{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.tools import plotting\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gender  FSIQ  VIQ  PIQ  Weight  Height  MRI_Count\n",
      "0  Female   133  132  124   118.0    64.5     816932\n",
      "1    Male   140  150  124     NaN    72.5    1001121\n",
      "2    Male   139  123  150   143.0    73.3    1038437\n",
      "3    Male   133  129  128   172.0    68.8     965353\n",
      "4  Female   137  132  134   147.0    65.0     951545\n",
      "5  Female    99   90  110   146.0    69.0     928799\n",
      "6  Female   138  136  131   138.0    64.5     991305\n",
      "7  Female    92   90   98   175.0    66.0     854258\n",
      "8    Male   141   93   84   134.0    66.3     904858\n",
      "9    Male   133  114  147   172.0    68.8     955466\n"
     ]
    }
   ],
   "source": [
    "data = pandas.read_csv('brain_size.csv', sep=';', na_values=\".\")\n",
    "data=data.drop(labels='Index',axis=1)\n",
    "print data[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Uni-variate description of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>count</th>\n",
       "      <td>40</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.00000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>4.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>117.450000</td>\n",
       "      <td>112.350000</td>\n",
       "      <td>111.02500</td>\n",
       "      <td>151.052632</td>\n",
       "      <td>68.525641</td>\n",
       "      <td>9.087550e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>31.136548</td>\n",
       "      <td>23.616107</td>\n",
       "      <td>22.47105</td>\n",
       "      <td>23.478509</td>\n",
       "      <td>3.994649</td>\n",
       "      <td>7.228205e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>72.00000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>7.906190e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>90.750000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>88.25000</td>\n",
       "      <td>135.250000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>8.559185e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>131.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>115.00000</td>\n",
       "      <td>146.500000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>9.053990e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>138.250000</td>\n",
       "      <td>129.750000</td>\n",
       "      <td>128.00000</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>70.500000</td>\n",
       "      <td>9.500780e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.00000</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.079549e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Gender        FSIQ         VIQ        PIQ      Weight     Height  \\\n",
       "count      40   40.000000   40.000000   40.00000   38.000000  39.000000   \n",
       "unique      2         NaN         NaN        NaN         NaN        NaN   \n",
       "top      Male         NaN         NaN        NaN         NaN        NaN   \n",
       "freq       20         NaN         NaN        NaN         NaN        NaN   \n",
       "mean      NaN  117.450000  112.350000  111.02500  151.052632  68.525641   \n",
       "std       NaN   31.136548   23.616107   22.47105   23.478509   3.994649   \n",
       "min       NaN   77.000000   71.000000   72.00000  106.000000  62.000000   \n",
       "25%       NaN   90.750000   90.000000   88.25000  135.250000  66.000000   \n",
       "50%       NaN  131.000000  113.000000  115.00000  146.500000  68.000000   \n",
       "75%       NaN  138.250000  129.750000  128.00000  172.000000  70.500000   \n",
       "max       NaN  240.000000  150.000000  150.00000  192.000000  77.000000   \n",
       "\n",
       "           MRI_Count  \n",
       "count   4.000000e+01  \n",
       "unique           NaN  \n",
       "top              NaN  \n",
       "freq             NaN  \n",
       "mean    9.087550e+05  \n",
       "std     7.228205e+04  \n",
       "min     7.906190e+05  \n",
       "25%     8.559185e+05  \n",
       "50%     9.053990e+05  \n",
       "75%     9.500780e+05  \n",
       "max     1.079549e+06  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boxplots and histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Boxplots\n"
     ]
    },
    {
     "data": {
      "image/png": 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A04ALgP9afJG0mZmN2YIFPSIeA17pmXs4Ig4Vw8eB1cX1i4E7I+KNiNhDu9ifPbpw05Fz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x86b29b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.boxplot(column=['FSIQ', 'VIQ', 'PIQ'],meanline=False,showmeans=True,return_type='dict')\n",
    "print \"Boxplots\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Histograms\n"
     ]
    },
    {
     "data": {
      "image/png": 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sEu22+1BtGeXUlBRITnHsP4i7BvfK/zryC1HQuZkAI5F2Eu0+UniaHsmbm+T/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\nOpa7cOGAleq6l9f3dF/Hj/g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6cLdkjzWgT0Kl4xbj7hAS6tItg4bkSJqOu0s5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\nEzv5SCQSGcL8P4SBQOkOahVvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8989198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist(column=['FSIQ', 'VIQ', 'PIQ','Weight', 'Height', 'MRI_Count'],  bins=10)\n",
    "print \"Histograms\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identify errors or ouliers - unicariate analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>16</th>\n",
       "      <td>Female</td>\n",
       "      <td>240</td>\n",
       "      <td>132</td>\n",
       "      <td>120</td>\n",
       "      <td>127.0</td>\n",
       "      <td>68.5</td>\n",
       "      <td>852244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Gender  FSIQ  VIQ  PIQ  Weight  Height  MRI_Count\n",
       "16  Female   240  132  120   127.0    68.5     852244"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.FSIQ>data.FSIQ.mean()+2*data.FSIQ.std()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Easy solution: remove it (last resource)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\inanna\\anaconda2\\lib\\site-packages\\ipykernel\\__main__.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Gender, FSIQ, VIQ, PIQ, Weight, Height, MRI_Count]\n",
       "Index: []"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new= data.drop(16)\n",
    "new[data.FSIQ>data.FSIQ.mean()+2*data.FSIQ.std()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Boxplots\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa178a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new.boxplot(column=['FSIQ', 'VIQ', 'PIQ'],meanline=False,showmeans=True,return_type='dict')\n",
    "print \"Boxplots\"\n",
    "data=new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histograms for qualitative data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa3dc358>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAEbCAYAAAARetX+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n2gCDiDbAIKINMMj/AyNl6HdTzZKWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa045b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.Gender.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Univariate analysis depending on label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>FSIQ</th>\n",
       "      <th>Height</th>\n",
       "      <th>MRI_Count</th>\n",
       "      <th>PIQ</th>\n",
       "      <th>VIQ</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">Female</th>\n",
       "      <th>count</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.900000e+01</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>110.842105</td>\n",
       "      <td>65.621053</td>\n",
       "      <td>8.632025e+05</td>\n",
       "      <td>109.947368</td>\n",
       "      <td>108.263158</td>\n",
       "      <td>137.736842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>23.845016</td>\n",
       "      <td>2.255999</td>\n",
       "      <td>5.736996e+04</td>\n",
       "      <td>22.428835</td>\n",
       "      <td>21.586627</td>\n",
       "      <td>17.242847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>7.906190e+05</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>106.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>89.500000</td>\n",
       "      <td>64.500000</td>\n",
       "      <td>8.243520e+05</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>124.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>101.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>8.564720e+05</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>139.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>133.000000</td>\n",
       "      <td>66.500000</td>\n",
       "      <td>8.864400e+05</td>\n",
       "      <td>129.500000</td>\n",
       "      <td>129.000000</td>\n",
       "      <td>146.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>140.000000</td>\n",
       "      <td>70.500000</td>\n",
       "      <td>9.913050e+05</td>\n",
       "      <td>147.000000</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>175.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">Male</th>\n",
       "      <th>count</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>2.000000e+01</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>117.600000</td>\n",
       "      <td>71.431579</td>\n",
       "      <td>9.548554e+05</td>\n",
       "      <td>111.600000</td>\n",
       "      <td>115.250000</td>\n",
       "      <td>166.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>24.843510</td>\n",
       "      <td>3.283131</td>\n",
       "      <td>5.591135e+04</td>\n",
       "      <td>23.540335</td>\n",
       "      <td>25.640993</td>\n",
       "      <td>20.047656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>66.300000</td>\n",
       "      <td>8.799870e+05</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>132.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>95.250000</td>\n",
       "      <td>68.900000</td>\n",
       "      <td>9.195292e+05</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>95.250000</td>\n",
       "      <td>148.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>133.000000</td>\n",
       "      <td>70.500000</td>\n",
       "      <td>9.472415e+05</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>110.500000</td>\n",
       "      <td>172.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>140.000000</td>\n",
       "      <td>73.750000</td>\n",
       "      <td>9.734960e+05</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>180.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>144.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.079549e+06</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>192.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    FSIQ     Height     MRI_Count         PIQ         VIQ  \\\n",
       "Gender                                                                      \n",
       "Female count   19.000000  19.000000  1.900000e+01   19.000000   19.000000   \n",
       "       mean   110.842105  65.621053  8.632025e+05  109.947368  108.263158   \n",
       "       std     23.845016   2.255999  5.736996e+04   22.428835   21.586627   \n",
       "       min     77.000000  62.000000  7.906190e+05   72.000000   71.000000   \n",
       "       25%     89.500000  64.500000  8.243520e+05   92.000000   90.000000   \n",
       "       50%    101.000000  66.000000  8.564720e+05  110.000000  112.000000   \n",
       "       75%    133.000000  66.500000  8.864400e+05  129.500000  129.000000   \n",
       "       max    140.000000  70.500000  9.913050e+05  147.000000  136.000000   \n",
       "Male   count   20.000000  19.000000  2.000000e+01   20.000000   20.000000   \n",
       "       mean   117.600000  71.431579  9.548554e+05  111.600000  115.250000   \n",
       "       std     24.843510   3.283131  5.591135e+04   23.540335   25.640993   \n",
       "       min     80.000000  66.300000  8.799870e+05   74.000000   77.000000   \n",
       "       25%     95.250000  68.900000  9.195292e+05   86.000000   95.250000   \n",
       "       50%    133.000000  70.500000  9.472415e+05  117.000000  110.500000   \n",
       "       75%    140.000000  73.750000  9.734960e+05  128.000000  145.000000   \n",
       "       max    144.000000  77.000000  1.079549e+06  150.000000  150.000000   \n",
       "\n",
       "                  Weight  \n",
       "Gender                    \n",
       "Female count   19.000000  \n",
       "       mean   137.736842  \n",
       "       std     17.242847  \n",
       "       min    106.000000  \n",
       "       25%    124.500000  \n",
       "       50%    139.000000  \n",
       "       75%    146.500000  \n",
       "       max    175.000000  \n",
       "Male   count   18.000000  \n",
       "       mean   166.444444  \n",
       "       std     20.047656  \n",
       "       min    132.000000  \n",
       "       25%    148.750000  \n",
       "       50%    172.000000  \n",
       "       75%    180.750000  \n",
       "       max    192.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupby_gender = data.groupby('Gender')\n",
    "groupby_gender.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Boxplots separated by Gender\n"
     ]
    },
    {
     "data": {
      "image/png": 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qch/Xij3+EEKoGfeJP+qD1efzPcrH2/b0Fg/kv1aP+8Tvkcdr9YRQJ3GtnlB7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\nIvGHEELNROIPIYSaicQfQgg18/8BY7kfyeXjQv8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa9dc6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "groupby_gender.boxplot(column=['FSIQ', 'VIQ', 'PIQ'],meanline=False,showmeans=True,return_type='dict')\n",
    "print 'Boxplots separated by Gender'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bi-variate analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scatter matrixes for correlations betwen numerical variables \n"
     ]
    },
    {
     "data": {
      "image/png": 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0d3fXncrXCzGRRigU36oFWwjZOURVcTa6byvvwVrHN7pXm1W5huU+9dRFzJy5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\n7zImTTqDxx//IiJdvP3tZ7N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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa242fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8JuC1JwR1+QK6porIyXEGeZlXimFUJzZ2S09e5S8i7xCRc0VkHc7i7W+B/xnk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\nXe+rb37+VYz5iVc3Jr/oqOj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+xZt5OeoUunDzG+ACVX3De1BEfpKnXqOITFXVt1T1KRG5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\n/x8zfOExtksk6AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x864ff28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter matrices for different columns\n",
    "plotting.scatter_matrix(data[['Weight', 'Height', 'MRI_Count']])\n",
    "plotting.scatter_matrix(data[['PIQ', 'VIQ', 'FSIQ']])\n",
    "print 'Scatter matrixes for correlations betwen numerical variables '"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               FSIQ       VIQ       PIQ    Weight    Height  MRI_Count\n",
      "FSIQ       1.000000  0.897984  0.865884 -0.075450 -0.118544   0.374143\n",
      "VIQ        0.897984  1.000000  0.778131 -0.052778 -0.071560   0.360769\n",
      "PIQ        0.865884  0.778131  1.000000  0.013634 -0.076802   0.399081\n",
      "Weight    -0.075450 -0.052778  0.013634  1.000000  0.710607   0.503301\n",
      "Height    -0.118544 -0.071560 -0.076802  0.710607  1.000000   0.606555\n",
      "MRI_Count  0.374143  0.360769  0.399081  0.503301  0.606555   1.000000\n"
     ]
    }
   ],
   "source": [
    "print data.corr(method='pearson')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding errors (or outliers) with bi-variate analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a= 0.925554782663 b= 11.7609688234\n",
      "11.9904462053\n",
      "8    51.492429\n",
      "Name: FSIQ, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\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>8</th>\n",
       "      <td>Male</td>\n",
       "      <td>141</td>\n",
       "      <td>93</td>\n",
       "      <td>84</td>\n",
       "      <td>134.0</td>\n",
       "      <td>66.3</td>\n",
       "      <td>904858</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<div>\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>8</th>\n",
       "      <td>Male</td>\n",
       "      <td>141</td>\n",
       "      <td>93</td>\n",
       "      <td>84</td>\n",
       "      <td>134.0</td>\n",
       "      <td>66.3</td>\n",
       "      <td>904858</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's do regression between a pari of numeric varaibales\n",
    "from scipy.optimize import curve_fit\n",
    "def fit_func(x, a, b):\n",
    "    return a*x+b\n",
    "params = curve_fit(fit_func, data.PIQ,data.FSIQ )\n",
    "print 'a=',params[0][0], 'b=',params[0][1]\n",
    "\n",
    "# Find errors or outliers\n",
    "error = data.FSIQ-[fit_func(x,params[0][0],params[0][1]) for x in data.PIQ]\n",
    "print np.std(error)\n",
    "print error[error>np.mean(error)+2*np.std(error)]\n",
    "\n",
    "data[data.FSIQ-[fit_func(x,params[0][0],params[0][1]) for x in data.PIQ]==max(data.FSIQ-[fit_func(x,params[0][0],params[0][1]) for x in data.PIQ])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solving the error susbtituting current value by predicted value with linear regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>118.0</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>NaN</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>143.0</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>172.0</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>147.0</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>146.0</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>138.0</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>175.0</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>134.0</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>172.0</td>\n",
       "      <td>68.8</td>\n",
       "      <td>955466</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<div>\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>118.0</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>NaN</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>143.0</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>172.0</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>147.0</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>146.0</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>138.0</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>175.0</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>134.0</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>172.0</td>\n",
       "      <td>68.8</td>\n",
       "      <td>955466</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.set_value(8, 'FSIQ', fit_func(data['PIQ'][8],params[0][0],params[0][1]))\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scatter matrixes for correlations betwen numeriacal variables \n"
     ]
    },
    {
     "data": {
      "image/png": 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SkKVAh0Y2sHui9bTahnJqK8szOodO0E8b8bB7ovW0zYaSFKnYYAu3QPISYCNwv6qeFhx3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\n7zImTTqDxx//IiJdvP3tZ7N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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa9963c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EzgUuAf4OeAy4D8cls5i4Y39A19txKnpFHuQVBvMqMIzKwcZjtOTy9rkOuAf4\ndKYcPwXiD+j6M04px8AgrziKuZhXQWnw+/kbRiHYeIyWWIO8gJPwBXThKP/AIK84+mkzjWiwBV/3\niLbgmxc2Houn4GIuJThxpiCvRfgCuuII8vITVcRgPu3rJeLYlL97RFP+kwgzVnLt4/+uXsZTPpRN\n+ZeKUiv/qGYU+Rw3lUrR17ecwcERurraWL9+dc3esKb83SOa8p+g2DHob/+xj32QxYs/URfjKR+K\ncvWsRaLKnZ3PcROJBIODI8yefSeDgyMkEomS9MEwqoFix6C//dDQkI2nPIld+YtIo4jcKyKPiMjn\n3G0rRGSziNwtIo1RnTvtNtbc3BxJxGA+kYitra10dbWxc+dldHW10draWpI+GEalo6qoKj09HQWP\nQf9Y6+zstPGUJ7GbfUTkYuBYVf28iKwBvgVco6rvE5EVwIuq+i1fm5Indluy5BKSyWTJIwbN5j+V\ntNng/vvv55lnngnV5jOf+Qxm9qk9s493HHZ3d9DXd0HBwVtm8w+m0MRuUXEM8JT7/ufAiTj5g8AJ\n/LoE5wchJ/kuFo2NjbF160scddQytm5dS19fcpLbWL4Lvdn2T0cihqHeohY/9KEljI0tAoJmeS8E\nfF9Z5F+YvbbwjgUg4/v0Pe4dhwMDa1m8WCZ+XMKOP+++3rHW0NBAe3t7RP9l7VEO5f8ccCaOF9BZ\nwDM4aSDACfI6POgAYRaLMi0IjY8n2LjxSrq62mhubs7rePmevxT/Qy2i+k/AkQF7/QD4txh6Uxoc\nxR/2yaO2mHwfd6AK27YN+94fvL+bm5unjMN8xkK9jpsoKMez0YPADBH5EbCPkJW8brjhhonXww8/\nHLhY5F8Q2r17N01NrVx88ddpamolmUxm3Tdo8akUC8a1XPh806ZNk+Rl1C7e+7i//wX6+3815b33\n/k4mk1PGYT5joZbHTdzEPvNX1RRwNYCI3IbzY3ArTvrohbhJ4Px4lYiqMjz8MgMD2ReL0gtC6X1a\nW1vp6ZnDwMAaenrmTGrj3zdo8Snf/aM6RqXij8BeuXJl+TpjRIr3Pu7tnevO9ie/997fzv5Tx2HY\nsVDL4yZuyrHgexSwASey9y5VvUtErgHOA4aBy9wiMN42UxZ8CwkQySdgJOi7UgSJ1Uua2vS1P+SQ\nIxkdfYZwZp+/o1oWfMMvpNbmgm8+Nv+0xx0wYa8fGxujubk5tANGvYybUmBBXkVg9sjiMeVf2Pmr\nRfmHJZNZIe4oAAAdLUlEQVTH3bp199p4iRAL8ioCs0caRmnwj49EImHjpYyUI8hrhoh8T0QeFZH7\nRWSaiHxJRPpF5Mu52pYyt3fYY+UTuGXl5oxaJex4ybWff3w463A2XspFOWz+FwFvV9UbReQ6nEXn\no1T1ChFZC3xdVXf42mgqlSqZSaUQ106z4xeOmX0KO38lmX1uvXV94HgJ64Iddh3OKJ5KM/v8moNR\nPofh3I2BlbxKaVLJ91j5BGPVW+CWUR+EGS9hxpV/fNh4KR/lUP7PAz0i8jRwKk4lrz3ud1mDvEpp\nUjHzjGHkR5jxYuOquiiH2ecKoEVVvygin8J5CnhGVTe6JqGjVfX/+tro9ddfj6ry+uuvc+6553LW\nWWcV1Q973IwOfyWvlStXVqHZZzqwP+S+RHD+qPpamNknlUqFGi82riqLinL1FJErgX2qeoeIfBj4\naxybf6yVvOwmjY9qtfnX+/mDXD1tDFU+lWbzvwf4gDilHS8FvgrsF5F+YNyv+KMgvTC1bNkabr/9\nnrKUqDOMasbGUPUTu/JX1VdV9b2qepaqvkdVX1HV5araqxlKOEaB+eMbRnHYGKp+6jLIyxamDKM4\nbAxVP+Ww+b8HuNb9+DbgCuCtwAXAEE5un3FfG7P5VzFm86/O85vNv/qpKJu/qv6Ha/I5CyeR2w5g\ngaqegVPk5cI4+mH+xYZRHDaGqpuymX1EZA4wApzM5EpeGYO8KoVSppgwjFrDxkf1UI5KXmn+G3A/\nTpRvYJBXJWBZOw0jOzY+qotyKv/zgIuAbuAod1vOSl5p/MVC4mKyh8Mq+vrGQtfrrSf8QV6TeYzg\n3/dwRd6NysLGR3VRlnz+ItKGU8jlPSJyJPANVT1PRFYAL6nqRt/+9gxpGIZRABWz4OtyAfAAgKru\nBjaLyGYc+/93MjVQ1bxf6ZQQcbSrhXOlUiluvXU9ixffyK23rieVSpWkj0BNXJ9qPFemNtnknEt+\npf7/a6F9Kc7tareQL/++12fdzyu7bESq/EVktojsEJGkiHjPNRO4xPO5HafXu9VXwtGIDwvcqQ9M\nzgZEP/P/E3A2nqLsIjINOAn3J0pE3gk0q2ov8EYROTXiPtUNqpM9L/yf/VjgTn3gyLmDX//6Jnp6\nOqbIWVV57bXXAmeORnUTqfJX1ddU9VWc6JI0lwN3ej53EyKffyEUuihcSLtynyuVSrFr1y5SqRQw\nNfdKKpVi794DOXOxiAhLl17K2rVXT/LUCOpjph8Vf5tyX59KOpeq0tXVlbdyLWX/VEE1hb8Lqspt\nt61n69ZhbrttfWAfi3W8qOb25e47FNc+lgVfN4nbOTg/NutV9YMi0q+qvW41rydU9Ycicg7Qrao3\n+tqrzUKyk0ql6OtbzuDgCF1dbaxfv5pkMsmVV67mqKOW8Yc/rGXVqsu55pqv09GxguHhVaxde3VJ\nPDHSPzJe9z5gUuRnqQqA1wJhrlfUjI6OsmzZambPXs7OnatZu3b5xL2wZ88eTj75/bzyyls47LDn\n+fnP/51DDz3U5BcR+VZXCxvJnZZXrgjfuFw90z1ejJPVEw4+DbyC4+IJFe7qWakkEgkGB0eYPftO\nBgcvI5FI0Nrayvh4go0br6Srq40jjzySnp4O+vtvord3bslMOn73vkWLRrn++s/xyCPbePObD+PU\nU99RkvPUCpmu14YN3y3aN141fKqF5uZmDhxIsHHjR+nqaqO5uXlS/0ZHhTe84UJGR79g6wE1TFze\nPuK+3gZcKSIPASeIyMeBbcBCd7+FeNYHvNxwww0TL1P8k2ltbaWrq42dOy+jq6uN1tZWkskkTU2t\nXHzx12lqcj77H/WD1gDC4F8nAPjjH5s5//yH+au/OpUVK1aU4D+sHTJdr3wXXzOt5eSTXjmZTNLY\n2MrFF99KY6Nzb6Rpa2tj4cK30NJyOwsXvoW2trbC/1mjool05i8iTcBDOAu8DwPXqeq17nf9qnqL\n+36fm8//ZxpDPv9ao6GhgfXrV0/M+BsaGlwlM4eBgTX09MwBYNu2YebO/Qzbtq2ir680M870OkFf\n39jE00RPTycDA7ZonIlir1cms1G+wVUtLS3Mnz+HgYFbmT9/zqRzNjQ0sGHDmkn3klGblCXIK1/M\n5l8YXlMAMElpLFp0Ph//+FdKvgbgP6/Z/IPJx2Tj2OvXTJJbS0tL3mkV8jmnyS86ymnzN+VfI4QZ\nzP4fg9tu20B//6/o7X0rV1yxKLLFRlMexZFKpSZm4iKSUdHno8zzxeQXHfWw4GtESCZTQCYFkE7B\nm24jAiINWO6tyiWTJ5fXbJSWs1e2hhEGM+jVAI7N9yVmz76agYGXJhYN/b7/aVSVkZERtm4d4thj\nr2NgYLgor45SLBzXCqW4Ft5jJBIJtm/fxRFHrGH79l0kEokS9taoZ6Je8J0NfA84Hielw/HAOuAA\n8IKqftTd70vAacAOVf1ElH2qRZqbm3n99RH+7d8+wumnt9Pc3JxxxtjQ0OB5SniJ8fEEQ0M3T1n0\ny4ewTx31QCmuhf8YH/3o3zM+/luefPKDHH30fo444gi73kZJiDu9w7OqOl9VzwRERE619A7FMzY2\nxosv/onm5rfy4ot/YmxszOf7PzIxYzzoGXINTU2trFp1eVEKxPLEHKQU18J/jOHhYcbGZnHooSsY\nG5vF8PCwXW+jJMSa3kEn1+bdD/yWCNM71BfTUe0GpgOZff/B72c+h7a2tqJmjpYP6CCluBb+vDtH\nHnkkhxwCqdRDHHIIbrCeXW+jeGJN76CqKRE5D7gJeA74IHANlt6hKBwTz9Vs2zZCd3cb69evoaGh\nYZKXiNdfu9SeIUHHqydvkWKvrZNb56AX1tKll3L77Rv4yU9+ydlnH88VV/QB8aaDqCf5xU09ePtM\n9FhVHwQeFJGvAO8D/oyldygKJ5q3jb//+39l504nr8/MmTNpaGigvb19yv6l9gzxHy93Ja/apthr\nOzY2NikYb/HiJFdc0cfixZOVvXn2GMUS58x/IdCoqq+5224E+oHdwBJVvVJEbgHu8Ef52sw/N5W+\n6Gozx/BUoixNftFRs0FenvQOpwBPAptxFoAVeF5Vl7j7rXb3+ZmqXpXhOKb8A4gyyKdYTHnkR6XJ\n0uQXHTWr/EuFKf/qxpRHdWPyi45yKn8L8jIMw6hDTPkbhmHUIbEWcBeReSKyVUT+U0S+6Nnv0yKy\nWUTuFpHGKPtkGIZhRO/qmY7wvd/9PAScpaqvich6EXk7MAIsUNUzRGQFcCHwrYj7ZRhGHuzbt487\n7riDAwcOBO7b1NTERz7yEaZPnx5Dz4xCiVT5u26dr4nrsqCq3qxUB4BxYB6wyd32CHAJpvwNo6J4\n6KGH+OQnV6H6d4H7inyf2bNnc+GFF8bQM6NQYg/yAhCRk4AjVPVZETkF2ON+9SpweEx9MgwjD6ZN\nO5k9e74auN+sWb+NoTdGscSez19EDge+Arzf3fQKcJT73iJ8a4B6jvA1jGohtPIXkVkAqronaN9M\nzd1jNAHrgRWqutv97nHgSuALBBRwN6oD/4/zypUry9cZwzAyEujtIyLLReT3wEvAsIj8SkQ+6H73\n5oC2TSLyI5wC7v8BXIeTt//zIvITEelyfwQ2i8hm4GTgO8X9S4ZhGEYQOWf+InIDzoLsGar6orvt\nGGCNiHQAHwPmZmuvqgeAc32bP5thv5uBm/PquWEYhlEwQWafRcA7VHVfeoOqvigif4+TkO3SKDtn\nGIZhREOQ2SflVfxpVPUvwO9V9bvRdMswDMOIkiDl/zu3wMokRORs4PfRdMkwjGrnIx+5EhEJfLW3\nd4Y+Znt7Z6hj5nvceiXI7HMV8ICIbAF24PjrvwuYD5wfdPAMBdzbvJ9VNeXuZwXcDaOGeOWVXYTJ\nQDkyEj5l9cjIcKhj5nvceiXnzF9VfwGciFN0pRM4xn1/oqo+E+L4/gLu/s9YAXfDMIz4CfTzd23+\n3yjk4BnSO0z67JKpgPuOQs5nGIZhhCPI1XMvmZ+zBFBVnZXhu0zkelY7DHjBff8qcELIYxqGYRgF\nklP5q+ohEZ3X+2PwClbAvaaw9A6GUfkEzfybgddV9XX389uAvwWGVPX+XG39h3JfmT5vA5YAG3HS\nO9yR6QCW3qF6sPQOhlH5BLl6Poyz0IuIzMVR1McA/0NEPhd0cF96h4dF5F3+z6r6U2C/iPQD46r6\nROH/jmEYhhGGoAXfw1X1eff9h4F7VfV/isg0nEXZa3M1zpLewf8ZVV0esr+GYRhGCQia+Xtt82cD\nP4IJr51UVJ0yDKNeeGPowC2jtAQp/6dE5Asi8gmcBG4/BBCRwyLvWR2QSqXYtWsXqZT9jsaFXfNK\nYz/OHDPMyyglQcr/Y8Afcez+/1VVk+72E3Dy7xsFkkql6Otbzvz5V9PXt9yUUQzYNTeMgwQp/yNV\n9XOqerWq/jy9UVUHVPXuoIOLyGwR2SEiSRFpcLd9SUT6ReTLnv2mbKt1EokEg4MjzJ59J4ODIyQS\nieBGRlHYNTeMgwQp/4nCKiJSSFH1SekcfKkcponIqXGmd1BVRkdHUS3/I2RraytdXW3s3HkZXV1t\ntLa2lrtLNU+U17yS7i3DCEOQt493leWYfA/uSeeQ3uRN5fCI+zlFDOkdVJXbb7+HgYEheno6Wbr0\n0kgWkVSVsbExWlpach6/oaGB9etXk0gkaG1tpaEhsKiaUSQNDQ3cffeXGRoaorOzs2TXPK57yzBK\nST7ePqWY0hwGpGsAvwocDhyaYVvJGRsbY2BgiI6OFQwMDDE2Nlbyc6SVwLJla7j99nsCZ4EiwsyZ\nM01RlJBcM3BV5Wtfu4+VK+/ja1+7r2Sz9DjuLcMoNUEz/5NFZA/OE8AM9z3kn9snjT+Vw5+BcWJI\n79DS0kJPTycDA6vo6emkpaUlr/ZhmKwEVtHXN8bMmTMz7lvLs8VypXcIuqb5yCcf4ri3DKPUBOX2\naSzRedLpHDKlchjPsG0KxaZ3EBGWLLmECy90zCxRKNp8lEBUiqgSKFd6h6BrWqySzmbSExGWLr2U\nvr5gc59hVAqBKZ2LQUSagIdw0zkA13EwlcPP0qkcRGTKtlKjqqxbd2/gTDuszT4T+SgBmy2WnqBr\nmks+QXIPeqpIm/AMo1qIVPlnSe/weIb9Ik/vEGamXQpTTFglYLPF0hPmmmaSTxi51/KTmlGf1I2L\nSXpWODycfaYd98KdLfiWnkKuaRi5h7l/DKOaiHTmX0mEmRWaKaY+CSN3e1Izag2JOyhFRBqB9UAr\n8LiqXisiK3AKwg8Bl6nquK+NxtXPsDb/YtYG6g0RiS34qVC5mDyzIyJ8+9vf5rLL7mLPnuAyHrNm\nXciePQ8QzjtcQu6X/77VEHDn3Gul/v8P/u/u2Mt4Q5fD7HMRzsLuOTjuo73Amap6BvAUcGEZ+jRB\nGLNBLn9+i/SMD/+1zjfOwouZ4Ix6oxzK/xgcJQ/wc+BEYJP7+RGcCN/IKUZJZ7MRF6N8jPzIdK3D\n2O7tx9kwHMqh/J8DznTfn0VMEb5wcOCnUqkpiiMfpZBt8c8iPeMj07VOy2Vo6GZOOaWd5ubmSXK1\nH2fDOEg5FnwfBM52yzkOEVMBd6873ymntPPkkzvp6LiGgYFVLFo0yoYN383q6ue3B2db/LMFY4c4\nInxbWlro7u6gv/9GenvfOiGHJUsuYf/+O9ixYyfr1t2DKmzbNkxPTyeLFp1v7pqG4RK78lfVFHA1\ngIjchvNjcCtOfYCFuBlA/RQb4eudKe7YcTOnnTabHTscJQ1kVQrZfMAz+YubR4hDXBG+IiDSgPcy\nJ5NJduzYRUfHNfT334RqirlzP+P+yGM/zobhErvyF5GjgA04aR3uUtXfichmEdkMDAOR5PT3zsrn\nz5/DkiWXkEwmJxRANqWQb3CPRXrGgyOXYY499rpJcvHKubd3rjvzd+Q6c+ZM+3E2DJfYXT0LoVSu\nnqlUKmsK5WyufrWcgC0uonD1zCaXtI0fmPgRNhfO4jBXz+gop6tn3Sj/YpS4+YAXR1R+/n652A91\nNJjyj4568/MvC8V44pgPeGXil4t5WxlGeGpa+Xvd/Pzumc3NzQX5e5ufeOEUe+2C2qc9gF544Ua6\nuztsQdcwclCOBd8ZwL8DLThunR8APgecBuxQ1U+U4jyZTADpxb7m5uZQ6Z3DHNOeBsJR7LUL2z6T\nB5BhxE17eycjI8Pl7kZOyjHzfy+wXVXPwknvfC0FFHAPmgVmMgGkzQTJZLIg84CZFQqn2GsXpv3Y\n2Bhbtw7x5jdfxdatJh+jfDiKX0O8ykc5lP+vcWb94NT0VaYWcM9JmEjNXCl4ne86+PWvb6KnJ7x5\nwNL6Fk6Ya5frBz1M++bmZsbHE2zceCXj4wmam5sj+V8MoxYoR4Tv80CPiDwNJHAUvje9wwlBBwjj\nex8UcKUKqinyMT9bEFfhBMsjuFJW0LVPJpM0NbVy8cU3sXPnapLJpMVcGEYWyqH8Pwx8V1W/KCKf\nwnkKyCu9w5lnnhkqUjNbwNXY2Bjbtg0zd+5n2LZtFYsXhw/ztyCuYLKld8h17cL+oOe69s7TwRwG\nBtbQ0zPHnswMIxfphFdxvYArgY+47z8M/G/gVvfzLcBpGdqoqmoqldK9e/dqKpWa9D4bjz76aMbt\nqVRKb711vS5efKPeeuv6KcfI1i4XhbSphnMV2s7bBgg8RjaZeNuFkXkqldIf/OAHOfcJ0+co28R5\nrlL0D9Bvf/vbOmvWhZp+Zj74enTKtlmzLnAN2v59M73C7pdt36nnT+8b5fUJahvu/3q0yP8/+H93\n32fUxeWw+d8DfEBEHgUuBb7KwaLu45qlgLv67PxAoO99tuRiaRPC2rVXZ/QaKSQpWaGJzCr9XIW2\n87cJOkY2maTb+eWvWex1IsLg4GBBJrlKl0Vl9q+w45eO4s5fTALC4pMXlrd97MpfVV9V1feq6lmq\n+h5VfUVVl6tqr6pela1dqT1tLHCr8sglE/O0MozSUjU1fC1dcn1j8i8vhxxyCPv2PcKsWe+YtH3f\nvhGmT/+Wb1tl+7cbDlWT26fcfTAMw6hGtJoTuxmGYRilpaZz+xiGYRiZMeVvGIZRh5jyNwzDqENM\n+RuGYdQhVePqGQY3I+jpwOE4aSK2Zwsa87RpBC70twO+o6oHSnmuOurjXwFvcr/6I/DnMP0t5NyF\nXpsi/s+ak18+/Su0T/mep1T/Wynbl7v/xf7vU45XK94+IvJl4I04ieJexckTtBAnajhr8JiI3A08\nBTzia3eyqvaV+Fz10sf/DQzh1J3rBFYG9bfQcxdybYo4V03KL2z/Cu1TMdeh2P+txO3L1v9i+56R\nbHkfqu0F9Oez3fP95ny2F3muuuhjmPelOnch16aIc9Wk/ML2r9A+FSurEp6/avtfbN8zvWrJ7POE\niNzGwRTRs4BzgCcD2n1XRL6Hkygj3e5M4MEIzlVouwfK3MeFefZxl4g8g1Or4Zci8ukQ/S20z4XI\nr9Bz1ar8/P07FOjN0L9C+5SmUFmV6vzFti9EjqU6f6HjMiu1ZPaZhlMVTIEDwBvc919V1Yxpoj1t\ne4ETcW76V4AHVfV3AW3eCXR72mxX1Z+G6Ge63WFuu20h270JmOdp97iq7s7jXH+OsY9nAz8HXsdx\nKgjb34JkWIj83HZ5y7BW5Remf8WMMc8xCpJVhv8tr3GXoX1e8vO0z1uOOc4fWqal6LufWpr5fxOn\nLORhwKnAD3AWG78JvCdbIxH5HDADR1l1A/uAk0VkQFXvynG+BvfVBDS6r0BcYU0SmIi8S1UfD2ja\nBryoqs962nWp6mBAHzcCLwPvA44spI8i8nF/n7O0+yPwAxG5E+fm/ANwB85N+ucQp85bhkXIDwqQ\nYa3KzyO7E3GUcyfgV2oFjTFPP4qRVZqCxl2aIuSXphA5eilIplD4uMxGLc38H1WnLjAi8rSqvsO/\nPUu7R1T1HM/nH6nquSLyY1VdmKXNl4FpTF34CVqMy+RaK8DDqnpujnZfxLnpDgBHAP+gqrtF5Ceq\nenaWNl93j70f5wb7A87jYquqLslxrs1MLi4qONXVfqFOneVs7R5W1feKyHLgM+7rv+LM0Brdfjyg\nqmtzHCNvGRYiP3e/vGVYq/Lzye4c4PvAfOD3qnqtZ7+CxpinfUGy8uxf0LjztC9Ifp72ecvR174g\nmbptCxqXuailmf+YiHwGpzLYy+JUCXsZ50LnIiEi/wtnFf9M4Bl3e64ZxakZLvj94tQkyMUojmtY\nOtGSuu9PCmh3mqqeCSAiJwH/LiIrAtrM9bR5WlUvdt8/GtDufrc/d6rqJrfNQ6r6NwHtprl/LwKe\nVtXbgNtEZIuqvltE2oALAo5RiAwLkR8UJsNalZ9XdmepagpXdr79Ch1jaQqVVZpCx12aQuWXphA5\neilUplD4uMxKLSn/9wPvxSkQ/1mcKmHTgQ8EtOvDuenfAWzj4OLNohxtCl24+SVwkaq+6t0oIj8K\naNckItNU9TVVfUpELgLWA2/P1cbz/jrv6XKdSFW/5Np2LxeRK3CK74ThBBG5CzgWp1pbmunucUeA\ndQHHKESGhcgPCpNhrcrPK7s3An9xt0/37VfoGEtTqKzSFLtgW6j80hQix0ntPe9DyxSKGpdZqRmz\nT9xkWnwBmnLZDkVkNvAnVX3Ntz2nzVFE5gFDqprwbGsCrlPVz2Zp83bgWVUd92ybBlybrU2GYzQB\ni4G3Ad8K6GOH5+NOVX1NRGYCnwx7vrjJV4a1Kr9qkl0h487TtiD5efbLW46+9kXL1HPOUOMyJ1qg\nj2g9vzi46OR9NQI/irFdQ652FdLHwHbVJMNalV+1yK7YfkbUPqccS3X+KGRUS2afOEnbDr2EsR16\nbY7pR66o2hVq3yxFu3z+t3JRiAxrVX7VIrtCx52/faH/ZynbQ35rDsWuV0yl3L/m1fgCdgCHZtge\nNCuLrV019LHaZFir8qsW2RXbz2puH4WMzOZfADlsh02aO1FXbO2qoY/lpNKvaTWcK26K7Wc1t49C\nRqb8DcMw6hDL528YhlGHmPI3DMOoQ0z5G4Zh1CGm/GNCRMZF5EkReVpEviki093tez37vF1EHhGR\n50TkeRG5oWwdNgAn9F5EzvVtu1pEvi8iT3u2vVtEBkXkl+5rWfy9NdJ4xttP3b9/LSIzRGS9iDzl\njsN+EWl296+7cWjKPz7GVPUUdZJhvQ5c4W5XAPfH4AHgJlV9G04I/Dxxkm0Z5eMe4BLftg8CN3FQ\ndu3ABmCJqh6PkxTtH0QkKJeRER3p8fZO9+9vgKuBXap6kjsOP4ozFqEOx6Ep//KwGZjr23YpsEVV\nHwFQ1X3A/wDySRxllJ5vAX8nIm+AiVQIs4Hfe/ZZBtyhqj8HUNWXgWvcl1EeMuXLmSQ3VX1eVV/3\n7VM349CUf3wITOTl+BuczIZe3o4TyDGBqr4ITBeRWbH00JiCq8gfw0loBs6s/5tMTq87RXbAE8Dx\nkXfQyMYMj9nnW+62bwDXishWEfk/IuKfgEEdjUNL7xAfM0QknX1wM86N6MUbMu7FfqDLz304Sv9B\n9+9HfN9nk51RPpKqeop3g6r+XETm4NSZOBd4TES6VfU5z251Mw5N+cfHlJvRxy9w6qZOICLHALtV\ndU+kPTOC+A7wRTej5HRV/ZkvE+YvgHcB3/NsOw1n9m9UEKqaxJHnd0QkBfwt4FX+dTMOa+7XrILJ\nlrM7vX0DMF9EzgYQkRnAGuCfY+ibkQNVHQP+E+dp7V7PV2nZ3QJ8WEROBhCRI4AbcXLeG+VhyngT\nkR4ROcx9Pw2nEtaQb/+6GYem/OMjm1lAYWJh6XzgMyLyHE791C2qel9M/TNycy9OBkWvPNKy24VT\nqGSdiDwL/A5Yo6r+SlhGfGQab8cC/ykiP8ex6z+uqvd796+ncWi5fSoUETkf+BJOWb3flrs/RnhE\n5EocV95e9VWNMqqLWh6HpvwNwzDqEDP7GIZh1CGm/A3DMOoQU/6GYRh1iCl/wzCMOsSUv2EYRh1i\nyt8wDKMOMeVvGIZRh/x/nZwHRNbGhqgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xda9efd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotting.scatter_matrix(data[['Weight', 'Height', 'MRI_Count']])\n",
    "plotting.scatter_matrix(data[['PIQ', 'VIQ', 'FSIQ']])\n",
    "print 'Scatter matrixes for correlations betwen numeriacal variables '"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender       False\n",
       "FSIQ         False\n",
       "VIQ          False\n",
       "PIQ          False\n",
       "Weight        True\n",
       "Height        True\n",
       "MRI_Count    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>140</td>\n",
       "      <td>150</td>\n",
       "      <td>124</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.5</td>\n",
       "      <td>1001121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Male</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>892420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<div>\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>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>140</td>\n",
       "      <td>150</td>\n",
       "      <td>124</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.5</td>\n",
       "      <td>1001121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Male</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>892420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.Weight.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>20</th>\n",
       "      <td>Male</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>892420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<div>\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>20</th>\n",
       "      <td>Male</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>892420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.Height.isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's solve the case 1 of Weight with regression according to Height because both variables are highly correlated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "array must not contain infs or NaNs",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-20-f2b7979543c6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mfit_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mparams\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcurve_fit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfit_func\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mHeight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mWeight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m'a='\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'b='\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Inanna\\Anaconda2\\lib\\site-packages\\scipy\\optimize\\minpack.pyc\u001b[0m in \u001b[0;36mcurve_fit\u001b[1;34m(f, xdata, ydata, p0, sigma, absolute_sigma, check_finite, bounds, method, jac, **kwargs)\u001b[0m\n\u001b[0;32m    652\u001b[0m     \u001b[1;31m# NaNs can not be handled\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    653\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mcheck_finite\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 654\u001b[1;33m         \u001b[0mydata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray_chkfinite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mydata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    655\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    656\u001b[0m         \u001b[0mydata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mydata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Inanna\\Anaconda2\\lib\\site-packages\\numpy\\lib\\function_base.pyc\u001b[0m in \u001b[0;36masarray_chkfinite\u001b[1;34m(a, dtype, order)\u001b[0m\n\u001b[0;32m   1031\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchar\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtypecodes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'AllFloat'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1032\u001b[0m         raise ValueError(\n\u001b[1;32m-> 1033\u001b[1;33m             \"array must not contain infs or NaNs\")\n\u001b[0m\u001b[0;32m   1034\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1035\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: array must not contain infs or NaNs"
     ]
    }
   ],
   "source": [
    "def fit_func(x, a, b):\n",
    "    return a*x+b\n",
    "params = curve_fit(fit_func, data.Height,data.Weight)\n",
    "print 'a=',params[0][0], 'b=',params[0][1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course, an error because...\n",
    "\n",
    "### First we have to filter the NaNs to compute anything"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>118.0</td>\n",
       "      <td>64.5</td>\n",
       "      <td>816932</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>143.0</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>172.0</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>147.0</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>146.0</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>138.0</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>175.0</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>134.0</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>172.0</td>\n",
       "      <td>68.8</td>\n",
       "      <td>955466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Female</td>\n",
       "      <td>132</td>\n",
       "      <td>129</td>\n",
       "      <td>124</td>\n",
       "      <td>118.0</td>\n",
       "      <td>64.5</td>\n",
       "      <td>833868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Male</td>\n",
       "      <td>141</td>\n",
       "      <td>150</td>\n",
       "      <td>128</td>\n",
       "      <td>151.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1079549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Male</td>\n",
       "      <td>135</td>\n",
       "      <td>129</td>\n",
       "      <td>124</td>\n",
       "      <td>155.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>924059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Female</td>\n",
       "      <td>140</td>\n",
       "      <td>120</td>\n",
       "      <td>147</td>\n",
       "      <td>155.0</td>\n",
       "      <td>70.5</td>\n",
       "      <td>856472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Female</td>\n",
       "      <td>96</td>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "      <td>146.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>878897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Female</td>\n",
       "      <td>83</td>\n",
       "      <td>71</td>\n",
       "      <td>96</td>\n",
       "      <td>135.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>865363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Male</td>\n",
       "      <td>100</td>\n",
       "      <td>96</td>\n",
       "      <td>102</td>\n",
       "      <td>178.0</td>\n",
       "      <td>73.5</td>\n",
       "      <td>945088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Female</td>\n",
       "      <td>101</td>\n",
       "      <td>112</td>\n",
       "      <td>84</td>\n",
       "      <td>136.0</td>\n",
       "      <td>66.3</td>\n",
       "      <td>808020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Male</td>\n",
       "      <td>80</td>\n",
       "      <td>77</td>\n",
       "      <td>86</td>\n",
       "      <td>180.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>889083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Male</td>\n",
       "      <td>97</td>\n",
       "      <td>107</td>\n",
       "      <td>84</td>\n",
       "      <td>186.0</td>\n",
       "      <td>76.5</td>\n",
       "      <td>905940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Female</td>\n",
       "      <td>135</td>\n",
       "      <td>129</td>\n",
       "      <td>134</td>\n",
       "      <td>122.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>790619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Male</td>\n",
       "      <td>139</td>\n",
       "      <td>145</td>\n",
       "      <td>128</td>\n",
       "      <td>132.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>955003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Female</td>\n",
       "      <td>91</td>\n",
       "      <td>86</td>\n",
       "      <td>102</td>\n",
       "      <td>114.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>831772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Male</td>\n",
       "      <td>141</td>\n",
       "      <td>145</td>\n",
       "      <td>131</td>\n",
       "      <td>171.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>935494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Female</td>\n",
       "      <td>85</td>\n",
       "      <td>90</td>\n",
       "      <td>84</td>\n",
       "      <td>140.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>798612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Male</td>\n",
       "      <td>103</td>\n",
       "      <td>96</td>\n",
       "      <td>110</td>\n",
       "      <td>187.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>1062462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Female</td>\n",
       "      <td>77</td>\n",
       "      <td>83</td>\n",
       "      <td>72</td>\n",
       "      <td>106.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>793549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Female</td>\n",
       "      <td>130</td>\n",
       "      <td>126</td>\n",
       "      <td>124</td>\n",
       "      <td>159.0</td>\n",
       "      <td>66.5</td>\n",
       "      <td>866662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Female</td>\n",
       "      <td>133</td>\n",
       "      <td>126</td>\n",
       "      <td>132</td>\n",
       "      <td>127.0</td>\n",
       "      <td>62.5</td>\n",
       "      <td>857782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Male</td>\n",
       "      <td>144</td>\n",
       "      <td>145</td>\n",
       "      <td>137</td>\n",
       "      <td>191.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>949589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Male</td>\n",
       "      <td>103</td>\n",
       "      <td>96</td>\n",
       "      <td>110</td>\n",
       "      <td>192.0</td>\n",
       "      <td>75.5</td>\n",
       "      <td>997925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Male</td>\n",
       "      <td>90</td>\n",
       "      <td>96</td>\n",
       "      <td>86</td>\n",
       "      <td>181.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>879987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Female</td>\n",
       "      <td>83</td>\n",
       "      <td>90</td>\n",
       "      <td>81</td>\n",
       "      <td>143.0</td>\n",
       "      <td>66.5</td>\n",
       "      <td>834344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Female</td>\n",
       "      <td>133</td>\n",
       "      <td>129</td>\n",
       "      <td>128</td>\n",
       "      <td>153.0</td>\n",
       "      <td>66.5</td>\n",
       "      <td>948066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Male</td>\n",
       "      <td>140</td>\n",
       "      <td>150</td>\n",
       "      <td>124</td>\n",
       "      <td>144.0</td>\n",
       "      <td>70.5</td>\n",
       "      <td>949395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Female</td>\n",
       "      <td>88</td>\n",
       "      <td>86</td>\n",
       "      <td>94</td>\n",
       "      <td>139.0</td>\n",
       "      <td>64.5</td>\n",
       "      <td>893983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Male</td>\n",
       "      <td>81</td>\n",
       "      <td>90</td>\n",
       "      <td>74</td>\n",
       "      <td>148.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>930016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Male</td>\n",
       "      <td>89</td>\n",
       "      <td>91</td>\n",
       "      <td>89</td>\n",
       "      <td>179.0</td>\n",
       "      <td>75.5</td>\n",
       "      <td>935863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<div>\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>118.0</td>\n",
       "      <td>64.5</td>\n",
       "      <td>816932</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>143.0</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>172.0</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>147.0</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>146.0</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>138.0</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>175.0</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>134.0</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>172.0</td>\n",
       "      <td>68.8</td>\n",
       "      <td>955466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Female</td>\n",
       "      <td>132</td>\n",
       "      <td>129</td>\n",
       "      <td>124</td>\n",
       "      <td>118.0</td>\n",
       "      <td>64.5</td>\n",
       "      <td>833868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Male</td>\n",
       "      <td>141</td>\n",
       "      <td>150</td>\n",
       "      <td>128</td>\n",
       "      <td>151.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1079549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Male</td>\n",
       "      <td>135</td>\n",
       "      <td>129</td>\n",
       "      <td>124</td>\n",
       "      <td>155.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>924059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Female</td>\n",
       "      <td>140</td>\n",
       "      <td>120</td>\n",
       "      <td>147</td>\n",
       "      <td>155.0</td>\n",
       "      <td>70.5</td>\n",
       "      <td>856472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Female</td>\n",
       "      <td>96</td>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "      <td>146.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>878897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Female</td>\n",
       "      <td>83</td>\n",
       "      <td>71</td>\n",
       "      <td>96</td>\n",
       "      <td>135.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>865363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Male</td>\n",
       "      <td>100</td>\n",
       "      <td>96</td>\n",
       "      <td>102</td>\n",
       "      <td>178.0</td>\n",
       "      <td>73.5</td>\n",
       "      <td>945088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Female</td>\n",
       "      <td>101</td>\n",
       "      <td>112</td>\n",
       "      <td>84</td>\n",
       "      <td>136.0</td>\n",
       "      <td>66.3</td>\n",
       "      <td>808020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Male</td>\n",
       "      <td>80</td>\n",
       "      <td>77</td>\n",
       "      <td>86</td>\n",
       "      <td>180.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>889083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Male</td>\n",
       "      <td>97</td>\n",
       "      <td>107</td>\n",
       "      <td>84</td>\n",
       "      <td>186.0</td>\n",
       "      <td>76.5</td>\n",
       "      <td>905940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Female</td>\n",
       "      <td>135</td>\n",
       "      <td>129</td>\n",
       "      <td>134</td>\n",
       "      <td>122.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>790619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Male</td>\n",
       "      <td>139</td>\n",
       "      <td>145</td>\n",
       "      <td>128</td>\n",
       "      <td>132.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>955003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Female</td>\n",
       "      <td>91</td>\n",
       "      <td>86</td>\n",
       "      <td>102</td>\n",
       "      <td>114.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>831772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Male</td>\n",
       "      <td>141</td>\n",
       "      <td>145</td>\n",
       "      <td>131</td>\n",
       "      <td>171.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>935494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Female</td>\n",
       "      <td>85</td>\n",
       "      <td>90</td>\n",
       "      <td>84</td>\n",
       "      <td>140.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>798612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Male</td>\n",
       "      <td>103</td>\n",
       "      <td>96</td>\n",
       "      <td>110</td>\n",
       "      <td>187.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>1062462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Female</td>\n",
       "      <td>77</td>\n",
       "      <td>83</td>\n",
       "      <td>72</td>\n",
       "      <td>106.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>793549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Female</td>\n",
       "      <td>130</td>\n",
       "      <td>126</td>\n",
       "      <td>124</td>\n",
       "      <td>159.0</td>\n",
       "      <td>66.5</td>\n",
       "      <td>866662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Female</td>\n",
       "      <td>133</td>\n",
       "      <td>126</td>\n",
       "      <td>132</td>\n",
       "      <td>127.0</td>\n",
       "      <td>62.5</td>\n",
       "      <td>857782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Male</td>\n",
       "      <td>144</td>\n",
       "      <td>145</td>\n",
       "      <td>137</td>\n",
       "      <td>191.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>949589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Male</td>\n",
       "      <td>103</td>\n",
       "      <td>96</td>\n",
       "      <td>110</td>\n",
       "      <td>192.0</td>\n",
       "      <td>75.5</td>\n",
       "      <td>997925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Male</td>\n",
       "      <td>90</td>\n",
       "      <td>96</td>\n",
       "      <td>86</td>\n",
       "      <td>181.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>879987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Female</td>\n",
       "      <td>83</td>\n",
       "      <td>90</td>\n",
       "      <td>81</td>\n",
       "      <td>143.0</td>\n",
       "      <td>66.5</td>\n",
       "      <td>834344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Female</td>\n",
       "      <td>133</td>\n",
       "      <td>129</td>\n",
       "      <td>128</td>\n",
       "      <td>153.0</td>\n",
       "      <td>66.5</td>\n",
       "      <td>948066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Male</td>\n",
       "      <td>140</td>\n",
       "      <td>150</td>\n",
       "      <td>124</td>\n",
       "      <td>144.0</td>\n",
       "      <td>70.5</td>\n",
       "      <td>949395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Female</td>\n",
       "      <td>88</td>\n",
       "      <td>86</td>\n",
       "      <td>94</td>\n",
       "      <td>139.0</td>\n",
       "      <td>64.5</td>\n",
       "      <td>893983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Male</td>\n",
       "      <td>81</td>\n",
       "      <td>90</td>\n",
       "      <td>74</td>\n",
       "      <td>148.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>930016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Male</td>\n",
       "      <td>89</td>\n",
       "      <td>91</td>\n",
       "      <td>89</td>\n",
       "      <td>179.0</td>\n",
       "      <td>75.5</td>\n",
       "      <td>935863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data.dropna(axis=0)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a= 4.11620824141 b= -129.9238153\n"
     ]
    }
   ],
   "source": [
    "def fit_func(x, a, b):\n",
    "    return a*x+b\n",
    "params = curve_fit(fit_func, df.Height,df.Weight)\n",
    "print 'a=',params[0][0], 'b=',params[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>118.000000</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>168.501282</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>143.000000</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>172.000000</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>147.000000</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>146.000000</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>138.000000</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>175.000000</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>134.000000</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>172.000000</td>\n",
       "      <td>68.8</td>\n",
       "      <td>955466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Female</td>\n",
       "      <td>132</td>\n",
       "      <td>129</td>\n",
       "      <td>124</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>64.5</td>\n",
       "      <td>833868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Male</td>\n",
       "      <td>141</td>\n",
       "      <td>150</td>\n",
       "      <td>128</td>\n",
       "      <td>151.000000</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1079549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Male</td>\n",
       "      <td>135</td>\n",
       "      <td>129</td>\n",
       "      <td>124</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>69.0</td>\n",
       "      <td>924059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Female</td>\n",
       "      <td>140</td>\n",
       "      <td>120</td>\n",
       "      <td>147</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>70.5</td>\n",
       "      <td>856472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Female</td>\n",
       "      <td>96</td>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>66.0</td>\n",
       "      <td>878897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Female</td>\n",
       "      <td>83</td>\n",
       "      <td>71</td>\n",
       "      <td>96</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>68.0</td>\n",
       "      <td>865363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Male</td>\n",
       "      <td>100</td>\n",
       "      <td>96</td>\n",
       "      <td>102</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>73.5</td>\n",
       "      <td>945088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Female</td>\n",
       "      <td>101</td>\n",
       "      <td>112</td>\n",
       "      <td>84</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>66.3</td>\n",
       "      <td>808020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Male</td>\n",
       "      <td>80</td>\n",
       "      <td>77</td>\n",
       "      <td>86</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>70.0</td>\n",
       "      <td>889083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Male</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>892420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Male</td>\n",
       "      <td>97</td>\n",
       "      <td>107</td>\n",
       "      <td>84</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>76.5</td>\n",
       "      <td>905940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Female</td>\n",
       "      <td>135</td>\n",
       "      <td>129</td>\n",
       "      <td>134</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>62.0</td>\n",
       "      <td>790619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Male</td>\n",
       "      <td>139</td>\n",
       "      <td>145</td>\n",
       "      <td>128</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>68.0</td>\n",
       "      <td>955003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Female</td>\n",
       "      <td>91</td>\n",
       "      <td>86</td>\n",
       "      <td>102</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>63.0</td>\n",
       "      <td>831772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Male</td>\n",
       "      <td>141</td>\n",
       "      <td>145</td>\n",
       "      <td>131</td>\n",
       "      <td>171.000000</td>\n",
       "      <td>72.0</td>\n",
       "      <td>935494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Female</td>\n",
       "      <td>85</td>\n",
       "      <td>90</td>\n",
       "      <td>84</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>68.0</td>\n",
       "      <td>798612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Male</td>\n",
       "      <td>103</td>\n",
       "      <td>96</td>\n",
       "      <td>110</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>77.0</td>\n",
       "      <td>1062462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Female</td>\n",
       "      <td>77</td>\n",
       "      <td>83</td>\n",
       "      <td>72</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>63.0</td>\n",
       "      <td>793549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Female</td>\n",
       "      <td>130</td>\n",
       "      <td>126</td>\n",
       "      <td>124</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>66.5</td>\n",
       "      <td>866662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Female</td>\n",
       "      <td>133</td>\n",
       "      <td>126</td>\n",
       "      <td>132</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>62.5</td>\n",
       "      <td>857782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Male</td>\n",
       "      <td>144</td>\n",
       "      <td>145</td>\n",
       "      <td>137</td>\n",
       "      <td>191.000000</td>\n",
       "      <td>67.0</td>\n",
       "      <td>949589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Male</td>\n",
       "      <td>103</td>\n",
       "      <td>96</td>\n",
       "      <td>110</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>75.5</td>\n",
       "      <td>997925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Male</td>\n",
       "      <td>90</td>\n",
       "      <td>96</td>\n",
       "      <td>86</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>69.0</td>\n",
       "      <td>879987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Female</td>\n",
       "      <td>83</td>\n",
       "      <td>90</td>\n",
       "      <td>81</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>66.5</td>\n",
       "      <td>834344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Female</td>\n",
       "      <td>133</td>\n",
       "      <td>129</td>\n",
       "      <td>128</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>66.5</td>\n",
       "      <td>948066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Male</td>\n",
       "      <td>140</td>\n",
       "      <td>150</td>\n",
       "      <td>124</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>70.5</td>\n",
       "      <td>949395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Female</td>\n",
       "      <td>88</td>\n",
       "      <td>86</td>\n",
       "      <td>94</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>64.5</td>\n",
       "      <td>893983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Male</td>\n",
       "      <td>81</td>\n",
       "      <td>90</td>\n",
       "      <td>74</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>74.0</td>\n",
       "      <td>930016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Male</td>\n",
       "      <td>89</td>\n",
       "      <td>91</td>\n",
       "      <td>89</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>75.5</td>\n",
       "      <td>935863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<div>\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>118.000000</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>168.501282</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>143.000000</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>172.000000</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>147.000000</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>146.000000</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>138.000000</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>175.000000</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>134.000000</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>172.000000</td>\n",
       "      <td>68.8</td>\n",
       "      <td>955466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Female</td>\n",
       "      <td>132</td>\n",
       "      <td>129</td>\n",
       "      <td>124</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>64.5</td>\n",
       "      <td>833868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Male</td>\n",
       "      <td>141</td>\n",
       "      <td>150</td>\n",
       "      <td>128</td>\n",
       "      <td>151.000000</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1079549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Male</td>\n",
       "      <td>135</td>\n",
       "      <td>129</td>\n",
       "      <td>124</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>69.0</td>\n",
       "      <td>924059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Female</td>\n",
       "      <td>140</td>\n",
       "      <td>120</td>\n",
       "      <td>147</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>70.5</td>\n",
       "      <td>856472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Female</td>\n",
       "      <td>96</td>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>66.0</td>\n",
       "      <td>878897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Female</td>\n",
       "      <td>83</td>\n",
       "      <td>71</td>\n",
       "      <td>96</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>68.0</td>\n",
       "      <td>865363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Male</td>\n",
       "      <td>100</td>\n",
       "      <td>96</td>\n",
       "      <td>102</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>73.5</td>\n",
       "      <td>945088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Female</td>\n",
       "      <td>101</td>\n",
       "      <td>112</td>\n",
       "      <td>84</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>66.3</td>\n",
       "      <td>808020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Male</td>\n",
       "      <td>80</td>\n",
       "      <td>77</td>\n",
       "      <td>86</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>70.0</td>\n",
       "      <td>889083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Male</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>892420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Male</td>\n",
       "      <td>97</td>\n",
       "      <td>107</td>\n",
       "      <td>84</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>76.5</td>\n",
       "      <td>905940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Female</td>\n",
       "      <td>135</td>\n",
       "      <td>129</td>\n",
       "      <td>134</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>62.0</td>\n",
       "      <td>790619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Male</td>\n",
       "      <td>139</td>\n",
       "      <td>145</td>\n",
       "      <td>128</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>68.0</td>\n",
       "      <td>955003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Female</td>\n",
       "      <td>91</td>\n",
       "      <td>86</td>\n",
       "      <td>102</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>63.0</td>\n",
       "      <td>831772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Male</td>\n",
       "      <td>141</td>\n",
       "      <td>145</td>\n",
       "      <td>131</td>\n",
       "      <td>171.000000</td>\n",
       "      <td>72.0</td>\n",
       "      <td>935494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Female</td>\n",
       "      <td>85</td>\n",
       "      <td>90</td>\n",
       "      <td>84</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>68.0</td>\n",
       "      <td>798612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Male</td>\n",
       "      <td>103</td>\n",
       "      <td>96</td>\n",
       "      <td>110</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>77.0</td>\n",
       "      <td>1062462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Female</td>\n",
       "      <td>77</td>\n",
       "      <td>83</td>\n",
       "      <td>72</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>63.0</td>\n",
       "      <td>793549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Female</td>\n",
       "      <td>130</td>\n",
       "      <td>126</td>\n",
       "      <td>124</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>66.5</td>\n",
       "      <td>866662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Female</td>\n",
       "      <td>133</td>\n",
       "      <td>126</td>\n",
       "      <td>132</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>62.5</td>\n",
       "      <td>857782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Male</td>\n",
       "      <td>144</td>\n",
       "      <td>145</td>\n",
       "      <td>137</td>\n",
       "      <td>191.000000</td>\n",
       "      <td>67.0</td>\n",
       "      <td>949589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Male</td>\n",
       "      <td>103</td>\n",
       "      <td>96</td>\n",
       "      <td>110</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>75.5</td>\n",
       "      <td>997925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Male</td>\n",
       "      <td>90</td>\n",
       "      <td>96</td>\n",
       "      <td>86</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>69.0</td>\n",
       "      <td>879987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Female</td>\n",
       "      <td>83</td>\n",
       "      <td>90</td>\n",
       "      <td>81</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>66.5</td>\n",
       "      <td>834344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Female</td>\n",
       "      <td>133</td>\n",
       "      <td>129</td>\n",
       "      <td>128</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>66.5</td>\n",
       "      <td>948066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Male</td>\n",
       "      <td>140</td>\n",
       "      <td>150</td>\n",
       "      <td>124</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>70.5</td>\n",
       "      <td>949395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Female</td>\n",
       "      <td>88</td>\n",
       "      <td>86</td>\n",
       "      <td>94</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>64.5</td>\n",
       "      <td>893983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Male</td>\n",
       "      <td>81</td>\n",
       "      <td>90</td>\n",
       "      <td>74</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>74.0</td>\n",
       "      <td>930016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Male</td>\n",
       "      <td>89</td>\n",
       "      <td>91</td>\n",
       "      <td>89</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>75.5</td>\n",
       "      <td>935863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = data.Weight.isnull()\n",
    "data.set_value(idx, 'Weight', fit_func(data['Height'][idx],params[0][0],params[0][1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For the remainding Nas, we will replace the value with the average of the column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>118.000000</td>\n",
       "      <td>64.500000</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>168.501282</td>\n",
       "      <td>72.500000</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>143.000000</td>\n",
       "      <td>73.300000</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>172.000000</td>\n",
       "      <td>68.800000</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>147.000000</td>\n",
       "      <td>65.000000</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>146.000000</td>\n",
       "      <td>69.000000</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>138.000000</td>\n",
       "      <td>64.500000</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>175.000000</td>\n",
       "      <td>66.000000</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>134.000000</td>\n",
       "      <td>66.300000</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>172.000000</td>\n",
       "      <td>68.800000</td>\n",
       "      <td>955466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Female</td>\n",
       "      <td>132</td>\n",
       "      <td>129</td>\n",
       "      <td>124</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>64.500000</td>\n",
       "      <td>833868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Male</td>\n",
       "      <td>141</td>\n",
       "      <td>150</td>\n",
       "      <td>128</td>\n",
       "      <td>151.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1079549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Male</td>\n",
       "      <td>135</td>\n",
       "      <td>129</td>\n",
       "      <td>124</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>924059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Female</td>\n",
       "      <td>140</td>\n",
       "      <td>120</td>\n",
       "      <td>147</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>70.500000</td>\n",
       "      <td>856472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Female</td>\n",
       "      <td>96</td>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>878897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Female</td>\n",
       "      <td>83</td>\n",
       "      <td>71</td>\n",
       "      <td>96</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>865363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Male</td>\n",
       "      <td>100</td>\n",
       "      <td>96</td>\n",
       "      <td>102</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>73.500000</td>\n",
       "      <td>945088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Female</td>\n",
       "      <td>101</td>\n",
       "      <td>112</td>\n",
       "      <td>84</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>66.300000</td>\n",
       "      <td>808020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Male</td>\n",
       "      <td>80</td>\n",
       "      <td>77</td>\n",
       "      <td>86</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>889083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Male</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>86</td>\n",
       "      <td>152.144771</td>\n",
       "      <td>68.526316</td>\n",
       "      <td>892420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Male</td>\n",
       "      <td>97</td>\n",
       "      <td>107</td>\n",
       "      <td>84</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>76.500000</td>\n",
       "      <td>905940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Female</td>\n",
       "      <td>135</td>\n",
       "      <td>129</td>\n",
       "      <td>134</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>790619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Male</td>\n",
       "      <td>139</td>\n",
       "      <td>145</td>\n",
       "      <td>128</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>955003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Female</td>\n",
       "      <td>91</td>\n",
       "      <td>86</td>\n",
       "      <td>102</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>831772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Male</td>\n",
       "      <td>141</td>\n",
       "      <td>145</td>\n",
       "      <td>131</td>\n",
       "      <td>171.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>935494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Female</td>\n",
       "      <td>85</td>\n",
       "      <td>90</td>\n",
       "      <td>84</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>798612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Male</td>\n",
       "      <td>103</td>\n",
       "      <td>96</td>\n",
       "      <td>110</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>1062462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Female</td>\n",
       "      <td>77</td>\n",
       "      <td>83</td>\n",
       "      <td>72</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>793549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Female</td>\n",
       "      <td>130</td>\n",
       "      <td>126</td>\n",
       "      <td>124</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>66.500000</td>\n",
       "      <td>866662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Female</td>\n",
       "      <td>133</td>\n",
       "      <td>126</td>\n",
       "      <td>132</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>857782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Male</td>\n",
       "      <td>144</td>\n",
       "      <td>145</td>\n",
       "      <td>137</td>\n",
       "      <td>191.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>949589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Male</td>\n",
       "      <td>103</td>\n",
       "      <td>96</td>\n",
       "      <td>110</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>75.500000</td>\n",
       "      <td>997925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Male</td>\n",
       "      <td>90</td>\n",
       "      <td>96</td>\n",
       "      <td>86</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>879987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Female</td>\n",
       "      <td>83</td>\n",
       "      <td>90</td>\n",
       "      <td>81</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>66.500000</td>\n",
       "      <td>834344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Female</td>\n",
       "      <td>133</td>\n",
       "      <td>129</td>\n",
       "      <td>128</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>66.500000</td>\n",
       "      <td>948066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Male</td>\n",
       "      <td>140</td>\n",
       "      <td>150</td>\n",
       "      <td>124</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>70.500000</td>\n",
       "      <td>949395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Female</td>\n",
       "      <td>88</td>\n",
       "      <td>86</td>\n",
       "      <td>94</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>64.500000</td>\n",
       "      <td>893983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Male</td>\n",
       "      <td>81</td>\n",
       "      <td>90</td>\n",
       "      <td>74</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>930016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Male</td>\n",
       "      <td>89</td>\n",
       "      <td>91</td>\n",
       "      <td>89</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>75.500000</td>\n",
       "      <td>935863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<div>\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>118.000000</td>\n",
       "      <td>64.500000</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>168.501282</td>\n",
       "      <td>72.500000</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>143.000000</td>\n",
       "      <td>73.300000</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>172.000000</td>\n",
       "      <td>68.800000</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>147.000000</td>\n",
       "      <td>65.000000</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>146.000000</td>\n",
       "      <td>69.000000</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>138.000000</td>\n",
       "      <td>64.500000</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>175.000000</td>\n",
       "      <td>66.000000</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>134.000000</td>\n",
       "      <td>66.300000</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>172.000000</td>\n",
       "      <td>68.800000</td>\n",
       "      <td>955466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Female</td>\n",
       "      <td>132</td>\n",
       "      <td>129</td>\n",
       "      <td>124</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>64.500000</td>\n",
       "      <td>833868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Male</td>\n",
       "      <td>141</td>\n",
       "      <td>150</td>\n",
       "      <td>128</td>\n",
       "      <td>151.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1079549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Male</td>\n",
       "      <td>135</td>\n",
       "      <td>129</td>\n",
       "      <td>124</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>924059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Female</td>\n",
       "      <td>140</td>\n",
       "      <td>120</td>\n",
       "      <td>147</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>70.500000</td>\n",
       "      <td>856472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Female</td>\n",
       "      <td>96</td>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>878897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Female</td>\n",
       "      <td>83</td>\n",
       "      <td>71</td>\n",
       "      <td>96</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>865363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Male</td>\n",
       "      <td>100</td>\n",
       "      <td>96</td>\n",
       "      <td>102</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>73.500000</td>\n",
       "      <td>945088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Female</td>\n",
       "      <td>101</td>\n",
       "      <td>112</td>\n",
       "      <td>84</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>66.300000</td>\n",
       "      <td>808020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Male</td>\n",
       "      <td>80</td>\n",
       "      <td>77</td>\n",
       "      <td>86</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>889083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Male</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>86</td>\n",
       "      <td>152.144771</td>\n",
       "      <td>68.526316</td>\n",
       "      <td>892420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Male</td>\n",
       "      <td>97</td>\n",
       "      <td>107</td>\n",
       "      <td>84</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>76.500000</td>\n",
       "      <td>905940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Female</td>\n",
       "      <td>135</td>\n",
       "      <td>129</td>\n",
       "      <td>134</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>790619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Male</td>\n",
       "      <td>139</td>\n",
       "      <td>145</td>\n",
       "      <td>128</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>955003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Female</td>\n",
       "      <td>91</td>\n",
       "      <td>86</td>\n",
       "      <td>102</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>831772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Male</td>\n",
       "      <td>141</td>\n",
       "      <td>145</td>\n",
       "      <td>131</td>\n",
       "      <td>171.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>935494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Female</td>\n",
       "      <td>85</td>\n",
       "      <td>90</td>\n",
       "      <td>84</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>798612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Male</td>\n",
       "      <td>103</td>\n",
       "      <td>96</td>\n",
       "      <td>110</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>1062462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Female</td>\n",
       "      <td>77</td>\n",
       "      <td>83</td>\n",
       "      <td>72</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>793549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Female</td>\n",
       "      <td>130</td>\n",
       "      <td>126</td>\n",
       "      <td>124</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>66.500000</td>\n",
       "      <td>866662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Female</td>\n",
       "      <td>133</td>\n",
       "      <td>126</td>\n",
       "      <td>132</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>857782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Male</td>\n",
       "      <td>144</td>\n",
       "      <td>145</td>\n",
       "      <td>137</td>\n",
       "      <td>191.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>949589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Male</td>\n",
       "      <td>103</td>\n",
       "      <td>96</td>\n",
       "      <td>110</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>75.500000</td>\n",
       "      <td>997925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Male</td>\n",
       "      <td>90</td>\n",
       "      <td>96</td>\n",
       "      <td>86</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>879987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Female</td>\n",
       "      <td>83</td>\n",
       "      <td>90</td>\n",
       "      <td>81</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>66.500000</td>\n",
       "      <td>834344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Female</td>\n",
       "      <td>133</td>\n",
       "      <td>129</td>\n",
       "      <td>128</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>66.500000</td>\n",
       "      <td>948066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Male</td>\n",
       "      <td>140</td>\n",
       "      <td>150</td>\n",
       "      <td>124</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>70.500000</td>\n",
       "      <td>949395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Female</td>\n",
       "      <td>88</td>\n",
       "      <td>86</td>\n",
       "      <td>94</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>64.500000</td>\n",
       "      <td>893983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Male</td>\n",
       "      <td>81</td>\n",
       "      <td>90</td>\n",
       "      <td>74</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>930016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Male</td>\n",
       "      <td>89</td>\n",
       "      <td>91</td>\n",
       "      <td>89</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>75.500000</td>\n",
       "      <td>935863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.fillna(data.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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