Workshop_Data_Analysis_With_Pandas_Numpy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculations witn Numpy - Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_scores = [70,65,95,88]\n",
    "type(test_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = np.array(test_scores)\n",
    "type(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.5"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95 65\n"
     ]
    }
   ],
   "source": [
    "print(scores.max(), scores.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#More Calculations\n",
    "income = np.array([75000, 55000, 88000, 125000, 64000, 97000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "84000.0\n",
      "125000\n",
      "55000\n"
     ]
    }
   ],
   "source": [
    "print(income.mean())\n",
    "print(income.max())\n",
    "print(income.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1786285.7142857143"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income = np.append(income, 12000000)\n",
    "income.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88000.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(income)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4169786.007331644"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Standard Deviation\n",
    "income.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.379418403139947"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "318"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.30087333, 0.18694582, 0.32318268, 0.66574957, 0.5669708 ],\n",
       "       [0.39825396, 0.37941492, 0.01058154, 0.1703656 , 0.12339337],\n",
       "       [0.69240128, 0.87444156, 0.3373969 , 0.99245923, 0.13154007],\n",
       "       [0.50032984, 0.28662051, 0.22058485, 0.50208555, 0.63606254],\n",
       "       [0.63567694, 0.08043309, 0.58143375, 0.83919086, 0.29301825]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MATRICES\n",
    "np.random.seed(seed=60)\n",
    "# create a 5 by 5 Matrix\n",
    "random_square = np.random.rand(5,5)\n",
    "random_square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.30087333, 0.18694582, 0.32318268, 0.66574957, 0.5669708 ])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_square[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.30087333, 0.39825396, 0.69240128, 0.50032984, 0.63567694])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get column specified e.g. 0\n",
    "random_square[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_square[0,0] == random_square[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.42917627159618377"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_square.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4087444389228477"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean of specified Row\n",
    "random_square[0].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5055070681931644"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean of specified column\n",
    "random_square[:,0].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.35019700684996913"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean of last column\n",
    "random_square[:,-1].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Computation Times Calculations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 216 ms, sys: 43.6 ms, total: 260 ms\n",
      "Wall time: 547 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# How long it takes to generate a 100000 by 100 matrix\n",
    "np.random.seed(seed=60)\n",
    "big_matrix = np.random.rand(100000, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 178 ms, sys: 36.7 ms, total: 215 ms\n",
      "Wall time: 235 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# duration to calculate mean\n",
    "big_matrix = np.random.rand(100000, 100)\n",
    "big_matrix.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dealing with NPArrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,\n",
       "        27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,\n",
       "        40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,\n",
       "        53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,  65,\n",
       "        66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,\n",
       "        79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,  91,\n",
       "        92,  93,  94,  95,  96,  97,  98,  99, 100])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(1,101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1,   2,   3,   4,   5],\n",
       "       [  6,   7,   8,   9,  10],\n",
       "       [ 11,  12,  13,  14,  15],\n",
       "       [ 16,  17,  18,  19,  20],\n",
       "       [ 21,  22,  23,  24,  25],\n",
       "       [ 26,  27,  28,  29,  30],\n",
       "       [ 31,  32,  33,  34,  35],\n",
       "       [ 36,  37,  38,  39,  40],\n",
       "       [ 41,  42,  43,  44,  45],\n",
       "       [ 46,  47,  48,  49,  50],\n",
       "       [ 51,  52,  53,  54,  55],\n",
       "       [ 56,  57,  58,  59,  60],\n",
       "       [ 61,  62,  63,  64,  65],\n",
       "       [ 66,  67,  68,  69,  70],\n",
       "       [ 71,  72,  73,  74,  75],\n",
       "       [ 76,  77,  78,  79,  80],\n",
       "       [ 81,  82,  83,  84,  85],\n",
       "       [ 86,  87,  88,  89,  90],\n",
       "       [ 91,  92,  93,  94,  95],\n",
       "       [ 96,  97,  98,  99, 100]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reshape to 20 by 5\n",
    "np.arange(1, 101).reshape(20,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-49, -48, -47, -46, -45],\n",
       "       [-44, -43, -42, -41, -40],\n",
       "       [-39, -38, -37, -36, -35],\n",
       "       [-34, -33, -32, -31, -30],\n",
       "       [-29, -28, -27, -26, -25],\n",
       "       [-24, -23, -22, -21, -20],\n",
       "       [-19, -18, -17, -16, -15],\n",
       "       [-14, -13, -12, -11, -10],\n",
       "       [ -9,  -8,  -7,  -6,  -5],\n",
       "       [ -4,  -3,  -2,  -1,   0],\n",
       "       [  1,   2,   3,   4,   5],\n",
       "       [  6,   7,   8,   9,  10],\n",
       "       [ 11,  12,  13,  14,  15],\n",
       "       [ 16,  17,  18,  19,  20],\n",
       "       [ 21,  22,  23,  24,  25],\n",
       "       [ 26,  27,  28,  29,  30],\n",
       "       [ 31,  32,  33,  34,  35],\n",
       "       [ 36,  37,  38,  39,  40],\n",
       "       [ 41,  42,  43,  44,  45],\n",
       "       [ 46,  47,  48,  49,  50]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat1 = np.arange(1, 101).reshape(20,5)\n",
    "mat1 - 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  10,   20,   30,   40,   50],\n",
       "       [  60,   70,   80,   90,  100],\n",
       "       [ 110,  120,  130,  140,  150],\n",
       "       [ 160,  170,  180,  190,  200],\n",
       "       [ 210,  220,  230,  240,  250],\n",
       "       [ 260,  270,  280,  290,  300],\n",
       "       [ 310,  320,  330,  340,  350],\n",
       "       [ 360,  370,  380,  390,  400],\n",
       "       [ 410,  420,  430,  440,  450],\n",
       "       [ 460,  470,  480,  490,  500],\n",
       "       [ 510,  520,  530,  540,  550],\n",
       "       [ 560,  570,  580,  590,  600],\n",
       "       [ 610,  620,  630,  640,  650],\n",
       "       [ 660,  670,  680,  690,  700],\n",
       "       [ 710,  720,  730,  740,  750],\n",
       "       [ 760,  770,  780,  790,  800],\n",
       "       [ 810,  820,  830,  840,  850],\n",
       "       [ 860,  870,  880,  890,  900],\n",
       "       [ 910,  920,  930,  940,  950],\n",
       "       [ 960,  970,  980,  990, 1000]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat1 * 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  2,   4,   6,   8,  10],\n",
       "       [ 12,  14,  16,  18,  20],\n",
       "       [ 22,  24,  26,  28,  30],\n",
       "       [ 32,  34,  36,  38,  40],\n",
       "       [ 42,  44,  46,  48,  50],\n",
       "       [ 52,  54,  56,  58,  60],\n",
       "       [ 62,  64,  66,  68,  70],\n",
       "       [ 72,  74,  76,  78,  80],\n",
       "       [ 82,  84,  86,  88,  90],\n",
       "       [ 92,  94,  96,  98, 100],\n",
       "       [102, 104, 106, 108, 110],\n",
       "       [112, 114, 116, 118, 120],\n",
       "       [122, 124, 126, 128, 130],\n",
       "       [132, 134, 136, 138, 140],\n",
       "       [142, 144, 146, 148, 150],\n",
       "       [152, 154, 156, 158, 160],\n",
       "       [162, 164, 166, 168, 170],\n",
       "       [172, 174, 176, 178, 180],\n",
       "       [182, 184, 186, 188, 190],\n",
       "       [192, 194, 196, 198, 200]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat1 + mat1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[    1,     4,     9,    16,    25],\n",
       "       [   36,    49,    64,    81,   100],\n",
       "       [  121,   144,   169,   196,   225],\n",
       "       [  256,   289,   324,   361,   400],\n",
       "       [  441,   484,   529,   576,   625],\n",
       "       [  676,   729,   784,   841,   900],\n",
       "       [  961,  1024,  1089,  1156,  1225],\n",
       "       [ 1296,  1369,  1444,  1521,  1600],\n",
       "       [ 1681,  1764,  1849,  1936,  2025],\n",
       "       [ 2116,  2209,  2304,  2401,  2500],\n",
       "       [ 2601,  2704,  2809,  2916,  3025],\n",
       "       [ 3136,  3249,  3364,  3481,  3600],\n",
       "       [ 3721,  3844,  3969,  4096,  4225],\n",
       "       [ 4356,  4489,  4624,  4761,  4900],\n",
       "       [ 5041,  5184,  5329,  5476,  5625],\n",
       "       [ 5776,  5929,  6084,  6241,  6400],\n",
       "       [ 6561,  6724,  6889,  7056,  7225],\n",
       "       [ 7396,  7569,  7744,  7921,  8100],\n",
       "       [ 8281,  8464,  8649,  8836,  9025],\n",
       "       [ 9216,  9409,  9604,  9801, 10000]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat1 * mat1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   55,   130,   205,   280,   355,   430,   505,   580,   655,\n",
       "          730,   805,   880,   955,  1030,  1105,  1180,  1255,  1330,\n",
       "         1405,  1480],\n",
       "       [  130,   330,   530,   730,   930,  1130,  1330,  1530,  1730,\n",
       "         1930,  2130,  2330,  2530,  2730,  2930,  3130,  3330,  3530,\n",
       "         3730,  3930],\n",
       "       [  205,   530,   855,  1180,  1505,  1830,  2155,  2480,  2805,\n",
       "         3130,  3455,  3780,  4105,  4430,  4755,  5080,  5405,  5730,\n",
       "         6055,  6380],\n",
       "       [  280,   730,  1180,  1630,  2080,  2530,  2980,  3430,  3880,\n",
       "         4330,  4780,  5230,  5680,  6130,  6580,  7030,  7480,  7930,\n",
       "         8380,  8830],\n",
       "       [  355,   930,  1505,  2080,  2655,  3230,  3805,  4380,  4955,\n",
       "         5530,  6105,  6680,  7255,  7830,  8405,  8980,  9555, 10130,\n",
       "        10705, 11280],\n",
       "       [  430,  1130,  1830,  2530,  3230,  3930,  4630,  5330,  6030,\n",
       "         6730,  7430,  8130,  8830,  9530, 10230, 10930, 11630, 12330,\n",
       "        13030, 13730],\n",
       "       [  505,  1330,  2155,  2980,  3805,  4630,  5455,  6280,  7105,\n",
       "         7930,  8755,  9580, 10405, 11230, 12055, 12880, 13705, 14530,\n",
       "        15355, 16180],\n",
       "       [  580,  1530,  2480,  3430,  4380,  5330,  6280,  7230,  8180,\n",
       "         9130, 10080, 11030, 11980, 12930, 13880, 14830, 15780, 16730,\n",
       "        17680, 18630],\n",
       "       [  655,  1730,  2805,  3880,  4955,  6030,  7105,  8180,  9255,\n",
       "        10330, 11405, 12480, 13555, 14630, 15705, 16780, 17855, 18930,\n",
       "        20005, 21080],\n",
       "       [  730,  1930,  3130,  4330,  5530,  6730,  7930,  9130, 10330,\n",
       "        11530, 12730, 13930, 15130, 16330, 17530, 18730, 19930, 21130,\n",
       "        22330, 23530],\n",
       "       [  805,  2130,  3455,  4780,  6105,  7430,  8755, 10080, 11405,\n",
       "        12730, 14055, 15380, 16705, 18030, 19355, 20680, 22005, 23330,\n",
       "        24655, 25980],\n",
       "       [  880,  2330,  3780,  5230,  6680,  8130,  9580, 11030, 12480,\n",
       "        13930, 15380, 16830, 18280, 19730, 21180, 22630, 24080, 25530,\n",
       "        26980, 28430],\n",
       "       [  955,  2530,  4105,  5680,  7255,  8830, 10405, 11980, 13555,\n",
       "        15130, 16705, 18280, 19855, 21430, 23005, 24580, 26155, 27730,\n",
       "        29305, 30880],\n",
       "       [ 1030,  2730,  4430,  6130,  7830,  9530, 11230, 12930, 14630,\n",
       "        16330, 18030, 19730, 21430, 23130, 24830, 26530, 28230, 29930,\n",
       "        31630, 33330],\n",
       "       [ 1105,  2930,  4755,  6580,  8405, 10230, 12055, 13880, 15705,\n",
       "        17530, 19355, 21180, 23005, 24830, 26655, 28480, 30305, 32130,\n",
       "        33955, 35780],\n",
       "       [ 1180,  3130,  5080,  7030,  8980, 10930, 12880, 14830, 16780,\n",
       "        18730, 20680, 22630, 24580, 26530, 28480, 30430, 32380, 34330,\n",
       "        36280, 38230],\n",
       "       [ 1255,  3330,  5405,  7480,  9555, 11630, 13705, 15780, 17855,\n",
       "        19930, 22005, 24080, 26155, 28230, 30305, 32380, 34455, 36530,\n",
       "        38605, 40680],\n",
       "       [ 1330,  3530,  5730,  7930, 10130, 12330, 14530, 16730, 18930,\n",
       "        21130, 23330, 25530, 27730, 29930, 32130, 34330, 36530, 38730,\n",
       "        40930, 43130],\n",
       "       [ 1405,  3730,  6055,  8380, 10705, 13030, 15355, 17680, 20005,\n",
       "        22330, 24655, 26980, 29305, 31630, 33955, 36280, 38605, 40930,\n",
       "        43255, 45580],\n",
       "       [ 1480,  3930,  6380,  8830, 11280, 13730, 16180, 18630, 21080,\n",
       "        23530, 25980, 28430, 30880, 33330, 35780, 38230, 40680, 43130,\n",
       "        45580, 48030]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#take the dot product of mat1 and mat1.T\n",
    "np.dot(mat1, mat1.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas Section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create dictionary of test scores\n",
    "test_dict = {'Corey':[63,75,88], 'Kevin':[48,98,92], 'Akshay': [87, 86, 85]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create DataFrame\n",
    "df = pd.DataFrame(test_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Corey</th>\n",
       "      <th>Kevin</th>\n",
       "      <th>Akshay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>48</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75</td>\n",
       "      <td>98</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>88</td>\n",
       "      <td>92</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Corey  Kevin  Akshay\n",
       "0     63     48      87\n",
       "1     75     98      86\n",
       "2     88     92      85"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Corey</th>\n",
       "      <th>Kevin</th>\n",
       "      <th>Akshay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>75.333333</td>\n",
       "      <td>79.333333</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.503333</td>\n",
       "      <td>27.300794</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>63.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>69.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>85.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>75.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>81.500000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>86.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Corey      Kevin  Akshay\n",
       "count   3.000000   3.000000     3.0\n",
       "mean   75.333333  79.333333    86.0\n",
       "std    12.503333  27.300794     1.0\n",
       "min    63.000000  48.000000    85.0\n",
       "25%    69.000000  70.000000    85.5\n",
       "50%    75.000000  92.000000    86.0\n",
       "75%    81.500000  95.000000    86.5\n",
       "max    88.000000  98.000000    87.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Corey</th>\n",
       "      <td>63</td>\n",
       "      <td>75</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kevin</th>\n",
       "      <td>48</td>\n",
       "      <td>98</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Akshay</th>\n",
       "      <td>87</td>\n",
       "      <td>86</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0   1   2\n",
       "Corey   63  75  88\n",
       "Kevin   48  98  92\n",
       "Akshay  87  86  85"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transpose the DataFrame\n",
    "df = df.T\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>Quiz_1</th>\n",
       "      <th>Quiz_2</th>\n",
       "      <th>Quiz_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Corey</th>\n",
       "      <td>63</td>\n",
       "      <td>75</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kevin</th>\n",
       "      <td>48</td>\n",
       "      <td>98</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Akshay</th>\n",
       "      <td>87</td>\n",
       "      <td>86</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Quiz_1  Quiz_2  Quiz_3\n",
       "Corey       63      75      88\n",
       "Kevin       48      98      92\n",
       "Akshay      87      86      85"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename the columns\n",
    "df.columns = ['Quiz_1', 'Quiz_2', 'Quiz_3']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Quiz_1    63\n",
       "Quiz_2    75\n",
       "Quiz_3    88\n",
       "Name: Corey, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Access first row by index number\n",
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Corey     63\n",
       "Kevin     48\n",
       "Akshay    87\n",
       "Name: Quiz_1, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Quiz_1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Corey     63\n",
       "Kevin     48\n",
       "Akshay    87\n",
       "Name: Quiz_1, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Access first column using dot notation\n",
    "df.Quiz_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quiz_1</th>\n",
       "      <th>Quiz_2</th>\n",
       "      <th>Quiz_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Corey</th>\n",
       "      <td>63</td>\n",
       "      <td>75</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kevin</th>\n",
       "      <td>48</td>\n",
       "      <td>98</td>\n",
       "      <td>92</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Quiz_1  Quiz_2  Quiz_3\n",
       "Corey      63      75      88\n",
       "Kevin      48      98      92"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Limit DataFrame to first 2 rows\n",
    "df[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quiz_2</th>\n",
       "      <th>Quiz_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Corey</th>\n",
       "      <td>75</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kevin</th>\n",
       "      <td>98</td>\n",
       "      <td>92</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Quiz_2  Quiz_3\n",
       "Corey      75      88\n",
       "Kevin      98      92"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Defining a new DataFrame from first 2 rows and last 2 columns \n",
    "rows = ['Corey', 'Kevin']\n",
    "cols = ['Quiz_2', 'Quiz_3']\n",
    "df_spring = df.loc[rows, cols]\n",
    "df_spring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quiz_2</th>\n",
       "      <th>Quiz_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Corey</th>\n",
       "      <td>75</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kevin</th>\n",
       "      <td>98</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Quiz_2  Quiz_3\n",
       "Corey      75      88\n",
       "Kevin      98      92"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select first 2 rows and last 2 columns using index numbers \n",
    "df.iloc[[0,1], [1,2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quiz_1</th>\n",
       "      <th>Quiz_2</th>\n",
       "      <th>Quiz_3</th>\n",
       "      <th>Quiz_Avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Corey</th>\n",
       "      <td>63</td>\n",
       "      <td>75</td>\n",
       "      <td>88</td>\n",
       "      <td>75.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kevin</th>\n",
       "      <td>48</td>\n",
       "      <td>98</td>\n",
       "      <td>92</td>\n",
       "      <td>79.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Akshay</th>\n",
       "      <td>87</td>\n",
       "      <td>86</td>\n",
       "      <td>85</td>\n",
       "      <td>86.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Quiz_1  Quiz_2  Quiz_3   Quiz_Avg\n",
       "Corey       63      75      88  75.333333\n",
       "Kevin       48      98      92  79.333333\n",
       "Akshay      87      86      85  86.000000"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define new column as mean of other columns\n",
    "df['Quiz_Avg'] = df.mean(axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quiz_1</th>\n",
       "      <th>Quiz_2</th>\n",
       "      <th>Quiz_3</th>\n",
       "      <th>Quiz_Avg</th>\n",
       "      <th>Quiz_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Corey</th>\n",
       "      <td>63</td>\n",
       "      <td>75</td>\n",
       "      <td>88</td>\n",
       "      <td>75.333333</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kevin</th>\n",
       "      <td>48</td>\n",
       "      <td>98</td>\n",
       "      <td>92</td>\n",
       "      <td>79.333333</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Akshay</th>\n",
       "      <td>87</td>\n",
       "      <td>86</td>\n",
       "      <td>85</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Quiz_1  Quiz_2  Quiz_3   Quiz_Avg  Quiz_4\n",
       "Corey       63      75      88  75.333333      92\n",
       "Kevin       48      98      92  79.333333      95\n",
       "Akshay      87      86      85  86.000000      88"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new column\n",
    "df['Quiz_4'] = [92, 95, 88]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quiz_1</th>\n",
       "      <th>Quiz_2</th>\n",
       "      <th>Quiz_3</th>\n",
       "      <th>Quiz_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Corey</th>\n",
       "      <td>63</td>\n",
       "      <td>75</td>\n",
       "      <td>88</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kevin</th>\n",
       "      <td>48</td>\n",
       "      <td>98</td>\n",
       "      <td>92</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Akshay</th>\n",
       "      <td>87</td>\n",
       "      <td>86</td>\n",
       "      <td>85</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Quiz_1  Quiz_2  Quiz_3  Quiz_4\n",
       "Corey       63      75      88      92\n",
       "Kevin       48      98      92      95\n",
       "Akshay      87      86      85      88"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Delete Column\n",
    "del df['Quiz_Avg']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create new DataFrame of one row\n",
    "df_new = pd.DataFrame({'Quiz_1':[np.NaN], 'Quiz_2':[np.NaN], 'Quiz_3': [np.NaN], 'Quiz_4':[71]}, index=['Adrian'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quiz_1</th>\n",
       "      <th>Quiz_2</th>\n",
       "      <th>Quiz_3</th>\n",
       "      <th>Quiz_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adrian</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Quiz_1  Quiz_2  Quiz_3  Quiz_4\n",
       "Adrian     NaN     NaN     NaN      71"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quiz_1</th>\n",
       "      <th>Quiz_2</th>\n",
       "      <th>Quiz_3</th>\n",
       "      <th>Quiz_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Corey</th>\n",
       "      <td>63.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kevin</th>\n",
       "      <td>48.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Akshay</th>\n",
       "      <td>87.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adrian</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Quiz_1  Quiz_2  Quiz_3  Quiz_4\n",
       "Corey     63.0    75.0    88.0      92\n",
       "Kevin     48.0    98.0    92.0      95\n",
       "Akshay    87.0    86.0    85.0      88\n",
       "Adrian     NaN     NaN     NaN      71"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenate DataFrames\n",
    "df = pd.concat([df, df_new])\n",
    "# Display new DataFrame\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quiz_1</th>\n",
       "      <th>Quiz_2</th>\n",
       "      <th>Quiz_3</th>\n",
       "      <th>Quiz_4</th>\n",
       "      <th>Quiz_Avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Corey</th>\n",
       "      <td>63.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>92</td>\n",
       "      <td>79.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kevin</th>\n",
       "      <td>48.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>95</td>\n",
       "      <td>83.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Akshay</th>\n",
       "      <td>87.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>88</td>\n",
       "      <td>86.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adrian</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71</td>\n",
       "      <td>71.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Quiz_1  Quiz_2  Quiz_3  Quiz_4  Quiz_Avg\n",
       "Corey     63.0    75.0    88.0      92     79.50\n",
       "Kevin     48.0    98.0    92.0      95     83.25\n",
       "Akshay    87.0    86.0    85.0      88     86.50\n",
       "Adrian     NaN     NaN     NaN      71     71.00"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Quiz_Avg'] = df.mean(axis=1, skipna=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Corey     92.0\n",
       "Kevin     95.0\n",
       "Akshay    88.0\n",
       "Adrian    71.0\n",
       "Name: Quiz_4, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Quiz_4.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quiz_1</th>\n",
       "      <th>Quiz_2</th>\n",
       "      <th>Quiz_3</th>\n",
       "      <th>Quiz_4</th>\n",
       "      <th>Quiz_Avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Corey</th>\n",
       "      <td>63.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>92</td>\n",
       "      <td>79.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kevin</th>\n",
       "      <td>48.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>95</td>\n",
       "      <td>83.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Akshay</th>\n",
       "      <td>87.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>88</td>\n",
       "      <td>86.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adrian</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71</td>\n",
       "      <td>71.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Quiz_1  Quiz_2  Quiz_3  Quiz_4  Quiz_Avg\n",
       "Corey     63.0    75.0    88.0      92     79.50\n",
       "Kevin     48.0    98.0    92.0      95     83.25\n",
       "Akshay    87.0    86.0    85.0      88     86.50\n",
       "Adrian     NaN     NaN     NaN      71     71.00"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_df = pd.read_csv('data/HousingData.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      "CRIM       486 non-null float64\n",
      "ZN         486 non-null float64\n",
      "INDUS      486 non-null float64\n",
      "CHAS       486 non-null float64\n",
      "NOX        506 non-null float64\n",
      "RM         506 non-null float64\n",
      "AGE        486 non-null float64\n",
      "DIS        506 non-null float64\n",
      "RAD        506 non-null int64\n",
      "TAX        506 non-null int64\n",
      "PTRATIO    506 non-null float64\n",
      "B          506 non-null float64\n",
      "LSTAT      486 non-null float64\n",
      "MEDV       506 non-null float64\n",
      "dtypes: float64(12), int64(2)\n",
      "memory usage: 55.4 KB\n"
     ]
    }
   ],
   "source": [
    "housing_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>486.000000</td>\n",
       "      <td>486.000000</td>\n",
       "      <td>486.000000</td>\n",
       "      <td>486.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>486.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>486.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.611874</td>\n",
       "      <td>11.211934</td>\n",
       "      <td>11.083992</td>\n",
       "      <td>0.069959</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>68.518519</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>12.715432</td>\n",
       "      <td>22.532806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.720192</td>\n",
       "      <td>23.388876</td>\n",
       "      <td>6.835896</td>\n",
       "      <td>0.255340</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>27.999513</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>7.155871</td>\n",
       "      <td>9.197104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.081900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>45.175000</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>7.125000</td>\n",
       "      <td>17.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.253715</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208500</td>\n",
       "      <td>76.800000</td>\n",
       "      <td>3.207450</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>391.440000</td>\n",
       "      <td>11.430000</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.560262</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>93.975000</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>16.955000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.976200</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>37.970000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  486.000000  486.000000  486.000000  486.000000  506.000000  506.000000   \n",
       "mean     3.611874   11.211934   11.083992    0.069959    0.554695    6.284634   \n",
       "std      8.720192   23.388876    6.835896    0.255340    0.115878    0.702617   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.081900    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.253715    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      3.560262   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
       "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  486.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    68.518519    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
       "std     27.999513    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
       "25%     45.175000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
       "50%     76.800000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
       "75%     93.975000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT        MEDV  \n",
       "count  486.000000  506.000000  \n",
       "mean    12.715432   22.532806  \n",
       "std      7.155871    9.197104  \n",
       "min      1.730000    5.000000  \n",
       "25%      7.125000   17.025000  \n",
       "50%     11.430000   21.200000  \n",
       "75%     16.955000   25.000000  \n",
       "max     37.970000   50.000000  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  PTRATIO  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
       "\n",
       "        B  LSTAT  MEDV  \n",
       "0  396.90   4.98  24.0  \n",
       "1  396.90   9.14  21.6  \n",
       "2  392.83   4.03  34.7  \n",
       "3  394.63   2.94  33.4  \n",
       "4  396.90    NaN  36.2  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 14)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM        True\n",
       "ZN          True\n",
       "INDUS       True\n",
       "CHAS        True\n",
       "NOX        False\n",
       "RM         False\n",
       "AGE         True\n",
       "DIS        False\n",
       "RAD        False\n",
       "TAX        False\n",
       "PTRATIO    False\n",
       "B          False\n",
       "LSTAT       True\n",
       "MEDV       False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_df.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>AGE</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.9</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.8</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.02985</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58.7</td>\n",
       "      <td>5.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS   AGE  LSTAT\n",
       "0  0.00632  18.0   2.31   0.0  65.2   4.98\n",
       "1  0.02731   0.0   7.07   0.0  78.9   9.14\n",
       "2  0.02729   0.0   7.07   0.0  61.1   4.03\n",
       "3  0.03237   0.0   2.18   0.0  45.8   2.94\n",
       "4  0.06905   0.0   2.18   0.0  54.2    NaN\n",
       "5  0.02985   0.0   2.18   0.0  58.7   5.21"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show some records with NULL values\n",
    "housing_df.loc[:5, housing_df.isnull().any()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>AGE</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>486.000000</td>\n",
       "      <td>486.000000</td>\n",
       "      <td>486.000000</td>\n",
       "      <td>486.000000</td>\n",
       "      <td>486.000000</td>\n",
       "      <td>486.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.611874</td>\n",
       "      <td>11.211934</td>\n",
       "      <td>11.083992</td>\n",
       "      <td>0.069959</td>\n",
       "      <td>68.518519</td>\n",
       "      <td>12.715432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.720192</td>\n",
       "      <td>23.388876</td>\n",
       "      <td>6.835896</td>\n",
       "      <td>0.255340</td>\n",
       "      <td>27.999513</td>\n",
       "      <td>7.155871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.730000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.081900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.175000</td>\n",
       "      <td>7.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.253715</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>76.800000</td>\n",
       "      <td>11.430000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.560262</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>93.975000</td>\n",
       "      <td>16.955000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.976200</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>37.970000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         AGE       LSTAT\n",
       "count  486.000000  486.000000  486.000000  486.000000  486.000000  486.000000\n",
       "mean     3.611874   11.211934   11.083992    0.069959   68.518519   12.715432\n",
       "std      8.720192   23.388876    6.835896    0.255340   27.999513    7.155871\n",
       "min      0.006320    0.000000    0.460000    0.000000    2.900000    1.730000\n",
       "25%      0.081900    0.000000    5.190000    0.000000   45.175000    7.125000\n",
       "50%      0.253715    0.000000    9.690000    0.000000   76.800000   11.430000\n",
       "75%      3.560262   12.500000   18.100000    0.000000   93.975000   16.955000\n",
       "max     88.976200  100.000000   27.740000    1.000000  100.000000   37.970000"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data Description of columns with any Null values\n",
    "housing_df.loc[:, housing_df.isnull().any()].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"line-height:1.7; font-size: 16px;\">The choice depends on what you ultimately want to do with the data. If the goal is \n",
    "a straightforward data analysis, eliminating the rows with null values is worth \n",
    "consideration. However, if the goal is to use machine learning to predict data, \n",
    "then perhaps more is to be gained by changing the null values to a suitable \n",
    "replacement. It's impossible to know in advance.</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace Null values in the data\n",
    "housing_df['AGE'] = housing_df['AGE'].fillna(housing_df.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_df['CHAS'] = housing_df['CHAS'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_df = housing_df.fillna(housing_df.median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      "CRIM       506 non-null float64\n",
      "ZN         506 non-null float64\n",
      "INDUS      506 non-null float64\n",
      "CHAS       506 non-null float64\n",
      "NOX        506 non-null float64\n",
      "RM         506 non-null float64\n",
      "AGE        506 non-null float64\n",
      "DIS        506 non-null float64\n",
      "RAD        506 non-null int64\n",
      "TAX        506 non-null int64\n",
      "PTRATIO    506 non-null float64\n",
      "B          506 non-null float64\n",
      "LSTAT      506 non-null float64\n",
      "MEDV       506 non-null float64\n",
      "dtypes: float64(12), int64(2)\n",
      "memory usage: 55.4 KB\n"
     ]
    }
   ],
   "source": [
    "housing_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VISUALIZATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.7.6 (default, Dec 30 2019, 19:38:26) \\n[Clang 11.0.0 (clang-1100.0.33.16)]'"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# !pip uninstall seaborn -y\n",
    "import sys\n",
    "sys.version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip3 install seaborn --upgrade\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEBCAYAAACQbKXWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAF0BJREFUeJzt3X1QVPe9x/HPIoh6wWklZ62l1pmmdghpK504WmoGxnQU4kJpwWnFNoxjEmtr1djU8QGUNg31IWRoHetMOmPsNLGTIFVUyqzt1NSp4phKUxwjmbEpkKAUFvKgIM+c+0fubC6X9BbOnmXB3/v1F3v2cH7f/XL2s4ezu7/jsW3bFgDAKFGRLgAAMP4IfwAwEOEPAAYi/AHAQIQ/ABiI8AcAAxH+AGAgwh8ADET4A4CBCH8AMNCow7+zs1NZWVlqbm6WJL322mv65je/KZ/Ppx/+8Ifq6+uTJNXX1ysvL08ZGRkqLCzUwMBAeCoHADg2qvCvq6tTfn6+GhsbJX3wQrBx40Y99dRT+v3vfy9JqqiokCRt3bpVu3bt0pkzZ2TbtsrLy8NTOQDAsVGFf3l5uYqLi+X1eiVJFy5cUEpKipKSkiRJRUVFWrZsmW7cuKGenh6lpKRIknJzc+X3+8NUOgDAqejRrFRSUjLsdlNTk2bMmKENGzborbfe0sKFC7V9+3Zdu3ZNlmUF17MsS62trWMq6N13uzQ0NHknGk1IiFNHR2eky5gw6Mdw9OND9GI4p/2IivLo4x//rzH/3qjC//8aHBzU+fPn9fLLL+uTn/ykCgsL9atf/UpLliwZsa7H4xnTtp08iIkmISEu0iVMKPRjOPrxIXox3Hj2w1H433PPPVqwYIHmzp0rSXr44Yf14osvKjc3V+3t7cH1AoFA8FTRaHV0dE7qI3/LilcgcDvSZUwY9GM4+vEhejGc035ERXkcvWg4+qjngw8+qNdff10tLS2SpFdeeUX333+/EhMTFRsbq9raWklSZWWl0tLSnAwBAAgjR0f+c+bM0VNPPaX169ert7dX9913n7Zt2yZJKi0tVVFRkbq6upScnKyCggJXCwYAhM4z0S7jyGmfuwv9GI5+fIheDDcpTvsAACY3wh8ADET4A4CBHL3hC8TPnK5psaPbfSwr3rVxe3oHdPtWt2vbA0xF+MORabHRyn7y5LiPe/rZHPEWIRA6TvsAgIEIfwAwEOEPAAYi/AHAQIQ/ABiI8AcAAxH+AGAgwh8ADET4A4CBCH8AMBDhDwAGIvwBwECEPwAYaFTh39nZqaysLDU3Nw9bfvToUT3yyCPB2zdv3tS3v/1tZWZm6nvf+566urrcrRYA4Ir/GP51dXXKz89XY2PjsOX/+Mc/9Nxzzw1b9pOf/ESrV6+W3+/X5z//eR06dMjVYgEA7viP4V9eXq7i4mJ5vd7gsr6+Pu3evVubN28OLuvv79df//pXZWRkSJJyc3Pl9/vDUDIAIFT/8WIuJSUlI5Y9++yzysvL06c+9angsnfffVdxcXGKjv5gk5ZlqbW11cVSAQBuGfOVvC5cuKCWlhbt2LFDly5dCi63bXvEuh6PZ8wFJSTEjfl3Jho3L1uIkSZ7fyd7/W6iF8ONZz/GHP5VVVW6fv26cnJydOfOHbW3t+uJJ57QM888o87OTg0ODmrKlCkKBALDThWNVkdHp4aGRr6QTBaWFa9A4O6/0GAkn7STub+m7B+jQS+Gc9qPqCiPo4PmMYf/nj17gj9funRJBw8e1M9//nNJ0sKFC1VdXa3s7GxVVlYqLS1tzAUBAMLP1c/5FxcXq7y8XCtWrNDly5f1xBNPuLl5AIBLRn3kf/bs2RHLFi9erMWLFwdvJyYm6oUXXnCnMgBA2PANXwAwEOEPAAYi/AHAQIQ/ABiI8AcAAxH+AGAgwh8ADET4A4CBCH8AMBDhDwAGIvwBwECEPwAYiPAHAAMR/gBgIMIfAAxE+AOAgQh/ADAQ4Q8ABhp1+Hd2diorK0vNzc2SpJdffllZWVnKzs7Wjh071NfXJ0mqr69XXl6eMjIyVFhYqIGBgfBUDgBwbFThX1dXp/z8fDU2NkqSGhoadPjwYb300ks6deqUhoaG9Nvf/laStHXrVu3atUtnzpyRbdsqLy8PW/EAAGdGFf7l5eUqLi6W1+uVJE2dOlU//vGPFRcXJ4/Ho8997nO6efOmbty4oZ6eHqWkpEiScnNz5ff7w1c9AMCR6NGsVFJSMux2YmKiEhMTJUnvvPOOjh49qj179qitrU2WZQXXsyxLra2tLpYLAHDDqML/32ltbdVjjz2mvLw8LV68WH/7299GrOPxeMa0zYSEuFBKmhAsKz7SJdzVJnt/J3v9bqIXw41nPxyH/5tvvqnHH39c3/nOd7R27VpJ0uzZs9Xe3h5cJxAIBE8VjVZHR6eGhmynZUWcZcUrELgd6TLCLpJP2sncX1P2j9GgF8M57UdUlMfRQbOjj3p2dnbq0Ucf1ebNm4PBL31wOig2Nla1tbWSpMrKSqWlpTkZAgAQRo6O/CsqKtTe3q7nn39ezz//vCTpoYce0ubNm1VaWqqioiJ1dXUpOTlZBQUFrhYMAAjdmML/7NmzkqQ1a9ZozZo1H7lOUlKSKioqQi4MABA+fMMXAAxE+AOAgQh/ADAQ4Q8ABiL8AcBAhD8AGIjwBwADEf4AYCDCHwAMRPgDgIEIfwAwEOEPAAYi/AHAQIQ/ABiI8AcAAxH+AGAgwh8ADET4A4CBRh3+nZ2dysrKUnNzsySppqZG2dnZWr58ucrKyoLr1dfXKy8vTxkZGSosLNTAwID7VQMAQjKq8K+rq1N+fr4aGxslST09Pdq5c6cOHTqk6upqXb16VefOnZMkbd26Vbt27dKZM2dk27bKy8vDVjwAwJlRhX95ebmKi4vl9XolSVeuXNG8efM0d+5cRUdHKzs7W36/Xzdu3FBPT49SUlIkSbm5ufL7/eGrHgDgSPRoViopKRl2u62tTZZlBW97vV61traOWG5ZllpbW10qFQDgllGF//9l2/aIZR6P598uH4uEhDgnJU0olhUf6RLuapO9v5O9fjfRi+HGsx+Own/27Nlqb28P3m5ra5PX6x2xPBAIBE8VjVZHR6eGhka+iEwWlhWvQOB2pMsIu0g+aSdzf03ZP0aDXgzntB9RUR5HB82OPuq5YMECNTQ0qKmpSYODg6qqqlJaWpoSExMVGxur2tpaSVJlZaXS0tKcDAEACCNHR/6xsbHau3evNm7cqN7eXqWnpyszM1OSVFpaqqKiInV1dSk5OVkFBQWuFgwACN2Ywv/s2bPBn1NTU3Xq1KkR6yQlJamioiL0ygAAYcM3fAHAQIQ/ABiI8AcAAxH+AGAgwh8ADET4A4CBCH8AMBDhDwAGIvwBwECEPwAYiPAHAAMR/gBgIMIfAAxE+AOAgQh/ADAQ4Q8ABnJ0JS8gUvr6ByN2/eCe3gHdvtUdkbEBtxH+mFSmxkxR9pMnIzL26WdzxOXGcbcI6bTPyZMn5fP55PP5tG/fPklSfX298vLylJGRocLCQg0MDLhSKADAPY6P/Lu7u1VSUiK/36+ZM2cqPz9fNTU1+tnPfqann35aKSkp2rlzp8rLy7V69Wo3a8b/iJ85XdNi+ecNwNg5To7BwUENDQ2pu7tbM2bM0MDAgKKjo9XT06OUlBRJUm5urg4cOED4h8m02OiIngIBMHk5Dv+4uDht3rxZDz/8sKZNm6ZFixYpJiZGlmUF17EsS62tra4UCgBwj+Pwf+ONN/S73/1Or7zyiuLj4/WjH/1IFy5cGLGex+MZ03YTEuKcljRhROrTKAg/N/627B8fohfDjWc/HIf/+fPnlZqaqoSEBEkfnOI5fPiw2tvbg+sEAgF5vd4xbbejo1NDQ7bTsiLOsuIVCIzPZ0J44oy/UP+247l/THT0Yjin/YiK8jg6aHb8aZ+kpCTV1NTozp07sm1bZ8+e1aJFixQbG6va2lpJUmVlpdLS0pwOAQAIE8dH/g8++KCuXbum3NxcxcTE6Atf+ILWrVunZcuWqaioSF1dXUpOTlZBQYGb9QIAXBDS5wTXrVundevWDVuWlJSkioqKkIoCAIQXc/sAgIEIfwAwEOEPAAYi/AHAQIQ/ABiI8AcAAxH+AGAgwh8ADET4A4CBCH8AMBDhDwAGIvwBwECEPwAYiPAHAAMR/gBgIMIfAAxE+AOAgQh/ADBQSOF/9uxZ5ebmKjMzU08//bQkqaamRtnZ2Vq+fLnKyspcKRIA4C7H4f/222+ruLhYhw4d0unTp3Xt2jWdO3dOO3fu1KFDh1RdXa2rV6/q3LlzbtYLAHCB4/D/4x//qBUrVugTn/iEYmJiVFZWpunTp2vevHmaO3euoqOjlZ2dLb/f72a9AAAXRDv9xaamJsXExOjRRx9VIBDQ0qVLNX/+fFmWFVzH6/WqtbXVlUIBAO5xHP6Dg4O6fPmyXnjhBc2YMUPf//73NX369BHreTyeMW03ISHOaUkThmXFR7oEhIkbf1v2jw/Ri+HGsx+Ow/+ee+5RamqqZs2aJUn66le/Kr/frylTpgTXaWtrk9frHdN2Ozo6NTRkOy0r4iwrXoHA7XEbC+Mr1L/teO4fEx29GM5pP6KiPI4Omh2f81+6dKnOnz+vW7duaXBwUH/5y1+UmZmphoYGNTU1aXBwUFVVVUpLS3M6BAAgTBwf+S9YsECPPfaYVq9erf7+fi1ZskT5+fn6zGc+o40bN6q3t1fp6enKzMx0s14AgAsch78krVy5UitXrhy2LDU1VadOnQqpKABAePENXwAwUEhH/vhA/Mzpmhb7YSt5IxbAREf4u2BabLSynzw57uOefjZn3McEcHfgtA8AGIjwBwADEf4AYCDCHwAMRPgDgIEIfwAwEOEPAAYi/AHAQIQ/ABiI8AcAAxH+AGAgwh8ADET4A4CBCH8AMBDhDwAGciX89+3bp+3bt0uS6uvrlZeXp4yMDBUWFmpgYMCNIQAALgo5/C9evKgTJ04Eb2/dulW7du3SmTNnZNu2ysvLQx0CAOCykK7k9d5776msrEzr16/XG2+8oRs3bqinp0cpKSmSpNzcXB04cECrV692pVggkvr6B125ROdYt9HTO6Dbt7pDHhf430IK/927d2vLli1qaWmRJLW1tcmyrOD9lmWptbU1tAqBCWJqzJSIXa7z9riPirud4/A/duyY5syZo9TUVB0/flySZNv2iPU8Hs+YtpuQEOe0JOCu5cZ/HBPR3fq4nBrPfjgO/+rqagUCAeXk5Oj999/XnTt35PF41N7eHlwnEAjI6/WOabsdHZ0aGhr5IjKRsQMj3AKBu+/Y37Li78rH5ZTTfkRFeRwdNDsO/yNHjgR/Pn78uF599VXt2bNHWVlZqq2t1QMPPKDKykqlpaU5HQIAECYhnfP/KKWlpSoqKlJXV5eSk5NVUFDg9hAAgBC5Ev65ubnKzc2VJCUlJamiosKNzQIAwoRv+AKAgQh/ADAQ4Q8ABiL8AcBAhD8AGIjwBwADEf4AYCDCHwAM5Po3fAG4y62ppMeKqaTvboQ/MMExlTTCgdM+AGAgwh8ADET4A4CBCH8AMBDhDwAGIvwBwECEPwAYiPAHAAOFFP4HDx6Uz+eTz+fT/v37JUk1NTXKzs7W8uXLVVZW5kqRAAB3OQ7/mpoanT9/XidOnFBlZaVef/11VVVVaefOnTp06JCqq6t19epVnTt3zs16AQAucDy9g2VZ2r59u6ZOnSpJuvfee9XY2Kh58+Zp7ty5kqTs7Gz5/X6lp6e7U+3/I37mdE2LZbYKAM5FMkf6+gfHdTzHj3L+/PnBnxsbG1VdXa1HHnlElmUFl3u9XrW2to5puwkJcU5Lisj8J9IHc6AAd6NwTygXiQnr/pNI5sh49iPkl7jr16/ru9/9rrZt26bo6Gg1NDQMu9/j8Yxpex0dnRoassdcx0TciYDJLhAI39RulhUf1u07EekccdKPqCiPo4PmkN7wra2t1Zo1a/Tkk0/qG9/4hmbPnq329vbg/W1tbfJ6vaEMAQAIA8fh39LSog0bNqi0tFQ+n0+StGDBAjU0NKipqUmDg4OqqqpSWlqaa8UCANzh+LTP4cOH1dvbq7179waXrVq1Snv37tXGjRvV29ur9PR0ZWZmulIoAMA9jsO/qKhIRUVFH3nfqVOnHBcEAAg/vuELAAYi/AHAQIQ/ABiI8AcAAxH+AGAgwh8ADET4A4CBCH8AMBBzIAOYcJiiPfzoLoAJZ1psdESmVjZpenZO+wCAgTjyB/CR+voHjbyYiykIfwAfaWrMFK6OdxfjtA8AGIjwBwADEf4AYCDCHwAMRPgDgIHCEv6nT5/WihUrtGzZMh09ejQcQwAAQuD6Rz1bW1tVVlam48ePa+rUqVq1apUWL16sz372s24PBQBwyPXwr6mp0Ze//GV97GMfkyRlZGTI7/frBz/4wah+PyrK43hs78enO/7dUEVqbB6zGWObNm4kx47kY3aSf04z02Pbtu3oN/+N5557Tnfu3NGWLVskSceOHdOVK1f005/+1M1hAAAhcP2c/0e9lng8zo/mAQDucz38Z8+erfb29uDttrY2eb1et4cBAITA9fD/yle+oosXL+qdd95Rd3e3/vCHPygtLc3tYQAAIXD9Dd/Zs2dry5YtKigoUH9/v1auXKkvfvGLbg8DAAiB62/4AgAmPr7hCwAGIvwBwECEPwAYiPAHAAMR/iHq7OxUVlaWmpubJX0wvUV2draWL1+usrKyCFc3vg4ePCifzyefz6f9+/dLMrsfv/jFL7RixQr5fD4dOXJEktn9kKR9+/Zp+/btkqT6+nrl5eUpIyNDhYWFGhgYiHB146egoEA+n085OTnKyclRXV3d+E+IacOxv//973ZWVpZ9//3322+//bbd3d1tp6en22+99Zbd399vr1271v7zn/8c6TLHxYULF+xvfetbdm9vr93X12cXFBTYp0+fNrYfly5dsletWmX39/fb3d3d9tKlS+36+npj+2Hbtl1TU2MvXrzY3rZtm23btu3z+ezXXnvNtm3b3rFjh3306NFIljduhoaG7CVLltj9/f3BZf/617/spUuX2u+++67d1dVlZ2dn29evXw9rHRz5h6C8vFzFxcXBbzBfuXJF8+bN09y5cxUdHa3s7Gz5/f4IVzk+LMvS9u3bNXXqVMXExOjee+9VY2Ojsf1YtGiRfvOb3yg6OlodHR0aHBzUrVu3jO3He++9p7KyMq1fv16SdOPGDfX09CglJUWSlJuba0wv/vnPf8rj8ejxxx/X1772Nb344ovDJsScMWNGcELMcCL8Q1BSUqKFCxcGb7e1tcmyrOBtr9er1tbWSJQ27ubPnx98Ijc2Nqq6uloej8fYfkhSTEyMDhw4IJ/Pp9TUVKP3j927d2vLli2aOXOmpJHPFcuyjOnFrVu3lJqaql/+8pf69a9/rZdeekk3b94c932D8HeRzaR2un79utauXatt27bp05/+9Ij7TevHpk2bdPHiRbW0tKixsXHE/Sb049ixY5ozZ45SU1ODy0x+rnzpS1/S/v37NWPGDM2aNUsrV67UgQMHRqwX7n64Pr2DyUyf1K62tlabNm3Szp075fP59OqrrxrbjzfffFN9fX267777NH36dC1fvlx+v19TpkwJrmNKP6qrqxUIBJSTk6P3339fd+7ckcfjGbZvBAIBI3ohSZcvX1Z/f3/wxdC2bSUmJo77c4UjfxctWLBADQ0Nampq0uDgoKqqqoyZ1K6lpUUbNmxQaWmpfD6fJLP70dzcrKKiIvX19amvr09/+tOftGrVKiP7ceTIEVVVVenkyZPatGmTHnroIe3Zs0exsbGqra2VJFVWVhrRC0m6ffu29u/fr97eXnV2durEiRN65plnxn1CTI78XRQbG6u9e/dq48aN6u3tVXp6ujIzMyNd1rg4fPiwent7tXfv3uCyVatWGduP9PR01dXV6etf/7qmTJmi5cuXy+fzadasWUb246OUlpaqqKhIXV1dSk5OVkFBQaRLGhdLly4N7htDQ0NavXq1HnjggXGfEJOJ3QDAQJz2AQADEf4AYCDCHwAMRPgDgIEIfwAwEOEPAAYi/AHAQIQ/ABjovwHFzbEaZV+S7QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(housing_df['MEDV'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(housing_df['MEDV'])\n",
    "plt.title('Median Boston Housing Prices')\n",
    "plt.xlabel('1980 Median Value in Thousands')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX',\n",
       "       'PTRATIO', 'B', 'LSTAT', 'MEDV'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Tweak more\n",
    "title = 'Median Boston Housing Prices'\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.hist(housing_df['MEDV'])\n",
    "plt.title(title, fontsize=15)\n",
    "plt.xlabel('1980 Median Value in Thousands')\n",
    "plt.ylabel('Count')\n",
    "plt.savefig(title, dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_hist(column, title, xlab, ylab):\n",
    "    title = title\n",
    "    plt.figure(figsize=(10,6))\n",
    "    plt.hist(column)\n",
    "    plt.title(title, fontsize=15)\n",
    "    plt.xlabel(xlab)\n",
    "    plt.ylabel(ylab)\n",
    "    plt.savefig(title, dpi=300)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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z5fvxvxIXFxd7HdWrV9e7776rRx99VM8995xmzpwpJyenS57In5eXZ183F6vn3Lo8f/39s7bzl/mfunXrptDQUH3zzTdKTEzUa6+9pvfee09Lly697Lo552Lvudy2JkkVKlTI1y7e3t46fPiw/vvf/+qFF1644np3cnLS1KlT1b9/f3377bdav369+vfvr8jISL322msXfK6g7Xgty1KlShVVqlQp37AjR47Y/3+5dpUuvt2fP146W//s2bO1bds2rV27VuvWrdOcOXM0YMAA9e/fv8DfGxfbDowxBdr2zufk5HTBevnnNjd48GB17tzZ3j4vv/yy4uPj9dFHH110Xrj1cdEBLOPIkSNav369nnjiCS1ZssT+b+nSpWrevLn94oN77rlH/v7++uKLL7R69ep8PSb33XefkpKSVL16dfu/3bt3a8qUKZec77x583T33Xfr3Xff1VNPPaUWLVrYeziMMfL29paTk5O2bNli/8zx48d14MCB65rvP91///3q27ev3nnnnXwXB5z7Ij958qR92LkLLK7Xud5D6ewy7d+/X/fff7+qV68uV1dXpaam5lumZcuWXVUPYI0aNbRt27Z8vSy///67Tpw4oRo1alxz3dWqVdPgwYO1bt06+4nt52r+5xW3P/74o+677z57PRcb7+rqmq9XraCOHTum0aNHKzc3V4899pimTJmiDz74QPv27dNvv/12jUt3bc4FAGOMatSoof379+c7Sf3o0aPav3+/vU1ee+013XfffXrqqaf0/vvvKzo62n6S/D+DfmG1Y0G4u7urUqVKF203Dw8P3X777XJ1dVVeXl6+exqev49s2LBB06dPl4+Pj/r166dPP/1UnTp1si/v9e6/Bdn2zlerVi3t27fPfjGVpHwX/CQnJ2vkyJHy8PBQ586d9dZbb2nChAn6/vvvb7r7DuLGIbDBMj777DPZbDb16tVLXl5e+f716tVLWVlZ9qvrHn30Uc2aNUtubm5q0aKFfRp9+/bVF198oRkzZigpKUlr1qzRiBEjVKpUqUv2dlSqVEl//PGHNmzYoD/++ENLly61f1GfOXNGVatWVVBQkGJiYrRp0ybt2rVLL774ok6fPm3/YbuW+V7M008/rRo1auS7mainp6cqV65sDwKbN2/W1KlTr9h7VhATJ07Ut99+q127dumFF17QXXfdpdDQUJUuXVo9evTQ66+/ruXLlyslJUUfffSRpk+ffsXDu+fr0qWLMjIy9PLLL2vPnj3avHmzXnjhBdWsWVMBAQHXVftjjz2mRo0aadKkSTp8+LBKlSqlnj17aurUqVq5cqWSkpI0Y8YMffnll/Ye2759+2rFihWaOXOmkpKStGLFCsXFxemxxx675D3gLuf222/X2rVrNWLECP322286cOCAFi1apHLlyunee++9ruW7nMzMTB05ckRHjhxRamqqVq9erQ8//FCtW7dW2bJlFRERofLly+v555/Xjh07tGPHDj3//PMqV66c2rZtq7Jly2rOnDmaPHmykpOTtXPnTn3zzTeqV6+eJNkP1e3cuVMZGRmF2o4F0bdvX3300UeaP3++Dhw4oPj4eM2ePVs9evSQk5OT/Pz85OTkpLi4OB08eFDLly+399ZLZ//omT59uj766COlpKTo559/1vfffy9fX1/79K9n/y3Itne+kJAQ3X777XrppZe0e/durVu3TnFxcfbxd955p1asWKFRo0Zp79692rt3r1asWHHZW9/g1kdgg2UsWbJELVu2VOXKlS8YFxAQoJo1ayo+Pl7S2S+83NxchYWF5TvkEBgYqNjYWC1btkxhYWEaMWKE2rVrp5iYmEvOt1u3bmrTpo2io6MVERGhOXPm6NVXX1WZMmW0bds2SdKYMWNUp04d9enTR127dlXt2rVVuXJle+/Xtcz3YlxdXfXaa6/lWyYnJyfFxsbqxIkTioiI0MiRIzVo0KDLHqYrqGeffVZjx45Vhw4dZLPZ9O6779p/oJ577jl16tRJsbGxCgkJ0dy5cxUTE6OoqKgCT/+uu+7Se++9p9TUVLVv3179+vVTrVq19P7771/VIdGLcXJyUkxMjM6cOWNfz//5z3/073//W+PGjbPfVmHy5MkKCQmRdPZcpgkTJmjJkiUKCwvTxIkT1a1bt2u+cbGzs7PeeecdSWdvrRIREaHff/9ds2bNuqYAWFAxMTFq3ry5mjdvrtatW2vs2LEKDQ21PymiZMmS9j9ounTpou7du9tDWrly5XTPPfdo+vTp2rBhgyIiItStWzdVqlRJkydPlnS2xyk4OFjR0dGKi4sr1HYsiI4dOyo6OlrvvPOO2rZtq/fff19DhgxRr169JJ09J/XVV1/VqlWrFBISovj4+Hy3mWnUqJHGjRun+Ph4tW3bVv369VPDhg3t7X4j9t8rbXvnc3d314cffmjvmY2JiVHv3r3t48uWLauZM2cqJSVFjz/+uDp06KAzZ85oxowZN2S/x83JyVzpBAOgmMvOzta6devUtGlTlSlTRtLZE6kbN25s/2IHAKAwcdEBcAVubm4aNWqUAgMD1bt3b9lsNnvPQmBgoKPLAwAUA/SwAQWwY8cOxcbGavv27bLZbPL399eLL76oWrVqObo0AEAxQGADAACwOM5eBAAAsDgCGwAAgMUR2AAAACzulrlK9Nixk7LZOB1PkipUcFdaWqajyyjWaANroB0cjzZwPNrA8c5vA2dnJ915521X+MSFbpnAZrMZAtt5WBeORxtYA+3geLSB49EGjne9bcAhUQAAAIsjsAEAAFgcgQ0AAMDiCGwAAAAWR2ADAACwOAIbAACAxRHYAAAALI7ABgAAYHEENgAAAIsjsAEAAFgcgQ0AAMDiCGwAAAAWV+gPf8/MzFTHjh319ttva+/evZo8ebJ9XGpqqnx9ffXOO+/ojTfe0MKFC1WuXDlJ0uOPP67OnTsXdnkAAACWV6iBbcuWLRo+fLiSkpIkSQ8++KAefPBBSdKRI0fUqVMnvfzyy5Kk7du3a/LkyfL39y/MkgAUsrLlSqtUyUL/W7DAPDzKXvNns7JzlZF++gZWAwDXplC/VePj4zVy5Ei99NJLF4yLjY1Vx44ddc8990g6G9hmzpyplJQUNWzYUIMHD1bJkiULszwAhaBUSReFD1rq6DJuiGWvRyrD0UUAgAr5HLaxY8fqgQceuGB4UlKSfvjhB3Xr1k2SdPLkSdWqVUuDBw/W4sWLlZ6erjfffLMwSwMAALhpOOS4xbx58/TEE0/Izc1NknTbbbdp5syZ9vFPPvmkhg4dqujo6AJPs0IF9xte583seg4D4cagDW4NtOP1Yx06Hm3geNfbBg4JbF9//bVmzZplf33o0CElJiaqQ4cOkiRjjFxcrq60tLRM2WzmhtZ5s/LwKKsjRziQ40jFuQ1utR+G4tqON0px3hesgjZwvPPbwNnZ6Zo6mYr8th5Hjx5VVlaWqlatah9WqlQpTZw4USkpKTLGaM6cOWrTpk1RlwYAAGBJRd7DdvDgQVWqVCnfsPLlyysmJkZ9+/ZVTk6O6tevr549exZ1aQAAAJZUJIEtISHB/v969eopPj7+gvcEBwcrODi4KMoBAAC4qfCkAwAAAIsjsAEAAFgcgQ0AAMDiCGwAAAAWR2ADAACwOAIbAACAxRHYAAAALI7ABgAAYHEENgAAAIsjsAEAAFgcgQ0AAMDiCGwAAAAWR2ADAACwOAIbAACAxRHYAAAALI7ABgAAYHEENgAAAIsjsAEAAFgcgQ0AAMDiCGwAAAAWR2ADAACwOAIbAACAxRHYAAAALI7ABgAAYHEENgAAAIsjsAEAAFgcgQ0AAMDiCGwAAAAWR2ADAACwOAIbAACAxRHYAAAALI7ABgAAYHEENgAAAIsjsAEAAFgcgQ0AAMDiCj2wZWZmKiwsTAcPHpQkvfzyywoKClJkZKQiIyP11VdfSZISExMVHh6uoKAgTZkypbDLAgAAuGm4FObEt2zZouHDhyspKck+bPv27Zo9e7Y8PT3tw7KysjR06FB9/PHHuvvuu9WnTx99++23evDBBwuzPAAAgJtCoQa2+Ph4jRw5Ui+99JIk6dSpUzp06JBeeeUVHTp0SG3atFH//v21detWVa9eXVWrVpUkhYeHa+XKlQQ2AA51JidPHh5lHV3GDZGVnauM9NOOLgPANSrUwDZ27Nh8r9PS0tSkSRPFxMSoTJky6tOnjxYsWKAyZcrIw8PD/j5PT0+lpqYWZmkAcEVuriUUPmipo8u4IZa9HqkMRxcB4JoVamD7p6pVq2r69On21127dtWSJUv0yCOPXPBeJyenq5p2hQru113freRW6RW4mdEGsBpHbZPsC45HGzje9bZBkQa2Xbt2KSkpScHBwZIkY4xcXFxUsWJF/f333/b3HT58ON85bgWRlpYpm83c0HpvVh4eZXXkCH9LO1JxbgN+GKzLEdtkcd4XrII2cLzz28DZ2emaOpmK9LYexhiNGzdOJ06cUE5OjubNm6c2bdrI19dX+/fv14EDB5SXl6fPP/9cgYGBRVkaAACAZRVpD1vNmjX19NNPq1OnTsrNzVVQUJDCwsIkSePHj9eAAQOUnZ2tBx988KKHSQEAAIqjIglsCQkJ9v937txZnTt3vuA9AQEB+uyzz4qiHAAAgJsKTzoAAACwOAIbAACAxRHYAAAALI7ABgAAYHEENgAAAIsjsAEAAFgcgQ0AAMDiCGwAAAAWR2ADAACwOAIbAACAxRHYAAAALI7ABgAAYHEENgAAAIsjsAEAAFgcgQ0AAMDiCGwAAAAWR2ADAACwOAIbAACAxRHYAAAALI7ABgAAYHEENgAAAIsjsAEAAFgcgQ0AAMDiCGwAAAAWR2ADAACwOAIbAACAxRHYAAAALI7ABgAAYHEENgAAAIsjsAEAAFgcgQ0AAMDiCGwAAAAWR2ADAACwOAIbAACAxRHYAAAALK7QA1tmZqbCwsJ08OBBSdK8efMUFham8PBwvfzyyzpz5owk6Y033lCrVq0UGRmpyMhIzZkzp7BLAwAAuCm4FObEt2zZouHDhyspKUmStH//fs2aNUuLFi3SbbfdpiFDhuiTTz5Rjx49tH37dk2ePFn+/v6FWRIAAMBNp1B72OLj4zVy5Eh5enpKktzc3DRq1Ci5u7vLyclJXl5eOnTokCRp+/btmjlzpsLDwxUTE6Ps7OzCLA0AAOCmUaiBbezYsXrggQfsrytXrqymTZtKko4ePao5c+booYce0smTJ1WrVi0NHjxYixcvVnp6ut58883CLA0AAOCmUaiHRC8lNTVVvXr1Uvv27dW4cWNJ0syZM+3jn3zySQ0dOlTR0dEFnmaFCu43vM6bmYdHWUeXUOzRBrAaR22T7AuORxs43vW2QZEHtr1796p3797q0qWLnnzySUnSoUOHlJiYqA4dOkiSjDFycbm60tLSMmWzmRte783Iw6OsjhzJcHQZxVpxbgN+GKzLEdtkcd4XrII2cLzz28DZ2emaOpmK9LYemZmZeuqppzRw4EB7WJOkUqVKaeLEiUpJSZExRnPmzFGbNm2KsjQAAADLKtIetgULFujvv//We++9p/fee0+S1Lp1aw0cOFAxMTHq27evcnJyVL9+ffXs2bMoSwMAALCsIglsCQkJkqQePXqoR48eF31PcHCwgoODi6IcAACAmwpPOgAAALA4AhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsrlADW2ZmpsLCwnTw4EFJUmJiosLDwxUUFKQpU6bY37dz5061b99ewcHBGjZsmHJzcwuzLAAAgJtKoQW2LVu2qFOnTkpKSpIkZWVlaejQoXrzzTe1fPlybd++Xd9++60k6cUXX9Qrr7yiVatWyRij+Pj4wirRbitWAAAceElEQVQLAADgplOgwDZ06NALhg0YMOCyn4mPj9fIkSPl6ekpSdq6dauqV6+uqlWrysXFReHh4Vq5cqX++OMPZWVlyc/PT5IUFRWllStXXu1yAAAA3LJcLjdy5MiRSk1N1Y8//qijR4/ah+fm5mrfvn2XnfDYsWPzvT58+LA8PDzsrz09PZWamnrBcA8PD6Wmpl7VQgAAANzKLhvYOnTooD179mjXrl0KDg62Dy9RooT8/f2vakbGmAuGOTk5XXL41apQwf2qP3Mr8/Ao6+gSij3aAFbjqG2SfcHxaAPHu942uGxg8/HxkY+Pj5o2bapKlSpd14wqVqyov//+2/768OHD8vT0vGD4kSNH7IdRr0ZaWqZstgvDX3Hk4VFWR45kOLqMYq04twE/DNbliG2yOO8LVkEbON75beDs7HRNnUyXDWznJCcn68UXX9SJEyfy9YgtW7aswDPy9fXV/v37deDAAVWpUkWff/652rdvr8qVK6tkyZL68ccf1aBBAy1ZskSBgYFXvSAAAAC3qgIFtpiYGLVv3161a9e+psOVklSyZEmNHz9eAwYMUHZ2th588EE98sgjkqRJkyZp+PDhOnnypGrXrq1u3bpd0zwAAABuRQUKbK6ururZs+c1zSAhIcH+/4CAAH322WcXvKdmzZpasGDBNU0fAADgVleg23rcf//92rVrV2HXAgAAgIsoUA9bSkqK2rdvr//7v/9TyZIl7cOv5hw2AAAAXJsCBbbo6OjCrgMAAACXUKDA5uXlVdh1AAAA4BIKFNiaNGliv8ntuatEPTw8tHbt2kItDgAAAAUMbL/99pv9/zk5Ofryyy/zDQMAAEDhKdBVoudzdXVV27ZttWHDhsKoBwAAAP9QoB6248eP2/9vjNH27duVnp5eaEUBAADgf676HDZJqlChgoYNG1aohQEAAOCsqz6HDQAAAEWrQIHNZrNp1qxZWrt2rXJzc9WsWTM988wzcnEp0McBAABwHQp00cHrr7+u7777Tt27d1fPnj31888/KzY2trBrAwAAgArYw7Zu3TotXLhQrq6ukqSWLVsqIiJCQ4cOLdTiAAAAUMAeNmOMPaxJkpubW77XAAAAKDwFCmw1a9bUuHHjlJycrOTkZI0bN47HVQEAABSRAgW2kSNHKj09XR07dtTjjz+uY8eO6ZVXXins2gAAAKArBLYzZ85o8ODB+u677zR+/HglJiaqXr16KlGihNzd3YuqRgAAgGLtsoEtLi5OmZmZ8vf3tw8bPXq00tPTNW3atEIvDgAAAFcIbGvWrNHrr7+uChUq2IdVrFhRsbGxWr16daEXBwAAgCsENldXV5UqVeqC4e7u7nJzcyu0ogAAAPA/lw1szs7OyszMvGB4ZmamcnNzC60oAAAA/M9lA1tYWJiGDx+uU6dO2YedOnVKw4cPV1BQUKEXBwAAgCsEtu7du6ts2bJq1qyZHn/8cXXo0EHNmjVTuXLl1K9fv6KqEQAAoFi77KOpnJ2dNXr0aPXp00e//vqrnJ2d5ePjo4oVKxZVfQAAAMVegZ4lWqVKFVWpUqWwawEAAMBFFOhJBwAAAHAcAhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFFejRVDfS/PnzNXv2bPvrgwcPKjIyUqdPn9aPP/6o0qVLS5L69++vNm3aFHV5AAAAllPkge2xxx7TY489Jknas2eP+vXrp/79+6t79+6aPXu2PD09i7okAAAAS3PoIdFRo0YpOjpapUqV0qFDh/TKK68oPDxccXFxstlsjiwNAADAMhwW2BITE5WVlaWQkBClpaWpSZMmGjdunOLj47V582YtWLDAUaUBAABYSpEfEj3n008/Vc+ePSVJVatW1fTp0+3junbtqiVLlujxxx8v8PQqVHC/4TXezDw8yjq6hGKPNoDVOGqbZF9wPNrA8a63DRwS2M6cOaNNmzZp/PjxkqRdu3YpKSlJwcHBkiRjjFxcrq60tLRM2Wzmhtd6M/LwKKsjRzIcXUaxVpzbgB8G63LENlmc9wWroA0c7/w2cHZ2uqZOJoccEt21a5fuuecelSlTRtLZgDZu3DidOHFCOTk5mjdvHleIAgAA/H8O6WFLSUlRpUqV7K9r1qypp59+Wp06dVJubq6CgoIUFhbmiNIAAAAsxyGBLTQ0VKGhofmGde7cWZ07d3ZEOQAAAJbGkw4AAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIc9qQDAEDROZOTd8s86SArO1cZ6adv6DQBqyOwAUAx4OZaQuGDljq6jBti2euR4r79KG44JAoAAGBxBDYAAACLI7ABAABYHIENAADA4ghsAAAAFkdgAwAAsDgCGwAAgMUR2AAAACyOwAYAAGBxBDYAAACLI7ABAABYHIENAADA4ghsAAAAFkdgAwAAsDgCGwAAgMUR2AAAACyOwAYAAGBxBDYAAACLI7ABAABYHIENAADA4ghsAAAAFkdgAwAAsDgCGwAAgMUR2AAAACyOwAYAAGBxBDYAAACLI7ABAABYHIENAADA4lwcMdNu3bopLS1NLi5nZx8TE6Pk5GS99dZbysnJUY8ePdS5c2dHlAYAAGA5RR7YjDHat2+f1qxZYw9sqampio6O1qJFi+Tm5qaOHTuqcePGuu+++4q6PAAAAMsp8sC2b98+OTk5qXfv3kpLS9Pjjz+u2267TU2aNNEdd9whSQoODtbKlSvVv3//oi4PAADAcor8HLb09HQFBARo+vTp+uCDD/Tpp5/q0KFD8vDwsL/H09NTqampRV0aAACAJRV5D5u/v7/8/f0lSWXKlFGHDh302muv6Zlnnsn3Picnp6uaboUK7jesxluBh0dZR5dQ7NEGQOFh/7o6rC/Hu942KPLAtnnzZuXk5CggIEDS2XPaKleurL///tv+nsOHD8vT0/OqppuWlimbzdzQWm9WHh5ldeRIhqPLKNaKcxvww4CiUFz3r2tRnL+PrOL8NnB2drqmTqYiPySakZGh2NhYZWdnKzMzU4sXL9bEiRO1ceNGHT16VKdPn9aXX36pwMDAoi4NAADAkoq8h61Vq1basmWL2rVrJ5vNpieeeEINGjRQdHS0unXrppycHHXo0EH16tUr6tIAAAAsySH3YXvuuef03HPP5RsWHh6u8PBwR5QDAABgaTzpAAAAwOIIbAAAABZHYAMAALA4h5zDBuBCZcuVVqmS7JIAgAvx6wBYRKmSLgoftNTRZVy3Za9HOroEALjlcEgUAADA4ghsAAAAFkdgAwAAsDgCGwAAgMUR2AAAACyOwAYAAGBxBDYAAACLI7ABAABYHIENAADA4ghsAAAAFkdgAwAAsDgCGwAAgMUR2AAAACyOwAYAAGBxBDYAAACLI7ABAABYHIENAADA4ghsAAAAFkdgAwAAsDgCGwAAgMUR2AAAACyOwAYAAGBxBDYAAACLI7ABAABYHIENAADA4ghsAAAAFkdgAwAAsDgCGwAAgMUR2AAAACzOxREzfeONN7RixQpJ0oMPPqiXXnpJL7/8sn788UeVLl1aktS/f3+1adPGEeUBAABYSpEHtsTERK1fv16LFy+Wk5OTevXqpa+++krbt2/X7Nmz5enpWdQlAQAAWFqRHxL18PDQkCFD5ObmJldXV9WoUUOHDh3SoUOH9Morryg8PFxxcXGy2WxFXRoAAIAlFXlgu//+++Xn5ydJSkpK0vLly9WiRQs1adJE48aNU3x8vDZv3qwFCxYUdWkAAACW5JBz2CRpz5496tOnjwYPHqx//etfmj59un1c165dtWTJEj3++OMFnl6FCu6FUeZNy8OjrKNLKPZoA6DwsH9dHdaX411vGzgksP3444/6z3/+o6FDh6pt27batWuXkpKSFBwcLEkyxsjF5epKS0vLlM1mCqPcm46HR1kdOZLh6DKKtWtpA75QgYLjO67g+E1wvPPbwNnZ6Zo6mYr8kOiff/6pfv36adKkSWrbtq2kswFt3LhxOnHihHJycjRv3jyuEAUAAPj/iryHbdasWcrOztb48ePtwzp27Kinn35anTp1Um5uroKCghQWFlbUpQEAAFhSkQe24cOHa/jw4Rcd17lz5yKuBgAAwPp40gEAAIDFOewqUQAArsWZnLxb4iKdrOxcZaSfdnQZuEkQ2AAANxU31xIKH7TU0WVct2WvR4prN1FQHBIFAACwOAIbAACAxRHYAAAALI7ABgAAYHEENgAAAIsjsAEAAFgcgQ0AAMDiCGwAAAAWR2ADAACwOAIbAACAxRHYAAAALI7ABgAAYHE8/B03tbLlSqtUSWtuxh4eZR1dAgDgFmHNXzqggEqVdFH4oKWOLuOGWPZ6pKNLAFCEzuTkFdkfdoU9n6zsXGWkny7UeRR3BDYAABzAzbXELfUHZ4aji7jFcQ4bAACAxRHYAAAALI7ABgAAYHEENgAAAIsjsAEAAFgcgQ0AAMDiuK0HAAC4LkV5T7nCZtV7yhHYAADAdeGecoWPQ6IAAAAWR2ADAACwOAIbAACAxRHYAAAALI7ABgAAYHEENgAAAIvjth7FUNlypVWqJE0PAMDNgl/tYqhUSZdb6n45AADc6jgkCgAAYHGWCmzLli1TaGio2rRpozlz5ji6HAAAAEuwzCHR1NRUTZkyRYsWLZKbm5s6duyoxo0b67777nN0aZJuvvO+bpVnugEAAAsFtsTERDVp0kR33HGHJCk4OFgrV65U//79HVzZWZz3BQAAHMUyge3w4cPy8PCwv/b09NTWrVsL/HlnZ6fCKCsfzztLF/o8igrLYk23yrLcKsshsSxWdassy62yHNKttSyFkSnOTfNap+1kjDE3sqBr9fbbb+v06dOKjo6WJM2fP1/btm1TTEyMgysDAABwLMtcdFCxYkX9/fff9teHDx+Wp6enAysCAACwBssEtqZNm2rjxo06evSoTp8+rS+//FKBgYGOLgsAAMDhLHMOW8WKFRUdHa1u3bopJydHHTp0UL169RxdFgAAgMNZ5hw2AAAAXJxlDokCAADg4ghsAAAAFkdgAwAAsDgCGwAAgMUR2AAAACzOMrf1wI0zYcIEHTt2TOPHj3d0KcVSt27dlJaWJheXs7tXTEyMfH19HVxV8ZKQkKA33nhDp06dUvPmzTV8+HBHl1SszJ8/X7Nnz7a/PnjwoCIjIzVixAgHVlX8LF26VDNmzJAkBQYGavDgwQ6uqPiZMWOGFi5cKDc3N4WGhqpv377XPjGDW0piYqJp3LixGTx4sKNLKZZsNptp1qyZycnJcXQpxVZycrJp3ry5+fPPP82ZM2dMp06dzJo1axxdVrG1e/du06ZNG5OWluboUoqVU6dOmYYNG5q0tDSTk5NjOnToYDZs2ODosoqVDRs2mLCwMJORkWFyc3NNnz59zKpVq655ehwSvYUcP35cU6ZM0TPPPOPoUoqtffv2ycnJSb1791ZERES+XgYUja+++kqhoaGqVKmSXF1dNWXKFHo4HWjUqFGKjo5W+fLlHV1KsZKXlyebzabTp08rNzdXubm5KlmypKPLKlZ+/fVXNW/eXO7u7ipRooRatGih1atXX/P0CGy3kBEjRig6OlrlypVzdCnFVnp6ugICAjR9+nR98MEH+vTTT7VhwwZHl1WsHDhwQHl5eXrqqacUERGhTz75RLfffrujyyqWEhMTlZWVpZCQEEeXUuy4u7tr4MCBCgkJUWBgoCpXrqz69es7uqxipU6dOlq/fr2OHz+u7OxsJSQk5Htm+tUisN0i5s+fr7vvvlsBAQGOLqVY8/f3V2xsrMqUKaPy5curQ4cO+vbbbx1dVrGSl5enjRs3auLEiYqPj9e2bdu0ePFiR5dVLH366afq2bOno8soln777TctXLhQ33zzjdavXy9nZ2fNmjXL0WUVKwEBAYqKilLXrl3Vq1cvNWjQQK6urtc8PQLbLWL58uXasGGDIiMjFRcXp4SEBI0bN87RZRU7mzdv1saNG+2vjTH2iw9QNO666y4FBASofPnyKlWqlB566CFt3brV0WUVO2fOnNGmTZvUunVrR5dSLK1fv14BAQGqUKGC3NzcFBUVpR9++MHRZRUrmZmZatOmjZYtW6aPP/5YpUuXVtWqVa95egS2W8T777+vzz//XEuXLtV//vMftW7dWkOHDnV0WcVORkaGYmNjlZ2drczMTC1evFht2rRxdFnFSqtWrbR+/Xqlp6crLy9P69atU506dRxdVrGza9cu3XPPPSpTpoyjSymWatasqcTERJ06dUrGGCUkJMjHx8fRZRUrBw8eVL9+/ZSbm6uMjAzNnz//uk4P4E9/4AZq1aqVtmzZonbt2slms+mJJ56Qv7+/o8sqVnx9fdWrVy898cQTysnJUbNmzdS+fXtHl1XspKSkqFKlSo4uo9hq3ry5fv31V0VFRcnV1VU+Pj56+umnHV1WsVKzZk0FBQUpIiJCeXl56tGjhxo0aHDN03MyxpgbWB8AAABuMA6JAgAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFEdgAKCcnR82bN9dTTz3l6FKuire3t6ZMmZJv2MqVK9W1a9cbNo+uXbtq5cqVN2x6l5OXl6e+ffsqODj4gufQLlq0SA0aNFBkZKQiIyMVERGh1q1b66WXXlJ2dnaR1AfAcbgPGwB99dVX8vb21o4dO7R3717VqFHD0SUV2AcffKDmzZurYcOGji7luqWmpmr9+vX65ZdfVKJEiQvGP/DAA3rnnXfsr7Ozs9WpUyctXrxYHTt2LMpSARQxAhsAzZ07V6Ghoapevbo+/PBDxcTEaNCgQapdu7a9123u3Ln6/vvvNXXqVCUkJOitt95STk6OSpUqpcGDB8vf31/Tpk3TL7/8osOHD8vb21tDhgzRiBEjlJaWpiNHjqhy5cqaOnWqKlSooK1bt2rUqFHKyclRtWrVdOjQIQ0ZMkSNGze+5PQvJjo6Wi+++KKWLl16wUPep02bpmPHjmnEiBEXvO7atavq1Kmj7777TmlpaerWrZvS0tL0ww8/6PTp05o6daq8vb0lnQ20M2bMUFZWlsLDw9W3b19J0k8//aRJkybp9OnTcnJy0oABA9SqVSstWrRICxYs0OnTp+Xu7q6PP/44X12bN29WbGysTp8+LVdXVz333HOqX7++evXqpdzcXEVFRWnatGmqVq3aZdvt+PHjyszMtC/3nj17FBMTo+PHj8vJyUlPPvmk2rVrJ0maN2+ePv74Yzk7O+uuu+7SK6+8onvvvVdDhgxRyZIltW3bNv39998KCQlR+fLl9c033+jIkSMaM2aMAgICtHnzZo0fP142m02S1KdPHwUHBxd4GwNwnQyAYm3Pnj2mbt265tixY2bLli2mXr165ujRo2bjxo0mLCzM/r4OHTqYDRs2mP3795uwsDBz9OhRY4wxu3fvNs2aNTMnT540cXFxJjg42OTk5BhjjPnggw/MO++8Y4wxxmazmV69eplZs2aZnJwcExgYaNasWWOMMWbjxo3G29vbfPfdd5ed/j95eXmZtLQ08/zzz5sBAwYYY4xZsWKF6dKlizHGmLi4OPPqq6/a33/+6y5dupj+/fsbY4z55ZdfjJeXl/n666+NMcaMHTvWDB8+3P6+Pn36mJycHJORkWEeeeQRs2bNGnP8+HETFBRkUlJSjDHG/PXXXyYwMND88ccfZuHChaZhw4YmIyPjgpqPHj1qAgICzC+//GJfvkaNGpnk5GSTkpJi/Pz8LtpOCxcuNPXr1zcRERHmkUceMY0bNzb//ve/zdy5c40xxuTk5JiHHnrIrFq1yl5PixYtzE8//WQSExPNww8/bNLS0uzTCgkJMTabzQwePNg89thj5syZM+bw4cPGy8vLfPTRR/b269mzpzHGmG7dupnPP//cGGPMzp07zahRoy5aJ4DCQQ8bUMzNnTtXLVu21B133KE77rhDVapU0bx589SnTx9lZ2dr27ZtKl26tI4ePaqAgAB98sknOnz4sHr06GGfhpOTk5KTkyVJfn5+9gfed+/eXZs3b9b777+vpKQk7dmzR76+vtq9e7ck6cEHH5QkNWnSRPfff78kacOGDZecfs2aNS+6DK+++qoiIyM1f/58lS1btsDLfu45r+ceyNyiRQtJUrVq1fI9KLtDhw5ycXGRu7u7goODlZiYKEk6cuSI+vXrl6/OXbt2STp7fp27u/sF89y6dauqVasmX19fSdL999+v+vXr64cfflDjxo0vW++5Q6I2m01vvvmmli1bpoceekiSlJSUpOzsbAUFBUmSKlasqKCgIK1bt05ZWVkKDQ1V+fLlJUlRUVEaO3asDh48KOnsI9VcXV3l4eGhMmXK5FsPx48flySFhIQoJiZGCQkJatq0qZ5//vmCrWQANwSBDSjGTp06pSVLlqhkyZJq3bq1JCkzM1Nz5szRU089pQ4dOmjp0qVydXVVhw4d5OTkJJvNpoCAAE2dOtU+nT///FOenp766quv8j3se+LEidq6davat2+vxo0bKzc3V8YYlShRQuYfT8U7d87W5aZ/Ke7u7po0aZJ69+6tXr162Yc7OTnlm09OTk6+z7m5ueV77erqetHpn38+mTFGLi4uysvLU40aNTR//nz7uNTUVJUvX17Lli275EPPzx1SPJ8xRrm5uZdcvn9ydnZW//799fPPP2vYsGGaMWPGZaf7z3X9z3n+cz2cC9zn69ixo1q1aqUNGzZo3bp1euONN/TZZ59dVUAGcO24ShQoxpYtW6Y777xT69atU0JCghISErR69WqdOnVKK1as0KOPPqqEhAStWrVKUVFRks72hm3YsEF79+6VJH377beKiIi46JWK69evV/fu3dWuXTtVqFBBiYmJ9qDj5uamtWvXSjrb67R79245OTld1fTP5+/vr549e2r69On2YXfeead27NghY4xOnTql9evXX9N6WrJkiYwxOnHihFasWKHAwED5+fnpwIED2rRpkyRp586dCg4O1uHDhy87LV9fX+3fv19bt26VdPa8s02bNqlRo0ZXXdfIkSO1ceNGrV69Wvfee69cXV315ZdfSjobHletWqWmTZuqefPmWr58uY4ePSpJWrhwoe644w5Vr169wPPq2LGjdu7cqaioKI0ePVrp6ek6ceLEVdcM4NrQwwYUY3PnzlXPnj3z9SCVK1dOXbt21YcffqiIiAjVrl1bubm5qlixoqSzh/BiYmL0/PPP23ub3nrrrYv2KPXr10+xsbF68803VaJECdWvX1/JyclycXHRtGnTNHLkSE2ePFn33HOP7rrrLpUqVeqqpv9Pffv21caNG+2vIyIitG7dOgUFBalixYry9/e/aG/TlZQtW1ZRUVHKyspSly5d7Icu4+LiFBsbq+zsbBljFBsbq8qVK192WuXLl9d///tfjR49WllZWXJyctJrr72me++9136IsqCqVaum3r1767XXXlOLFi305ptvasyYMZo2bZry8vLUr18/NWnSRJLUo0cPde/eXTabTeXLl9c777wjZ+eC/83+wgsvaNy4cZo6daq9h69KlSpXVS+Aa+dkruXbCwCu04QJE/TUU0/prrvu0p9//qnIyEitXr1a5cqVc3RpAGA59LABcIjKlSurR48ecnFxkTFGY8aMIawBwCXQwwYAAGBxXHQAAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALC4/wfBsx3uALzaoAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_hist(housing_df['RM'], 'Average Number of Rooms in Boston Households', 'Average Number of Rooms', 'Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Optimizing solution\n",
    "def my_hist(column, title, xlab, ylab, bins=10, alpha=0.7, color='c'):\n",
    "    title = title\n",
    "    plt.figure(figsize=(10,6))\n",
    "    plt.hist(column, bins=bins, range=(3,9), alpha=alpha, color=color)\n",
    "    plt.title(title, fontsize=15)\n",
    "    plt.xlabel(xlab)\n",
    "    plt.ylabel(ylab)\n",
    "    plt.savefig(title, dpi=300)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_hist(housing_df['RM'], 'Average Number of Rooms in Boston Households', 'Average Number of Rooms', 'Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SCATTER PLOTS\n",
    "x = housing_df['RM']\n",
    "y = housing_df['MEDV']\n",
    "plt.scatter(x, y) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CRIM</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.185359</td>\n",
       "      <td>0.392632</td>\n",
       "      <td>-0.055585</td>\n",
       "      <td>0.410971</td>\n",
       "      <td>-0.220045</td>\n",
       "      <td>0.343427</td>\n",
       "      <td>-0.366025</td>\n",
       "      <td>0.601224</td>\n",
       "      <td>0.560469</td>\n",
       "      <td>0.277964</td>\n",
       "      <td>-0.365336</td>\n",
       "      <td>0.437417</td>\n",
       "      <td>-0.383895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN</th>\n",
       "      <td>-0.185359</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.507304</td>\n",
       "      <td>-0.032992</td>\n",
       "      <td>-0.498619</td>\n",
       "      <td>0.312295</td>\n",
       "      <td>-0.535341</td>\n",
       "      <td>0.632428</td>\n",
       "      <td>-0.300061</td>\n",
       "      <td>-0.304385</td>\n",
       "      <td>-0.394622</td>\n",
       "      <td>0.170125</td>\n",
       "      <td>-0.398838</td>\n",
       "      <td>0.362292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDUS</th>\n",
       "      <td>0.392632</td>\n",
       "      <td>-0.507304</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.054693</td>\n",
       "      <td>0.738387</td>\n",
       "      <td>-0.377978</td>\n",
       "      <td>0.614248</td>\n",
       "      <td>-0.698621</td>\n",
       "      <td>0.592735</td>\n",
       "      <td>0.716267</td>\n",
       "      <td>0.385366</td>\n",
       "      <td>-0.354840</td>\n",
       "      <td>0.564508</td>\n",
       "      <td>-0.476394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHAS</th>\n",
       "      <td>-0.055585</td>\n",
       "      <td>-0.032992</td>\n",
       "      <td>0.054693</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.070867</td>\n",
       "      <td>0.106797</td>\n",
       "      <td>0.074984</td>\n",
       "      <td>-0.092318</td>\n",
       "      <td>-0.003339</td>\n",
       "      <td>-0.035822</td>\n",
       "      <td>-0.109451</td>\n",
       "      <td>0.050608</td>\n",
       "      <td>-0.047279</td>\n",
       "      <td>0.183844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOX</th>\n",
       "      <td>0.410971</td>\n",
       "      <td>-0.498619</td>\n",
       "      <td>0.738387</td>\n",
       "      <td>0.070867</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.302188</td>\n",
       "      <td>0.711864</td>\n",
       "      <td>-0.769230</td>\n",
       "      <td>0.611441</td>\n",
       "      <td>0.668023</td>\n",
       "      <td>0.188933</td>\n",
       "      <td>-0.380051</td>\n",
       "      <td>0.573040</td>\n",
       "      <td>-0.427321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RM</th>\n",
       "      <td>-0.220045</td>\n",
       "      <td>0.312295</td>\n",
       "      <td>-0.377978</td>\n",
       "      <td>0.106797</td>\n",
       "      <td>-0.302188</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.239518</td>\n",
       "      <td>0.205246</td>\n",
       "      <td>-0.209847</td>\n",
       "      <td>-0.292048</td>\n",
       "      <td>-0.355501</td>\n",
       "      <td>0.128069</td>\n",
       "      <td>-0.604323</td>\n",
       "      <td>0.695360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <td>0.343427</td>\n",
       "      <td>-0.535341</td>\n",
       "      <td>0.614248</td>\n",
       "      <td>0.074984</td>\n",
       "      <td>0.711864</td>\n",
       "      <td>-0.239518</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.724354</td>\n",
       "      <td>0.447088</td>\n",
       "      <td>0.498408</td>\n",
       "      <td>0.261826</td>\n",
       "      <td>-0.268029</td>\n",
       "      <td>0.575022</td>\n",
       "      <td>-0.377572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIS</th>\n",
       "      <td>-0.366025</td>\n",
       "      <td>0.632428</td>\n",
       "      <td>-0.698621</td>\n",
       "      <td>-0.092318</td>\n",
       "      <td>-0.769230</td>\n",
       "      <td>0.205246</td>\n",
       "      <td>-0.724354</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.494588</td>\n",
       "      <td>-0.534432</td>\n",
       "      <td>-0.232471</td>\n",
       "      <td>0.291512</td>\n",
       "      <td>-0.483244</td>\n",
       "      <td>0.249929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD</th>\n",
       "      <td>0.601224</td>\n",
       "      <td>-0.300061</td>\n",
       "      <td>0.592735</td>\n",
       "      <td>-0.003339</td>\n",
       "      <td>0.611441</td>\n",
       "      <td>-0.209847</td>\n",
       "      <td>0.447088</td>\n",
       "      <td>-0.494588</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.910228</td>\n",
       "      <td>0.464741</td>\n",
       "      <td>-0.444413</td>\n",
       "      <td>0.467765</td>\n",
       "      <td>-0.381626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAX</th>\n",
       "      <td>0.560469</td>\n",
       "      <td>-0.304385</td>\n",
       "      <td>0.716267</td>\n",
       "      <td>-0.035822</td>\n",
       "      <td>0.668023</td>\n",
       "      <td>-0.292048</td>\n",
       "      <td>0.498408</td>\n",
       "      <td>-0.534432</td>\n",
       "      <td>0.910228</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.460853</td>\n",
       "      <td>-0.441808</td>\n",
       "      <td>0.524156</td>\n",
       "      <td>-0.468536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTRATIO</th>\n",
       "      <td>0.277964</td>\n",
       "      <td>-0.394622</td>\n",
       "      <td>0.385366</td>\n",
       "      <td>-0.109451</td>\n",
       "      <td>0.188933</td>\n",
       "      <td>-0.355501</td>\n",
       "      <td>0.261826</td>\n",
       "      <td>-0.232471</td>\n",
       "      <td>0.464741</td>\n",
       "      <td>0.460853</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.177383</td>\n",
       "      <td>0.370727</td>\n",
       "      <td>-0.507787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.365336</td>\n",
       "      <td>0.170125</td>\n",
       "      <td>-0.354840</td>\n",
       "      <td>0.050608</td>\n",
       "      <td>-0.380051</td>\n",
       "      <td>0.128069</td>\n",
       "      <td>-0.268029</td>\n",
       "      <td>0.291512</td>\n",
       "      <td>-0.444413</td>\n",
       "      <td>-0.441808</td>\n",
       "      <td>-0.177383</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.370993</td>\n",
       "      <td>0.333461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LSTAT</th>\n",
       "      <td>0.437417</td>\n",
       "      <td>-0.398838</td>\n",
       "      <td>0.564508</td>\n",
       "      <td>-0.047279</td>\n",
       "      <td>0.573040</td>\n",
       "      <td>-0.604323</td>\n",
       "      <td>0.575022</td>\n",
       "      <td>-0.483244</td>\n",
       "      <td>0.467765</td>\n",
       "      <td>0.524156</td>\n",
       "      <td>0.370727</td>\n",
       "      <td>-0.370993</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.723093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEDV</th>\n",
       "      <td>-0.383895</td>\n",
       "      <td>0.362292</td>\n",
       "      <td>-0.476394</td>\n",
       "      <td>0.183844</td>\n",
       "      <td>-0.427321</td>\n",
       "      <td>0.695360</td>\n",
       "      <td>-0.377572</td>\n",
       "      <td>0.249929</td>\n",
       "      <td>-0.381626</td>\n",
       "      <td>-0.468536</td>\n",
       "      <td>-0.507787</td>\n",
       "      <td>0.333461</td>\n",
       "      <td>-0.723093</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM        ZN     INDUS      CHAS       NOX        RM       AGE  \\\n",
       "CRIM     1.000000 -0.185359  0.392632 -0.055585  0.410971 -0.220045  0.343427   \n",
       "ZN      -0.185359  1.000000 -0.507304 -0.032992 -0.498619  0.312295 -0.535341   \n",
       "INDUS    0.392632 -0.507304  1.000000  0.054693  0.738387 -0.377978  0.614248   \n",
       "CHAS    -0.055585 -0.032992  0.054693  1.000000  0.070867  0.106797  0.074984   \n",
       "NOX      0.410971 -0.498619  0.738387  0.070867  1.000000 -0.302188  0.711864   \n",
       "RM      -0.220045  0.312295 -0.377978  0.106797 -0.302188  1.000000 -0.239518   \n",
       "AGE      0.343427 -0.535341  0.614248  0.074984  0.711864 -0.239518  1.000000   \n",
       "DIS     -0.366025  0.632428 -0.698621 -0.092318 -0.769230  0.205246 -0.724354   \n",
       "RAD      0.601224 -0.300061  0.592735 -0.003339  0.611441 -0.209847  0.447088   \n",
       "TAX      0.560469 -0.304385  0.716267 -0.035822  0.668023 -0.292048  0.498408   \n",
       "PTRATIO  0.277964 -0.394622  0.385366 -0.109451  0.188933 -0.355501  0.261826   \n",
       "B       -0.365336  0.170125 -0.354840  0.050608 -0.380051  0.128069 -0.268029   \n",
       "LSTAT    0.437417 -0.398838  0.564508 -0.047279  0.573040 -0.604323  0.575022   \n",
       "MEDV    -0.383895  0.362292 -0.476394  0.183844 -0.427321  0.695360 -0.377572   \n",
       "\n",
       "              DIS       RAD       TAX   PTRATIO         B     LSTAT      MEDV  \n",
       "CRIM    -0.366025  0.601224  0.560469  0.277964 -0.365336  0.437417 -0.383895  \n",
       "ZN       0.632428 -0.300061 -0.304385 -0.394622  0.170125 -0.398838  0.362292  \n",
       "INDUS   -0.698621  0.592735  0.716267  0.385366 -0.354840  0.564508 -0.476394  \n",
       "CHAS    -0.092318 -0.003339 -0.035822 -0.109451  0.050608 -0.047279  0.183844  \n",
       "NOX     -0.769230  0.611441  0.668023  0.188933 -0.380051  0.573040 -0.427321  \n",
       "RM       0.205246 -0.209847 -0.292048 -0.355501  0.128069 -0.604323  0.695360  \n",
       "AGE     -0.724354  0.447088  0.498408  0.261826 -0.268029  0.575022 -0.377572  \n",
       "DIS      1.000000 -0.494588 -0.534432 -0.232471  0.291512 -0.483244  0.249929  \n",
       "RAD     -0.494588  1.000000  0.910228  0.464741 -0.444413  0.467765 -0.381626  \n",
       "TAX     -0.534432  0.910228  1.000000  0.460853 -0.441808  0.524156 -0.468536  \n",
       "PTRATIO -0.232471  0.464741  0.460853  1.000000 -0.177383  0.370727 -0.507787  \n",
       "B        0.291512 -0.444413 -0.441808 -0.177383  1.000000 -0.370993  0.333461  \n",
       "LSTAT   -0.483244  0.467765  0.524156  0.370727 -0.370993  1.000000 -0.723093  \n",
       "MEDV     0.249929 -0.381626 -0.468536 -0.507787  0.333461 -0.723093  1.000000  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CORRELATION - Data points Correlation\n",
    "housing_df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using Seaborn to visualize the Correlation\n",
    "corr = housing_df.corr()\n",
    "plt.figure(figsize=(12,10))\n",
    "sns.heatmap(corr, xticklabels=corr.columns.values, \n",
    "yticklabels=corr.columns.values, cmap=\"Blues\", linewidths=1.25, alpha=0.8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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JOepCJICs7SJVITs0WbaHVy+P4KeXhpFIO4Xj+3c0or+vG/e11Wzg6NaOYYKIiIioQpWjFqOSBFsXImE7PhIZB47rBzPANUhlHZx78w5eeesOMlau2FsRAn33N+N4bzfaGsMbPMJgMEwQERER0YYIoi5EiNxrUmkHlrvxxdXTSQs/vTiMn10ZLYQaXVXwyANteOpwJxprQxs7wIAxTBAREdGmx+ZvlWmtdSGe7yOVdZGx3A0PEWPTGZweiGHg+ji82eVVpqHisQMdeOJgB2rC+sYOsEwYJoiIiGhTq+bmb5s9BK22LsSXMhcisi78DU4RQ+MpnHpjCG/dmkR+JLVhHU8e6sQH9rfBNKrncXs1W9FWz9URERERrUK1Nn+r5hC0EiutC0lbLlIZp/Dt/0aQUuLmcByn3ojh+tBM4XhTbQjHervw0N5W6Frl94gAcsvEDE1FxNRW1WGbYYKIiIg2teIi33TWQTztwHV9TExncfHGeMU+mFdrCCqXrOMhmbHhuhsXInwpceXdKZwaiOG90WTheEdTBP19XTi4qxmqUh3buwoBRMM6wiEVuqqsepkYwwQRERFtavkiX8/zMZmwkH/UEwIV/U0/O2Dn7pHl+EhmHNiOt2Hj8HwfF65P4PSFGEanMoXjPe01eLqvG/t6GqqiR4QQgD47C9HeGMHkbIJYy0oxhgkiIiLa1PJFvjNJezZICAgh0VAbgqKIiv2mf6t0wF6M60sk0zYsZ+N2aHJcH69dGcWZizFMJ+3C8b33NaC/rws7O+s2ZmArpCgCpqEiHNKgqwKAgKoGswyLYYKIiIg2tXxQ+J/PX4KUErqmoC4aQjikQUqJ8ZlsRRY6b4UO2AvxfIlU1tnQHZoylovzb4/g7KVhpLIugNy3+gd3NqO/rwtdLdGNGdgKCAFoqkDY1GHqKpQyzZwwTBAREdGmd3h3C3Z31y/4Tb9pqBVZ6LwVOmAXk5BIZ12ksi78DSquTqRtnL10B+ffHoE1u6xKVQQe2tuK471daK6CWSFFCIRmZyGMdSgCZ5ggIiKiLWGxb/o1oVRsofNm74B98cY4Tl+IIZ11oakCD+9rw76exnUfx2Q8izMXh/Hzq6NwvVyQMXQFRx9sx5OHOlEXNdZ9TCtVPAuxnkXgDBNERES0JSz2Tf+3X7y25QudN8JbtyZw8tVB2K4Py/JguR5eOHsLzwDrFijuTKZxeiCGizfGkZ8MiYQ0PHGoA4/t77jn96LSKELA0BVEQhp0XcVGlIBX9idEREREFKCFvulvqR/c0oXO602I3Of7H2+8j+mkBUXkluIYmgobwJkLsbKHicGRBF56I4Yrg1OFY/VRA08d7sSjD7TBKPpdqESaImCaGsKGCm0N27oGMpaNe2siIiKijbeaQudKLNiuBr7vI5l1kbFc3I4lYM5rkqarCqYSVlneW0qJd96fwamBIdwaThSOt9Sb6O/rQu+eFmgB7XBUDvnmcmFTg1kUdja4ATjDBBEREW1tKy103iqdqYPkS4m05SJdVFzdWBtCPOPA0O4+GDuej8baULDv7Uu8dXsSpwZiiI2nCse7W6Lo7+vC/h1NUCq40ZyiCIRDGsKGBl0TGx4e5mOYICIioi1vJYXO7ExdOimBjOMinXbgztuh6VhvF144ews2cjMSjpebETrW2xXIe7uejzfeyRV4TxTVv+zqqsPTfd3Y3V1XsY3m8s3lwiEVpqFCzFZDVFqQABgmiIiIiFaEnalLIWc7V9tw3IWfgPf1NOIZ5GokphIWGmtDONbbteZ6Ccvx8LPLo/jppWHEU3cbze3f0Yj+vi7c11a7pvOXU765nGloMLRcc7lKxzBBREREtAJbvTP1UoQALMdHKuPAdjws90X6vp7GZcPD1cGpkgJHOuvg5bdGcO7NO8hYuUZzigB697TgeF8X2hsjq72sshIANK38zeXKhWGCiIiIaAW2amfq5bi+j1TGRdYOrnP11cEpvHD2FlRVgRnSEM8492wfO5Oy8dOLMfzs8ihs1weQ67nwyANtOHa4K/AajKCsd3O5cmGYICIi2gK4+1Bwtlpn6uX4UiKVdZHJuvADXtR/5kIMqqoUirSLt49trjNx+kIMb7wzDm+2HiOkq3j8QDueONSJmrAe6FiCcre5nLLh27oGgWGCiIhok+PuQ8Hb7J2pSyEhkbY8pDJOYYemoE0lrHu2j4UE3htN4i/+8UJhGVVNWMeThzpwdH87TKPyHm+FAEKz27oaRc3lqj1IAAwTREREmx53H6IgSeSKnJNpG65X3qfh/PaxuqrAdnwkMw4sx5vz58d6O/Hw3jboFbhUSFVEri+EoULfBLMQC2GYICIi2uS4+xAFQ8J2fCQyDpzZ2oRye+pwJ/759E1M296c4NJQY+DDj/bg0O5mqBXWI0IIQJudBaz0bV2DwDBBRES0yXH3IVoLIXK/L6m0A8v11uWh2PMlLt4Yx6mBGFIZt3A8pCt48lAnfvHhbRW369FmKaheKYYJIiKiTY67D9Fqeb6PZMA7NC3FcX38/OoozlwcxlTCKhy/f1s9+vu6sbOztuIazWmKgGlqCBvqpiioXimGCSIiok2Ouw/RSpVzh6aFZG0X598ewdlLd5DMOABy/RcO7GpCf183uluiZR/DSuQ7VEdMDSFd2fRLmZbCMEFERLQFcPchKlXacpHKOIXtVsspmXFw9tIwXnlrpFBYrSoCR/a24nhvJ1rqw2Ufw0rMXcpUHR2qy41hgoiIiGjLk8g6PpIZG65b/hAxlcjizIVhvHZ1tFBYbWgKHn2wDU8d6kR9TWU1mtOKdmVSla1TD1GKDQ0TX/va1zA1NYU//dM/xeXLl/GlL30JyWQSjzzyCP7oj/4ImsasQ0RERFQuQgAZy8FUwp6z5Wq5jEymcfpCDBeujyM/8REOaXjiYAceP9COiFk5jeaKlzKZRZsX0Fwb9rT+8ssv4/vf/z6efvppAMAf/MEf4E/+5E/Q19eHL37xi/jHf/xHfOpTn9qo4RERERGti43qTu76EsmMDUuKsgeJ90YTeOmNGC6/O1U4Vhc18NShTjz6YNucncY2mqIImIYK0+BSplJsSJiYnp7GX/zFX+Bzn/scrly5gqGhIWSzWfT19QEAPvGJT+Ab3/gGwwQRERFtahvRndzzJVJZBxkrt0NTpEy1zVJKXB+awamBGG7G4oXjzfUm+nu70Hd/CzS1cpYMaYpAOKzD1NWK611RyTYkTPzhH/4hPv/5z2N4eBgAMDo6itbW1sKft7a2YmRkZEXnbG6uCXSMdK/W1tqNHgJtAN73rYv3fmvifV9fP/neRYRmvwUHAENXkbVd/OSNGD742M5A38v1fCTTDhzLgRkJwYzcrUtoagouUfi+xMC1MZx85TYG7yQKx+9rr8WJx7fjyN42KBX0sK6rCiJhDeGQvuVCRBB/39c9TPzTP/0TOjs78fjjj+P5558HkEuu8610D+GJiST8ddh1YKtqba3F2Fhi+R+kTYX3fevivd+aeN/X3/BYEhFTm9NRWhECw2PJQO9F2naRTjtwF3hWamqKYnIyteb3cD0fA++M4/SF2Jzu6js7a9Hf1437t9VDCIHp6fSa32utFEUgHNIQNjQoQiKT9JBJWsu/cBNZ6O+7oogVf0G/7mHihz/8IcbGxvCrv/qrmJmZQTqdhhAC4+PjhZ8ZGxtDW1vbeg+NiIiIaF2Vszu5BGA5HpJpu7BjUjnYjoefXRnFTy8OYyZlF44/0NOIp490oae9Mma7BABNEwibuaVM+Q7aW7E3RJDWPUx861vfKvzz888/j1dffRVf/epX8bGPfQw///nP8fDDD+Nf/uVfcPz48fUeGhEREdG6Kld3csvxkMw4c2Y8gpbOunj5rTs49+YdZCwXAKCIXK3H8b4udDRFyvbeK6EIAUNXEAnpMPTKqdHYLCpm79U/+7M/w5e+9CWkUins378fn/70pzd6SERERERlFWx3cgnbkUhmHdhl3J1pJmXj7MVhvHp5BPZsWNFUgYf3teHY4U401a19ViUImiJgmhrCugpNUzgDUSZCLlSwUIVYM1FeXEe7NfG+b12891sT73t1EgKwHB/pjAPL9Vb80LxUzcTVwSmcuRDDVMJCTViHGdJwMxYvdMcO6SoeO9COJw52oDZirPVS1oy9IUpXtTUTRERERLR2QgCOK5HM2rDslYeI5VwdnMILZ29BSiBre5hK3q2HiIZ1PHmwA48daC/sRLWR8r0hwiENBmch1tXG330iIiIiWhHP95HKuoVeEeXw4s8Gkcy4c3eaUgQaawz8/rO90LWNrz/QNIFISIdpsKB6ozBMEBEREVUJz5dIWy4yWRd+GZ6apZS4OjiNUwMxDE9kCsc1VaAmbMA0FFi2t6FBQgggNLuUSddV9qfeYAwTRERERBXOl7kQkc66ZakR9XyJSzcncHoghjuTd/tAaKpAXcRAyFAhhIDtemisDS1xpvIpFFQbKjSVS5kqBcMEERERUYWSkEhbHtIZp1D0HCTH9XH6jfdx8txtTCbuNm27f1s9dnXW4fzlO1DU3Hf/tuvB83wc6+0KfByLKS6oDukKBLiUqdIwTBAREVFgLt4YD2ib061NSiDjLN61eq2ytotX3x7F2UvDSGQcALmmbgd2NaG/twvdrbkdfTqaI4XdnBprQzjW24V9PY2Bj2e+fEG1aWgwNDE7OqpEDBNEREQUiIs3xvGdH1+DqiqImBqmUza+8+NrAMBAsQJZx0MyY8N1gw8RyYyDc2/ewStv3UHWzvWiUBWBI/e34HhvF1oawnN+fl9P47qEhzxNvduhWlUYIKoBwwQREREF4uT5QaiqgtDs/v4hXYU1e5xhYjnlbTg3nbRw5sIwXrsyCsfL7c6kawo+8GAbPnZsN6Sbe8/ivhLrNRPBgurqxjBBREREgRifySJizn20MDQF4zPZDRpR5RMCsF0fqfTqGs4tZ3Qqg9MXhjDwzkRh96dwSMXjBzrwxMEOREwdjXUmJidThb4SqqrADGmIZxy8cPYWngHKEigURSAc0hAOqdBZUF21GCaIiIi2oIVqGz7YWrumc5q6gjsTaXi+hKYpqIvoUFUFLfVmQKPeXDzfRzLjImsH3yvi/dEkXhoYwuXbU8ifui6i46nDXXj0wbbC7FGxMxdiUFUFhpb7M0NTYc8eDypMCAFos8vgTENlQfUmwDBBRES0xSxW21BfH8H2lsiqzxnPuPB8H4CA5/mYjFuImho++Yt7gr2AKudLmWs4F3CvCCklbgzFcerCEG4MxQvHm+tMHO/txJG9rdDUxftDTCUsmKG5j4a6qmCqaJen1VKEQKjQoZoF1ZsJwwQREdEWkZ+NuDE0AyEE6msMCF0t1DY8/9J1fP7Zw6s698nzg4Vvm+MpG67nQ1UE6qIG6yVm5bd5TWWcQHtF+FLi8u0pnBoYwvtjqcLxzuYI+vu6cXBnE5QSipkba0OIZ5zCzAQAOJ6/pr4SLKje/BgmiIiItoDi2QhfSgig0FcgYuowNAWjRc3KVipfLyFEbh08kPumPJ11gxh+VZPIbcWaCnibV8/3MfDOOE5fiGFs+m5dyo7OWjzd1437t9VDiNIf4I/1duGFs7dgIzcj4Xj+qvpKCJFbIhUxNRiaihUMgaoQwwQREdEWULzTkq6pcD0JAYl42kHE1GG7PtqaVrfECQBa6k1Mp+w5a/Ft19/i9RIStuMjkXHguH5gZ7VdD69dGcWZC8OYSdmF4w/0NKC/rxvbO1ZX+7KvpxHPAKvezalQUG1o0DXBOogtgmGCiIhoCyjeaakuamAynoWUgOv6sJxcZ+NPPL362oYTR3vwnR9fg4XcDk62m/tW+8TRnsLPbJWGdkIAluMjlclt8xrUM3XGcvHyW3dw7s07hRkfIYDDu5vR39eNjjWEwbyV9pUoLqgO6SoUwYLqrYZhgoiIaAsonjkIhzQ01ZmYTliQEmiIGjhxtAePPNiOsbHEqs6fDwXFYeGBngacPD+Ib794DaauIJ5xETG1Td3QzvV9pALeoSmetnH24jDOXx6B7eRmODRV4KG9rTje24WmuvWf/ZlbUL14UTdtfgwTREQUuK3yDXQ1mT9zoCi5AuznPrS3LPcmmbbxH2/ECuEht2Wsn9tlt4JPAAAgAElEQVQOVIhN19DO8yVSWQdZywtsh6aJeBanB2J4/doYvNlai5Cu4uj+djx5qAO1ESOQ91kJTREIzxbaa+wNQWCYICKigC227Siwub6BrjYLzRwEGfLm3/f54SH3MCwQT9mFAm3X9XBjKI4v/PW5ksZTiSHVlxJpy0U66wa2Q9PwRAqnBmK4dHOi8LAeNTU8cbATjx1oL3x+6yVfUB02NYSKCqoZJAhgmCAiooAVF/oC2HTfQFezw7tbynYP5t/3+eFB0xR4ng/Xyy3TSWcdTCVsqIooKXRWWkiVkMjMbvPqBRQibt+J49QbMVx9b7pwrKHGwLHDXXj4gdY5W7auBxZUUykYJoiIKFDFhb55hqZgfCa7yCtoM5h/3+eHh7qIjsm4BVURkFJiJmkDkGioNUta9lQpIbWwzWvGgeut/elaSomr703j1BsxvDtyt16ltSGM/r4u9O5phqqsX02CEICuqQiHVBZUU0kYJoiIKFDcInRrmn/f54cHVVUQNTXURQ2ksy6klGiqM+cs2VkqdFZCSM06HlIZG4679idrz5d48+YETg3EcKeov8e21iiePtKNB7Y3Fh7k10N+FsI0VOgqO1RT6RgmiIgoUKVsEUobqxy1B/Pv+/zw0FJv4pO/uKfwPl//7uuYTtnIWG6hY7YiBNoawwuef+NCaq5XRDLrwna8NZ/NcX28fm0MZy7GMBm3Csf3dNejv68Lu7rqVtRobi3y27rWRXRo0lzX8EKbB8MEEREFqtyFvrQ2S9UefLB1dc3OgIXve3F4mO/E0R5884dXkMrYAASEyH1bH0/ZuHhj/J7XrXdIDapXxNXBKZy5EMNkPAtFUZCxXWSsXCgRAPbvaEL/kS5sa60JbOzLmbutq0BtNIRs2l7+hUQLYJggIqLAlbPQdysKciZhqdqDDz62c03jXMl9P7y7BXVhDVnLhefnlkHVRXSoqrJgHcR6hVQhcjMeqYwDy/HWVCtwdXAK/3LmJmzXR9a+ey4hgCP3t+J4XxfaGhaeiSkHbutK5cAwQUREVMGC3sUoX3tQvLxIVQTSGafwfkE+sC91vqzjo6M5MmdZj5Ry0TqIcofUIBvOTSctfP/0TcTTTuGYABAyVLQ1mHj26d1re4MS5QuqI6YGs2iJGIMEBYVhgoiIqIIFvYtRS72JkakMEunc8iJFCLiehC89/P2LV/Dj8+8GFlyWC0KVUqzv+7maiIy19hAxOp3B6YEYBt4ZLzSvEwKImjqiYR2KABJFAaNcFEXANFSYRm4pEwuqqVzY/5yIiKiCjc9kYWhz/3e9ll2MThztQTLtQEpAQOYeeEWuKdq/nr5ZCC757Vrzy45W4+T5QbiexHTCQmw8hemEBdeThfOdONoDz/NnlxNJzCQtTMxkERtP4evffR0Xb4yv6n1L5UuJRMbB+Iw1u8PU6s/1/lgS33nxGv7qHy/g9Wtj8KUs9NBob4ygLmpAVQQcz0djbSi4i5hH0wTqogZa6k3URYzZ3x0GCSofzkwQERFVsKC/vT+8uwVmSIXt5HpAaFquViEc0vD+WArNdXMfdNcSXGLjKaSyDoRQCjMg8ZQFb7b3RHEdxNB4ClnLQ01ER21EL2tTOl9KZCwXqTV2rZZS4mYsjlMDMVwfmikcb6oL4XhvF2pMDf/+yrtwfR+6UOB4uaLxY71dQVxGgRBAaHYpk66rjA60rhgmiIiIKlg5djHqboneE1Asx0M4pMF2/cCCS66pm4Ay+3SrCMCTYk6zt3wdRH6r2HI3pUvbLtJpB+4aQoQvJa68O4WX3hjC+2OpwvHO5giO93bh4K5mqLMXraoKzlyIYSphobE2hGO9XdjX07jm6wByBdWmqSHMgmraQAwTREREFawcuxgtFlB+9fgu/Pj8u4EFF1UVgJt7+BbIdY+GmD0+Tzmb0knkwlIyY8NdQ8M5z/dx4foETl+IYXQqUzi+vaMWT/d1Ye99Dff0iNjX0xhYeABysxCGpuZ2ZWJBNVUAhgkiIqIKF/QuRosFlA8+thNtdaHAgkt3SxR3JtPI2B5cN7ekKmyo6GiK3POz5SnGzjWcS2QcOK6/6rPYroefX8k1mptO3u3HsK+nAf19XdjRUbeGMZYm36E6bGjQNcHwQBWDYYKIiGgLWiygBBlc8jMgZkhbdqYjyOVcQTWcy1guzr89grOXhpHKuoVzH9rVjP6+LnQ2R1d55tIUb+sa0hWI2WoIBgmqJAwTREREVBbzC6w9T0JTRWE3p+LQEsRyrqAaziXSNs5eGsb5t0dhOblu1aoi8PC+Vhzr7UJzXXm3ruUsBFUThgkiIiIqm3wYyPebMDRl0Z2a1jIrEkTDucl4FqcvxPD6tbFCkXhIV3F0fxueONSJuoixuhOXgLMQVK0YJoiIiLaw4g7Vna01+OCRrsC3Yg268V4xz/eRyrrIWl6hSRwAXB2cKnkXpTuTaZwaGMLFGxOFh/eIqeHJg5147EA7wqHyPS5xFoKqHcMEERHRFjW/Q/VUPFOW3g4r3ampOOAsttzJlxKp2a7V83tFXB2cwgtnb0FVFZghDfGMgxfO3sIzwJxA8e6dBE4NDOHK4HThWH3UwLHeTjzyQBsMTUU5cBaCNhOGCSIioipRykP2Ss71v154G1nHgz7buK6+JlToUB1kmChlp6b8tcXGU8jYHqKmhrqocc+SqFIazp08P4hk2sl1oVYV1IT1Qr+Hvfc14J33Z/DSwBBuDycKr2ltMHG8twu9e1qgqcqC510rVREwOQtBmwzDBBERURWYP4uwlg7R+XNZjgtVCHiej8mEBW22pqGU3g7zg80DPQ24Mji9YNBZbqem4muzHA++L5HIONA1BRFThyMEzlyMYc+2BqQzSzecuzo4hdGpDITIPbx7nsRM0kJtRMfIpIP/8fwlDE+kCz/f3RpFf1839u9ohCKC7x1d3BeCsxC0GTFMEBERVYEg6w7y59I1Fa4noYjcsqHppIX6mtCyvR3mB5s7k2lce38a9dEQaiP6PUFnuZ2aiq/N8yVURcCXEsmMg9aGMHRdxehUBvGUveiY8s5ciEFTRaF+QkDCk8B00gEApKxckNjVVYen+7qxu7vunkZzQdAUgVAo151a19idmjYvhgkiIqIqEGSH6Py56qIGJuNZ+DL3fbntlNbbYX6wydgeBAQylou6qLFg0Flop6b87Ma196ahayrqojo0VYHrSURNDdGwDs8HxsdTqDFLe2SZSlioCWuYTuWWOc1/iN+/oxH9fd24r62mpPOtBGchaCtimCAiIipBkPUKqxFkh+j8ucIhDU11JuIpG47rwwzpeO5De5e9rvnBxnV9KAJwvbtdppcLOsWzG7qmwJ1datVSF4KiKHBcH1MJq7Ak6lhvac3r6qIGRqczgMScZnWmoeB3fvUg2hvv7b69VpyFoK2MYYKIiGgZQdYrLPUeS4WVIDtEF5/LNFQoSgie5+N3n+3D9pblH7bnBxtNU+C6PnTtbuHyckGneHajviaEZMYpFEpbjouZhI2QoaIurC+5rWveTNLCTy8OY2g8VegRgdnrM3QFv35sV6BBgrMQRDkME0RERMsoZ58EoLSwEkSH6LzFzvXIg+0YG0ss+dqLN8aRzDgYnUxDUxXURXWEDRVx10M4pEFKWVLQyc9uqIpAe1MEja6P2FgCybSLnZ21+NjjO5YNEAAwNp3B6QsxDLwzDm+2MFvXFERNDdKXaK43SwojpSrMQoRU6CpnIYgYJoiIiJYRZL3CQuaHFd+XmEna+J/Pv4nd3XWF0FBcd5Cfyfj2i9dWFSxW0226OPQ015uYSdqYjFvoao7g6b6uRXdzWkhrgwnXl4iaOpIZB+msi5ChobU+jP/+8QPLjmVoLIlTAzG8dWuysJypJqzjqUOd+MD+NphGcI84xbMQZtEyMwYJIoYJIiKiZQVZr7CQ4rCSsVxMxrOFB9WFZinWY9nVQuaEHl1FxNRhOR5qIgaeeWoXnin5TBIfeWw7/vmlG5hOWtAUBY6Xr43oWvxVUuLmcByn3ojh+tBM4XhjbQjHe7vw0N7WOUut1kpTBExTg2lwFoJoMQwTREREywiyXmEhxWElt/2pgBCy8OA+f0lVuZddLWatMzRCAJbjI5Vx0FIfxtNHunHmQgxTCQuNtaFFlyP5UuLKu1M4NRDDe6PJwvGOpgiO93Xh0K5mqMrd7V2vDk6VdN7FxshZCKLSMUwQEREtI8h6hYUUhxXH9SCEgARQF9EBAJ7n48bQDL7w1+fQUm8iNp5CQ21ozjmCXHa1mFJnaOYXk//K49vx4PZGJDMusrZbeDDf19O45EO+5/u4eH0Cpy7EMDqVKRzvaa/B033d2NfTcE+PiKuDU3jh7C2oqgIzpCGecfDC2Vt4Zvb9FsNZCKLVYZggIiJawvwH4//64eW3Tl2p4rAyMZ2BEAL1NQYipl5Y9qQqd5c0ZWwPatpBXdQonCPIZVeLKWWGpngJVn2NAaEI/ODcbUwnLezpbijpfRzXx2tXR3HmQgzTybuN6vbe14D+vi7s7KzD1cEp/L//9vY9sw9nLsSgqgoMLRd4DE2FjVwzu/lhQgggpKkIhzWENM5CEK0GwwQREdEi1rM2IV8QXfyeUkpMJywAuXAhhEBIVxE1NSTTDkKGWpZlV0uNEVh6hubk+UGEDA1NtSFAAIm0g0TaxkuvDy0bJrK2i1feGsHZN+8glcl1rBYCOLizGf19XehqiQJYevZhKmHBDM19vNFVBVMJq/DvnIUgCg7DBBER0SI2ojah+IF9aDwFx/UhBBBP5x6uI6aOuqgBz5doiBoYn8nC1BVoujq7s9NgWRvqLbcLlOP5aK4LIZFxkLU8APc+zM+XSNs4e+kOzr89AsvJvUZVBB7a24rjvV1onjfjstTsQ2NtCPGMU/iz/Jjam8Iwdc5CEAWNYYKIiGgR5d4SdjH5h/Xv/PgaLNuD50t4sx2iAUBVFXS3RPGFTz20YTs7zSchkbY8mLqKofHUPQ/zjfNqPABgMp7FmYvD+PnV0UKjOUNT8IH97XjqUOecZVzFRdWJtIPaiAYUvUc+sDzz1E68cPYWbOSWMGm6gtqIjo8+sQONdSGGB6KAMUwQEREtYqmC4+U6Vq9VflakoTaEyXgWgIBArv9EfY1RWNK0UTs75Unkliel0g5cX+KRB9oKD/O6uvCWr3cm0zg9EMPFG+OY7TOHSEjD4wc78PiBjnsC3PxlTcmMg5mUAwiBcChXpJ4PLPt6GvHrisDr74wha3uImhqO93bhwI4mBgmiMmCYICIiWsRiBccP9DSUfTYgPysihEBTnYl4yobj+pAAnvvQ3SLwoGZPLt4Yx0++dxHDY8kSw5GE7fhIZBw4rl84uq+nEc8AC27NOjiSwKmBGC6/O1X4+fqogacOd+LRB9pgFIW2YvOXNdVGDUwnLCTTDkxDg+P5kFLig49sQ01Ex+MHO3Cst4vhgWgdMEwQEREtYrGC4yBnAxab4SieFQmHNIRDGizHQ0PUmPMeQTTUyy+VChnqsuGouFeE7XhY6Hm9eMtXKSWuD83g//nB27g1HJ8z7v6+LvTuaYGmLt1obn5RtWloqK+RSKRs+L7EfW1R/MKRbTiwoxn5nWIZJIjWB8MEERHREhYqOP72i9cCmw1YbIaj1EZ5QTTUy4cj09DguP6i4cjz/Xt6RSzG9yXeuj2JUwMxxMZThePdLVH093Vh/44mKIpY4gx3zS+qFgKoi4Zw/7YGfPbj+6FxRyaiDcMwQUREtEJBzAYAS9c7fOFTDxV+Zqm6jCAa6i23VMqXEqmsi0zWhT/71L5Yl2nX8zHwzjhOX4jNCVe7uurQ39eFPd319zSaW86x3i68cPYWpKagPmJA0xSksw6eOtQJVWGQINpIGxIm/uqv/go/+tGPIITAs88+i8985jM4d+4cvvrVr8KyLHzkIx/B5z//+Y0YGhER0bLmzwbEUzZSWRfpjIOvf/f1kh7mL94Yx42hOHwpoWkK6iI6IqY+5yF+sW1YF1oalQ8fq5EPR8U1C64nsb2jBikrd12ef/eJfaE+D//605vYs60B196bQTx1t9Hcg9sb8fSRLtzXVrvq8T24owm1UR0/uzyG4YkUokLDM0/uxMFdzas+JxEFY93DxKuvvopXXnkFL7zwAlzXxa/8yq/g8ccfxxe/+EX83d/9HTo7O/E7v/M7OHXqFPr7+9d7eERERMua3wsia3moieiojeglFWPnlzcJAUDinm1fl5rhKF4aJQRwMxbHN753EV3NETz7C3tWVQCeD0dZ24UqBDRNQTik4+F9bUgUBYO84oJo35fIWh5SWQevXRkDACgC6N3TguO9XWhviqx4PAAgAKiaQMTUYeoq2hrCeOj+tlWdi4jKZ93DxAc+8AH87d/+LTRNw8jICDzPQzwex/bt23HfffcBAD7+8Y/j5MmTDBNERFQRFiuSPry7BV//7utzljyVUoxd6ravS73W9+VsIzgBRREYnc6uaEep+df05MEOvD+RRjKdu5Yj97dgR0fdgq+dSljQNQUzSQvprDunCPux/e041tuJxtqVLfnKEyL3GUZCOnRdwcoWRBHRetuQZU66ruMb3/gGvvnNb+LEiRMYHR1Fa2tr4c/b2towMjKyonM2N9cEPUyap7V19VPUVL1437eutd771y6P4PmXrmNkMo32pgg+8fQePPJge0CjWz+vXR7BP/zkOjRNoL7GQDLr4B9+ch319RE88mA7JpM2asPanDoATRWYStqLfobFr1FVBdMJK7czkgB+99m+JT+n/Gtj42kIIaAIASlzdQ0hQ8VP3ojhg4/tXPRann/pOgbvxJHOuqiLGmioMeD4Pq4NxfGxJ3di17aGJT+Pkck0PAlMTd+thxACCIc0dDRH8dvPHFzy9YtRBBAO6Qib2pxaFFof/G/91hTEfd+wAuzf//3fx2c/+1l87nOfw+3bt+/585UWZ01MJOH7rMAql9bWWoyNJTZ6GLTOeN+3rrXe++KlOKauYGwqjf/7ewNz+iNUg4s3xvG/XngbluNC11TURQ2EQxpc4eF/v3gF21siaKox7inGthwPjTXGop9h8WsMTUFbY7iw7ev2lsiSn33+tY7rzQYJCV9KqKoCRQgMjyUXfH3xPclaHnxfImO5aG0woWkKJmey+JfTN/HbJ/Yt+L6x8RRODQzhzZuThZkIIYCacG4GQfoSxw51YHIyteDrFyIEoM3uZKUbKpysDSd777IqKi/+t35rWui+K4pY8Rf0S2/sXAY3btzA5cuXAQDhcBgf/vCHcf78eYyPjxd+ZnR0FG1tXBdJRFStincpEkIgpKtQVQUnzw9u9NBKln/4zjq5h3bXk5iMZ5Gx3DlF0ieO9sDzfFiOByklLMdbdmvW1bxm/msVIeD5uSAhAdRF9CV3lJq/c1RLfQj1UQOx8RQmZyxICUzMZOa8RkqJm7E4vvXDy/gfz1/Cpdkg0VgbwtH9bdjeXgNNEWiIGHjmyZ2F3hLLUYRAOKShsdZEc10IYUOD4IImoqq07jMT77//Pr7xjW/g7//+7wEAP/nJT/DJT34SX//61/Huu+9i27Zt+Ld/+zf8xm/8xnoPjYiIAhJUV+ZyW6wWArj78K1ryuzDO+BLgXjKhqKE0FJvFl6ftT14ngtNFehqiS67m9NatnPN/8z3XrqB2HgKqqKgLqpDVZUlA8n4TBZ1UR3RsA5fhjGTyCJjefClRE3YgOP5aK4PA8gtmbo6OI1TA0MYHEkWztHeGEZ/XzcO7W6GWmKPiGKaImCaGsKGyt4QRJvEuoeJ/v5+XLhwAb/2a78GVVXx4Q9/GB/96EfR1NSE3/u934NlWejv78eJEyfWe2hERBSQoPowlNNSDeMO724pBKK6iI7JhAVfSggAzmxTuAd6Ggqvb6wNzWkWV+6lXPni7+Iw1BA1lnzvHR21SDseMpaHZNpGMusCElBVAdvNzYz80qP3YeCdcZwaGMLI1N1ZipCu4MlDnfjFh7dBWeEyZAFA03KfsWmohRkIBgmizUFIuTn+OrNmory4nnJr4n3fuoKsmSjuylxJNRPzd2ECUKhb+MKnHprz5+msg3jageP6MHUV/8cz+3Hy/OCSr1/Ken4+vpTI2B4uvDOG50/fgKIo0FUFyYyDjOUipKtobTDR3hTBjaH4nNkjXVNQG9EhRK6j9UqWMgkBhDQ11ztDX/dV1bQC/G/91hRUzQQ7YBMRUeCC6MocpIWWMy23FKu4MV04pBWWEeUf+L/94rUFXz80nsLXv/v6kte9VOfroD4jCYm05SGTceD6Eru66vHxJ3YWula31ps4ur8dkwkLZy/dwe07ueVMAsgVRGsKIqZeOJ/tejh5fnDBrtfFFEXANGa3dtUEZyCINjmGCSIiKovFujevt8WWM5mGCtv1F12KtVwgWmgpVzxlI2t5mE7Zhff65r9fRl3UQNb2Sg4yK7m24vF95LEeHNzVjIzlIT0bIort62nEvp5GJDMOzl4axvfP3ELW9gAAqiLw2MFOHH2gFd/64WWYobnj8zwfEwkbzQ1moev1C2dv4ZnZ82pqrqjaNLRCPQWDBNHmxzBBRESb2mKzAJAyt6MSMGepUXEB81KBKD9zMWO5SGVdOK4PAIUlQUIIeJ6PVNZF1vbQ0Ry5G2R0ZckgU4rikFQb0eFJif/v/CASaQe7uuoXfM1UIoszF4bx2tVRuF7uSd/QFDz6YBueOtSJnT1NmJxMobE2hHjGgaHdHV8y7UBTReGYoamAruLizXE8fqADuq5yPyaiLYhhgoiINrXFZgHSWRf/9cN7V70U6/DuFtwejuMH596FVzQDICUwPp1BS0MY8bSTCxdSFrbItQBACHiut2SQWc73/vM6kmkH4dklSa6VCy3/8fP37wkTI5NpnL4Qw4Xr48gPNRzS8MTBDjx+oH3OciYAONbbhRfO3oINQFcVOJ4P15NoqNFz27qaGiIhDb70MTaVhcEmc0RbFsMEERFtCott87rUzlJrXYp1ZXAaqiJmO1EDjudDylygiKdsuLOzFbp2twA5iCBz6eY4UpaHpvoQ0hkHw+NJ+BKoj+iYSviFn3tvNIGX3ojh8rtThWN1UQNPHerEow+2Ldppel9PI54B5tRHhHUFoZCOmogBx/GQzNhIZV00RI1VfHJEtFkwTBARBWCpfgVUfktt81pcSL3aWYDFjM9k4fk+VEWBLyXyraElAMv2cj0qfB91RQ/cQWyRe+7NOwAkRidz27cKCAhIJDMutrVG8c770zg1EMPNWLzwmuZ6E/29Xei7vwWauvzuSvn6CkXJ1UIMjiTwT/95HSMTKegBf45EVL0YJoiI1mi5fgVUfkvtjpTfprUcYa+l3kQ8ZcPz5ZylTkAuUNSGNTh+bocjKWXhAby4R8VKfmfStot0xsE7700DyC2fAnKzIr4PeJCIpx1864dXCq/pao6g/0g3DuxoglJiozkhAF1TETE1hHQFAgIHdjTBO76LoZmI5mCYICJao/XY5pOWttzuSOXaWerE0R5884dXEE/Zc44rAqivCaG1MYwTR3vueQBfye+MlEDWcZHKOIWi6YaaXIF0fU0IibQN15P5SRFMxC0AwM7OWvT3deP+bfUQJTaaEyK3LWw4pEFXBTCvpLpSdugiosrBMBEALm8g2tqC2uaTVm+jOm4f3t2C//YrD+D/+udL8Pxch2xNFWisM2EaKsZnsgs+gH/7xWsAJKYSFlzXh6YpqA1rc35nJICs7SKVdeC6c2c9jvV24Z/+8zqytof5/VojIRX9fd041ttV8nVoSq6ourUhjGnu50pEK8CWlGuUX95QvKf4d358DRdvjG/00IhonbTUm7Bdf86x9XiQpbtOHO3JbfPqeJBSwnK8dVvPf3h3C+7fVo+O5gh6OmrR1VqDcEhb8nfA1BVMJWx4ng9F5Ho4TCVshGc7RVuOh8l4BjNJ+54gkc66uHB9HGnr3iBRF9YQCet45e07uDo4haUIARi6iobaEFoawoiaOnSNuzIR0cpwZmKNuLyBiMpZ4LsZrMfsbTk7bpcy/hX/DgiB3NyDAiFyS5mipoLG+jAmExZsx7vnJfGUjZ9eGsarl0dgO3fDq6LkTiUEkHF91ERV2MjtxDS/OzUAKLNb1IZNDYZ271ImIqKVYJhYIy5vIKJyPshWu5UUp681dJRjPX+p41/p70DW9tBUZyKRdqBrChpqDBiaguHx1D1BYnwmgzMXhvH6tbE5Rd5RU0NNRMf4dKZQ4O3lt6JVFUwlrDnnyS9lMg0VmqqwOzURBYJhYo02ap0uEVUWFqYurNTZ20rdEWsls88r+R1oqTeRsT10tdbA83wk0g5m0g7qInebx8XGUzg1EMObtyYKD/7RsI4nD3bg6uAUUpYLVVGgqgq82cJsdbafheP5aKwN5ZYyablZiJCmIl+HzSBBREFhmFgjLm8gIlpcqbO3lbpktFyzzx97Ygf+9ewtjE1nIH0Jx8v9v+Opw524NRzHqYEhXHtvpvDzDTUGjvV24ZF9bdA1BZ3NkUKH6qipYSaZ202qJqTCcT2EdAUffXIHmuvC0DVRCA/cMISIgsYwsUZc3kBEtLhSZ28rdcnoSmafS3lQ93yJtOWipT6MJw50FDpMN9QY2NlVj5cGYhgcSc55TV1Ex8ef2IEHdzQVjs3vUN3aEIamCmiqgs6WKJ481IkDsz8vZW5s33vpBmLjKWiqgrqoXjGzP0RU3RgmAsDlDUTEb3wXVurs7UYvGV3s/pU6/uWWafkyFyLSWRf+bN3Dvp5G7NnWgEs3J3B6IIb/eH1ozjmV2cLsZMbB82du4VlFzCmoLu5QbRoqTGPhgur82GaSNoQQ8KXEVNJGU20Iqqps+OwPEVU3hgkiojWq1PX+laDU2duNXDJayv1bbvwLLdPyVIGfXhzG7m0NSGecOcXTb9+awMnzg5hMWHO2dw0bKmzXg5SAOtut2klojpkAACAASURBVJeAbbtzdmcSAFRNIBLSYRoqlCWa0uXH5ktZdM5cp+z2xvCGz/4QUXVjmCAiWqNKXe9fKUqZvd3IJaPL3b9Sxl+8TEtTBcIhDaoiMDyRQqKoO3bWdvHv527j9XfG5xRB65rALz/ag7OXhmE5HpSibKCI3PKoqYQFRQgYuoJISIehl9YqKj82TVXgehKKyIUR1/W5YQgRrRnDBBHRGlXqev9qs1FLRoO4fy31JpJZB401BlRFQTLjYCZpoTac250pmXFw7s07eOWtO8jad7d+jYQ01IR1+JB4+/YkGmtDSGYc+FLOWaxUHzWwt6cBzfWhFW/rml9CVhc1MBnPwpe5M6uK4IYhRLRm7IBNRLRG7IBd3YK4fx97YgdqwjpmUjZGJ9OYTlpwPR9H9rbiB+du4//87ht46Y2hQpCImhraG8NoqA1B05RCX4hjvV0wdBVS5mY46qIGWhsjiIR1PHGwA6qy8v4Q+e7giiLQWBuCIgDfl2hrMPHch/Zy9oyI1oRhgohojfIPa5bjQUoJy/H4jW8VWcv9k5BIWS5aG8I4+mA7NCGQsVyYmoKmWhPfP30LL795B47nIxxS8YsPdWN7exRhU4Oq3v1fcL4vxL6eRnzyl+7HA9sb0FgbhudLqArwX57ejUO7VvfQf3h3C5770F40RA1ICezqqsPvP3sYf/zfH2OQIKI14zInIqI14hbR1W219y9tuUhnHLhFuzNFTR0vDQzh8u0p5CcQaiM6njrciQ880I6QoeK+tppCjwhdVeB4PiAlfunR+1AXNfD4gQ48ebAz8Gvk7yMRlQPDBBFRAPiwtrhq2Da31PsnJZB1XKQyDtzZrtNSStyI5RrN3RiKF362uc7E8d5OHNnbCq1oFqK4R0Qq6+C+tih+4cg2HNjZjMX3ZCIiqkwME0REVDalbptb6YFDArAcD8mMDdfNhQhfSly+PYVTA0N4fyxV+NnO5gj6+7pxcGcTFGXhePDgjiY8tK8NYUOFrq28DoKIqFIwTBAR0aotFwLy2656no+RhAXX9aEqAn938gpaGyMYn8nC1BXEMy4iphZYn478uGLjKbiehKoKdLdElw0pxdfTWm/io09sx67uBqQyNpzZEOH5Pi5cn8CpgRjGpjOF17Y3hiEAZLIOXn37DkK6MqfJHABoikA4rCNc1BuiOEhUYqiqxDERUeVgmCAiolUpZdYht71qruOyQL5ngo+JhA1PAnVRA3cm0vB8H6ahQvz/7L15sFxnfef9eZ6z9Onl9l10tZtrCwlZThx5CXltsEHGjj0GB5MEZ/DEMJOXSRU1RSVVqZpxMkwqf1DZysk7pJIQqqiEN1OA30AcFiUQlR3AmwSKsWwL40W2LPtaulru3uvZn/eP031u91V3313r86kikXs55znP6e77279CrFino7muMFJU3QAQEMKpqVpPJ6X5PtOQrOvPIE3JPz1xlD3Xb2XnOwbxw4gfv3KGp144yWyLdsSukQG2be7jRy+dxjAkWceiVA/Yu/8Y9wK7rhzEMg1yjonTovC9nP0811yIa9JoNBcW2pnQaDSay5SVRpwXI9Y33O/wxlip4Ug01JcbvQalqk+l0XsgRfLf2UzyZ2m+zsNi13r46ARf3PsSXhCiFAghMKQgVoqqGyKAz3/jRbZvLXbMohRyFv35DK4fUar41P2QHxxKypieeiERlGuybXMfH75lG5uGcvztP/8Uw5DYptFYvwGWwU/emOI9127GNgUs0BGxXPHDtcwcaEFGjUazENqZ0Gg0msuA+QbnrpEB9r94akUR58WIvd190wh/+chhhEhkl2NFOuUoVmAKgUARK9q0Hlp1HprR8SCMqXsRU2WPo2Mvcs/NI9x76zvbrvGrjx3BDSIMIQhiBUohBESRQjFXT/T2mUrb9QqRrCtrm0yVXMJIEcUxdTdkYsblrdOV9L25jEnGlsxUPGYrHpuGckyXPZyMSd0LiBVkbQNDSo6PV7DNpSlV99rP+ax15kALMmo0moXQzoRGo9Fc4nQyOL/zw1HyWZOckyg0LycKXnNDwiimv5BJn58v9rZ7+zBbhvOcma4TK4VpSBqTUGkkKpCS5LFYoZTCD+M2nYd9B0cJwphyPUhi+0oRhIpvPf0mP351nPtu287u7cNpFN0ykx4NIZLzNKcutVJ1QwAef+4EhhR8/7njjJ4qEUaKrGMSRopa4zVNbEsy2JfBkLJxrRFPvTDG1SODrCs61PyQrGPiBzHlqo8bxEghOHx0YlGGfVOpOtNSCrWQeN5aZw6WsyaNRnN5oUXrNBqN5hKn1eBs9iTEKonyA9TcgFNTNcan6xw9UeLw0YmOxzl8dIIvffcV3hgrMV128cKI2arP+HSNU5M13j5TYXLWZdfIQNv77rttO/0Fm+GBLBsGs+njgmSsqhACIUBKQc0NGcjbbcrME7MudS9KXx+3+AZnput89bEjHD46wcSsi21KijkLRdKf0Y1sxqSQs5kqeXz50Vd5+0wV05QEkaJUDdocCcdODOkwjAlasieWISnVfCxLcu/7tuH5EZMzLqWKjxcmGZFCzmLfwdFF3KXliec1r7mV1cwcaEFGjUazEDozodFoNJc4nUpVzIZYWs0NmCp7aTW/EPDVx47Q35/jyuFc23seefwo1bqPEBJDyqRkSSlqXoQhE+M651jsf/EUV20usnv7cJrJcP2IKAoxDUHWNgFFpBID3TQlhazBpqEcD/76jUDiuDz08KE0AxKEEYYUtCYZhEjGsxqGZN/B0TSK3sy2zFR8ojhuu4ZsxqQvZxErxXTJRQpBJmPgelFbPwQkzkh/wSabsRifrROFMZV6kGZPHNukL2tzerLKtdvWpY5GMwNTzGdwbGPRhv1yxPPWOnOgBRk1Gs1CaGdCo9FoLnE6GZw5x6JSD5htTFkCgRCKgb4MUgq+8fjr/M59u9uOc3qqBoh0IlNrhmBdf5ZsxqTmJsf8/Dd+wsahHKWqTy5rMdiXScuXbr92U9qvYZvyrLKm+WVZYRRT84BYtY1RlUJgNo4xMevy8bt28tXHjjDrhdS9qC2L0HQilILZip86DlKA2+JEmIYg71jMVn02DuWSXg+g4JjMVn2kEBiGpGCZVGo+nh/yf/a9yi3XbiKMFGEUY5kGxbxNNmPiBVFb78dCRvlSxQ/vvmmErz52BA867mUvOq3njvV9Z71OCzJqNJpeaGdCo9FoLiE6GYidDE7TENxz8wjf+eFbKKWwzCSSns2YKKU4M1XrcPTE4YjimKg94M9UySXvmFQaE5OUUpyZrhPFioxtICwjred/ZXSGB+7c2dWwnt8H0F/I4AdRWpbVJI4Vjm2kkfjd24d582SJ7/xwlFglCyxkTfJZmyiO25yI9BgN58QyJYWshWMbBFGMYyX/vzmdabDokHNM/EBRqQfUvRDZKM0C+M6PRsk7Jl4oCMKYydk6xXwG0xDcfdPImjVKLzdz0G09nTJSGo1G0wvtTGg0mkuey0V0q5uB+MCdO7sa76+MznQsk9kwdLZBuXHQ4eRUnXmVQw0E5VrQMK4FlikJG03QpVqQlh41swi9ot2dyrKGB7Kcma7hh3F6fiGgXPOJFdx/+w4gcVTyWRMhBLZlEEYxM2XvLCeiyVBfhplKIqZXqftEsYkhBbfu3sRzr09gW5L+vM1sxWNipv04sQKhoFoPEVLQX8hgmZJSLSAIYzw/4r/c+zPs3j7MQw8f6tkovZLP6HIyB50at0t+xJ9/9VmytnFJf080Gs3qop0JjUZzSXM5iW71muzz4K/f2PF6u5XJ/OptO8567X0f2MGXvvMypVoANBqoSaYx0RjvKhQIoSjmM5SqPkEYE3YZ+dqN1rKsuhc2jhOhVGKwm4agVAtSNe1i1kyvbaoxojWOY2ZKLl7Y0fMh55h84IYtHHjxFPmshetHSTmVG3LHjVu58/+6kt07hnniuURjolwL8MMonRDVvPZYJQNnbUM2jmuRc6ykl8QN25rIu41YPR+f0fnrSfbZAwTriplL+nui0WhWF+1MaDSaS5rLSXSraSDW3CA1tk1DUq0HXd/TWiYzNlEljBSGkfRM3HHDlrY92r19mE/ec00qCtfsDVBKNXovFIYUDPQlo2KjWBHFyVSjmhtgGLJrPX9rZN6xJDU3xPOj1MBFCJRSlKoe6/qzbGpkTppGuxDgBcn1Ts3WCaOYLn4EpgEZU/Lym9OJYnXGoC8HGdsABS+PzvDSW9MUHIv/0IjOP/iFA0RxMs1JMaeV0ezh8IKIU1M1irnEmZjvNPVqlD4fn9H56ylVfUBgW3JVVMg1Gs3lg3YmNBrNJc3lJLo13O9waqqW6jFIAWEUE8eqp9ZB8/GvPnaEXKOheWy8zOe/+SJZ22DLcJ5dIwO8MjrDxKzLUDFDqSrIZa00m9FfsLml0VgdhHHqBMjED2By1mXLcJ77b995VlmPYxtpo3bOMRPxuoaToBolU8WclTpITaVsARiGYMcV/UyVPPwwYrrs4gadvYhmNiGMoO5F+GGd/rxNzjHJOSauH3FivELdi7hifZ7pluj8cL9DFCvKNT9pBGfOkchYSUlXGMZMlT2CMMYyZZvT1KtR+iuPHjnnn9H56wnCCIRgoEUz5FL9nmg0mtWlpzPxb//2b9xxxx3pNAvNxcXlUieu0fRiLUZnLue7dS6+j3ffNMLnv/kiqKQxOG6UHLVqHSym6bk5kUmpuYj7keMz9Ocz9OWSqDtCYAqouWHbsa7aXOSLe19qcwJyjoUXRBSyVupItJb1nJqsJerWfkQcK0xTkrUN4hiuWJ9P/wZ5foTnR6ggolzzGSo6RFHErpEBnnrhBN/90Vv44dkCdU1an3EcA6ESh8ixDar1kBPjFYIwRgo4PV1Py6ge+cHr3PeBHXz1sSP05WxqbpA4aSoplxoeyLaVY/lBzG98cNdZWZ1u+z/cP3rOheHmrydjmdiWJJ+10ilYWpxOo9Eshp7OxF/+5V/yx3/8x3zsYx/j137t1xgaGjpX69KskMupTlyj6cVKRmd2YjnfrXP1fdy9fZisbSTCYnG71sHYRLXnGlozODMVP1WNjmNFrEIEgroXUszbqdFbyNl8tqEL0bqGxMB2Uieg5gaUqgFnpmo89PAhKvWgrawnjJLSoSCMsQxBFMWU6zEqTvQcmg5O3U/On3NMPD/i7VNlrtuxju8fOsHo6cqi9sg0JIWcRTFnEYQRx89UcGyD+lk6E4lTESvF2GQy2WquiV0w3O9wYqLKYKOkK5sx00lYrb0S8/dmKX0ray0M17qe5mfU9UOkEOdsDRqN5uKnpzOxd+9ennvuOb7+9a/zwQ9+kD179vDxj3+c3bt393qb5gLgcqoT12h6sdqiW4/84HVmK35irDci703RtG7HXOvvY2vWI4wUOceimLfT570gSh7vsYZmBieOVZs+AyRGviETo781Aj85Uz+rfOrw0QlqbshU2cMyJRlTUvVCUMn41Zmqz5mpGuv6HWiJxDcRQiBI+i0sM+mx8AXEMawfyCaN0mGEFIIY+PdXxhfcH8sUmIZBPmtiGRLXCzk1WSOKmz0lIbGK0zIoSHoxpJQolWhPdGpif+jhQ2dlFEpVHz+IefALB3p+1uZnqm65dlNaRnY+MsnNc33vuTFOjld0Nluj0SyaBXsmbrjhBm644QYqlQrf/va3+YM/+AMMw+ATn/gEv/zLv3wu1qhZBpdTnbhGsxCrJbp1+OgEY5M1pGwIt0VJjfxgwe753VrL72MzohyEcRpdr3khdS9EqURETQqJYSTn7LaGZnR8tuK3vcYwBFGkiOLEKJ8quSR6E8n/mtkNSBSyxyaqSClQsSJsjEdt9jb0FzJkLAPTkMxW5pSqmyOSmgW1ifaDIpux+C93X83Th082DG0P2zTwgvgsh6cTUiTTlfrzFl4YU60nStoyLd0VBKFiw1A2nRx1ZrqerkE01tFfyHS8V/MzCqWqT6nm05/P9MxAdcpU7X/xFA/cufO8Gu+7tw9zx83bGB8vn7c1aDSai49FN2AXCgUeeOABPvKRj/BXf/VXfOYzn9HOxAXMWtSJazSXO/sOjmIastGLkDT0xkpRqga8c0ux6/tW8n2cP+UIIXD9KI0c7zs4ShDGadO1ZQiCSOH6EVKAZRpkM0lPQKnq09/SYNu6hqYR+/lvvNh2/jBqNfIVNPowFNBfsDEMySM/eB0vTEThmqVNQibGfAQgYKjokM0kf3KKeYupUqLZYJsSUwoCpRp7q7Atg3XFHFuHc2wYynHPe6/i6InDRBFUgnDBPTONRIAumzGp+yHTZT9xrhrPG1Il6xRJGVfTycpmTCxTJhObGhmJYj5RBB9oyfS03hfPjwijMHW6+vOZNCvULQOlM8cajeZSQi78koRnnnmG3/3d3+UXf/EXKZVKfO1rX1vLdWlWyN03jRBFMV4QoZRKaqh1/atGsyImZl36CzZNTQVIJvqEC3y3lvt9bEawZ6o+oDg5VefkZBUhSKPeJyaq1L2oMb2pmTFI3m9bBpvW5egvZCjkLKpu2HMNu7cPs3HQwWhkXppOhAIKjoFsPGIYkqG+DDknmeZ0etrFaDgCyXsb/zMkmcao0aYjAWCaBlvW5RjI29TckA0DDn05m41DWa4eGWDbliJSwI4r+nny+ZP8P//wPOOzLlF8dnO1FNCQeCBjGazrd1jX7xBGMaenqsyUkylPhjF3TVGcXENf1iKXaUyPajDYl8GQSaZhw2AWKcVZ+9R6Xwb6MhQLNo5tYBiCvpzVtr5OGaiJWbdnlkij0WguJnpmJs6cOcM3v/lN/umf/gmAj33sY/zP//k/GRgYOCeL0yyf1a4T12g0cxmGoaJDqeoTRsm0nw2DuZ7freV+H1sj2NNlr+EkSMq1gI1DOTwgikKCKMZoGbrXHFkaRnNGcl/OIopiBvJ27zWIpHPAMGQj8wJKxQz0ORSyVpphqbkBp6aSKUwqVkTR3ASgJlEcMVDIUKkHaRai2dh7/7ySnpfemuSpF05yYryKKQVDRYdvPnmMqptkIkRjafP9CSHAsQ1yjt1QxA6oe3PZC9nYl6a4nGEIolihYkWp6jNYSBwaSAx6KQX5rE0xa541qarTfYG5zEIUhWnDeJNOGSidOdZoNJcSPZ2J22+/nVtvvZX/9b/+F+9///v1iNiLjNWqE9doNAnNGnnDSKLWTcP4vtu2L/je5XwfW3stwsbIUiHmnATblJiGwIgEcawQqHQKEyRNzHUvJNuIvm8ZzvPgvOlL83H9iKGiQ6XRX2Aakr6cg+tH3Hfb9qSvwgsp14Ok5AmFNAQTMy7z8wZKJeNc77l5pGtzcRTH1LyI4f4ct994Bft/cpKDL53hzcZ0JkMKbty5njfGZpgu+xgyySxAUpbUl7NQCso1P+nPkALLkOSzJlU3JAzjdE+MhmehFARR0nBdrocYEsyclToP99++o+e9mph1EQJOlz3CKG7skYXZmES10FSmpUxvulDHEGs0Gk2Tns7Evn37uOKKK87VWjQazXlCGx+LY7Uzft32vfn4bMVr9DnYmI3JRkktf1Ii03QQdo0M8M8H3mpzJCAxmsdn6gwUMpiGWFSZYzNqvnV9Pm1y9oKIgbydXucX974ESqUK2J4fMlvtrLIdRjH33vpO7m15TIhk7XUvxPUiJmbrPPnCGIeOjKfXYFuSm67ZyC27N1PM2fzB3x5EiKSUK58zyDo2YZT0aTRHuiY9IpIwjKi6YZKBaDlvs0xKikSHo9nzEoXJZj3039674P5AkglJys0kUgjCSDFVctm8Ls99t21f8POx2M/RhTyGWKPRrB4X+9/gns7EFVdcwWOPPcbf/u3fcuTIEbLZLDt37uSTn/wk73//+8/VGjUazRrSy/i4Y33feV7dhcdqTobqtO9vniyx/8VTGIZkoC/DVMllquSRcwxqIUBMX85p63nYvX2YH79yhhOTNZRi3ojTROfhv/3ytWdNFHrk8aOcnqoBgo2DDvd9YAd33zTCl77zMtOlZMysIQWObXD/7TvS62/Vkah7YVqK1ErSyyAIopiHHj7EVMnjig15dr6jn1dGZxgbr2KbEssyePNUOS3NyjkmO98xwHTJ5cU3JjkxXuF9121BCEUuk4y89cOImYqH5ydORLMESkhBMWcxXYpSR6h1L5o0nZLm8zGK09NL6FdQKj2ySg+eTKRa7OdjMa9bTqO2bu7WaC4uLoUAQE9n4lvf+hZ/8zd/w2//9m+za9cuhBAcPnyYP/zDP+S///f/zl133XWu1qnRaNaIXsbHHTdvO7+Lu4Tptu+PPnOcYqEhDGcZCCGYKXu4XszmoWw6zWkgb7dFr9ygUQYlxVkZivljVPc+/Qb/fOCtlkh90tz9pe+8zO03bm30TSRmNjRqq1porfkvVX06meyxAhUlWhExis3r85yedjl8dBLblESxwgvm1tWft3nfdUkW4l8PvoVhSJyMSdkN+P6h42zfOsD4TJ2JWY84jtOJWpBoUzTVtpvnbtK6KtnouYjiZCJV06FQjQlVi8UNYgb7bMr1pIzKNCV9WRM3WHhc7VJYzkhhPRZco7m4uBQCAD2diS9/+cv8/d//PVu2bEkf2759O9dffz2f+cxntDOh0VwCaOPj/NBt310/ZNica8TNZkwc26Dmhnz2N2/uerzhfqchJtcekRckZVHNP0yHj07wnR+Otk1GSjQVFOV6wLeffhPLlKwbyKYTh7wgavvD1lrzH4RRKjTXao47tkEhZ2EbkvEZl1JlNu11CKNWpemEIIx46dgUVTdRx3Ysk5xjYhiC8ek6pyZriZMgAKUwpCSXs7n9hi1pJsc2JacmawgBhkj6SNqciYb+hVIQRQppilTTYuNQvsfdOnuvZ6o+m4bahQHnj49dKctp1NbN3RrNxcWl8De452jYIAjaHIkm27Ztw/O8NVuURqM5dwz3O20TeEAbH+eCbvvu2Oay7sfdN43gZMy58qbmEyLRdWj+Ydp3cJRYzTkcreNfGzISRLHi9GSVsYkqx89UmC55nJioAnO1va4fUaokWQkpBGZj9Go+a7FxKJeMoq2HjE3WmCl7RAsE7WtexBsny4kwnSEZHswSRhFHT8wyXfaIFWQsSRwnY3lNQ3L7DVu499Z38sCdO9Mxs0ophorJeFjDaM+oKKAvayFF8zrjxprtRTXRt+71uRi9vZzz6LHgGs3FxaXwN7hnZsIwjK7PKbX4lLBGo7lwWcpkGc3q0W3f7/qFK9j/4qkl3Y+9T7/Bo88cp+639y6Yhkh0EwyZRs0nZt1EHC6O08xFk2bvgVKJwR6HMYZMou5eEPE//mY/QRiTy1oM9mXww5haPUAaiZGfc0w8P2Kq5GIZgrqXZCAW8+fCkIK+nI2TMai4IdFEhdmKTxzPlTN5YZw6LsWCzf4XT3HV5mJb/8FDDx9KI/NDRYeJmXpaEtXUxpBS4Adx0vuxjGbHpTbiL7e5cjkN/3osuEZzcXEp/A1etAK2RqO5NNHGx/mh175ftbm46Pux9+k32HvgTQQCs9Ev0bTdm1OG8o6VNlAP9ztEsSKI/KTkp+VYQiR6FK2TmZoZBUPCVNlDCkHGNhCWQd6xKOYtRAwnp+uMT9eJ4kQxexFC1UDi8BRyiehbtR4wOeMRROB6kiiOEUKgaDRMK5WIyMWqa11x6x9mxzboL2SYrXoUczbZjIkXRFim5Dc+uGtFn/HFNlqvtLlyOQ3/eiy4RnPxcCn8De7pTLz66qvceOPZM8mVUvi+v2aL0mg055ZLxfi40MbrLbSebvu+lPvx6DPHEQgMKVJhtlaiGOp+yCOPH+Urjx7BsQ2iKKY/b1Ou+WmztgCGB7JkMyblWpA2MQsSobdEOVohJHhBzOZ1NkIIKnWf0VNlMhmDuPGmxeStm9oMGdugUgs4XUlKZw0psIwkm9FU9O4v2EyVPEARNNZ7eqpGX846q654/h/mjYNZ9ly3uavOxVpzKTRXajSateVi/xvc05l47LHHztU6NBqNZkVcaOP1ztV6XD9MxdjmT3FqCrwFoeL4eBXLEEw3SphsU5LNmGxt6FTsf/FU0qCsFKYhU40Gs3HsWClyGZN8Y2LSm6fLiep2Q2chrIUdnYjmBKVEcC8pUdowmCWKVeJETHlpGVQiyJf8Ry5jpCVfhiGRAhpyEphGu7bDfDr9Yb73rFedGy6F5kqNRqPpRU9nQgjRsQEb4Mknn2Tr1q1rsiiNRqNZKhdaBPhcrcexk9Id1NkdzvObnoMWZ8MPYvww5o2xEmMT1WSsqhDU3JANAw5VL6JaDxInwjHJZizCKKZUTZSmm07CQlimIAgVUkqGijZDxUQjozTrUszbuH6IHzYyGo3jGRIytsH+F09xy7WbeGV0hjNTtfS5hsIDTW2HCxk9XUmj0Vzq9Jzm9OlPfzr992/91m+1Pfe5z31ubVak0Wg0y2Bi1k1HmTY5nxHg1VzP4aMTPPTwIR78wgEeevgQh49OpM/d9QtXoFALTkuaT3N6kx/GlGoBY5M1Tk/XedfWIoWcjWhMgRoeyGJIyVTJZXLWTYXiWh2J+dfZShQLrtzcx8jGPFvW5ZmYccmYBv/3h67hvtu201/IMNiXaWsEL2QTcTrDkLwyOsODv34jQ0WH4f4MpmkQKzAMyWCfveraDquNnq6k0WgudXpmJlonNr399ttdn9NoNJrzzXIiwGvZY7GY9Szm/AuVS9176zsB+NbTby64pk5q0E2a05sOvTbJuv4M27YUmZx1eftMBdUYxzqfRgUUUQdPxjQkxbyNZUomZ13CUFGqBm3X+dDDhzAMSSKON7fGqhsy0NfufJ0rbYfV/kxcCs2VGo1G04sFy5w6/bvTfy+Fv/7rv+Zf//VfAdizZw8PPvggBw4c4E/+5E/wPI8PfvCD/M7v/M6yj6/RaC4vDh+doFLzOTNVwzQk/YUkqt0rArzWPQ0Ljftb7PkfefxoeV+8GgAAIABJREFUMiK10cvQjNjvOzgKzBmpvRwF6O1ICJGI4xWyFgqYqSTid6YhiSJFt1/7Tg5GqxNRqflMlZKxToN9mbOuM3EUFNMVv00fI4wUNTcgjBSeH/HgFw7gWJKamxxrrcYnrtVn4mJvrtRoNJpeLDozsVocOHCAp59+mm9+85sIIfjN3/xN/uVf/oU///M/58tf/jKbN2/mU5/6FE888QR79uxZ9fNrNJpLi1YDcKiYoVQNmJx12TKc5/7bd3Y14ta6p2GhiHSv8zefH5uoUqq1jGmNIiaCOuv6HcYmqnzpu6/geiFRQzOiG0IkU5LmN2ibhiDnWOQcEz+Ima34Sf8F4AdzKtVtKtLzeiWa/906nalcC5gqtZdzVeoBpaqPaUqytsG+g6MM9zu8MVZqqHS3r29ixkUBAwU7WV8Yg1KYMunrWI0I//wsRKUeXFB9NxqNRnMx0NOZiOOY2dlZlFJEUZT+G5I/asth/fr1/N7v/R62naSmt2/fzptvvsmVV17JO97xDgA+/OEPs2/fPu1MaDSaBWk1yjOWQT5r4wURhWwydeihhw91NObPxZSdXhHpbucfm6imzlHNaxdraPY5TJUS4bkwihFCYkiJUnGbkS8a/ydrG4Ag55g4tkHdC6l7IYWchZRJtL+pD9FKq2EvgGLBRqiYqhsRN54zZLLmfNbCNBMnYqbideyJDhoCeFEUU67HRBNV/us91/CXjxxuZLrb35RoS4BlSoQQqYFfyFp89r/e1GXHF0+nLMSZqRrr+h1oKU3Tk5c0Go2mNz2diSNHjnDzzTenDsRNN839gC+3zOld73pX+u8333yT7373u3ziE59g/fr16eMbNmzg9OnTSzruunWFZa1Hs3jWr+8730vQnAcu9Ps+VfHpy5ptv0mmITg5VeMfvvc6QRRTrQdMlz2Ojr3Ir93xLv7TXbvYvL7AdKmO02I4un7I5vWFc3LN3c4fqWSSkWObnJ6stb2naXJHMShiUBCrmLCD8S4E5LImBcfmU7+6G4DvHjhGuR5gScHEbJ2TE7Wz39hCxjbI2ga3Xr+VI6MzTM7W2ba1j5MTFaI4Ju9YOLaZiM3N1glChWGIxJlQioxt4vohSiVrjxXYhiSME8fnjpu38fC/vcb4TD11QKQEQ0rCMMaQgko9oL+QAebu62f/z485MV4BYMv6PL9xz8/y7ms2Lmn/v/fI4XSfAWzLYNqUlKpz52vek3P1mbhQuJyuVTOHvu+XJ6tx33s6Ez/84Q/Tfwsh2sqeVtIzAfDaa6/xqU99it/93d/FNE2OHTvW9vxSjz85WUkFkzSrz/r1fYyPl8/3MjTnmIvhvg8V7LManb0gIghipIgo13xoiLpFseIfv/caG4oZ7rhhC1997AhhpNpq8O+4YcuSr3k5Tbt33LCFL333Fc5M1YniGENKnIyJFIlYWxCe3dTc/IWTDa+i10+eAjwv5q53b6RacfnOD9/CC5N9qfshUz2i7YJkpGscxZSqEfsOHGNdf5Zfes+V7BoZ5Gs/eI2qG3FmqspMxU8chTjJInz6V67lK48eIeckDt7bZyqoppieSoTvUApDwPd+dIxKLWjLZMQxoGJMUxLHCj+I072YrXhUagGVxj0VAo6frvC5h5/lk/dcs6RSpJPjFXKO2bbPfTmLqZJHpR6s+DNxsXIxfOc1q4++75cnne67lGLJAfqezsR73vOejka9UgohBC+//PKSTtbk2Wef5bd/+7f5zGc+wz333MO///u/MzExN+rwzJkzbNiwYVnH1mg0lxfdGp1NQ1BzE0O1NXofK8W+g6M8+Os3AiufsrOUpt1Wp8OxJEHYLBdN1KsrNR8hBJ4fMdCXwTRlR6dCiMSQjqPu3kTWNti0Ls+JiSpH3p5holTHMhKHSwrR0xG54+e3sv8np6i3jIGdLHl879kTDA843PHzV/Dlfa8ipUSKiDCKkUJyz83J/jn2UU5N1ohV4jjIFjkI0xBkMxk2DmZ55PGjeEGYOnpNpBQM9mWYnK03SrgUfhhTdUMQIJDpJKkYhetHS+prOHx0gpobMlX2sExJMWeRc5JSrS3rchRytp68pNFoNIukpzPxK7/yKxw6dIjbb7+dj370o+zYsWPFJzx58iSf/vSn+dznPsd73vMeAK677jqOHTvGW2+9xRVXXMG//Mu/8NGPfnTF59JoNJc+3Rqd9x0c5dW3Z+bUlZnrOTgxUU3fu1JDcbGN3POdjlOTNaI4Zl1/Fkj6IIQQCJLo/eRsnULWYnaeMyFlEkEv1wLEPM02KSDnWOSzFlEcc3Kyxth4hWzGBBTTZY+wQ01Ukg1J9ieO4YkXxggarzMNQV/OJmMblGoe3z3wFv/j12/k1+/c2dhzwXC/w66RAV4ZneH7f/kUVTckjudKnhRJ2dVwv9M2Zevz3/gJzaxRq/p1GCmkFOSzNsWsmTZc1+oBVTdAyrkgV3O/FtvX0LwPGdvAC0LCMGaq7BGEMZYpuf/O7k37Go1Gozmbns7En/zJn1Cv13n00Uf5oz/6I2q1Gvfeey8f/vCHKRaLyzrh3/3d3+F5Hn/6p3+aPnb//ffzp3/6p/zWb/0WnuexZ88e7r777mUdX6PRXH50cwpeHZ1J/900oQUQ9YjoL5XFNnLPdzqSSLygVPXTlUmR6DkMFTPMVnxqbhK1bzpCZiOKns2YVOshxbzNbMVDSkkhmzxe90OmSm5bRkMR4vUQd1MKIjW3R0Go2pyISktjtW0m19W6562OUnMalGjo2MlGTZZpSJSCgbzdEu0XCJGcVQqBacw5FAN5m/tv39F2Xx96+BBvjJWI1ZzGhSKZVLVYRenmfcg5BpYpKVV9gjDCD2J+44O7tCOh0Wg0S6SnMwGQzWb5yEc+wkc+8hFOnTrFt7/9bf7zf/7PXHXVVfzFX/zFkk/4+7//+/z+7/9+x+f27t275ONpNBpNJ5JyG0ndT4xoIRJHQogk2r5aLFYsb77TYZpJhD5sCL5JkZT6KAXTZQ9DChzbZMtwvmNPyMZBByEFQ8UMNTekXAs4PVXtWL7Uy5GA9jlKhhT05W2ceU5E6/U2aZZtHT1RQggY6MsQxQpDilSletNQDqUUNTfkof/23rbzbhx0ODlVJ2ZOy8KQgs1D2bQMrZW7bxrhS999hWrdJ1Iizcw4GWPRehOt9yGbMclmzHR92pHQaDSapSOX8uKpqSmmpqaYnp6mXNaNOhqN5sLmyk1FBvsyOLaBIQW2ZVDMZ9gynF+1c9x90whRFOMFEUopvCDqKKY23O8kWgkNijkLpRInItGAiGkKSUuRaC7U/YhdIwNtxw+jGMeSfOwX38WHb9mGZSTR9Wo96NkHsRCGFAwUMqwfzBJFMaenalTq7c3RhhRnie7NVBNBvShWaalW3JjeFDaut5sS+X0f2EHeMdMeDikEecfkvg90LqndvX2YT35oF5vX5ZECBInj0a35+vDRCR56+BAPfuEADz18iMNHJ866D73Wp9FoNJqFWTAzcfLkSfbu3cvevXuRUnLvvffy9a9/nY0blzaGT6PRaM41zebsgb7MmqkmN43YR37wemPUqmLjUK7rWpqN4oYhsc3EiPbDODXak6h+Yo3nHZNXRmd44M6dfO/Z49TckM3rHDYMZNn71DEmZl0G+zLcd9t2oljxlUePLHn9UgqKORsnY1BzQ85M1bo6JR9+75UdRfeaWRbS/IJKhey6OVdNinmbuldDCsGGAYd379rAvoOjfOXRIx0boBfb59KtMf6Wazex/8VTZzXs7xoZ6KpJotFoNJru9HQmPvGJT3Ds2DE+9KEP8Wd/9mf8zM/8zLlal0aj0ayYhVSoVxMvjFk34KQG6vyJTvPX4lgSyzLJOSa2KRsjVJOeAds2GhOGkubjXVcOEcYx3//xcX56bIpn/YhsxkwatGs+X//B6wiWVrolpaCv0X9Rc0PGp2r0aiW5YjjHvbe+M/3v1nKhYs5iquwhUMSxopjPUKkF2Jac1yMxR6uxv3k4jx/GlOoh3z90glzWWnAy1kJ0a4xvOmetn4ldIwPsf/HUoiZyaTQajaadns7EM888QyaT4R//8R955JFH0sebo2EPHTq05gvUaDSalbAaE5sWYqGJTvN1KD5+V2LMhip5bd0L0ybr5tSjwb5EOM2QgoMvneJbT72RNjjHsaJSDwjCZvnT4tcqBRRyduqoTM7MORFSnK1dIYC+vH1W6VGzVySOFeVagIoVMUmp0sbBLP/l7qt77nunPWtqXwwWnY772MpC2h69GuPnfyYeevjQoiZyaTQajeZsejoT3/ve987VOjQajeaipZfh2q3cxvUjBvsy1L1k+pKUAscykrGuUczEbB0/iPjwe7fxxHMnGmVRRjqJSilw/Sg9X8aS/NJ7ruLb+4+lE5FaaXUi6l7Imek6cZwI9g0VLKIYKrWAjJE4FEEUYwjBxqEc9922/SyjurUZGgRSJmNg8465qOxPpz2L4tZSqfZ9bGUx2h7zG+NrbsBsxUcpxUMPH2pb42Incmk0Go3mbHo6E1u3bj1X69BoLjmWo4p8uXCp7U2viU7dshZRFOKHMTU3oNgw8l0/olTx8cOIjGnwsTt2cPXIIHufPoZpSmbK3lmZg0LWSsuJfn7XBmarHv/27In0+ab2RCFn4XoRkzN1co7JFevzTJc9tg7nmZh12TjotGUTFlLE3b19mGLWxPXCZIKTkYytNQzZNSPTep8d20iF7UxDUszbGPLsmSCdmqMXo+3R2qMShhHTZR9QDBWds5yPxU7k0lzYXGq/KxrNxcKCDdgajWbpLEUV+XLjUtybbircd980wlcePdIx6l3ImvRlTQTgeiGnp2tEsaI/bzNkZ3C9kKtHBhmbqOKHMdMVv+0YAjAMgZMxiKKY9123BYDbf/4dADz+/BgZ22CgkIyOnZhJMhFSCipuiBCCrcP5jiNYF4sbxGxal0OIuWyCUqpnRqZJqeoTxQohIAhjJmfrZCwT0xB4QdSzYb5bJuHERLWtifqWazfxyugMR0+UkmlVfU5DwI8256PX/dNcHFyKvysazcWCdiY0mjVgsarIlyOXwt60RkAd2wClcP2IKAoxDcGW4XwaFR3uH02j3lIIHFsipGT9QJbrdwzz1UeP4IURpinpz1tkMxZeEOLYBv/vd1/mteOz6XmlFBSyiS5C3Y3IWAbFrMX7rtvC1SOD6et+8d0jXLNtiB/99DQ/fWMSP4wbjoREikQwr+qGbcZy2zVZEtMyqdT8nhHe5WRk9h0cBSCXtRqq2gFhGGNIyVBf0puxUHS503lLVR/Xi5ip+qkxuf/FUzxw587UoWt1elrLmM5lo75mbbgUflc0mosV7UxoNGuArsHuzsW+N60RUCHgxESVxiRXLFNimu09A3ffNMLXvv86tiXJOxalmk+56vKhm69k+9Z+PnbHDvbuP4ZhSEwpKNd9avWkdIipOgAbBrPsfEc/J85UmKn4DBYd3vf+dgcCkubtE+MVnnn5DG+fqVDM25iGZHggm5RQVf1kDG2siJVKDXsgvSZQnGycd6iY6RnhXU5Gpnmfc46JsAxyjgXQJhy3kPHX6bxVN6SQszoak4spYzoXjfqateNi/13RaC5mtDOh0awBuga7Oxf73rRGQMdm3blJSgLCKKZU9fmrf/oJ77qin3veeyU/u22I//iB7fzbj48zeqrEQCHDh26+MnUErh4Z5Jdixb5/H+XMjEvc0hQxsrHAnuu3cvXIAFJ0H/362vEZnn99gpmKx8SMi2lI+nIWp6ZqlOsBlXqAZRk4liSMYpQUmIZMHYVMQ/ciYxlMl71ELVwIyrWAjUO5rhHeXhH91oxMk9b7vJLPQKfz1uoBfTmr7XVNY/Ljd+0852VMun7/3HKx/65oNBcz2pnQaNYAXYPdnYt9b1ojoE2FZ0imKymSUa75rIVhSr711DH2XOex44oBHrjr6rbjvDo6zRPPn+DMdB0/jNsmML3rin72XL+VbZv72kpz5iOA0TNlfvjTU3hBzNh4jTCKEQLiOE56I0imM4VhzKwfIQBpCIp5K43en5522TycS69JiqRxO2xIcveK8HaL6C90n1f6Geg03nW+MVmuBXh+xFcePZKWo9XcsFHGZTSE8UZX3dDX9fvnnov9d0WjuZjRzoRGswboGuzuXOx70ykCCsl0opyTTFaquSGlik/dD3n8uRPsuGKg7bU/eWOCbz15DHeeRsSVm/r4pfdexdbh/ILrsC2DgmPyte+/xkwlWU8YJY6AIskqSCkwDEkYKyxTEvkRCBjqy6TlRbaZlDb5YdymZt1UsIalRXg79ZPU3LDjfV7Nz8B8Y7JcC5iteumkrKZxeevPbVpzgTpdv3/uudh/VzSaixntTGg0a4Suwe7Oxbw3rUarZSUGY86xiOKYaj1kqpQ0YReyFpYhmS576Xsr9YADPznJky+MtY14zWVMMrbEEPR0JF4dnea518bxg5hsxmTP9Vs4Pl5NMyVNR0AKCBUYJFkJ25RsHMpxarJKEKnUkYDEUdg4lMMLIjygL2syXU70GLK2wcmJKmEUY0rB4aMTPe/b/Ih804D/+F07O5ZIreZnYL4x6fkRxZxNfyER/2sa9I8+c5xiwV5TQ1/X758fLubfFY3mYkY7ExqNRrMEdm8fRkrBUy+MkXNMpksu0yUXv1HyJAQU8zaQCL8N9mWYLns8dXiMH79yJi1nEiRNyIVsos2glGpzPOZz7GSJgy+fpuaGjE/X8MOYF9+Yopi30qxCMWcxVfbSkauxAiEUxXxiUOcci0o9OGv06v237wTmDPHNQ1n8SDE+U2/IcsOZ6Tpf+u4rfPJDu7oabOc7It9qTD74hQMIAaenktKvZh+J64cMm+1ZljCMOHqixINfOLAqEW1dv6/RaC4ntDOh0Wg0i0bhh4qRjX189LYdxLHi1dFpnnphjNPTdTw/ItcY3To+XSOIFJWaz5//w3NpOVM2Y+JYEiEFjj33E9x0PObT7MF47sg4EzMuMxUPEBhSEsWKmYpPPmulx+5rTDaypSCMoJCzcGwDL4gwDcE9N4/wyuhMx1KQVgP6s//nx0hRRzTGycYKqnWfRx4/2tXQvpAi8o5tcHKyihASKQRhpJgquVgNJ6pVGXu67GNI0bHsaTmN1Lp+X6PRXE5oZ0Kj0aw5F/tkG4XCC2JqbkgQtvc5XD0ymE5menV0mn0HRzk9XafZNu36ScYilzG57Yat/MI1G3jzZIm9+4/hhxGWIQmiuE14DhJNibxjks2YSCF481SZcs0HBLJxcCkgUlDMmhRyNhOzLpuGcun+dtv3exdxzSfGKx3OJTg9Vev6ngsqIq8aKRVUy/1Kys+iKE4N/dlKoow90OcghDhLD2M5jdS96vcv9u+CRqPRzEc7ExqNZk25WCfbCJFkC+pehOs1dB96oFSiLj1bTZSqm682pCCbMVjf73Dr7s1zr48VE+UkYm+Zgj0N4TkpBFnHJJcxMeTcJKfhfofpsosh5dwxAMuQuEHMZzsoWa+0hnz+IKnkv7tPl7qQIvJuEDPYZ1Ouh4RhjGlK+rImIHjgzp2pQa+UYqg4p4wNc9mUlZRtddr7i/W7oNFoNL3QzoRGowE6Zw/uWN+34uOe7zr6paIAP4iouyHevCxEJ2KleOnYFE88P8aJiWr6uGXItMQIYKaSOBmvjk7zyONHqXshhkwC6FGkOPTaJLuuGuS67cNtDkOTu28a4eiJElGsGhObkv9lM8ayI/+9ouRb1uc5frpCjGrE95O1bhrqfq61mqiznGh+M0uyacim5gaUagFTZR+n8Tl8sOF8NUfKttLMpqx22dbF9l3QaDSaxaCdCY1G0zVi2t+f48qG/sByuZDq6HsRxQo3iKi7QZvmQzfCKOaF1yd44vmxtmtxbCNVu25qRPhhlPZDPPXCGH4QIQRIIbAsSV/OxjAETz4/xo3v2tDxfLu3D3PPe0b4zo9GiWKFZUiyGQPLlMuK/C8UJf+Ne36Wzz38LK4fEcUKQwqcjMF9H9jR87irPVFnudH8ZpZk1gsp1wNQIFBkbKPt/b2yKfsO9hbeWyoXy3dBo9FoloJ2JjQaTdeI6Tcef53fuW/3io59QdXRd8APY+peiOdHxAulIUiyFs+8coanD59MS5oAdo0Msm1zH4eOjHNmuk7dDSlkTQzTaOuHmG5MW7JNSbFgY5sGlXpAedbHD+JupwXg3lvfyVWbi6sS+V8oSv7uazbyyXuuOe/1/cuN5jef++Lel0ApLNOgmLfJZky8IErfv1A2ZTXLti7074JGo9EsB+1MaDSarhHTMz2abRfLhVRH3yRWCi+IqLlho0TpBNNlj8G+DO9r9C7Mp+6F/PCnpzjwk1PUvBBImpJ3bx/m/ddvYbbisXf/sTSCXnVDpisBlhmm/RAA6/odTENi25JqPWSm7BMrMKRMjcpeZT3LjfzPP+bYRJWBedOj5kfJV5plWI1m45VE83dvHybnmAwPOG1K4ou9ztUu27oQvwsajUazUrQzodFoukZMNwytrMQJLhxlWiEgCBV1P6TuhelY16YD4GRMSvWAvfuPcS+kxn+p6rP/Jyc5+PLpNHNgGoIbd67n/ddtYajo8OroNF/73ut4YYQhkzGkzX4IFDx7ZJyt6wv87DuHuPfWbfx/jx3h9FQdaLwGhePY3H3TyJo06XY6Zt2PMGpBqokBqxslX63rWGk0fzHvXwvnrRMXyndBo9FoVhPtTGg0mq4R05/bvo6HHj60YsPnfCrTKpX0LNTcEH9eQ/W+g29RqQVJT4ApKTgmhiF56oUxhgeyPPn8GIeOjKeTnDKWwU0/s5Fbfm4TfbnECG86JH4YYQjSfgulkklOikQ07pXRad5/3VaGi1k+dse7eOQHr3N62kUIxcahPPfdtp3d24d56OFDq96k26lUKO+YVGoBGdtY9Sj54aMTfHHvS3hB2F5etIzrWGk0f6H3r9WEpW4OilZp1mg0lxramdBoNB0jprtGBvj+M2+D4KIcYxkrRd3v3lD96ug0Z6ZdpEzKleIoZrbqk8+YvF3x+d9fez51PPKOyS0/t5mbfmZj2whRSBqqDUNimpIoas5YSkTe8hmTgb4MhazJmyfL6ajVpkHZanA2dQ3Wokm30zGLeZsoVgzk7VWfvPTVx47gBhFGi1jcUNHBsY0lX8dKo/kLvX8tJizpEbAajeZyQjsTGo0GODt78NDDhzBNkY4pvRjGWLZqQzRLmbrx1AtjmIYgVsn7lFLEMZTrYfqagYLN+3Zv4ed3rcc2jY7HmS57OBmTQtZituIByUSnpHxIEUWKyZJ3lrp1N4PTsSSlRhlSUx8haxtsWkHJWbdSn63D+XRE6mrRNM4tUxJFcUM9W1Cq+kiZWdVm4/nR/10jAx3VvXtlA9bCeVusg6IF7BaH3ieN5sJGOxMajaYjE7Mu/QW7Lap/IY+x9IOYmhssShsCEicgnzUpVQPmD1HqL9jc+e53cN2OdR01H1oZ7MtQqgc4tok1IBFCEMWKUs2nL2sRK9WxLKebwVn3I0o1H9FQnw7DmFIYcdv1WzqcfXGstFRoKcZc0zgv5iymyh6xSnQqgmWWUXVzut48WWL/i6fSx09P1zlyfIZizqaYtxedDViLCUuLcVB09mJx6H3SaC58ev+V1Gg0lxyHj07w0MOHePALB3jo4UMcPjrR8XXD/Q5eELU9dqGNsUxKmUImSy7TZRc3WJwjEcUKy5SUayGtyQsB9Odt/sd/uoEbd65f0JEAGiNfFbmsyaahHI5t4PkRw30ZlIKBvM0Dd+48y/CZmHWxzfbj26akVA3oz2ewTJkoXJuS/nyGV0ZnFr6wLuzePswDd+5kIG9Tc8Oua+pE05ibqfptxlyvz40fxuQci6G+DIYhiRRkLJNbrt3EvoOjC372Wml1uoQQZCwDw5A8+szxtsfrXohAUPejttc1y8e6cfdNI0RRjBdEqMaUr5X2jjT3oJX5351u17XQei839D5pNBc+OjOh0VxGLCXKd/dNI/zD914nFNEFNcYymcoUJ/0QC5QyzScIYw4dGeepF8aYKnvp47YlcWwDKeAjt25DtowRXYife+c61vU7fP/Z4xwfrzLc7/DJD+1atELz/Ig4KPpyVtuUJaVUGtVebsnHcht/O2VQZr2QL+59KRm7Om8NrVmQbCZpaI+imFuu3dSWSVhshLlblN/1Q/KRyemyRxjFRJFCyiST0/q6hTJpazFhaTGZIC1gtzj0Pmk0Fz7amdBoLiOW0my6e/sw/f05/u7bP+HkRBUQbBw8f1mJXlOZevHq6DRPPHeC09P1xKhrOB8CGNlYIAxjal7YU2OiE6YhKORsMpbBUNHh2m3r2p5fyOjvZnBuHMrhh3HHspvzUfIx35iruUFDUVoxPOCctYZuxvlyG527OV2WKZkquQghkUIQoYhisEzR9rrFjIBd7QlLi3FQtIDd4tD7pNFc+GhnQqO5jFhOlM8LItYNZFOD91zXK0exwg26T2XqxQuvj/Ptp4/hBXGb87Fja5FfumUbGwayS16PlIJ81iKf6f7zuRijv5vBCd1Vl9di8lCnte87OMpUxWeoYONYss25KdUCUGCZc2Un89fQyTj/yqNHlhVh7uZ0FRyTmWoAKJQCKSGKIY5jlFLnbARsNxZyULSA3eLQ+6TRXPhoZ0KjuYxYapTvG4+/vubGazf8MKbuhXh+RLzYNESDmYrHUy+c5Ec/PUXznYJkxK1tG8SxWrIjIYUg65jkHXPBMqjFGv29DM5OUe3lGuSLpdXg7ssmBnfNDZvKetimJAhjBKqtDGuhNRw+OkHNTXpbBIAQ2KYkmzHZONj7PnRzur7y6BEG+2zK9bAx9cogb0rqXkjNDVc8AnatJwitpLzqcppupIX+NJoLH+1MaDSXEUuN8p2equFYZzcJr1W9cqyaWYiQMIoXXcrU5Mx0nSdfOMHzr02mDogQkHee8M1PAAAgAElEQVQs8lkrEZFTiumWfomFEAIytkEha2PKzk7EfONubKLKwLxRsEvZt25OxlqXfHRqdgUwpaCQtZiYdXEsg4xttOlt9FpD00FpZhASwW+FH0T4YcSe6zYvuK5O+zHcP8pM1WfT0JxT4wURW7qMu11KVm6lWYzFGvvLKa+6HKcbaaE/jebCRjsTmjXncoqiXai03gPHNkCpjtHb+WwcyjE+XVvTeuWkoTqZyrTUhuomx89UePz5E7z85nSaiejLWViGBAmOlfzU1b2ASi1AKcXf/vNPF+yRsC2DQtYiY8mujk0n467uRxi1oC16vxr7ttYlH90M7pob8tn/ehMwd71e0Lkxf/73vVLz04lOUpA6FLECQ8CPXx3n3lvfueS1LnUvluKIraScbK2N/XNR6qbRaDRLQTsTmjXlcoyiXWjMvwdNo+vjdy08GvRXb9vB3zzy/JoZr80sxFIaqpsopTg6VuKJ509w9EQpfXxd0eH9123mhp3rOXpilr37j+GHEVEYMVsNgERHolQP2Lv/GPfCWQ6FZQryWRunYbDNX1urwVxzQzK2Qc6ZM+7yjkmlFpCxjVXdt7Uq+Whez2zFo1T16S/Y9BeSzMp8g7vXGjp9389M1RgqZgjDGCGg3VcUjE1UOXx0YsnXsNS9WIrzsZIJQmtt7OvpRhqN5kJDOxOaNUVH0c4/K7kH775mIw/cuXPVjFchIIxiXL9RyrSMLESsFC+9Oc2Tz5/g+Hg1fXzzuhx7rt/CtdvWIRvlSFePDPLz4xWePnwKt6GZ0ZezyGYsAHwSJeymM2EaSXO1Y5t064qYbzBPlT28IMRq9AAAFPM2UawYyNurnpFbSclHpywhkF7PQF+GqZLLVMnDkAKE6Ghwt67h8NEJHvnB63z+Gy8SRjGGIRjsyyAsg4xlYBqJdoZpSjx/TrdEJIfHkHLZvwdL2YulOB/LLSc7fHSCoydKxEphmpJiziLnWIsy9hebwdXTjTQazYWGdiY0a4qOop1/VnoPVqte2Q9ial6AH8RLbqgGiOKY51+b4MkXxhifmVv7VZv62HP9Fna+YwAxrzH61dFpnj0yTiFv4c8mhmzNDbBMmShWG5KZiocpBbmcRc5e+CdxvnNmmTJRqa76qTPhhzFbu9TurwWLMUS7ZQmbImAZy4BGr8RM2WOy5LF9S7GnE3T46ARf+s7LVN0QIZLypTBSTJaS+5NzLBzboFQLmN+zLhuvL+atc/Z7sNjP8nLKyZr7KwSgIIriVMvEMGRPY79XBhfaHaBdIwPsf/GUnm6k0WguGLQzoVlTdBTt/HM+70GzobrmBkShYukuBPz02GQyqrTstZUb7RoZYM/1W7lyU1/X9z71whiGIbFNA8OUxFEMCCr1AMc2MU3JyMYB1g04iK65iHbmO2fFnMVU2SMIo44jSbuxWr1Eiy0lbHWC6l5IqeoThBFxDMMDDjQ+H9mMmah4B/GCztC+g6O4foQQybQrIZIm6zhujJAFqm6IYQhMKfCCOUE50zQo5qwkI9LSW7ISlrqnvXQnmte32GM197eZ3QGBQDFbScrGen0eumUPH3n8KF4Qtd3b/S+e4pZrN/HK6IzuQ9NoNBcE2pnQrCl6Rvj551zfAyHmxrq6frSshmqAuhfyzwfe5IXXJ9qcCNuU/Ieb3sF7fnbhKUDTZQ+nkS0oOCazVR+BwjIM+gs2QRjx3ms3LdqRgLOds5xjEYRxknlZRFM7rG4v0WLL2JpOUN0LU2O3KfaWiL+JtszKhqHcgueemHWJYkVzyJUhRaoFEoYxsxUfUKwrZslmknNPztYxpGTjYHZVP4vd9vTNk6WOhvdC92CpGbnm/gohGCo6DWctRgEP3Nm7P6lb9vDkRJV1A9mz7u0rozPnLOul0Wg0C6GdCc2aomeEn3/O1T1QKLyGQR0so6G6Sanms//wSf795TN4wVyNfc4xKWQtYqX46RtTi3ImBvsylOoBtmmQzVhYpsSQEpTClIL/8J6r+Ll3Lm0fOjlnlin5jQ/uSo3UfQdH+cqjR7ru9Wr2Ei22jK3pBJWqPokjkTRDm4ZAKZgpezi2kRr4v3rbjgXPPdyfGM2xUgiS7IQhIY4VUiRjeIeKTuqkZDMmQ0WHmbK3aMdrsXTa01kv5Ds/HGXdgHOWw7Da/VytTmY2Y5LNmHhBxEDeXvB43bKHkOhxtKLLRDUazYWGdiY0a46eEX7+Wat70GyorvsR7jIbqptMlVyefGGMQ0fG25Su845JIWclTgAsSSfifddtYe/+Y0QyiRYLKShXPD66Z8ey92Op04w6ZRxaswSlqp80LktBrR4seT2dDNFS1ccPYh78woF0fU0nKAgjpBDEjRGt+YxJzQ3xw5iTE1U2DuW4//advPuajYyPl3ue++6bRtKeibihIwGKvrzNJz+0i30HEy2IVgxDsn1rf8fI+kpKvzo5VXUvIlZxR4dhtfu5VpIB7PbejYNOm/o46DJRjUZz4aGdCY1Gs2QUEAQRNS/EC5afhQA4OVnliefH+Mkbk+lxco7JLddu5sjb01S9MHUkAIIoZnCeIFw3rh4Z5D/aBi8cnWBsvEbOMfnonu0rdqy6OWeLjXYP9zucnq5TrjWzBEl5UKyiJY9JnW+Ilqo+pZpPfz7T5tA8cOdOHrhzJ1/c+xJuEGGZkowpqXphY62SwaLTlg1azD588p5reOQHr3N62kUIxcahPPfdNrfHizWwV1r61Tm6H4GCE+MVTENSzNs4tpE6K6vZS7SSDGC398Li90+j0WjOF9qZ0Gg0iyZWCtePqHkBYbgCDwJ461SZx58/waujM+ljA4X/v707jY6jPPMF/n9r61X73sILYLzbksHBGC9yICwm2BASziRwE5IzCbOQDIcPNzPhMEnOzJCF5B5mkslkhnNnkpzk5uaGYSeEgRmP5RWCwZK8YIOx8SZZshZr7aWquu6HVrclI9vqVnVXdff/9yVxIXe/6gXef1U9z6Nh7fIQVi6sgabICFX7E3MiAKiyBN1MbKbWNYUu+/jJDk3XL67HqsX1Ga0x3TPl0z3bffuq2fjJs/thWYAkrMTsBZG4CpPubTYXbkRjehxlAU9qYN7EQPP1+67Fg5sXpzbtA0NRwEpcYSoLeib97M03XDnt57/YetPZYM/0tqOpQpU1PigvGdb6hyIo8Wuoq/BlpZZoJlcAL/V3eZsoEbkZwwQRXYaFmGEhEptZQTWQuEXpvZPn0NrWiQ/PnL+Fpqbch5bmEJrmVU26CrFgdgU2I9GVaWA4iooSz2WnVkuSgN+rwO9RIF3YjzQNmZwpn+7Z7uVXV8PrkRHT4zDMeGomgc+jZHSbzcSN6Nd/uuuSgWbiBr+nfwyqIqEs6EnVNdh9T/50N9h2tDAGJoeqgFdBRDdTNR1xCxgZ0/HA7Qvypp6Lt4kSkdsxTBDRlCxYGB5NzBswzPiMbmWKxy3sP9aH1rZOdPWNpY5fURPAhhWNWDin4qIb/wWzKyaFh8MnBvC/XzrwkXAhRKLAN+hVIEnSlI+VjkzOlKdztruxOvCR4BHVzRnfDz+dQJPcoD7x63dc07rZjtuOJm68H/7R9sRME9MCBCAAqIoMTZVSP8ONOhHRzDFMUNbZ1U+fsk+IRE1COGoiHDVQDgm6Eb/8X7wIw4zjnffOYlt7J/qHzhdNX91YipbmRlwdKv3IoLlLOXxiAC/uPAZZluD1KBgK6/jdGx/C71PRPK8amiLNKPRMlMmZ8nTOdmerZW86j3u5n83ld9fO16Pjg15EookrEoosYCFR5+PzyKifRstbIiKaPoYJyio7++lT5i63KbSsRLFqOGIgOoO2rknRmIk/vNuNHfu6MDx2vkPR4rkV2NDciCtqgxk97sQhdJIQKA14oCgCu/d14WMLam0LEsClz5Rf6vWc7tnubN1mk87jXupn97zbndXv7lSv4f23zLfl9Xj1zRMI+lUMj8VgjbfBNeMWRiMGbl81myc4iIhsxDBBWWV3L3dK38UCnSwJLJxTiYhuIhzRJ7VjzdRoRMeu/WfwxoEzCEcTXYEkIdB8TRXWNzWitsI3o8cfGI7C51UQ8KkI+FREYwZGRmMYjRgzXvuFLnamfOHscts22dm6zSadx73Yzz679UjWvrsX+0zef8t8W4ax9Q5GUOJPzBVJtt5VZJH6XXiCg4jIPgwTlFV293Kn9E0MdJIQqAgqEJKErW2dqCn3I27D6fxzI1Hs6OjCW4d6UrdFqbKElYtqsW55A8qD02vlejmh6gAsSUBYwOBwFHHLQvQS99XP5Az0xc7aF0tA7u4fg1fNzsC0bL+GyatKyeFxAFID5Irl/SMiyhWGCcoqu3u5U/p6ByMoC2rwexUosoSxiIGx0Si6zuozDhJnz4Wxra0TbUd6YY53efJqMlYvqcfqpfUI+lQ7foVUh6aNN8zBr147DAiR9bkFyZ+7cKr14EgU5SUeYMJnuhADcl2lH2cHxrLy3c32SYZL1V/86rX3eIKDiMhGDBOUVdkqMqXpEQK4urEMg6NRjEVMRKJRWEjUR0x38NtUTp8dwda2Thw81o9kHCnxqVizvAHXL6qFV7PnXy3JDk0BrwpZElg8txKfvfmanMwtSLowlAyNxtA/FIEQInXWuxAD8j0b5uGf/r0tK9/dbJ9kuFQtSHXZCZ7gICKyEcMEZVW+9HIvRIlaCAOL51bg+e1HIctS2oPfJrIsC0e7htC6txNHTg+mjleOt2e9dn4NVGXmLVmBRIjwaDKCPg2KNLnbU67mFiRdGErKghr6h6I4NxyFV5MLNiCvXFRnW0H0RB0f9GJkLIae/jEosoSyoAZZlmx/DS/2OeEJDiIiezFMUNaxl3tuJNu6RmImIhEDxvhtR9dcUY7Na65Ma/DbRHHLwqHjA2ht68TJnpHU8fpKP1qaQ1h6VRVkKfPhcJN+BwCaKiPgU+FRZ9bm1a6z3xeGEr9XhWVZGBzRMRYxXBGQs9WdyO7vbvIqj2FakETi/Th7LrHm/3Hr/Jy8hume4GDnJyKiS2OYIMpz02nreuHgt+kw43G8sa8Lr+w6hp6BcOr4nLoStKwIYcGs8rRmRFyOqkgo8anQVAmAmHGbV7vOQE8VShRFxtWNPls6D81UPrVffvXNEzBMC8NjMUBI0JREy9bB0VhO1zHdkJRPry0RkVMYJohywO6zm0IkBsJFYokQkbwKYYeYYeLtQ2exvaMT50bOb/LmzypHS3MIVzaU2vZcAKDIAkG/Bo8qw75oYt8tdm68LWbi52ksYsCjyfB73d+dKLFeHRif/QAgNQPCjetl5yciostjmCDKMjvPbloAdD2OsaiOmB63pa1rUjhq4M2D3di5rys1t0EIYOmVVWhpDiFUHbDtuQBAlgQCPhV+T3r/GkonmNlxm47b6n4u/Dz1D0cR1Q2oipQqCHdrd6LqMi8GhiOQpfO1NRYSbYTduN5Cbm3N27eIyC4ME0RZZsfZTTNuJQqqozoMY3oB4vCJgWnVSQyPxbBzXxfePNiDqJ4YNCdLAtfOr8Gmlquh2DlWGufbvPo9CqQ0b5Ny6rYTN9X9TPw8haMGYFkwLaD3XBjV5T74PIpruxPdvmo2Pjg9BDOeqJmwkKjJsUwTgyMWnvj1O67a1BZqa+tLfY9urilxeHVElG/sab2SgZGREdx55504deoUAGDXrl3YtGkTbr31Vjz55JNOLYvIdr2DEWhKZsO/YkYcg6Mx9A1FMDwaSytIvLjzGIbCOrweBUNhHS/uPIbDJwZSP9M/FMHz24/iB/93L7a1dyGqm9BUCeuWN+B/fm4FPrX+KtRW+NP7ZS9BCMDvVVBV6kHQq6YdJIDJG2khEhONZVnCq2+esG2dbpf8PIWjBvqHzn+G4hbQNxjG0GjM8duwLmb51dX45OrZkCQB0wKEELDiACBQFlRTm9qOD3qdXiqARPgxzTiiugnLshDVTde+tung94iI7OTIlYn29nY89thj+PDDDwEAkUgEjz76KH75y1+ioaEBf/Inf4LW1la0tLQ4sTwiW6VzdjNRC5HYtIxFdZiGhUyuC2xv74QsS9CUxHNqiozY+PGyoAetbaex74M+JEst/B4FNy6rx+ol9albZewiBODVFAR86kfavKarkG87ma7k52loNAZAQJEFDDMOC4AFgWjMxAObF7vm7P6FNq+9CnMbSvHqmyfwwelBqIo0PlQxMeDQTTUJbrvFzS78HhGRnRwJE7/97W/xrW99C1//+tcBAB0dHZgzZw5mzZoFANi0aRNeffVVhgkqCNMp4E3WQoSjBqK6OeNaiIHhKLwXhAIrbuFkzwh+9O8dqWNlAQ3rmhqwckEttAlhZyrTvW0qSQjAo8gI+FVoyszavCYV6m0n6Uh+nnTDhCQE4hYgJIGqEg98HgVjEcP1m93kbWNf/+ku+L3KpK5gbtvUuukWN7vwe0REdnIkTDz++OOT/tzT04OamprUn2tra9Hd3Z3WY1ZVBW1ZG11cDe+lzcjNNSUoK/Pj2a1H0NM/htpKP+7ZMA8rF9VBN0yMRQyEozqgyvCpMnw2PGdNZQCDIxFoskAkZmJoNIaoHk/987pKP267YQ6uX1IPRb703Y6VlQHs/6AXv9t9HLIsEPSrGI0a+N3u4ygp8WLpFBstVZZQElDh86g2/Dbn/dGtC/Evz3bAjMcTtSe6CViJ48Xy+Ux+nn74f95GJGpAUyWUBz0I+FREYgYaaoK2vRbZfk0baoIYGArDO2FTa/fvcDF73u3Gs1uPoLt/DHUTvpPF4FLfI4D/ri9WfN+Lkx3vuysKsK0pTlmm27++r28EcRvbY9JkNTUlOHt22Oll5K051X488pnlqT/HjDiOfNiHaGzmVyGmsmpRDZ5t/QARPQ7TPP/4VaVe3LZqNhbPrYAkBIYGw5d4lESQ6O8fxSs7jgICkCUJZjz5vyZe2XEUoYrz8UdVJAR9KgQkjAyZGIG9Z5jnVPvx2ZvnfeS2kznV/qL6fM6p9uMrdy5KFdGqioSRsA7TjOPmFSFbXotcfOdvXhFKDbGbeNUu099huh2KJhYge1UJZwfG8E//3ob7b8nN4DynXep7BKCovkuUwP/GF6ep3ndJEmmfoHdFmKirq0Nv7/mCu56eHtTW1jq4IiL7xccLOMciRuIe9yxkX8OMY+/7vdjW3onRiJk67tVkrFvegA0rGjMaNDfVbVOqLGFgOAoge7MiplKIt51kohDu57fzd0in0xfnR/B7RET2cUWYaGpqwrFjx3D8+HFcccUVePnll/HpT3/a6WURzZgQgG5YCMcMhKNG1q6eRXUTb73bgx0dnRga01PHF8+tQEtzI2bVzuw2wIoSD4bCeqqgGwB0M47GmgBKA1rasyLIHoWwIbTrd0gnILAAmYjIPq7YAXg8Hnzve9/D1772NUSjUbS0tOD22293ellEMxLRE9OpY4aZlasQADAW0bFr/xnsPtCdmDkAQBICTfOqsL45hDqbWruuawrhxZ3HEEPiioSiSKj2e3H7qjkMEuQK6QQEFiATEdnH0V3Ali1bUv9/9erVePHFFx1cDRWaXE94TbR1jSMSS4QII4s1PIMjUezY14U/vNsD3UgUViuywMqFtVi3PISKEo+tz7dgdgU2A/jDwW4YFlAe1LBuWQMWz6209XkodwptAnI6AWE6HdaIiGh6eEqRClKuJyXH9DjGojpiejwrBdVJvefCaG3vRNv7vTDHw4pXk3HD4jrcuKwBQZ+93ZOShABWzK/B2uUNkCXHZl2STXL1/chlYEknIBRCvQkRkVswTFBBykWBZdyyEInNbLjcdJ3uHUXr3tM4cKw/9TxBn4o1y+qxanEdvFr2vspeTUbQp0FVRNZu13KzXJ/Bz8Xz5eL7ketAn25AKIR6EyIiN2CYoIKUvQJLC7ppIRw1EImZWW1HbFkWjnUNo7XtNN4/NZg6XlHiwfqmEK6dXwNVyc5VAgFAU2VUlnrgERYsC0UbJHK5Ic728yWDynsnz0FVZJQG1NTkabsLkJ3omFQMAaHQbk8jovzHMEEFye4CSwsWonocYxEDehYLqoHEFY/DJ86hte00TnSPpI7XV/qxvjmEZVdVQZay14BVU2UEvQo0VYLPo2JkyP0dbrK1wcr1htjO57vwNVk4uxw7959JzaUwzDj6x1v7+r2q7QXI7Jhkv1yHWyKi6WCYoIJkR4GlEIn2p5GYiUiWC6oBwIxb6PigF9vaOtE9cH6Y3Oy6IDY0N2LB7PKMZkRMl6IIBH2ZzYpw8mxpNjdYud4Q2/V8U70mv3vjBII+FX6vjLKgB/1DEVgWMDSqQ5Yl2wuQ2THJfpyPQURuxDBBBWkmBZYWAH18uFw0y1chAEA34nj7cA+2d3SlhsABwPxZZWhpbsTc+pKshghZEgj41IxbvDp9tjSbG6xcb4jter6pXhMzbmEsoqM0oMHnUVBZ6sXgSBS6YaI8oNkeANkxyX682kNEbsQwQQUr3funzbg1PhtCh2Fmv0AgEjPw5sFu7Nx3BiPhxKA5IYClV1aipbkRoepAVp9fkgT8XgV+jwJpBmHF6bOl2dxg5XpDbNfzTfWaqLIEwzw/Fd3nUSBJAuUBDV+/71o7lj8JOybZj1d7iMiNGCaoyFmIGRYisewXVCeNhHXs3NeFNw50I6onNneyJHDt/Bqsa2pAdZkvq88vRGIjGfCqttReOH22NJsbrFxviO16vqleE59HxmjYQlQ3c3aloBgKonOJV3uIyI0YJqgoWRhv6xoxYJjxnHQqGhiOYHt7F/Yc7kld+dAUCdcvrsOaZQ0oC2hZfX4hAM94m1fFxgJup8+WZnuDlesNsR3PN9VroioSPrl6Ng6dOMcrBXngUnVIvNpDRG7CMEFFQ4hEfUI4ZiIcNXJyFQIAuvvHsK29E+1HepF8Sp9HwY1L67F6Sf1HzurbLdnmNeBT4VGlaQWn5EamfySGyuCl76d3+mwpN1gfdanXZLPDa6PLu1wdUjF/tonIfRgmqOBZFhAzElchYjkoqE460T2M1rZOvHt8IHWsNKBh3fIGfGxhLbQJZ/KzRVUkBMdDBDC9oXMTNzIlvssXVGd7Mz+dTlHcYH1UMbwm6XQRy6f5DE7XIRERpYNhggqSEIAx3tY1bENb18MnBrC9vRMDw1FUlHiwrimEBbMrPvJzlmXhyOlBbN3biWNdQ6njVWVetDSF0HxNNRQ5O4PmJlLkRIcmr6ak3eZ14kZGCDGtjUy2Nq52dorKp80kXV46nw2nO46ly+k6JCKidDBMUMGJGXGEowaiMRNxGy5DHD4xgBd3HoMsS/B6FAyFdby48xg2A6lAEY9bOPBhP1rbOtHZO5r6u6EqP1pWNGLJ3EpIWRw0lyRLAn6fCr9Hhkg7RiS4aSNj1xnafNtM0uWl89nItzP9TtchERGlg2GCCkLcmtDW1bBg551M29s7IcsSNCXxH3ZNkREbP351Yxna3u/FtvbOSZvtKxtKsWFFCPMay7I6IyJJEgI+r4KAd2ZtXgF3bWTsCjb5tpmky0vns+GmgDwdTtchERGlg2GC8pYQ569CZLOt68BwFN4LBrrJkkBX3xj+12/aMDgaSx1fNKcCLc0hzK4rycpaLpRs8xr0KpAke26fmriRUWSBqG46tpGxK9jk22aSLi+dz4abAvJ0sKkAEeUThgnKS5HxCdV6DgqqK0o8GArr0BQZ8biF0YiOkbAOywLCMROSSPzHf31zCPWV/uwuZpwQibPrQZ8GVZleYfV0TdzIDIzEUHGZbk7ZZNcZ2nzbTNLlpfPZyMcz/cVQQE9EhYFhgvJCsqA6HDMRsaGgOh3rmkJ4fvtRjIR1RKNm6hYqWRJYubAW65Y3oLI0d5tSTZUR9KrwaIk2r9kIU8mNTE1NCc6eHbb/CdJYBzDzM7T5uJlMRzEWl6fz2eCZfiKi7GGYIFezAOh6HGNRHTE9bktBdTp6B8M48OEAhseM1HMLASyZW4lNa+aixJ/dQXMTKYpA0KfBO352PccvhWPsOENbyJvJYi4uT+ezwTP9RETZwTBBrhS3xidUR3WYNhdUT0dn7yha205j/7H+1KY94FOxZmk9blhSB6+Wu6+OIgn4/Sp8qoIc1HIXrELdTLK4nIiInMQwQS5iIWZYiMSyW1B90We3LHx4Zhitbafx3snB1PGKEg/WLW/AdQtqoSrZnxGRJEkCAa8Cn2fmHZqocLG4nIiInMQwQY6zYCGqx3NWUP2R57csHD5xDlvbTuNE90jqeG2FDy3NISy/ugqyTZ2SpiPZ5tXvUSDnYDYF5Te7issvrLv4o1sXYk51bhoKEBFR/mKYIEcIAehmHOGoiUjUgJnjqxAAYMYt7PugD61tp9E9EE4dn1UbxIbmEBbMqcjpFQEhAK+mIOBToOQwvFB+s6O4fKq6i395tgOfvXkeb5UiIqJLYpignLIAxHQT4YiBqANXIQBAN+J4+70ebG/vwsBwNHX8mivK0NIcwpUNpTkZNJckkOjQFPCp8KhS0RRWkz3sKC6fqu7CjMdZd0FERJfFMEE5YcYnTKg2ndktR2IG3jzYjZ37zmAkrANIbOSXXFWJlqYQGmuCOV+Tqkgo8anQVAmAvfMiqHjMtLh8qroLjyqz7oKIiC6LYYKyKjmhOhozc97WNWkkrGPXvi68cbAbkZgJIDEjYsU11VjfFEJ1uS/na1JkgaBfg0eVwaoIZzgxm8Gt8yCmqruI6iaH+hER0WUxTJDt4tbkqxBOnW0fGI5ie0cn9hzqSV0NURUJ1y+qxdplDSgLenK+JlkSCPhU+D386jnJidkMbp4HMVXdBSwUzFA/IiLKHu5oyBZCJK5CRGImwlEj521dJ+oeGMO2tk60H+lFchk+j4zVS+px49J6+L1qztckSQL+8Q5NbPPqPCdmM7h5HsRUdRfs5kRERNPBMEEz4nRb14lO9gxj695OvHt8IHWs1K9i7fIQPraodtItHLkiBMs6JjcAABotSURBVODzKAh4lZy2l6VLc2I2g9vnQVxYd1FTU4KzZ4cdXBEREeUDhglKW7KtayRmIhIxYDh4FcKyLBw5PYjWtk4c7RxKHa8q82J9UwgrrqmGIud+Ey9E4sxz0KdBkXklwm3sms3g9uckIiLKNoYJmjbLAmKGs21dk+KWhYPH+tHa3onTZ0dTx0NVfqxvbsTSKyshOTTwzcM2r65nx2yGfHhOIiKibGOYoEsSAjDMOAaHo+gbDDt6FQJIrKX9SC9a2zon3R5yZUMJWpobcc0VZTmdETGRqggEfRo8bPPqenbMZsiH5yQiIso2hgmakgVA1+MYi+qI6XGUS7KjQSKmm3jrUA92dHRhcDSWOr5wdgU2rAhhdl2JY2tTJIGAX4VXU9jmNY/MdDZDvjwnERFRNjFM0CRm3EJUNzEW1WEaFpw+uR6OGth94Ax27T+DsYgBAJBEYlO2vjmE+krnus1IqTavMgRjBBERERUhhgkCMN7WNWogopuOtnVNGhqLYWdHF958txsxPQ4gMejtugW1WLe8AZWlzhWtSkLA5010aGKbVyIiIipmDBNF7PxwOQOGGXfFPf59QxFsa+vEO++dhTkeajyqjFWL67BmWT1K/Jpja2ObVyIiIqLJGCaKTHK4XDhqIBJzx1UIAOjqG0VrWyf2He1LhZqAV8GaZQ1YtbgOPgcnRgsBeBQZAb8KTWGHJiIiIqIkhoki4abhchN9eGYIrXs7cfjkudSx8qCGdctDuG5hDTQl94PmJtJUGUGvCo+WCBFued2IiIiI3IBhooAlh8uFoyaiUWeHy01kWRbeO3kOW9s6cfzM+Qm7tRU+tDSFsHxeleO3ESnjbV694wPGGCKIiIiIPophogC5abjcRGbcwv6jfWht68SZ/rHU8StqAtiwohEL51Q4XtCsSAJ+vwq/xq8GERER0eVwx1QgksPlIrHxgmqXXIUAAN2I4533zmJ7eyf6h6Op4/May9DSHMJVoVLHBs0lSZJAwKvA52GHJiIiIqLpYpgoADE9UVAd1U3E3XIZAkA0ZuLNd7uxs6MLw2EdACAALL6yEi3NIVxRE3R2gUi0efV6ZAS8KmSJIYKIiIgoHQwTeep8W1cdhguGy000Etaxe/8Z7D5wBpGYCSCxaV9xTTXWNYdQW+5zeIWJKzleTUHAp0Bhm1ciIiKijDBM5BG3tnVNOjcSxfaOLux5twe6mRg0pyoSPrawFmuXN6A86HF4hYkrI5oqI+BT4VHZ5pWIiIhoJhgm8oBb27om9QyEsa39NNre70vdZuXVZKxeWo8bl9Yj4FUdXmGCqkgo8anQVAmAcN3rSERERJRvGCZcSohE4XI4ZiISNVLToN3kVM8ItradxrsfDqRusyrxq1i7rAHXL6qDR3N2RkSSIgsE/Ro8qgxWRRARERHZh2HChRK1EAZiLrwKYVkWPugcQmvbaXxweih1vLLUg/VNIVw7vwaK7I4aBFkS8PtU+D0yBGMEERERke0YJlwg2dY1HDMRcVlb16S4ZeHAsX60tp3GqbOjqeMNVX6sbwph2VVVkFzSDUmSBPxeBX62eSUiIiLKKoYJB1kAdN3E2HhbV7ddhQAAMx5H+5E+7NjXhTN95wfNza0vQUtzCPNnlTs+IyJJCMDnURD0KpDYoYmIiIgo6xgmHGDGJ7R1NV2YIJCYoL3n0Fns6OjEuZFY6viC2eXY0NyIOfUlDq5uMiEAjyoj6NOgyO4INkRERETFgGEih5JtXaMxdw2XmygcNfDGgW7s2t+F0YgBILFZX7moDjcsqkVDVcDhFU6mqTKCbPNKRERE5AiGiSybNFzOtFy74R0ai2HXvi68ebAHUT0xaE6WBK5bUIN1TSFcM7cK/f2jl3mU3FEVgaBPg4dtXomIiIgcwzCRBW4fLjdR/1AE29o78c57Z1O3XGmqhFWL6rBmeQNK/ZrDK5xMkQT8fhU+VYFLSjWIiIiIihbDhI3cPlxuoq6+UbS2dWLf0b7UOv1eBTcurcfqJfXwedz10ZAkgYBXgd+rsM0rERERkUu4a8eYh4QAdDOOcNS9w+UmOn5mGFvbTuPwiXOpY2UBDeuaGrByYS00xR2D5pIkIeAbb/Mqu6T1LBERERElMExkyLISHY/CEQNRl1+FsCwL7508h9a2Tnx4Zjh1vKbci5bmRjTNq4LsslaqQgBeTUHQp7hubURERESUwDCRhuRwuUgsESLcOFxuonjcwv5jfWht60TXhBkRV9QE0NLciEVzK1w31E0IwKPICPhVaAo7NBERERG5GcPENCSGy8UxFtUR0+OubeuaZJhx7H3vLLa1d6FvKJI6fnVjKVqaG3F1qNQ1g+Ym0lQZQa8KTRVghyYiIiIi93NVmHjppZfw05/+FLqu44tf/CLuv/9+R9cTtyxEYibGojpMw4Lb97bRmIk/HOrGzo4uDI3pAAABYPHcSrQ0h3BFbdDZBV6EkmrzKrO0moiIiCiPuCZMdHd348knn8Szzz4LTdPw2c9+FqtWrcK8efNyvBILMcNCJOb+tq5JoxEdu/efwe4DZxCOJmZESEKg+ZpqrG8KobbC5/AKpyZLAgGfCr/LOkcRERER0fS4Zhe3a9cu3HDDDSgvLwcA3HbbbXj11Vfx1a9+NSfPH7csRHUTYxEDhhnPi1tszo1EsaOjC28d6oFuxAEAqixh5aJarFvegPKgx+EVTk2SBPzjHZrcVrNBRERERNPnmjDR09ODmpqa1J9ra2vR0dEx7b9fVZXZLTzRqIGxqIFwzICsSSjR1IweJ5fO9I3itTeO480DZ1KtaP0eBRuuuwIfXzkLJVkaNFdZGZjR3xcY79DkV6Gp7mpBSxdXU1Pi9BLIIXzvixPf9+LE97042fG+uyZMWFNcCkinSLivbyStW5IieqIjU8zlbV0nOn12BFvbOnHwWH+qfqPEp2LN8gZcv6gWXk2BHtHRH9Ftf+7KygD6+0cz+rtCAB5NRtCnwYzpGIzZvz7KjpqaEpw9O3z5H6SCw/e+OPF9L05834vTVO+7JIm0T9C7JkzU1dVhz549qT/39PSgtrbWtsdPtnUNx0xE8qCta5JlWTjaNYTWvZ04cnowdbyy1IP1TSGsuKYGquLOOQwCiQ5NAZ8Kj8o2r0RERESFxjVh4sYbb8SPf/xj9Pf3w+fz4bXXXsPf/u3fzvhxE21dE7UQbh8uN1HcsnDo+ABa2zpxsmckdby+0o+W5hCWXlXl6onQaqpDkwS2eSUiIiIqTK4JE3V1dXjkkUfwhS98Abqu4zOf+QyWL1+e0WMlrkJY47cy6TDM/NnJmvE4Oo70obW9Ez0D4dTxOfUl2NAcwvxZ5a6cEZGkSAIBvwqvprDNKxEREVGBc02YAIBNmzZh06ZNM3qMmBFHOGogGjNdP1xuIt2IY8/hHuzo6MLAcDR1fP6scrQ0h3BlQ6mDq7s8SRIIeBX4vQoEYwQRERFRUXBVmJiJsG5iZDQKw7Ty6paaSMzAGwe6sXP/GYyGxwfNCWDplVVoaQ4hVD2zDkrZJgTg9yZmRbj5tisiIiIisl/BhImxMR26kT8pYngshp37zuDNg92I6olBc7IkcO38GqxrakB1mTsHzSVN7NCkMEQQERERFaWCCRP5on8ogu0dXXj7cE+qlkNTJFy/uA5rlzWgNJCdGRF20lQZQXZoIiIiIip6DBM5cqZ/DNvaOtHxQS+SXWn9HgWrl9Zj9ZJ6+L3ufyuU8Q5N3vGBcwwSRERERMXN/TvYPHeiexhb93bi0ImB1LGygIa1yxvwsYW1eTEJWpYESnwqNLj71isiIiIiyi2GiSywLAvvnxpEa9tpHOs6P1mwusyLluYQmuZVQ5HdOWhuIkkI+LwKAl4FpUEPzoZjTi+JiIiIiFyEYcJG8biFAx/2o3XvaXT2jaWON1YH0NIcwuK5lZDyoFhZCMCrKQj6FMiS+0MPERERETmDYcIGhhnH3vd7sa29E32DkdTxq0KlaGkOYV5jmasHzSUJJIqrAyyuJiIiIqJpYJiYgahu4q13e7BjXxeGRs/fArRoTgU2rAhhVm2Jg6tLjzpeXO1RJQCCQYKIiIiILothIgNjER279p/B7gPdCEcNAIAkgKZ51VjfFEJdpd/hFU6fIgn4/Sp8msK51URERESUFoaJNAyORLFjXxfeercHMSMOAFBkgZULa7FueQgVJR6HVzh9kiQQ8CrweRRIeXALFhERERG5D8PENPSeC2Nbeyf2vt8Lc3xIhFeTccPiOty4rAFBn+rwCqdPCMDnSXRoYnE1EREREc0Ew8QlnO4dReve0zhwrB/JEoKgT8WaZfVYtbgOXi1/Xj4hAI8qI+jToMi8EkFEREREM5c/u+EcsSwLx7qG0NrWifdPDaaOV5R4sK6pAdfNr4Wq5NcZfU2VEfSq8Gjs0ERERERE9mGYGBe3LBw+PoCtbZ042TOSOl5X4UNLcyOWXV0FOQ9mREykyAJBvwaPKkMADBJEREREZKuiDxNm3ELHB71obetEz0A4dXx2XRAtzY1YMLs87wqUJUkg4FPh98gQ7NFERERERFlStGFCN+J4+3APtnd0YWA4mjo+f1YZWpobMbe+JC8GzU0kCQGfN1FcnW8BiIiIiIjyT9GFiUjMwJsHu7Fj3xmMhnUAicnPS6+qREtzI0LVAWcXmAEhAK+mIOBToLBDExERERHlSNGEiZGwjp37uvDGgW5EdRMAIEsCK+bXYH1TA6rLfA6vMDMeVUbAp8KjsriaiIiIiHKr4MPEwHAE29u7sOdwDwwzsdvWFAnXL6rDmuUNKAtoDq8wM4oiUOLT4FElAIJBgoiIiIhyrmDDRHf/GLa1d6L9SC/G58zB51Fw49J6rF5SB783fwbNTaRIAn6/Cp+qgGURREREROSkggsTJ3uGsXVvJ949PpA6VhrQsG55Az62sBaaKju4usxJkoDfq8DvYXE1EREREblDwYSJD88M4Xe7j+No51DqWHWZF+ubQmi+phqKnJ+FyUIkrqgEvApkFlcTERERkYsUTJj4f1uOpOZEhKoDaGkOYcncSkh5NmguSYhEcXXQp0GR8/N3ICIiIqLCVjBhAgCubCjFhhUhzGssy7sZERNpqoygV4VHY4cmIiIiInKvggkTn791AarKvE4vY0YURSDo0+Adr+tgkCAiIiIiNyuYMBGqDqTmR+QbdmgiIiIionxUMGEiH0mSQMCrwO9VIMAUQURERET5hWHCAZIQ8I23eZXztECciIiIiIhhIoeEALyagoBPgcI2r0RERESU5xgmckAg0ebV71PhUdmhiYiIiIgKA8NElmmKhKBPhaZKAASDBBEREREVDIaJLFFkgaBfg0eVWVpNRERERAWJYcJmsiQQ8Knwe/jSEhEREVFh447XJpIk4B/v0CRxWAQRERERFQGGiRkSAvB5FAS8CmR2aCIiIiKiIsIwkSEhAI8mI+jToHBWBBEREREVIYaJNAkAmiojwDavRERERFTkGCbSoI63efWwzSsREREREcPEdChyokOTV1PY5pWIiIiIaBzDxCXIkoDfp8LvkSEYI4iIiIiIJmGYmIIkBHzeRIcmtnklIiIiIpoaw8QEyTavQa8CiW1eiYiIiIguiWEC421e1fE2rzKvRBARERERTUfRhwlNlVMdmtidiYiIiIho+oo2TCiKQNCnwavKAMAgQURERESUpqILE7KUaPPq0xSwtpqIiIiIKHNFEyYkScDvVeD3sEMTEREREZEdCj5MsEMTEREREVF2FGyYEEh0aAr4VWgKi6uJiIiIiOxWkGFCVQRKfBo0VQIgGCSIiIiIiLKgcMKEABRJwO8fL652ej1ERERERAWuYMKE36NADUgsriYiIiIiypGCqUj2sUsTEREREVFOFUyYICIiIiKi3GKYICIiIiKijDgWJv7hH/4BP/7xj1N/HhoawoMPPoiNGzfi/vvvx9mzZ51aGhERERERTUPOw8Tw8DAeffRR/Nu//duk43//93+PlStX4ve//z3uvfdePP7447leGhERERERpSHnYeK//uu/MHfuXHzpS1+adHzr1q3YtGkTAODOO+/Etm3boOt6rpdHRERERETTlPMwcffdd+PBBx+ELMuTjvf09KCmpgYAoCgKgsEg+vv7c708IiIiIiKapqzNmfj973+P7373u5OOXXXVVfj5z38+7ceQpOlnnaqq4LR/ljJTU1Pi9BLIAXzfixff++LE97048X0vTna871kLExs3bsTGjRun/fO1tbXo7e1FfX09DMPAyMgIysvLp/33+/pGEI9bmSyVpqGmpgRnzw47vQzKMb7vxYvvfXHi+16c+L4Xp6ned0kSaZ+gd01r2JaWFjz//PMAgFdeeQUrV66EqqoOr4qIiIiIiC4ma1cm0vXwww/jr/7qr/DJT34SJSUl+OEPf+j0koiIiIiI6BIcCxNf+9rXJv25vLwc//zP/+zQaoiIiIiIKF2uuc2JiIiIiIjyC8MEERERERFlhGGCiIiIiIgywjBBREREREQZYZggIiIiIqKMMEwQEREREVFGXDNnYqYkSTi9hILH17g48X0vXnzvixPf9+LE9704Xfi+Z/I5EJZlWXYtiIiIiIiIigdvcyIiIiIioowwTBARERERUUYYJoiIiIiIKCMME0RERERElBGGCSIiIiIiygjDBBERERERZYRhgoiIiIiIMsIwQUREREREGWGYICIiIiKijDBMEBERERFRRhSnF0D54fvf/z4GBgbwve99z+mlUI584QtfQF9fHxQl8a+Jv/mbv0FTU5PDq6Js27JlC/7xH/8RY2NjWLt2LR577DGnl0RZ9vTTT+NXv/pV6s+nTp3CXXfdhW9+85sOropy4YUXXsBTTz0FAFi/fj3+8i//0uEVUa489dRTeOaZZ6BpGu644w782Z/9WcaPxTBBl7V7924899xz2LBhg9NLoRyxLAtHjx7F1q1bU2GCCt/JkyfxrW99C08//TSqqqrwwAMPoLW1FS0tLU4vjbLo3nvvxb333gsAeP/99/HQQw/hq1/9qsOromwLh8N4/PHH8eqrr6K0tBSf+9znsGvXLtx4441OL42ybNeuXXjppZfwzDPPwOfz4aGHHsJrr72GW2+9NaPH421OdEnnzp3Dk08+iT/90z91eimUQ0ePHoUQAl/5ylewefPmSWctqXC9/vrruOOOO1BfXw9VVfHkk0/yalSR+fa3v41HHnkElZWVTi+Fssw0TcTjcYTDYRiGAcMw4PF4nF4W5cDBgwexdu1aBINByLKMdevW4T//8z8zfjyGCbqkb37zm3jkkUdQWlrq9FIoh4aGhrB69Wr85Cc/wc9//nP85je/wc6dO51eFmXZ8ePHYZom/viP/xibN2/Gr3/9a5SVlTm9LMqRXbt2IRKJYOPGjU4vhXIgGAzi4YcfxsaNG7F+/Xo0Njbi2muvdXpZlANLlizBjh07cO7cOUSjUWzZsgW9vb0ZPx7DBF3U008/jYaGBqxevdrppVCOrVixAk888QT8fj8qKyvxmc98Bq2trU4vi7LMNE3s3r0bP/jBD/Db3/4W+/btw3PPPef0sihHfvOb3+BLX/qS08ugHDl06BCeeeYZ/Pd//zd27NgBSZLwr//6r04vi3Jg9erVuOeee/D5z38eX/7yl3HddddBVdWMH49hgi7qlVdewc6dO3HXXXfhRz/6EbZs2YLvfOc7Ti+LcmDPnj3YvXt36s+WZbF2oghUV1dj9erVqKyshNfrxc0334yOjg6nl0U5EIvF8NZbb+Gmm25yeimUIzt27MDq1atRVVUFTdNwzz334A9/+IPTy6IcGBkZwS233IKXXnoJv/zlL+Hz+TBr1qyMH49hgi7qZz/7GV5++WW88MIL+Iu/+AvcdNNNePTRR51eFuXA8PAwnnjiCUSjUYyMjOC5557DLbfc4vSyKMs+/vGPY8eOHRgaGoJpmti+fTuWLFni9LIoBw4fPoy5c+fC7/c7vRTKkYULF2LXrl0YGxuDZVnYsmULli1b5vSyKAdOnTqFhx56CIZhYHh4GE8//fSMbm/kqUYi+oiPf/zjaG9vx9133414PI777rsPK1ascHpZlGVNTU348pe/jPvuuw+6rmPNmjX49Kc/7fSyKAdOnjyJ+vp6p5dBObR27VocPHgQ99xzD1RVxbJly/Dggw86vSzKgYULF+LWW2/F5s2bYZomvvjFL+K6667L+PGEZVmWjesjIiIiIqIiwduciIiIiIgoIwwTRERERESUEYYJIiIiIiLKCMMEERERERFlhGGCiIiIiIgywtawRERkiwULFmD+/PmQJAlCCITDYQSDQXz729/GsmXL8Oyzz+Ib3/gG/vzP/xwPP/xw6u9ZloVPfOIT8Pl8ePnllx38DYiIKF28MkFERLb5xS9+gRdeeAHPP/88/uM//gN33HEH/u7v/i71z0OhEF566aVJf2fPnj2IRCK5XioREdmAYYKIiLLCMAx0dXWhrKwsdWz+/Pnw+/145513Useee+45bN682YklEhHRDDFMEBGRbR544AFs3rwZa9euxW233QYA+O53vzvpZ+6++2688MILAIBwOIy3334b69aty/laiYho5hgmiIjINr/4xS/w4osv4qmnnkIkEsGKFStQVVU16Wc2bdqE119/Hbqu4/XXX8dNN90EWZYdWjEREc0EwwQREdlu8eLF+MY3voHHHnsMp06dmvTPampqsHjxYrS2tuL555/Hpz71KYdWSUREM8UwQUREWXHnnXeiubkZ3/nOdz7yz+6++2787Gc/w/DwMObPn+/A6oiIyA4ME0RElDV//dd/jW3btmH79u2Tjn/iE5/AoUOHcNdddzm0MiIisoOwLMtyehFERERERJR/eGWCiIiIiIgywjBBREREREQZYZggIiIiIqKMMEwQEREREVFGGCaIiIiIiCgjDBNERERERJQRhgkiIiIiIsrI/weMbrb4iqhxmgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 936x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# REGRESSION\n",
    "plt.figure(figsize=(13, 8))\n",
    "sns.regplot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   MEDV   R-squared:                       0.484\n",
      "Model:                            OLS   Adj. R-squared:                  0.483\n",
      "Method:                 Least Squares   F-statistic:                     471.8\n",
      "Date:                Wed, 15 Jan 2020   Prob (F-statistic):           2.49e-74\n",
      "Time:                        13:12:23   Log-Likelihood:                -1673.1\n",
      "No. Observations:                 506   AIC:                             3350.\n",
      "Df Residuals:                     504   BIC:                             3359.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const        -34.6706      2.650    -13.084      0.000     -39.877     -29.465\n",
      "RM             9.1021      0.419     21.722      0.000       8.279       9.925\n",
      "==============================================================================\n",
      "Omnibus:                      102.585   Durbin-Watson:                   0.684\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              612.449\n",
      "Skew:                           0.726   Prob(JB):                    1.02e-133\n",
      "Kurtosis:                       8.190   Cond. No.                         58.4\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# More details on Regression line\n",
    "import statsmodels.api as sm\n",
    "X = sm.add_constant(x)\n",
    "model = sm.OLS(y, X)\n",
    "est = model.fit()\n",
    "print(est.summary())\n",
    "# The strangest part of the code is adding the constant. This is basically the y-intercept. \n",
    "# When the constant is not added, the y-intercept is 0. In our case, it makes sense that \n",
    "# the y-intercept would be 0: if there are 0 rooms, the house should have no value. In \n",
    "# general, however, it's a good idea to keep a y-intercept, and it's the default choice of \n",
    "# the preceding Seaborn graph."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"font-weight: 400; font-size: 18px; background: #333366; color: #fff; line-height: 1.7; letter-spacing: 0.5px; padding: 10px;\">\n",
    "    <p>There's a lot of important information in this table. The first is the value of R^2. This suggests that 48% of the data can be explained by the regression line. The second is the coefficient constant of -34.6706. This is the y-intercept. The third is the RM coefficient of 9.1021. This suggests that for every one-bedroom increase, the value of the house increased by 9,102. (Keep in mind that this is from 1980.)</p>\n",
    "<p>\n",
    "The standard error suggests how far off the actual values are from the line on average, and the numbers underneath the [0.025 0.975] column give the 95% Confidence Interval of the value, meaning statsmodel is 95% confident that the true increase in the value of the average house for every one-bedroom increase is between 8,279 and 9,925.</p></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip3 uninstall statsmodels #--upgrade\n",
    "sys.version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# BOX PLOTS\n",
    "plt.figure(figsize=(12, 8))\n",
    "x = housing_df['RM']\n",
    "y = housing_df['MEDV']\n",
    "plt.boxplot(x)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# VIOLIN PLOTS\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.violinplot(x)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TASK:  Salary Visualization\n",
    "uk_salary_df = pd.read_csv('data/UKStatistics.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 51 entries, 0 to 50\n",
      "Data columns (total 19 columns):\n",
      "Post Unique Reference              51 non-null object\n",
      "Name                               51 non-null object\n",
      "Grade (or equivalent)              51 non-null object\n",
      "Job Title                          51 non-null object\n",
      "Job/Team Function                  49 non-null object\n",
      "Parent Department                  51 non-null object\n",
      "Organisation                       51 non-null object\n",
      "Unit                               49 non-null object\n",
      "Contact Phone                      46 non-null object\n",
      "Contact E-mail                     49 non-null object\n",
      "Reports to Senior Post             51 non-null object\n",
      "Salary Cost of Reports (£)         51 non-null int64\n",
      "FTE                                51 non-null float64\n",
      "Actual Pay Floor (£)               51 non-null int64\n",
      "Actual Pay Ceiling (£)             51 non-null int64\n",
      "Unnamed: 15                        0 non-null float64\n",
      "Professional/Occupational Group    51 non-null object\n",
      "Notes                              27 non-null object\n",
      "Valid?                             51 non-null int64\n",
      "dtypes: float64(2), int64(4), object(13)\n",
      "memory usage: 7.6+ KB\n"
     ]
    }
   ],
   "source": [
    "uk_salary_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 15\n",
       "count          0.0\n",
       "mean           NaN\n",
       "std            NaN\n",
       "min            NaN\n",
       "25%            NaN\n",
       "50%            NaN\n",
       "75%            NaN\n",
       "max            NaN"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# GATHER NULL INFO IN DATASET\n",
    "# housing_df.loc[:, housing_df.isnull().any()].describe()\n",
    "uk_salary_df.loc[:, uk_salary_df.isnull().any()].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: Unnamed: 15, dtype: float64)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# uk_salary_df.head()\n",
    "uk_salary_df['Unnamed: 15'].dropna()\n",
    "# uk_salary_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "del uk_salary_df['Unnamed: 15']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Post Unique Reference', 'Name', 'Grade (or equivalent)', 'Job Title',\n",
       "       'Job/Team Function', 'Parent Department', 'Organisation', 'Unit',\n",
       "       'Contact Phone', 'Contact E-mail', 'Reports to Senior Post',\n",
       "       'Salary Cost of Reports (£)', 'FTE', 'Actual Pay Floor (£)',\n",
       "       'Actual Pay Ceiling (£)', 'Professional/Occupational Group', 'Notes',\n",
       "       'Valid?'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uk_salary_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZsiTr1q3Lpk2b8sgjj2TEiBGt4/369Uv37t2zdOnSJMncuXNbx6dMmZJDDjkkt9xyS7p169beSwcA6LAqWt5tv3M3mz9/fu64445s2bIlX/nKVzJ58uRMnjw5l1xySQYNGpQVK1bk29/+djZu3JgBAwZkxowZeemllzJhwoQcdthh6dLl97vGVVVVueuuu3ZqbtuvAEBHtyvbr3sk6vYkUQcAdHQfi2fqAAD46Ik6AIACiDoAgAKIOgCAAog6AIACiDoAgAKIOgCAAog6AIACiDoAgAKIOgCAAog6AIACiDoAgAKIOgCAAog6AIACiDoAgAKIOgCAAog6AIACiDoAgAKIOgCAAog6AIACiDoAgAKIOgCAAog6AIACiDoAgAKIOgCAAog6AIACiDoAgAKIOgCAAog6AIACiDoAgAKIOgCAAog6AIACiDoAgAKIOgCAAog6AIACiDoAgAKIOgCAAog6AIACiDoAgAKIOgCAAog6AIACiDoAgAKIOgCAAog6AIACiDoAgAJ0eb/BIUOGpKKiYrvjLS0tqaioyH/913/ttoUBALDj3jfqfvrTn7bXOgAA+BDed/u1X79+rT/Lli3L7Nmz06tXr/zyl79Mv3792muNAAB8gB16pu7OO+/Mgw8+mLq6umzevDm33nprfvjDH+7utQEAsIN2KOoefvjh3HXXXdlrr72y//77Z/bs2bZmAQA6kB2Kui5duqRbt26tr/fZZ5906fK+j+O9r/nz52fs2LEZNWpUZs6cud348uXLM3HixIwePTpXXXVVmpubkySrVq3KpEmTMmbMmFx44YXZuHHjLq8BAKAkOxR1ffv2zeLFi1NRUZGmpqbcfvvtu/xM3erVq3PzzTfngQceSG1tbWbNmpWXXnqpzTlTpkzJtGnTsmjRorS0tGT27NlJkunTp+eMM85IXV1dBg4cmNtuu22X1gAAUJodirpp06blX/7lX/Liiy9m8ODBeeyxx3L11Vfv0oRPPvlkhg8fnv322y89evTI6NGjU1dX1zpeX1+fzZs3Z/DgwUmSmpqa1NXVZcuWLXn66aczevToNscBAPiArzT5gx49euS+++7Lpk2bsnXr1vTs2XOXJ2xoaEhlZWXr66qqqjz77LPvOV5ZWZnVq1dn/fr16dmzZ+u27x+OAwCwg1H3F3/xFzn22GPz1a9+NUOHDv1QE7a0tGx37P9+wfF7jX/QdTuqd+9dD1IAgI5qh6LuZz/7WX7605/mhhtuyO9+97uccsopmTBhQnr16rXTEx544IF55plnWl83NDSkqqqqzfjatWtbX69ZsyZVVVXp1atXNmzYkK1bt6Zz586tx3dWY+OGbNu2fSACAHQUnTpV7PSNqB16pm7vvffO6aefnoceeijf//73s2jRoowcOXKXFnn00UdnyZIlWbduXTZt2pRHHnkkI0aMaB3v169funfvnqVLlyZJ5s6dmxEjRqRr164ZOnRoFixY0OY4AABJRcu77Wu+i+effz5z5szJwoULM2jQoHz1q1/Nl770pV2adP78+bnjjjuyZcuWfOUrX8nkyZMzefLkXHLJJRk0aFBWrFiRb3/729m4cWMGDBiQGTNmpFu3bqmvr8/UqVPT2NiYvn375qabbsq+++67U3O7UwcAdHS7cqduh6Ju3Lhx2bRpUyZOnJiampoceOCBu7zIPU3UAQAd3W6LuieeeCJf/OIXd3lhHYmoAwA6ut32TN2f/dmf5ZprrsnZZ5+dN998M1dffbW/5gAA0IHsUNRdd9112XvvvdPY2Jju3btnw4YNu/zlwwAAfPR2KOqWL1+eyy67LF26dMlee+2VG2+8McuXL9/dawMAYAftUNR16tT2tK1bt253DACAPWeHvnz4yCOPzD/8wz9k8+bN+cUvfpEf/ehHGTZs2O5eGwAAO2iHPv26ZcuW3HnnnVm8eHG2bduWY445JhdffHG6devWHmv8SPn0KwDQ0X3kX2kybty49714/vz5OzVZRyDqAICOblei7n23X6dNm/ahFgQAQPvY4T8TVgp36gCAjm63ffkwAAAdm6gDACiAqAMAKICoAwAogKgDACiAqAMAKICoAwAogKgDACiAqAMAKICoAwAogKgDACiAqAMAKICoAwAogKgDACiAqAMAKICoAwAogKgDACiAqAMAKICoAwAogKgDACiAqAMAKICoAwAogKgDACiAqAMAKICoAwAogKgDACiAqAMAKICoAwAogKgDACiAqAMAKICoAwAogKgDACiAqAMAKICoAwAogKgDACiAqAMAKICoAwAogKgDACiAqAMAKICoAwAogKgDAChAu0fdqlWrMmnSpIwZMyYXXnhhNm7cuN05TU1NmTJlSqqrqzNhwoS8/PLLSZKNGzfm0ksvzbhx4zJu3Lg8/PDD7b18AIAOqd2jbvr06TnjjDNSV1eXgQMH5rbbbtvunPvvvz977bVXFi5cmG9961uZOnVqkuTOO+/MQQcdlPnz5+fee+/NjBkzsnbt2vZ+CwAAHU67Rt2WLVvy9NNPZ/To0UmSmpqa1NXVbXfe4sWLM378+CTJkUcemfXr12fVqlUZNmxYzjzzzCRJ7969s99++4k6AIAkXdpzsvXr16dnz57p0uX301ZWVmb16tXbndfQ0JDKysrW15WVlXnjjTfyxS9+sfXYggUL0tTUlMMOO2yn1tC7d89dXD0AQMe126Ju4cKFmTFjRptjhx566HbnVVRU7NDv69Tpf28qLly4MNdff33uvvvu1kDcUY2NG7JtW8tOXQMA0J46darY6RtRuy3qqqurU11d3ebYli1bctRRR2Xr1q3p3Llz1qxZk6qqqu2uraqqypo1a3LIIYckSZvz7r///txzzz2555570r9//921fACAj5V2faaua9euGTp0aBYsWJAkmTt3bkaMGLHdeSNHjkxtbW2S5Jlnnkn37t1z0EEH5dFHH829996bBx98UNABAPwfFS0tLe26F1lfX5+pU6emsbExffv2zU033ZR99903Dz74YBoaGnLppZfmnXfeydVXX53nnnsu3bp1y7XXXpsjjjgi48ePz7p169K7d+/W33fttddm0KBBOzy/7VcAoKPble3Xdo+6PU3UAQAd3a5Enb8oAQBQAFEHAFAAUQcAUABRBwBQAFEHAFAAUQcAUABRBwBQAFEHAFAAUQcAUABRBwBQAFEHAFAAUQcAUABRBwBQAFEHAFAAUQcAUABRBwBQAFEHAFAAUQcAUABRBwBQAFEHAFAAUQcAUABRBwBQAFEHAFAAUQcAUABRBwBQAFEHAFAAUQcAUABRBwBQAFEHAFAAUQcAUABRBwBQAFEHAFAAUQcAUABRBwBQAFEHAFAAUQcAUABRBwBQAFEHAFAAUQcAUABRBwBQAFEHAFAAUQcAUABRBwBQAFEHAFAAUQcAUABRBwBQAFEHAFAAUQcAUABRBwBQAFEHAFCAdo+6VatWZdKkSRkzZkwuvPDCbNy4cbtzmpqaMmXKlFRXV2fChAl5+eWX24w3Nzfn1FNPzU9+8pP2WjYAQIfW7lE3ffr0nHHGGamrq8vAgQNz2223bXfO/fffn7322isLFy7Mt771rUydOrXN+A9/+MP89re/bacVAwB0fO0adVu2bMnTTz+d0aNHJ0lqampSV1e33XmLFy/O+PHjkyRHHnlk1q9fn1WrViVJli5dmhdffDHHHXdc+y0cAKCD69Kek61fvz49e/ZMly6/n7aysjKrV6/e7ryGhoZUVla2vq6srMwbb7yRffbZJ9/5zndy++2358Ybb9ylNfTu3XPXFg8A0IHttqhbuHBhZsyY0ebYoYceut15FRUVO/T7OnXqlOnTp+ev/uqvcsABB+zyuhobN2TbtpZdvh4AYHfr1Klip29E7baoq66uTnV1dZtjW7ZsyVFHHZWtW7emc+fOWbNmTaqqqra7tqqqKmvWrMkhhxySJFmzZk0qKyuzZMmSrFy5Mj/4wQ/y+uuv5z//8z/TpUuX1q1aAIBPqnbdfu3atWuGDh2aBQsWZNy4cZk7d25GjBix3XkjR45MbW1thg4dmmeeeSbdu3dPv3798vjjj7eeM3Xq1AwbNkzQAQBkD3z69W//9m8ze/bsjB07Ns8880y+8Y1vJEkefPDB3HLLLUmSM888M01NTTnhhBNy3XXX5bvf/W57LxMA4GOloqWl5RP1gJln6gCAjm5XnqnzFyUAAAog6gAACiDqAAAKIOoAAAog6gAACiDqAAAKIOoAAAog6gAACiDqAAAKIOoAAAog6gAACiDqAAAKIOoAAAog6gAACiDqAAAKIOoAAAog6gAACiDqAAAKIOoAAAog6gAACiDqAAAKIOoAAAog6gAACiDqAAAKIOoAAAog6gAACiDqAAAKIOoAAAog6gAACiDqAAAKIOoAAAog6gAACiDqAAAKIOoAAAog6gAACiDqAAAKIOoAAAog6gAACtBlTy+gvXXqVLGnlwAA8L52pVcqWlpaWnbDWgAAaEe2XwEACiDqAAAKIOoAAAog6gAACiDqAAAKIOoA3sdTTz2ViRMn5qKLLsq4ceNyyimn5OWXX85vfvObfP3rX8+pp56a4447LhdeeGHeeeedzJs3L6eddlrr9atWrcoxxxyTpqamPfgugE8CUQfwAV544YWce+65mT9/fmpqajJlypTMnj07J598cmbNmpVHHnkkr732WhYvXpwxY8bk1VdfzUsvvZQkeeihhzJhwoR069ZtD78LoHSiDuADHH744Rk6dGiSZOLEiVm+fHkuuOCC9OrVK3fddVf+7u/+Lg0NDXn77bfTrVu3nHLKKZk9e3a2bt2aOXPm5NRTT93D7wD4JPjE/UUJgJ3VuXPnNq9bWlryzW9+Mz169Eh1dXWOPfbYvP766/nDd7mfeuqpOeWUUzJs2LD88R//cQ4++OA9sWzgE8adOoAPsGLFiqxYsSJJMmvWrHz+85/Pr371q1x88cUZO3ZsKioq8qtf/Spbt25Nkhx00EEZPHhwrr/++px++ul7cunAJ4g7dQAf4IADDsj3v//91NfXp1evXvnud7+bn//857n44ouz7777Zq+99sqRRx6ZV199tfWampqa/P3f/31Gjhy5B1cOfJKIOoAP0LNnz/zTP/1Tm2OTJk3KpEmT3vX8bdu25Re/+EXOOOOM7bZuAXYX268AH6ENGzbkqKOOyv/8z//ka1/72p5eDvAJUtHyhyd7AQD42HKnDgCgAKIOAKAAog4AoACiDgCgAKIOAKAAog4AoAD/D58JB/rBD5H7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram: Actual Pay Floor (£) [def my_hist(column, title, xlab, ylab, bins=10, alpha=0.7, color='c'):]\n",
    "my_hist(\"Actual Pay Floor (£)\", \"UK Actual Pay\", \"pay\", \"level\", bins=7, alpha=0.8, color='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = 'UK Salary'\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.hist(uk_salary_df['Actual Pay Floor (£)'])\n",
    "plt.title(title, fontsize=15)\n",
    "plt.xlabel('Salary in Thousands')\n",
    "plt.ylabel('Level')\n",
    "plt.savefig(title, dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot using the values for x as Salary Cost of Reports (£) and y as Actual Pay Floor (£).\n",
    "plt.figure(figsize=(14,8))\n",
    "x = uk_salary_df['Salary Cost of Reports (£)'].map(lambda x: x * 1000)\n",
    "y = uk_salary_df['Actual Pay Floor (£)']\n",
    "plt.scatter(x, y) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# BOX PLOT\n",
    "plt.figure(figsize=(14,8))\n",
    "plt.boxplot(x)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "plt.boxplot(y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Salary Cost of Reports (£)</th>\n",
       "      <th>FTE</th>\n",
       "      <th>Actual Pay Floor (£)</th>\n",
       "      <th>Actual Pay Ceiling (£)</th>\n",
       "      <th>Valid?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.100000e+01</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.651590e+06</td>\n",
       "      <td>0.982157</td>\n",
       "      <td>22745.098039</td>\n",
       "      <td>23823.313725</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.656579e+06</td>\n",
       "      <td>0.101771</td>\n",
       "      <td>45114.451404</td>\n",
       "      <td>47133.322796</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.105736e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.175629e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.672047e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>154999.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Salary Cost of Reports (£)        FTE  Actual Pay Floor (£)  \\\n",
       "count                5.100000e+01  51.000000             51.000000   \n",
       "mean                 1.651590e+06   0.982157          22745.098039   \n",
       "std                  2.656579e+06   0.101771          45114.451404   \n",
       "min                  0.000000e+00   0.300000              0.000000   \n",
       "25%                  0.000000e+00   1.000000              0.000000   \n",
       "50%                  1.105736e+06   1.000000              0.000000   \n",
       "75%                  2.175629e+06   1.000000              0.000000   \n",
       "max                  1.672047e+07   1.000000         150000.000000   \n",
       "\n",
       "       Actual Pay Ceiling (£)  Valid?  \n",
       "count               51.000000    51.0  \n",
       "mean             23823.313725     1.0  \n",
       "std              47133.322796     0.0  \n",
       "min                  0.000000     1.0  \n",
       "25%                  0.000000     1.0  \n",
       "50%                  0.000000     1.0  \n",
       "75%                  0.000000     1.0  \n",
       "max             154999.000000     1.0  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uk_salary_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 51 entries, 0 to 50\n",
      "Data columns (total 18 columns):\n",
      "Post Unique Reference              51 non-null object\n",
      "Name                               51 non-null object\n",
      "Grade (or equivalent)              51 non-null object\n",
      "Job Title                          51 non-null object\n",
      "Job/Team Function                  49 non-null object\n",
      "Parent Department                  51 non-null object\n",
      "Organisation                       51 non-null object\n",
      "Unit                               49 non-null object\n",
      "Contact Phone                      46 non-null object\n",
      "Contact E-mail                     49 non-null object\n",
      "Reports to Senior Post             51 non-null object\n",
      "Salary Cost of Reports (£)         51 non-null int64\n",
      "FTE                                51 non-null float64\n",
      "Actual Pay Floor (£)               51 non-null int64\n",
      "Actual Pay Ceiling (£)             51 non-null int64\n",
      "Professional/Occupational Group    51 non-null object\n",
      "Notes                              27 non-null object\n",
      "Valid?                             51 non-null int64\n",
      "dtypes: float64(1), int64(4), object(13)\n",
      "memory usage: 7.2+ KB\n"
     ]
    }
   ],
   "source": [
    "uk_salary_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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