Lab - Correlation Analysis in Python.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "brainFile = 'Datasets/brainsize.txt'"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "tx = []\n",
    "with open(brainFile, 'r') as f:\n",
    "    for i in f:\n",
    "       tx.append(i.split())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "del tx[-2:]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "[['Gender', 'FSIQ', 'VIQ', 'PIQ', 'Weight', 'Height', 'MRI_Count'],\n ['Female', '133', '132', '124', '118', '64.5', '816932'],\n ['Male', '140', '150', '124', 'NA', '72.5', '1001121'],\n ['Male', '139', '123', '150', '143', '73.3', '1038437'],\n ['Male', '133', '129', '128', '172', '68.8', '965353'],\n ['Female', '137', '132', '134', '147', '65.0', '951545'],\n ['Female', '99', '90', '110', '146', '69.0', '928799'],\n ['Female', '138', '136', '131', '138', '64.5', '991305'],\n ['Female', '92', '90', '98', '175', '66.0', '854258'],\n ['Male', '89', '93', '84', '134', '66.3', '904858'],\n ['Male', '133', '114', '147', '172', '68.8', '955466'],\n ['Female', '132', '129', '124', '118', '64.5', '833868'],\n ['Male', '141', '150', '128', '151', '70.0', '1079549'],\n ['Male', '135', '129', '124', '155', '69.0', '924059'],\n ['Female', '140', '120', '147', '155', '70.5', '856472'],\n ['Female', '96', '100', '90', '146', '66.0', '878897'],\n ['Female', '83', '71', '96', '135', '68.0', '865363'],\n ['Female', '132', '132', '120', '127', '68.5', '852244'],\n ['Male', '100', '96', '102', '178', '73.5', '945088'],\n ['Female', '101', '112', '84', '136', '66.3', '808020'],\n ['Male', '80', '77', '86', '180', '70.0', '889083'],\n ['Male', '83', '83', '86', 'NA', 'NA', '892420'],\n ['Male', '97', '107', '84', '186', '76.5', '905940'],\n ['Female', '135', '129', '134', '122', '62.0', '790619'],\n ['Male', '139', '145', '128', '132', '68.0', '955003'],\n ['Female', '91', '86', '102', '114', '63.0', '831772'],\n ['Male', '141', '145', '131', '171', '72.0', '935494'],\n ['Female', '85', '90', '84', '140', '68.0', '798612'],\n ['Male', '103', '96', '110', '187', '77.0', '1062462'],\n ['Female', '77', '83', '72', '106', '63.0', '793549'],\n ['Female', '130', '126', '124', '159', '66.5', '866662'],\n ['Female', '133', '126', '132', '127', '62.5', '857782'],\n ['Male', '144', '145', '137', '191', '67.0', '949589'],\n ['Male', '103', '96', '110', '192', '75.5', '997925'],\n ['Male', '90', '96', '86', '181', '69.0', '879987'],\n ['Female', '83', '90', '81', '143', '66.5', '834344'],\n ['Female', '133', '129', '128', '153', '66.5', '948066'],\n ['Male', '140', '150', '124', '144', '70.5', '949395'],\n ['Female', '88', '86', '94', '139', '64.5', '893983'],\n ['Male', '81', '90', '74', '148', '74.0', '930016'],\n ['Male', '89', '91', '89', '179', '75.5', '935863']]"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tx"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "f.close()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "brainFrame = pd.DataFrame(data=tx[1:], columns=tx[0])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "   Gender FSIQ  VIQ  PIQ Weight Height MRI_Count\n0  Female  133  132  124    118   64.5    816932\n1    Male  140  150  124     NA   72.5   1001121\n2    Male  139  123  150    143   73.3   1038437\n3    Male  133  129  128    172   68.8    965353\n4  Female  137  132  134    147   65.0    951545\n5  Female   99   90  110    146   69.0    928799\n6  Female  138  136  131    138   64.5    991305\n7  Female   92   90   98    175   66.0    854258\n8    Male   89   93   84    134   66.3    904858\n9    Male  133  114  147    172   68.8    955466",
      "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>Gender</th>\n      <th>FSIQ</th>\n      <th>VIQ</th>\n      <th>PIQ</th>\n      <th>Weight</th>\n      <th>Height</th>\n      <th>MRI_Count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Female</td>\n      <td>133</td>\n      <td>132</td>\n      <td>124</td>\n      <td>118</td>\n      <td>64.5</td>\n      <td>816932</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Male</td>\n      <td>140</td>\n      <td>150</td>\n      <td>124</td>\n      <td>NA</td>\n      <td>72.5</td>\n      <td>1001121</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Male</td>\n      <td>139</td>\n      <td>123</td>\n      <td>150</td>\n      <td>143</td>\n      <td>73.3</td>\n      <td>1038437</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Male</td>\n      <td>133</td>\n      <td>129</td>\n      <td>128</td>\n      <td>172</td>\n      <td>68.8</td>\n      <td>965353</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Female</td>\n      <td>137</td>\n      <td>132</td>\n      <td>134</td>\n      <td>147</td>\n      <td>65.0</td>\n      <td>951545</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Female</td>\n      <td>99</td>\n      <td>90</td>\n      <td>110</td>\n      <td>146</td>\n      <td>69.0</td>\n      <td>928799</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Female</td>\n      <td>138</td>\n      <td>136</td>\n      <td>131</td>\n      <td>138</td>\n      <td>64.5</td>\n      <td>991305</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Female</td>\n      <td>92</td>\n      <td>90</td>\n      <td>98</td>\n      <td>175</td>\n      <td>66.0</td>\n      <td>854258</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Male</td>\n      <td>89</td>\n      <td>93</td>\n      <td>84</td>\n      <td>134</td>\n      <td>66.3</td>\n      <td>904858</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Male</td>\n      <td>133</td>\n      <td>114</td>\n      <td>147</td>\n      <td>172</td>\n      <td>68.8</td>\n      <td>955466</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brainFrame.head(10)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 40 entries, 0 to 39\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   Gender     40 non-null     object\n",
      " 1   FSIQ       40 non-null     object\n",
      " 2   VIQ        40 non-null     object\n",
      " 3   PIQ        40 non-null     object\n",
      " 4   Weight     40 non-null     object\n",
      " 5   Height     40 non-null     object\n",
      " 6   MRI_Count  40 non-null     object\n",
      "dtypes: object(7)\n",
      "memory usage: 2.3+ KB\n"
     ]
    }
   ],
   "source": [
    "brainFrame.info()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "def toFloat(x):\n",
    "    try:\n",
    "        return float(x)\n",
    "    except:\n",
    "        return x"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [
    "for i in brainFrame.columns:\n",
    "    brainFrame[i] = brainFrame[i].apply(toFloat)\n",
    "brainFrame.replace('NA', np.nan, inplace=True)\n",
    "brainFrame = brainFrame.infer_objects()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [],
   "source": [
    "menDf = brainFrame[brainFrame.Gender == 'Male']\n",
    "womenDf = brainFrame[brainFrame.Gender == 'Female']"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "menMeanSmarts = menDf[['PIQ', 'FSIQ', 'VIQ']].mean(axis=1)\n",
    "plt.scatter(menMeanSmarts, menDf['MRI_Count'])\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGoCAYAAABYLN/cAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAABAHElEQVR4nO3dfXSU9Z3//9dkkpCEBEIzJfEgbGxIBETMHVJcEAkl4SZgXRCqLl17KG6NGFNBwPUHHGTBwxrEQ5UWb7ZB4XStENqAHoNLlLIVEkSgFJZjEoRikq8wyRLI/d31+4NmcAiQXDFk5kqej3Nyeuaaz8y8r3dHfc3nc93YDMMwBAAAYCE+ni4AAADALAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwnF4TYCoqKjR58mTl5+d3+DW5ublKTU1VbGysJk+erO3bt9/CCgEAQEf5erqA7nD48GEtW7ZMf/vb3zr8moMHD2rZsmV69dVXdf/99ys/P18LFixQTEyMRo0adQurBQAA7enxMzA7d+7U4sWL9ctf/rLNc5999plmz56txMRETZ8+XTk5Oa7nsrKy9NOf/lQTJkyQzWbTD3/4Q+3YsUNDhgzpzvIBAMB19PgAM27cOH388ceaNm2a2/ZTp07pySef1BNPPKH8/HytXr1aa9eu1f79+yVJf/nLXxQaGqonnnhCY8aM0YMPPqi//e1vCg0N9cBeAACAb+vxAeb73/++fH3brpT913/9lyZNmqTk5GTZ7XbFx8drzpw52rZtmySpsrJSb7/9tp588kn9+c9/1lNPPaVf/vKXOnbsWHfvAgAAuEavOAbmekpKSnTw4EElJia6tjU3N7uWiPz9/TVr1izFxcVJkpKTkzV27Fjl5ubqnnvu8UjNAADgil4bYCIiIvTQQw/pxRdfdG07f/68DMOQJEVFRamhocHtNc3Nza7nAQCA5/T4JaQbmT17tnbv3q3/+Z//UUtLi86cOaN//ud/1n/+539Kkh555BH97ne/02effaaWlhbl5uYqPz9fqampHq4cAAD02hmYe+65R6+88opeeeUVPfPMMwoMDFRqaqqeffZZSdKsWbPk4+Ojl156SV9//bUGDRqkDRs26K677vJw5QAAwGawJgIAACym1y4hAQAA6+p0gOnIpfn37dunGTNmKDY2VlOnTtUnn3zi9vybb76p+++/X7GxsZo3b55Onz7teq6mpkbPP/+8xowZo4SEBC1ZskTV1dWdLRcAAPQgnQowhw8f1ty5c296af4zZ87o6aef1jPPPKPPP/9cTz/9tDIyMvTNN99IunKF3HfffVdvv/228vPzdddddyk9Pd11ls/q1atVVlam3Nxc7dmzR2VlZcrMzOxMuQAAoIcxHWBudmn+a8clJibqRz/6kXx9fTVt2jSNHj1a7733niTp97//vR599FFFR0erT58+WrRokUpLS5Wfn6/a2lrt2rVL6enpCg0NVVhYmBYvXqzs7GzV1tZ2bk8BAECPYfospHHjxmnGjBny9fW9aYgpKipSTEyM27ahQ4fq1KlTrucXLFjges7Pz0+RkZE6deqUQkND1djY6Pb6qKgo1dXV6cyZMxo+fHiH662ouCxvP0zZZpO+970QS9TqLeiZefSsc+ibefTMPHp2VWsv2mM6wHz/+9/v0Ljq6moFBga6bQsICFBNTU27z1dVVUmSgoKCXM+1jjV7HExHmuAtrFSrt6Bn5tGzzqFv5tEz8+hZx92y68AEBgaqrq7ObVtdXZ369u3b7vOtwaW2ttY1vnXpKDg42FQd5eXen2ZtNiksLMQStXoLemYePesc+mYePTOPnl3V2ov23LIAExMToxMnTrhtKyoq0siRIyVJ0dHRKiws1MSJEyVJjY2NOnPmjGJiYnTHHXfIz89PRUVFrvsOFRcXu5aZzDAMWebLYKVavQU9M4+edQ59M4+emUfPOu6WXQdm5syZKigo0IcffqimpiZ9+OGHKigo0IMPPijpypVut27dqlOnTqm+vl7r16+Xw+FQYmKiAgMDNXXqVGVmZqqiokIVFRXKzMxUamqqAgICblXJAADAIro0wMTFxSknJ0fSlYNuX3/9dW3evFmjR4/Wpk2b9Ktf/Up33HGHpCv3Inr88cf11FNP6Yc//KFOnjypzZs3y8/PT5K0cuVKRUZGasaMGZoyZYpuv/12rVixoivLBQAAFtXjbyXgdHr/eqLNJjkcIZao1VvQM/PoWefQN/PomXn07KrWXrSHWwkAAADLIcAAAADLIcAAAADLuWWnUQMAAGtrbjF0tKRSzqoGOYL9FTuov+w+Nk+XJYkAAwAAriOv0Kn1eUU6X9Xg2jYw2F+LkoYqKdrhwcquYAkJAAC4ySt0amnOSbfwIknnqxq0NOek8gqdHqrsKgIMAABwaW4xtD6v6KZjXvmkWM0tnj3fmwADAABcjpZUtpl5udY3l+t1tKSymyq6PgIMAABwcbYTXsyOu1UIMAAAwMUR7N+l424VAgwAAHCJHdRfA9sJJ+EhfRQ7qH83VXR9BBgAAOBi97FpUdLQm455dmKUx68HQ4ABAABukqIdWjdzRJuZmPCQPlo3c4RXXAeGC9kBAIA2kqIdmhAVxpV4AQCAtdh9bEoYHOrpMq6LJSQAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5vmZfUF5eruXLl6ugoEB2u10zZ87U0qVL5evb9q2ys7P1xhtv6JtvvlFMTIwWL16s0aNHS5Li4uLcxra0tKiurk7r169Xamqqjh07prlz5yowMNA1ZsSIEdq2bZvZkgEAQA9jOsBkZGQoPDxc+/fvl9Pp1JNPPqmsrCz9/Oc/dxu3d+9erVy5Uhs3btT999+vvXv3asGCBcrOztYPfvADHTlyxG38kiVLVF5erilTpkiSjh8/rtGjR+vdd9/9DrsHAAB6IlNLSGfPnlVBQYGee+45BQYGavDgwUpLS7vurMju3buVmpqqiRMnym63Kzk5WYmJidqxY0ebsdnZ2frss8+UmZnpmsk5fvy4Ro4c2cndAgAAPZmpGZjCwkKFhoYqPDzctS0qKkqlpaW6dOmS+vXr59re3NysoKAgt9f7+Pjo9OnTbtsuX76sdevWaeXKlRowYIBr+/Hjx+VwOJScnKyqqirde++9WrZsmSIiIkztoM1marhHtNZohVq9BT0zj551Dn0zj56ZR8+u6mgPTAWY6upqt2NSJLke19TUuAWYlJQUrVixQikpKYqPj9enn36qAwcOuI6BafXOO+9o0KBBmjp1qmtbc3OzBg4cqPvuu0+PPPKIGhsbtXr1aj3xxBPauXOn7HZ7h2sOCwsxs4seZaVavQU9M4+edQ59M4+emUfPOs5UgAkKClJtba3bttbHffv2dds+ffp0VVRUaPny5aqsrNSECROUmprq9nrDMLR9+3alp6fL9q3IZbfblZWV5fZ+y5cv19ixY1VcXKyYmJgO11xeflmG0eHhHmGzXfnSWqFWb0HPzKNnnUPfzKNn5tGzq1p70R5TASY6OloXL16U0+mUw+GQJBUXFysiIkIhIe4fduHCBY0fP17z5s1zbZszZ46Sk5Ndj48fP+524G6rsrIyZWVlKT093RWMGhoaJEkBAQFmSpZhyDJfBivV6i3omXn0rHPom3n0zDx61nGmDuKNjIxUQkKC1q5dq6qqKp07d06bNm3S7Nmz24w9dOiQ5s2bp5KSEtXX1ysrK0tfffWVHnroIdeYw4cP66677mqzLDVgwAB98MEH2rBhg+rr61VRUaFVq1Zp7NixGjJkSCd3FQAA9BSmL2S3ceNGNTU1adKkSZozZ47Gjx+vtLQ0SVeu7ZKTkyNJmjZtmubOnau5c+dq7Nix2rt3r7Zs2aKwsDDXe507d87tgOBWAQEBeuutt1RcXKxx48YpJSVFwcHBevXVVzu5mwAAoCexGUbPnqxyOr1/PdFmkxyOEEvU6i3omXn0rHPom3n0zDx6dlVrL9rDrQQAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDl+Hq6AAAA4HnNLYaOllTKWdUgR7C/Ygf1l93H5umybogAAwBAL5dX6NT6vCKdr2pwbRsY7K9FSUOVFO3wYGU3xhISAAC9WF6hU0tzTrqFF0k6X9WgpTknlVfo9FBlN0eAAQCgl2puMbQ+r+imY175pFjNLUY3VdRxpgNMeXm50tLSlJiYqDFjxmjNmjVqamq67tjs7GxNmTJFcXFxmjt3rg4dOuR6rqWlRXFxcYqNjVVcXJzrr6amRpJUU1Oj559/XmPGjFFCQoKWLFmi6urqTu4mAAC41tGSyjYzL9f65nK9jpZUdlNFHWc6wGRkZCgoKEj79+/X9u3bdeDAAWVlZbUZt3fvXq1cuVJLly7V559/rvnz52vBggU6ffq0JKmoqEiNjY0qKCjQkSNHXH9BQUGSpNWrV6usrEy5ubnas2ePysrKlJmZ+d32FgAAuDjbCS9mx3UnUwHm7NmzKigo0HPPPafAwEANHjxYaWlp2rZtW5uxu3fvVmpqqiZOnCi73a7k5GQlJiZqx44dkqTjx4/rzjvvlL+/f5vX1tbWateuXUpPT1doaKjCwsK0ePFiZWdnq7a2tpO7CgCA92luMfT53y7qj0dL9PnfLnbrco0juO1/g7/LuO5k6iykwsJChYaGKjw83LUtKipKpaWlunTpkvr16+fa3tzc7JpNaeXj4+OagTl+/Ljq6+s1a9YslZSUKCoqSosWLVJ8fLzOnj2rxsZGxcTEuH1OXV2dzpw5o+HDh3e4Zpv3ngHm0lqjFWr1FvTMPHrWOfTNPHrWcXlfOpV5nbN/FicNVVLMrT/7J+72/hoY7H/TZaTwkD6Ku71/t/3/2dHPMRVgqqurFRgY6Lat9XFNTY1bgElJSdGKFSuUkpKi+Ph4ffrppzpw4IBGjx4tSQoICNCoUaP0zDPPqH///tq2bZvmz5+vnJwcVVVVSZJbAGr9HLPHwYSFhZga70lWqtVb0DPz6Fnn0Dfz6NnNffTXMi3NOalr51su/P3sn1//c7ymjLztltfx4o9H6smtX0iSWy2tOWLVg3cpfGC/Nq/zNFMBJigoqM0STuvjvn37um2fPn26KioqtHz5clVWVmrChAlKTU11jV+2bJnb+Pnz5ys7O1v79u1TfHy8671b37f1dcHBwWZKVnn5ZRned/C0G5vtyj/oVqjVW9Az8+hZ59A38+hZ+5pbDK34w1/bhBfpaohY+ccTihvY95ZfTC4xIljrZo5oOxMU0keLJkYpMSJYTuflW1rDt7V+f9pjKsBER0fr4sWLcjqdcjiuTG0VFxcrIiJCISHuH3bhwgWNHz9e8+bNc22bM2eOkpOTJUkbNmxQSkqKRowY4Xq+oaFBffr00R133CE/Pz8VFRXpnnvucX2On5+fIiMjzZQsw5Bl/gGyUq3egp6ZR886h76ZR89u7MjXHTv758jXlUoYHHrL65kY7dD9UWHXvRKvt/5/aOog3sjISCUkJGjt2rWqqqrSuXPntGnTJs2ePbvN2EOHDmnevHkqKSlRfX29srKy9NVXX+mhhx6SJH355Zdas2aNLly4oIaGBr322muqqqrS5MmTFRgYqKlTpyozM1MVFRWqqKhQZmamUlNTFRAQ0DV7DgCAh3jj2T92H5sSBocqZfhAJQwO9erbCEidOI1648aNampq0qRJkzRnzhyNHz9eaWlpkqS4uDjl5ORIkqZNm6a5c+dq7ty5Gjt2rPbu3astW7YoLCxMkvTSSy9pyJAhevDBBzVmzBgVFBTot7/9rUJDQyVJK1euVGRkpGbMmKEpU6bo9ttv14oVK7potwEA8Bwrn/3jLWyG4a2TQ13D6fT+NVibTXI4QixRq7egZ+bRs86hb+bRs/Y1txia+WZ+u2f//PHn93r9TEhXa/3+tIdbCQAA0M3sPjYtShp60zHPTozqdeHFDAIMAAAekBTt0LqZIzTwmmWi8JA+WjdzhNfeBdpbmDoLCQAAdJ2kaIcm/P3sn3ofH/VpaXGd/YObI8AAAOBBdh+bEoeEctyQSSwhAQAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAy/H1dAEAbp3mFkNHSyrlrGqQI9hfk78X7OmSAKBLEGCAHiqv0Kn1eUU6X9Xg2nZb7pf65YQfaGK0w4OVAcB3xxIS0APlFTq1NOekW3iRpP9XWaclOSeVV+j0UGUA0DUIMEAP09xiaH1e0XWfM/7+v698UqzmFuO6YwDACggwQA9ztKSyzczLtb65XK+jJZXdVBEAdD0CDNDDONsJL2bHAYA3IsAAPYwj2L9LxwGANzIdYMrLy5WWlqbExESNGTNGa9asUVNT03XHZmdna8qUKYqLi9PcuXN16NAh13P19fVas2aN7r//fiUkJOjhhx/WwYMHXc8fO3ZMw4YNU1xcnOvvscce68QuAr1L7KD+GthOOAkP6aPYQf27qSIA6HqmA0xGRoaCgoK0f/9+bd++XQcOHFBWVlabcXv37tXKlSu1dOlSff7555o/f74WLFig06dPS5IyMzP1xRdf6L333lNBQYEefvhh/eIXv1Bpaakk6fjx4xo9erSOHDni+tu2bdt321ugF7D72LQoaeh1n7P9/X+fnRglu4/tumMAwApMBZizZ8+qoKBAzz33nAIDAzV48GClpaVdN1js3r1bqampmjhxoux2u5KTk5WYmKgdO3ZIujIDk56erttuu012u11z5syRv7+/Tpw4IelKgBk5cmQX7CLQ+yRFO7Ru5og2MzER/QP0HzNHKInrwACwOFMXsissLFRoaKjCw8Nd26KiolRaWqpLly6pX79+ru3Nzc0KCgpye72Pj49rBubFF190e+7AgQO6fPmyhg0bJulKgHE4HEpOTlZVVZXuvfdeLVu2TBEREaZ20GaBH5mtNVqhVm9Bz9o3KcahB4aG6cjXlXJWN+j7wf760T236+L/VcngDOoO47tmHj0zj55d1dEemAow1dXVCgwMdNvW+rimpsYtwKSkpGjFihVKSUlRfHy8Pv30Ux04cECjR49u875Hjx5VRkaGFi5cqMGDB6u5uVkDBw7Ufffdp0ceeUSNjY1avXq1nnjiCe3cuVN2u73DNYeFhZjZRY+yUq3egp61b8rAfm6P6Vnn0Dfz6Jl59KzjTAWYoKAg1dbWum1rfdy3b1+37dOnT1dFRYWWL1+uyspKTZgwQampqW1e//7772vt2rVKT0/Xz372M0mS3W5vc1zN8uXLNXbsWBUXFysmJqbDNZeXX/b6X5s225UvrRVq9Rb0zDx61jn0zTx6Zh49u6q1F+0xFWCio6N18eJFOZ1OORxX1tCLi4sVERGhkBD3D7tw4YLGjx+vefPmubbNmTNHycnJkq4sMa1atUp79uzR66+/rvvuu881rqysTFlZWUpPT3cFo4aGK9esCAgIMFOyDEOW+TJYqVZvQc/Mo2edQ9/Mo2fm0bOOM3UQb2RkpBISErR27VpVVVXp3Llz2rRpk2bPnt1m7KFDhzRv3jyVlJSovr5eWVlZ+uqrr/TQQw9Jkl566SX96U9/0o4dO9zCiyQNGDBAH3zwgTZs2KD6+npVVFRo1apVGjt2rIYMGfIddhcAAPQEpk+j3rhxo5qamjRp0iTNmTNH48ePV1pamiQpLi5OOTk5kqRp06Zp7ty5mjt3rsaOHau9e/dqy5YtCgsLU0VFhbZt2yan06nU1FS3a73k5OQoICBAb731loqLizVu3DilpKQoODhYr776apfuPAAAsCabYfTsySqn0/vXE202yeEIsUSt3oKemUfPOoe+mUfPzKNnV7X2oj3cSgAAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFiO6QBTXl6utLQ0JSYmasyYMVqzZo2ampquOzY7O1tTpkxRXFyc5s6dq0OHDrk9/+abb+r+++9XbGys5s2bp9OnT7ueq6mp0fPPP68xY8YoISFBS5YsUXV1tdlyAQBAD2Q6wGRkZCgoKEj79+/X9u3bdeDAAWVlZbUZt3fvXq1cuVJLly7V559/rvnz52vBggWukLJz5069++67evvtt5Wfn6+77rpL6enpMgxDkrR69WqVlZUpNzdXe/bsUVlZmTIzM7/b3gIAgB7BVIA5e/asCgoK9NxzzykwMFCDBw9WWlqatm3b1mbs7t27lZqaqokTJ8putys5OVmJiYnasWOHJOn3v/+9Hn30UUVHR6tPnz5atGiRSktLlZ+fr9raWu3atUvp6ekKDQ1VWFiYFi9erOzsbNXW1nbNngMAAMvyNTO4sLBQoaGhCg8Pd22LiopSaWmpLl26pH79+rm2Nzc3KygoyO31Pj4+rhmYoqIiLViwwPWcn5+fIiMjderUKYWGhqqxsVExMTFun1NXV6czZ85o+PDhHa7ZZjOzh57RWqMVavUW9Mw8etY59M08emYePbuqoz0wFWCqq6sVGBjotq31cU1NjVuASUlJ0YoVK5SSkqL4+Hh9+umnOnDggEaPHn3D9woICFBNTY2qqqokyS0AtY41exxMWFiIqfGeZKVavQU9M4+edQ59M4+emUfPOs5UgAkKCmqzhNP6uG/fvm7bp0+froqKCi1fvlyVlZWaMGGCUlNTXeMDAwNVV1fn9pq6ujr17dvXFVxqa2td79v6uuDgYDMlq7z8sv5+WI3XstmufGmtUKu3oGfm0bPOoW/m0TPz6NlVrb1oj6kAEx0drYsXL8rpdMrhcEiSiouLFRERoZAQ9w+7cOGCxo8fr3nz5rm2zZkzR8nJya73Kiws1MSJEyVJjY2NOnPmjGJiYnTHHXfIz89PRUVFuueee1yf07rMZIZhyDJfBivV6i3omXn0rHPom3n0zDx61nGmDuKNjIxUQkKC1q5dq6qqKp07d06bNm3S7Nmz24w9dOiQ5s2bp5KSEtXX1ysrK0tfffWVHnroIUnSrFmztHXrVp06dUr19fVav369HA6HEhMTFRgYqKlTpyozM1MVFRWqqKhQZmamUlNTFRAQ0DV7DgAALMv0adQbN25UU1OTJk2apDlz5mj8+PFKS0uTJMXFxSknJ0eSNG3aNM2dO1dz587V2LFjtXfvXm3ZskVhYWGSpNmzZ+vxxx/XU089pR/+8Ic6efKkNm/eLD8/P0nSypUrFRkZqRkzZmjKlCm6/fbbtWLFiq7abwAAYGE2w+jZk1VOp/evJ9psksMRYolau1tzi6GjJZVyVjXIEeyv2EH9Zfex0bNOoGedQ9/Mo2fm0bOrWnvRHlPHwADdKa/QqfV5RTpf1eDaNjDYX4uShmpSjMODlQEAPI17IcEr5RU6tTTnpFt4kaTzVQ1amnNSeV86PVQZAMAbEGDgdZpbDK3PK7rpmPWfFKu5pZfPswJAL0aAgdc5WlLZZublWt9crlfBVxXdVBEAwNsQYOB1nO2El1bnL9e1PwgA0CMRYOB1HMH+HRo3MIRrAgFAb0WAgdeJHdRfA9sJMeEhfXTvHd/rpooAAN6GAAOvY/exaVHS0JuOWTQxSnYfbtsKAL0VAQZeKSnaoXUzR7SZiQkP6aN1M0coievAAECvxoXs4LWSoh2aEBV23SvxAgB6NwIMvJrdx6aEwaGeLgMA4GVYQgIAAJZDgAEAAJZDgAEAAJZDgAEAAJZDgAEAAJZDgAEAAJZDgAEAAJZDgAEAAJZDgAEAAJZDgAEAAJZDgAEAAJZDgAEAAJZDgAEAAJZDgAEAAJbj6+kCgJ6oucXQ0ZJKOasa5Aj2V+yg/rL72DxdFgD0GAQYoIvlFTq1Pq9I56saXNsGBvtrUdJQJUU7PFgZAPQcLCEBXSiv0KmlOSfdwoskna9q0NKck8ordHqoMgDoWQgwQBdpbjG0Pq/opmNe+aRYzS1GN1UEAD0XAQboIkdLKtvMvFzrm8v1OlpS2U0VAUDPRYABuoiznfBidhwA4MYIMEAXcQT7d+k4AMCNmT4Lqby8XMuXL1dBQYHsdrtmzpyppUuXyte37Vtt2bJFW7Zs0cWLFzVo0CAtXLhQKSkpkqS4uDi3sS0tLaqrq9P69euVmpqqY8eOae7cuQoMDHSNGTFihLZt22a2ZKBbxA7qr4HB/jddRgoP6aPYQf27sSoA6JlMB5iMjAyFh4dr//79cjqdevLJJ5WVlaWf//znbuP27dunzZs3a+vWrfrBD36g3NxcZWRk6OOPP9btt9+uI0eOuI1fsmSJysvLNWXKFEnS8ePHNXr0aL377rvfYfeA7mP3sWlR0lAtzTl5wzHPToziejAA0AVMLSGdPXtWBQUFeu655xQYGKjBgwcrLS3turMip0+flmEYrj+73S4/P7/rztRkZ2frs88+U2Zmpuv548ePa+TIkZ3cLcAzkqIdWjdzhAZes0wUHtJH62aO4DowANBFTM3AFBYWKjQ0VOHh4a5tUVFRKi0t1aVLl9SvXz/X9unTpys7O1vTpk2T3W6XzWbTyy+/rIiICLf3vHz5statW6eVK1dqwIABru3Hjx+Xw+FQcnKyqqqqdO+992rZsmVtXt8emwV+7LbWaIVavYU392xSjEMPDA3Tka8r5axukKOvv+Ju9/yVeL25Z96MvplHz8yjZ1d1tAemAkx1dbXbMSmSXI9ramrcAkxjY6OGDRumNWvWaNiwYdq1a5deeOEFRUVF6c4773SNe+eddzRo0CBNnTrVta25uVkDBw7Ufffdp0ceeUSNjY1avXq1nnjiCe3cuVN2u73DNYeFhZjZRY+yUq3ewpt7NmVgv/YHeYA398yb0Tfz6Jl59KzjTAWYoKAg1dbWum1rfdy3b1+37atXr1Z8fLxGjRolSZo1a5Z2796tnTt3atmyZZIkwzC0fft2paeny/atyGW325WVleX2fsuXL9fYsWNVXFysmJiYDtdcXn5ZhpdfN8xmu/KltUKt3oKemUfPOoe+mUfPzKNnV7X2oj2mAkx0dLQuXrwop9Mph+PKWn5xcbEiIiIUEuL+YaWlpW2OYfH19ZWfn5/r8fHjx90O3G1VVlamrKwspaenu4JRQ8OVMzsCAgLMlCzDkGW+DFaq1VvQM/PoWefQN/PomXn0rONMHcQbGRmphIQErV27VlVVVTp37pw2bdqk2bNntxmblJSkrVu36sSJE2ppadFHH32k/Px8TZs2zTXm8OHDuuuuu9osSw0YMEAffPCBNmzYoPr6elVUVGjVqlUaO3ashgwZ0sldBQAAPYXpC9lt3LhRTU1NmjRpkubMmaPx48crLS1N0pVru+Tk5EiSFi5cqMcee0xPP/20Ro8erTfeeEOvv/66hg8f7nqvc+fOuR0Q3CogIEBvvfWWiouLNW7cOKWkpCg4OFivvvpqJ3cTAAD0JDbD6NmTVU6n968n2mySwxFiiVq9BT0zj551Dn0zj56ZR8+uau1Fe7iVAAAAsBwCDAAAsBwCDAAAsBzT90ICAKA3aG4xdLSkUs6qBjmC/RU7yPNX1MZVBBgAAK6RV+jU+rwit7vLDwz216KkodzTzEuwhNRLNLcYOnzuonL/97wOn7uo5pZefpg7ANxAXqFTS3NOuoUXSTpf1aClOSeVV+j0UGX4NmZgegF+SQBAxzS3GFqfV3TTMa98UqwJUWEsJ3kYMzA9HL8kAKDjjpZUtvn35bW+uVyvoyWV3VQRboQA04N19JcEy0kAcIWznfBidhxuHQJMD8YvCQAwxxHs36XjcOsQYHowfkkAgDmxg/prYDvhJDykj2IH9e+minAjBJgejF8SAGCO3cemRUlDbzrm2YlRHMDrBQgwPRi/JADAvKRoh9bNHNHm35/hIX20buYIzt70EpxG3YO1/pJYmnPyhmP4JQEAbSVFOzQhKowr8XoxAkwP1/pL4trrwISH9NGzE6P4JQEAN2D3sSlhcKiny8ANEGB6AX5JAAB6GgJML8EvCQBAT8JBvAAAwHKYgQEAwEs0txg68jXL/R1BgAEAwAt89NcyrfjDX7nxbgexhAQAgIflfenUk1u/sMSNd5tbDB0+d1G5/3teh89d9Nj99JiBAQDAg5pbDGXmFelmMeCVT4o1ISrM48tJeYXONpfl8NQsETMwAAB4kFVuvJtX6NTSnJNeM0tEgAEAwIOscOPd5hZD6/OKbjrmlU+Ku3U5iQAD3IC3rPMC6NmscONdb5wl4hgY4Dq8aZ0XQM/WeuPdC1UNNzwOxtM33vXGWSJmYIBreNs6L4Ceze5j0+KkoTcd4+kb73rjLBEBBvgWb1znBeD9vuuSc1KMQ7/+53gNvCYAhIf00bqZIzw+89s6S3Qz3T1LxBIS8C1m1nm5txQAqeuWnKeMvE1xA/t65ZV47T42LUoaqqU5J284prtniZiBAb7FG9d5AXivrl5ybr3xbsrwgUoYHOoV4aVVUrRD62aO8JpZImZggG/xxnVeAN6po0vO3nABuq6SFO3QhKgwHS3x/CwRAQb4ltZ13pstI3n6bAAA3qG3Ljm3zhJ5muklpPLycqWlpSkxMVFjxozRmjVr1NTUdN2xW7ZsUVJSkuLj4zVjxgzl5ua6nmtpaVFcXJxiY2MVFxfn+qupqZEk1dTU6Pnnn9eYMWOUkJCgJUuWqLq6upO7CXRM6zrvzXj6bAAA3oElZ88yHWAyMjIUFBSk/fv3a/v27Tpw4ICysrLajNu3b582b96st956S1988YUWLlyojIwMff3115KkoqIiNTY2qqCgQEeOHHH9BQUFSZJWr16tsrIy5ebmas+ePSorK1NmZuZ321ugA7xtnReAd2LJ2bNMLSGdPXtWBQUF+tOf/qTAwEANHjxYaWlpevnll/Xzn//cbezp06dlGIbrz263y8/PT76+Vz7y+PHjuvPOO+Xv3/b/2NraWu3atUvvvPOOQkNDJUmLFy/WT3/6Uy1ZskSBgYGd3F2gY7xpnReAd2LJ2bNMBZjCwkKFhoYqPDzctS0qKkqlpaW6dOmS+vXr59o+ffp0ZWdna9q0abLb7bLZbHr55ZcVEREh6UqAqa+v16xZs1RSUqKoqCgtWrRI8fHxOnv2rBobGxUTE+P2OXV1dTpz5oyGDx/e4ZptFvjvTWuNVqjVW3RHz3ztNiUOCb11H9DN+J51Dn0zr7f0zNd+5QJ0S25yavGiiVHytbffiN7Ss47oaA9MBZjq6uo2sx+tj2tqatwCTGNjo4YNG6Y1a9Zo2LBh2rVrl1544QVFRUXpzjvvVEBAgEaNGqVnnnlG/fv317Zt2zR//nzl5OSoqqpKklzLSd/+HLPHwYSFhZga70lWqtVb0DPz6Fnn0DfzekPP5jhC1K9fgFbtOqmyyjrX9tv6B2jljBGaMvI2U+/XG3rWVUwFmKCgINXW1rpta33ct29ft+2rV69WfHy8Ro0aJUmaNWuWdu/erZ07d2rZsmVatmyZ2/j58+crOztb+/btU3x8vOu9W9+39XOCg4PNlKzy8ssyvPyiqTbblS+tFWr1FvTMPHrWOfTNvN7Ws8SIYP1h/ugrF6CrbpCjr7/ibr+y5Ox0Xu7Qe/S2nt1May/aYyrAREdH6+LFi3I6nXI4rhzIWFxcrIiICIWEuH9YaWmpRo4c6f5hvr7y8/OTJG3YsEEpKSkaMWKE6/mGhgb16dNHd9xxh/z8/FRUVKR77rnH9Tl+fn6KjIw0U7IMQ5b5MlipVm9Bz8yjZ51D38zrTT3zsbU9tbgz+96bevZdmToLKTIyUgkJCVq7dq2qqqp07tw5bdq0SbNnz24zNikpSVu3btWJEyfU0tKijz76SPn5+Zo2bZok6csvv9SaNWt04cIFNTQ06LXXXlNVVZUmT56swMBATZ06VZmZmaqoqFBFRYUyMzOVmpqqgICArtlzAABgWaZPo964caOampo0adIkzZkzR+PHj1daWpokKS4uTjk5OZKkhQsX6rHHHtPTTz+t0aNH64033tDrr7/uOgD3pZde0pAhQ/Tggw9qzJgxKigo0G9/+1vXWUcrV65UZGSkZsyYoSlTpuj222/XihUrumi3AQCAldkMo2dPVjmd3r+eaLNJDkeIJWr1FvTMPHrWOfTNPHpmHj27qrUX7eFmjgAAwHIIMAAAwHIIMAAAwHIIMAAAwHIIMAAAwHIIMAAAwHIIMAAAwHIIMAAAwHIIMAAAwHIIMAAAwHIIMAAAwHIIMAAAwHIIMAAAwHIIMAAAwHIIMAAAwHJ8PV0AvEtzi6GjJZVyVjXIEeyv2EH9ZfexebosAADcEGDgklfo1Pq8Ip2vanBtGxjsr0VJQ5UU7fBgZQBgDfwI7D4EGEi6El6W5pxss/18VYOW5pzUupkjCDEAcBP8COxeHAMDNbcYWp9XdNMxr3xSrOYWo5sqAgBraf0R+O3wIl39EZhX6PRQZT0XAQY6WlLZ5h+6a31zuV5HSyq7qSIAsA5+BHoGAQZythNezI6zsuYWQ4fPXVTu/57X4XMX+RcOgHbxI9AzOAYGcgT7d+k4q2L9GkBn8CPQM5iBgWIH9dfAdsJJeEgfxQ7q300VdT/WrwF0Fj8CPYMAA9l9bFqUNPSmY56dGNVjTwVk/RrAd8GPQM8gwECSlBTt0LqZI9r8Qxge0qfHn0LN+jWA76K3/wj0FI6BgUtStEMTosJ63UWYWL8G8F21/gi89ji68JA+enZiVI/+EegpBBi4sfvYlDA41NNldCvWrwF0hd76I9BTCDDo9VrXr2+2jMT6NYCO6I0/Aj2FY2DQ67F+DQDWQ4AB1LsPYgYAK2IJCfg71q8BwDoIMMC3sH4NANZgegmpvLxcaWlpSkxM1JgxY7RmzRo1NTVdd+yWLVuUlJSk+Ph4zZgxQ7m5ua7n6uvrtWbNGt1///1KSEjQww8/rIMHD7qeP3bsmIYNG6a4uDjX32OPPdaJXQQAAD2N6QCTkZGhoKAg7d+/X9u3b9eBAweUlZXVZty+ffu0efNmvfXWW/riiy+0cOFCZWRk6Ouvv5YkZWZm6osvvtB7772ngoICPfzww/rFL36h0tJSSdLx48c1evRoHTlyxPW3bdu277a3AACgRzAVYM6ePauCggI999xzCgwM1ODBg5WWlnbdYHH69GkZhuH6s9vt8vPzk6/vlVWr+vp6paen67bbbpPdbtecOXPk7++vEydOSLoSYEaOHNkFuwgAAHoaU8fAFBYWKjQ0VOHh4a5tUVFRKi0t1aVLl9SvXz/X9unTpys7O1vTpk2T3W6XzWbTyy+/rIiICEnSiy++6PbeBw4c0OXLlzVs2DBJVwKMw+FQcnKyqqqqdO+992rZsmWu13eUzQLHX7bWaIVavQU9M4+edQ59M4+emUfPrupoD0wFmOrqagUGBrpta31cU1PjFmAaGxs1bNgwrVmzRsOGDdOuXbv0wgsvKCoqSnfeeafbexw9elQZGRlauHChBg8erObmZg0cOFD33XefHnnkETU2Nmr16tV64okntHPnTtnt9g7XHBYWYmYXPcpKtXoLemYePesc+mYePTOPnnWcqQATFBSk2tpat22tj/v27eu2ffXq1YqPj9eoUaMkSbNmzdLu3bu1c+dOLVu2zDXu/fff19q1a5Wenq6f/exnkiS73d7muJrly5dr7NixKi4uVkxMTIdrLi+/LMPLbyJss1350lqhVm9Bz8yjZ51D38yjZ+bRs6tae9EeUwEmOjpaFy9elNPplMNx5cJexcXFioiIUEiI+4eVlpa2OYbF19dXfn5+kqTm5matWrVKe/bs0euvv6777rvPNa6srExZWVlKT093BaOGhiuXeQ8ICDBTsgxDlvkyWKlWb0HPzKNnnUPfzKNn5tGzjjN1EG9kZKQSEhK0du1aVVVV6dy5c9q0aZNmz57dZmxSUpK2bt2qEydOqKWlRR999JHy8/M1bdo0SdJLL72kP/3pT9qxY4dbeJGkAQMG6IMPPtCGDRtUX1+viooKrVq1SmPHjtWQIUO+w+4CAICewPRp1Bs3blRTU5MmTZqkOXPmaPz48UpLS5MkxcXFKScnR5K0cOFCPfbYY3r66ac1evRovfHGG3r99dc1fPhwVVRUaNu2bXI6nUpNTXW71ktOTo4CAgL01ltvqbi4WOPGjVNKSoqCg4P16quvdunOAwAAa7IZRs+erHI6vX890WaTHI4QS9TqLeiZefSsc+ibefTMPHp2VWsv2sPNHAEAgOUQYAAAgOVwM0cAADygucXQ0ZJKOasa5Aj21+TvBXu6JEshwAAA0M3yCp1an1ek81UNrm235X6pX074gSZGOzxYmXWwhAQAQDfKK3Rqac5Jt/AiSf+vsk5Lck4qr9DpocqshQADAEA3aW4xtD6v6LrPtZ589MonxWpu6eWnInUAAQYAgG5ytKSyzczLtb65XK+jJZXdVJF1EWAAAOgmznbCi9lxvRkBBgCAbuII9u/Scb0ZAQYAgG4SO6i/BrYTTsJD+ih2UP9uqsi6CDAAAHQTu49Ni5KGXvc529//99mJUbL72K47BlcRYAAA6EZJ0Q6tmzmizUxMRP8A/cfMEUriOjAdwoXsAADoZknRDk2ICnO/Em/s7fq/iqpefzPHjiLAAADgAXYfmxIGh0q6cgdmlo3MYQkJAABYDgEGAABYDgEGAABYDsfAAD1Ec4vhdkBg7KD+rKkD6LEIMEAPkFfo1Pq8Ird7rAwM9teipKGckgmgR2IJCbC4vEKnluacbHODuPNVDVqac1J5hU4PVQYAtw4BBrCw5hZD6/OKbjrmlU+K1dzChSUA9CwEGMDCjpZUtpl5udY3l+t1tKSymyoCgO5BgAEszNlOeDE7DgCsggADWJijnbvamh0HAFZBgAEsLHZQ/zY3hLtWeEgfxQ7q300VAUD3IMAAFmb3sWlR0tCbjnl2YhTXgwHQ4xBgAItLinZo3cwRbWZiwkP6aN3MEVwHBkCPxIXsgB4gKdqhCVFhXIkXQK9BgAF6CLuPTQmDQz1dBgB0C5aQAACA5RBgAACA5RBgAACA5ZgOMOXl5UpLS1NiYqLGjBmjNWvWqKmp6bpjt2zZoqSkJMXHx2vGjBnKzc11e/7NN9/U/fffr9jYWM2bN0+nT592PVdTU6Pnn39eY8aMUUJCgpYsWaLq6mqz5QIAgB7IdIDJyMhQUFCQ9u/fr+3bt+vAgQPKyspqM27fvn3avHmz3nrrLX3xxRdauHChMjIy9PXXX0uSdu7cqXfffVdvv/228vPzdddddyk9PV2GceWmc6tXr1ZZWZlyc3O1Z88elZWVKTMz87vtLW6J5hZDh89dVO7/ntfhcxe5cSAA4JYzFWDOnj2rgoICPffccwoMDNTgwYOVlpambdu2tRl7+vRpGYbh+rPb7fLz85Ov75UTn37/+9/r0UcfVXR0tPr06aNFixaptLRU+fn5qq2t1a5du5Senq7Q0FCFhYVp8eLFys7OVm1tbdfsObpEXqFTM9/M1y9+/xf9fx+e0i9+/xfNfDNfeYVOT5cGAOjBTJ1GXVhYqNDQUIWHh7u2RUVFqbS0VJcuXVK/fv1c26dPn67s7GxNmzZNdrtdNptNL7/8siIiIiRJRUVFWrBggWu8n5+fIiMjderUKYWGhqqxsVExMTFun1NXV6czZ85o+PDhHa7ZZoHLYLTWaIVavy3vS6eW5pxss/18VYOW5pzUf8wcoaSYW3MRNav2zJPoWefQN/PomXn07KqO9sBUgKmurlZgYKDbttbHNTU1bgGmsbFRw4YN05o1azRs2DDt2rVLL7zwgqKionTnnXde970CAgJUU1OjqqoqSVJQUFCbzzF7HExYWIip8Z5kpVqbWwxt2Fdww+dtkjbsO61ZP4y8pRdTs1LPvAU96xz6Zh49M4+edZypABMUFNRmCaf1cd++fd22r169WvHx8Ro1apQkadasWdq9e7d27typZcuWKTAwUHV1dW6vqaurU9++fV3Bpba21vW+rZ8THBxspmSVl1+W4eWHZNhsV760Vqi11ed/u6iyyrobPm9IKqus08dHv1bikNAu/3wr9szT6Fnn0Dfz6Jl59Oyq1l60x1SAiY6O1sWLF+V0OuVwXFkaKC4uVkREhEJC3D+stLRUI0eOdP8wX1/5+fm53quwsFATJ06UdGXG5syZM4qJidEdd9whPz8/FRUV6Z577nF9TusykxmGIct8GaxUq7OqocPjbuU+Waln3oKedQ59M4+emUfPOs7UQbyRkZFKSEjQ2rVrVVVVpXPnzmnTpk2aPXt2m7FJSUnaunWrTpw4oZaWFn300UfKz8/XtGnTJF2Zkdm6datOnTql+vp6rV+/Xg6HQ4mJiQoMDNTUqVOVmZmpiooKVVRUKDMzU6mpqQoICOiaPcd34rjmxoHfdRwAAGaYvhfSxo0b9eKLL2rSpEny8fHRj3/8Y6WlpUmS4uLitGrVKs2cOVMLFy6U3W7X008/rcrKSv3DP/yDXn/9ddcBuLNnz9bly5f11FNPqaKiQnfffbc2b97smqFZuXKl1q1bpxkzZqixsVGTJk3S8uXLu3DX8V3EDuqvgcH+On+TmZjwkD6KHdS/G6sCAPQWNsPo2ZNVTqf3ryfabJLDEWKJWr8tr/D6ZyG1WjdzhJKib91ZSFbsmSfRs86hb+bRM/Po2VWtvWgPtxIwgQu2uUuKdmjdzBEaeM0yUXhIn1saXgAAML2E1FvlFTq1Pq/IbclkYLC/FiUN7dX/oU6KdmhCVJiOllTKWdUgR7C/Ygf1v6WnTgMAQIDpgBstlbResK23zzbYfWxKGBzq6TIAAL0IS0jtaG4xtD6v6KZjXvmkuNcvJwEA0J0IMO04WlJ50zNtJOmby/U6WlLZTRUBAHBzveGYTZaQ2mHmgm0AAHhabzlmkxmYdnDBNgCAVbQes3ntykHrMZt5hU4PVdb1CDDtaL1g281wwTYAgKf1tmM2CTDtsPvYtChp6E3HPDsxitOGAQAe1duO2STAdAAXbAMAeLvedswmB/F2EBdsAwB4s952zCYBxgQu2AYA8Fa97Sa7LCEBANAD9LZjNgkwAAD0EL3pmE2WkAAA6EF6yzGbBBgAAHqY3nDMJktIAADAcggwAADAcggwAADAcggwAADAcggwAADAcggwAADAcggwAADAcggwAADAcggwAADAcnr8lXhtFrhycmuNVqjVW9Az8+hZ59A38+iZefTsqo72wGYYhnFrSwEAAOhaLCEBAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcB0sxMnTuixxx5TYmKixo0bp3//939XQ0ODJOnYsWN6+OGHFRcXp6SkJL3//vsertY7FBcXa/78+UpMTNQDDzygX//612ppaZFEz65VUVGhyZMnKz8/37WtvR7t3LlTkydPVmxsrP7pn/5JR44c6e6yPe56fZOkI0eO6O67724znp5dv2e5ubl68MEHFR8fr6SkJL322muuf1Yl+na9nm3btk3JycmKi4tTcnKytm7d6vaa3t6zmzLQbZqbm41//Md/NLZs2WI0NzcbZWVlRkpKivHaa68ZFy9eNO69915j69atRmNjo/HZZ58ZcXFxxrFjxzxdtkdVVVUZDzzwgPHCCy8Y1dXVxtdff22kpqYav/rVr+jZNT7//HPjRz/6kRETE2McPHjQMAyj3R4dPHjQiIuLMz7//HOjoaHB+O1vf2uMGTPGqKmp8eSudKvr9a2lpcV4//33jdjYWCMmJsZtPD27fs+OHz9ujBo1ysjLyzOam5uNoqIiY+LEicbbb79tGAZ9u17P9u7da4wePdo4fvy4YRiGcezYMePuu+82Dhw4YBgGPWsPMzDdqLKyUhcuXFBLS4uMv9+CysfHR4GBgdqzZ49CQ0P12GOPydfXV2PHjtWMGTO0bds2D1ftWYcPH1Z5eblWrFihoKAgDRo0SE8++aR+97vfKTc3l5793c6dO7V48WL98pe/dNve3vfq/fff1/Tp05WQkCA/Pz89/vjjGjBggD788ENP7Ea3u1Hf/u3f/k3vv/++0tPT27yGnl2/ZyUlJfrJT36iiRMnysfHR1FRUZo8ebIOHTokqXf37UY9S0pKUl5enkaOHKmmpib93//9n2w2m/r16yepd/esIwgw3WjAgAF6/PHHtW7dOt19992aMGGCIiMj9fjjj6uwsFAxMTFu44cOHapTp055qFrv0NLSIj8/P/n5+bm22Ww2OZ1OHTlyhJ793bhx4/Txxx9r2rRpbtvb+14VFRX16h7eqG/PPPOM3nvvPY0YMaLNa+jZ9XuWkpKi559/3vW4rq5On376qe666y5JvbtvN+qZJAUHB+v06dMaNWqUnnjiCT3yyCOu711v7llHEGC6UUtLiwICArR8+XIdPXpUu3fvVnFxsTZu3Kjq6moFBga6jQ8ICFBNTY2HqvUO8fHxCggI0Pr161VbW6uSkhK9/fbbrufp2RXf//735evr22Z7e9+r3v69u1HfIiIibvgaenb9nn1bVVWVnnrqKQUEBOjxxx+X1Lv71l7PBg8erGPHjmn79u364IMP9MYbb0jq3T3rCAJMN/r444+Vm5urRx99VP7+/oqOjtZTTz2l3/3udwoMDFRdXZ3b+Lq6OvXt29dD1XqHfv366c0339SxY8f0wAMPKCMjQz/+8Y8lSXa7nZ61o73vFd878+jZzZ0+fVo/+clP1NTUpHfeeUfBwcGS6NvNtM4y33333frpT3+q3bt3S6Jn7SHAdKOysjLXGUetfH195efnp5iYGBUWFro9V1RUpOjo6O4s0es0NDS4/kWYn5+v999/Xz4+Pho6dKhGjRpFz9rR3vcqOjqaHppEz25s3759evjhhzV+/Hi9/fbb6t+/v+s5+tZWVlaWMjIy3LY1NDS4+kbPbo4A043GjRunCxcu6De/+Y2am5t17tw5/frXv9aMGTM0efJkOZ1OZWVlqbGxUQcPHtSuXbs0a9YsT5ftcfPnz9f27dtlGIb++te/6je/+Y3+5V/+hZ51QHs9mj17tnbt2qWDBw+qsbFRWVlZKi8v1+TJkz1cufeiZ9d39OhRPfXUU3r++ee1dOnSNksm9K2txMRE/fd//7c+/PBDtbS06PDhw3rnnXf0yCOPSKJn7fL0aVC9zZ///Gfj4YcfNhISEowHHnjAeOWVV4z6+nrDMAzjL3/5izF37lwjLi7OmDRpkrFjxw4PV+sdCgoKjIceesiIjY01Jk2aZLzzzjuu5+hZW98+TdMw2u/RH/7wByMlJcWIjY01Zs+ebRw9erS7S/YK1/bNMK6cxnrtadSGQc9afbtn//qv/2rceeedRmxsrNvf/PnzXePpW9vv2d69e40ZM2YYcXFxRmpqqvHHP/7RbTw9uzGbYfz9fF4AAACLYAkJAABYDgEGAABYDgEGAABYDgEGAABYDgEGAABYDgEGAABYDgEGAABYDgEGAABYDgEGAABYDgEGAABYDgEGAABYDgEGAABYzv8P4GGw3PxB0KQAAAAASUVORK5CYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "womenMeanSmarts = womenDf[['PIQ', 'FSIQ', 'VIQ']].mean(axis=1)\n",
    "plt.scatter(womenMeanSmarts, womenDf['MRI_Count'])\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "               FSIQ       VIQ       PIQ    Weight    Height  MRI_Count\nFSIQ       1.000000  0.946639  0.934125 -0.051483 -0.086002   0.357641\nVIQ        0.946639  1.000000  0.778135 -0.076088 -0.071068   0.337478\nPIQ        0.934125  0.778135  1.000000  0.002512 -0.076723   0.386817\nWeight    -0.051483 -0.076088  0.002512  1.000000  0.699614   0.513378\nHeight    -0.086002 -0.071068 -0.076723  0.699614  1.000000   0.601712\nMRI_Count  0.357641  0.337478  0.386817  0.513378  0.601712   1.000000",
      "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>FSIQ</th>\n      <th>VIQ</th>\n      <th>PIQ</th>\n      <th>Weight</th>\n      <th>Height</th>\n      <th>MRI_Count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>FSIQ</th>\n      <td>1.000000</td>\n      <td>0.946639</td>\n      <td>0.934125</td>\n      <td>-0.051483</td>\n      <td>-0.086002</td>\n      <td>0.357641</td>\n    </tr>\n    <tr>\n      <th>VIQ</th>\n      <td>0.946639</td>\n      <td>1.000000</td>\n      <td>0.778135</td>\n      <td>-0.076088</td>\n      <td>-0.071068</td>\n      <td>0.337478</td>\n    </tr>\n    <tr>\n      <th>PIQ</th>\n      <td>0.934125</td>\n      <td>0.778135</td>\n      <td>1.000000</td>\n      <td>0.002512</td>\n      <td>-0.076723</td>\n      <td>0.386817</td>\n    </tr>\n    <tr>\n      <th>Weight</th>\n      <td>-0.051483</td>\n      <td>-0.076088</td>\n      <td>0.002512</td>\n      <td>1.000000</td>\n      <td>0.699614</td>\n      <td>0.513378</td>\n    </tr>\n    <tr>\n      <th>Height</th>\n      <td>-0.086002</td>\n      <td>-0.071068</td>\n      <td>-0.076723</td>\n      <td>0.699614</td>\n      <td>1.000000</td>\n      <td>0.601712</td>\n    </tr>\n    <tr>\n      <th>MRI_Count</th>\n      <td>0.357641</td>\n      <td>0.337478</td>\n      <td>0.386817</td>\n      <td>0.513378</td>\n      <td>0.601712</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brainFrame.corr(method='pearson')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "data": {
      "text/plain": "               FSIQ       VIQ       PIQ    Weight    Height  MRI_Count\nFSIQ       1.000000  0.944400  0.930694 -0.278140 -0.356110   0.498369\nVIQ        0.944400  1.000000  0.766021 -0.350453 -0.355588   0.413105\nPIQ        0.930694  0.766021  1.000000 -0.156863 -0.287676   0.568237\nWeight    -0.278140 -0.350453 -0.156863  1.000000  0.406542  -0.076875\nHeight    -0.356110 -0.355588 -0.287676  0.406542  1.000000   0.301543\nMRI_Count  0.498369  0.413105  0.568237 -0.076875  0.301543   1.000000",
      "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>FSIQ</th>\n      <th>VIQ</th>\n      <th>PIQ</th>\n      <th>Weight</th>\n      <th>Height</th>\n      <th>MRI_Count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>FSIQ</th>\n      <td>1.000000</td>\n      <td>0.944400</td>\n      <td>0.930694</td>\n      <td>-0.278140</td>\n      <td>-0.356110</td>\n      <td>0.498369</td>\n    </tr>\n    <tr>\n      <th>VIQ</th>\n      <td>0.944400</td>\n      <td>1.000000</td>\n      <td>0.766021</td>\n      <td>-0.350453</td>\n      <td>-0.355588</td>\n      <td>0.413105</td>\n    </tr>\n    <tr>\n      <th>PIQ</th>\n      <td>0.930694</td>\n      <td>0.766021</td>\n      <td>1.000000</td>\n      <td>-0.156863</td>\n      <td>-0.287676</td>\n      <td>0.568237</td>\n    </tr>\n    <tr>\n      <th>Weight</th>\n      <td>-0.278140</td>\n      <td>-0.350453</td>\n      <td>-0.156863</td>\n      <td>1.000000</td>\n      <td>0.406542</td>\n      <td>-0.076875</td>\n    </tr>\n    <tr>\n      <th>Height</th>\n      <td>-0.356110</td>\n      <td>-0.355588</td>\n      <td>-0.287676</td>\n      <td>0.406542</td>\n      <td>1.000000</td>\n      <td>0.301543</td>\n    </tr>\n    <tr>\n      <th>MRI_Count</th>\n      <td>0.498369</td>\n      <td>0.413105</td>\n      <td>0.568237</td>\n      <td>-0.076875</td>\n      <td>0.301543</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "menDf.corr(method='pearson')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "               FSIQ       VIQ       PIQ    Weight    Height  MRI_Count\nFSIQ       1.000000  0.955717  0.939382  0.038192 -0.059011   0.325697\nVIQ        0.955717  1.000000  0.802652 -0.021889 -0.146453   0.254933\nPIQ        0.939382  0.802652  1.000000  0.113901 -0.001242   0.396157\nWeight     0.038192 -0.021889  0.113901  1.000000  0.552357   0.446271\nHeight    -0.059011 -0.146453 -0.001242  0.552357  1.000000   0.174541\nMRI_Count  0.325697  0.254933  0.396157  0.446271  0.174541   1.000000",
      "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>FSIQ</th>\n      <th>VIQ</th>\n      <th>PIQ</th>\n      <th>Weight</th>\n      <th>Height</th>\n      <th>MRI_Count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>FSIQ</th>\n      <td>1.000000</td>\n      <td>0.955717</td>\n      <td>0.939382</td>\n      <td>0.038192</td>\n      <td>-0.059011</td>\n      <td>0.325697</td>\n    </tr>\n    <tr>\n      <th>VIQ</th>\n      <td>0.955717</td>\n      <td>1.000000</td>\n      <td>0.802652</td>\n      <td>-0.021889</td>\n      <td>-0.146453</td>\n      <td>0.254933</td>\n    </tr>\n    <tr>\n      <th>PIQ</th>\n      <td>0.939382</td>\n      <td>0.802652</td>\n      <td>1.000000</td>\n      <td>0.113901</td>\n      <td>-0.001242</td>\n      <td>0.396157</td>\n    </tr>\n    <tr>\n      <th>Weight</th>\n      <td>0.038192</td>\n      <td>-0.021889</td>\n      <td>0.113901</td>\n      <td>1.000000</td>\n      <td>0.552357</td>\n      <td>0.446271</td>\n    </tr>\n    <tr>\n      <th>Height</th>\n      <td>-0.059011</td>\n      <td>-0.146453</td>\n      <td>-0.001242</td>\n      <td>0.552357</td>\n      <td>1.000000</td>\n      <td>0.174541</td>\n    </tr>\n    <tr>\n      <th>MRI_Count</th>\n      <td>0.325697</td>\n      <td>0.254933</td>\n      <td>0.396157</td>\n      <td>0.446271</td>\n      <td>0.174541</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "womenDf.corr(method='pearson')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:>"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wcorr = womenDf.corr()\n",
    "sns.heatmap(wcorr)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}