len(dim)), [np.mean(s) for s in scores], [np.min(s) for s in scores], \n",
    "                 alpha=0.3, color=\"g\")\n",
    "plt.fill_between(range(len(dim)), [np.max(s) for s in scores], [np.mean(s) for s in scores], \n",
    "                 alpha=0.3, color=\"b\")\n",
    "plt.xticks(range(len(dim)), [f\"Dim={dim}\\nK={n}\" for dim, n in zip(dim,best_n)])\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write a short interpretation of the generated plot, answering the following questions:\n",
    "\n",
    "* What trend do you see in the plot for increasing dimensions?\n",
    "\n",
    "* Why do you think this is happening?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Your answer here: ** Optimal k drops as the num of dims increase. This makes sense as with more increased dims KNN has more data to connect each num to its neighbours, and a higher D essentially makes a better \"fingerprint\" for each digit, lessening the need to rely on juxtaposition with its neighbours."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Problem 3(d) \n",
    "\n",
    "**For AC209 Students**: Change the boxplot we generated above to also show the optimal value for $k$ chosen by the cross validation grid search. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 969,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Your code here ### \n",
    "# done above"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write a short interpretation answering the following questions:\n",
    "\n",
    "* Which trend do you observe for the optimal value of $k$?\n",
    "\n",
    "* Why do you think this is happening?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Your answer here: ** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discussion for Problem 3\n",
    "\n",
    "*Write a brief discussion of your conclusions to the questions and tasks above in 100 words or less.*\n",
    "\n",
    "---\n",
    "\n",
    "sklearn makes it almost trivial to classify well labeled training data. Here we used KNN on the principal components of two different data sets. With PCA, the number of dimensions matter for"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Submission Instructions\n",
    "\n",
    "To submit your homework, create a folder named **lastname_firstinitial_hw#** and place your IPython notebooks, data files, and any other files in this folder. Your IPython Notebooks should be completely executed with the results visible in the notebook. We should not have to run any code.  Compress the folder (please use .zip compression) and submit to the CS109 dropbox in the appropriate folder. *If we cannot access your work because these directions are not followed correctly, we will not grade your work.*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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