{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistical Thinking in Python (Part 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exploratory Data Analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    " # import\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn.datasets import load_iris\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['SectionC']\n"
     ]
    }
   ],
   "source": [
    "# reading excel file\n",
    "fh = pd.ExcelFile(\"dataset/EAVS.xlsx\")\n",
    "fh\n",
    "print(fh.sheet_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Jurisdiction</th>\n",
       "      <th>FIPSCode</th>\n",
       "      <th>QC1a</th>\n",
       "      <th>QC1b</th>\n",
       "      <th>QC1c</th>\n",
       "      <th>QC1d</th>\n",
       "      <th>QC1e</th>\n",
       "      <th>QC1f</th>\n",
       "      <th>QC1f_Other</th>\n",
       "      <th>...</th>\n",
       "      <th>QC5s</th>\n",
       "      <th>QC5s_Other</th>\n",
       "      <th>QC5t</th>\n",
       "      <th>QC5t_Other</th>\n",
       "      <th>QC5u_</th>\n",
       "      <th>QC5u_Other</th>\n",
       "      <th>QC5v</th>\n",
       "      <th>QC5_Total</th>\n",
       "      <th>QC5v_Other</th>\n",
       "      <th>QC5_Comment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AK</td>\n",
       "      <td>ALASKA</td>\n",
       "      <td>200000000</td>\n",
       "      <td>22861.0</td>\n",
       "      <td>19296.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>3158.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>73.0</td>\n",
       "      <td>Postmarked or witnessed after election day</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>552.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>AUTAUGA COUNTY</td>\n",
       "      <td>100100000</td>\n",
       "      <td>-999999.0</td>\n",
       "      <td>-999999.0</td>\n",
       "      <td>-999999.0</td>\n",
       "      <td>-999999.0</td>\n",
       "      <td>-999999.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL</td>\n",
       "      <td>BALDWIN COUNTY</td>\n",
       "      <td>100300000</td>\n",
       "      <td>-999999.0</td>\n",
       "      <td>-999999.0</td>\n",
       "      <td>-999999.0</td>\n",
       "      <td>-999999.0</td>\n",
       "      <td>-999999.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL</td>\n",
       "      <td>BARBOUR COUNTY</td>\n",
       "      <td>100500000</td>\n",
       "      <td>-999999.0</td>\n",
       "      <td>-999999.0</td>\n",
       "      <td>-999999.0</td>\n",
       "      <td>-999999.0</td>\n",
       "      <td>-999999.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL</td>\n",
       "      <td>BIBB COUNTY</td>\n",
       "      <td>100700000</td>\n",
       "      <td>-999999.0</td>\n",
       "      <td>-999999.0</td>\n",
       "      <td>-999999.0</td>\n",
       "      <td>-999999.0</td>\n",
       "      <td>-999999.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  State    Jurisdiction   FIPSCode      QC1a      QC1b      QC1c      QC1d  \\\n",
       "0    AK          ALASKA  200000000   22861.0   19296.0     101.0     306.0   \n",
       "1    AL  AUTAUGA COUNTY  100100000 -999999.0 -999999.0 -999999.0 -999999.0   \n",
       "2    AL  BALDWIN COUNTY  100300000 -999999.0 -999999.0 -999999.0 -999999.0   \n",
       "3    AL  BARBOUR COUNTY  100500000 -999999.0 -999999.0 -999999.0 -999999.0   \n",
       "4    AL     BIBB COUNTY  100700000 -999999.0 -999999.0 -999999.0 -999999.0   \n",
       "\n",
       "       QC1e  QC1f QC1f_Other     ...      QC5s  \\\n",
       "0    3158.0   NaN        NaN     ...      73.0   \n",
       "1 -999999.0   NaN        NaN     ...       NaN   \n",
       "2 -999999.0   NaN        NaN     ...       NaN   \n",
       "3 -999999.0   NaN        NaN     ...       NaN   \n",
       "4 -999999.0   NaN        NaN     ...       NaN   \n",
       "\n",
       "                                   QC5s_Other  QC5t QC5t_Other  QC5u_  \\\n",
       "0  Postmarked or witnessed after election day   NaN        NaN    NaN   \n",
       "1                                         NaN   NaN        NaN    NaN   \n",
       "2                                         NaN   NaN        NaN    NaN   \n",
       "3                                         NaN   NaN        NaN    NaN   \n",
       "4                                         NaN   NaN        NaN    NaN   \n",
       "\n",
       "  QC5u_Other QC5v QC5_Total  QC5v_Other QC5_Comment  \n",
       "0        NaN  NaN     552.0         NaN         NaN  \n",
       "1        NaN  NaN       NaN         NaN         NaN  \n",
       "2        NaN  NaN       NaN         NaN         NaN  \n",
       "3        NaN  NaN       NaN         NaN         NaN  \n",
       "4        NaN  NaN       NaN         NaN         NaN  \n",
       "\n",
       "[5 rows x 60 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = fh.parse(\"SectionC\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "150\n"
     ]
    }
   ],
   "source": [
    "## Loading the IRIS dataset\n",
    "irisds = load_iris()\n",
    "\n",
    "# selecting the data field only\n",
    "data = irisds['data']\n",
    "# saving into csv file\n",
    "\n",
    "col = irisds['feature_names']\n",
    "print(len(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 5.1,  3.5,  1.4,  0.2],\n",
       "       [ 4.9,  3. ,  1.4,  0.2],\n",
       "       [ 4.7,  3.2,  1.3,  0.2],\n",
       "       [ 4.6,  3.1,  1.5,  0.2],\n",
       "       [ 5. ,  3.6,  1.4,  0.2]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sample data\n",
    "print(col)\n",
    "data[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x29f41092e48>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sLgWwNcItAMAYEZH1Va12E6vLAGAh9twCAADAGIRbAAAAGINwCwAAAGMQbgEAAGAMwi0A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\nABiDcAsAAABjEG4BAABgDMItAAAAjPH/EUuwDAkj/kcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29f41041860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the sepal length ( sns style)\n",
    "sepal = data[:,0]\n",
    "\n",
    "sns.set()\n",
    "plt.hist(sepal)\n",
    "plt.xlabel(\"sepal length\")\n",
    "plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x29f41a69cf8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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N354vgLITGBigoKDy9Rrjr+8FV4u++a60e2WbcDt16lQtXrxYr7/+uurVq6d69erJ5XLJ\n6XRKkm655RYdOXJEy5cv9zncOp1h16Jk4/lb3/zt+QIoO05nmKpWrWh1GSXCa2PJ0Dfr2CLcjh8/\nXitWrNDUqVMLBddLwfaSOnXqaOvWrT6fPysrW3l5+Vddp78IDAyQ0xnmd33Lysq2ugQAhsrKytbp\n0+etLsMn/vpecLXom+8u9ay0WB5u58yZoxUrVmjGjBnq2LGjd3zWrFnatWuXFi5c6B07cOCAoqOj\nfb5GXl6+cnNZYL7yt77xIgTgWinPr6fluXYr0TfrWLohJDU1VUlJSUpISFBsbKwyMzO9f+6++25t\n375dCxcu1PHjx7Vs2TJ9+OGHGjhwoJUlAwAAwMYsvXO7fv165efnKykpSUlJSZIKPjHB4XDowIED\nmjVrlmbOnKmZM2cqKipK06ZNU9OmTa0sGQAAADZmabhNSEhQQkLCFY+7XC65XK4yrAgAAADlGZ9T\nAQAAAGMQbgEAAGAMwi0AAACMQbgFAACAMQi3AAAAMAbhFgAAAMYg3AIAAMAYhFsAAAAYg3ALAAAA\nYxBuAQAAYAzCLQAAAIxBuAUAAIAxCLcAAAAwBuEWAAAAxiDcAgAAwBiEWwAAABiDcAsAAABjEG4B\nAABgDMItAAAAjEG4BQAAgDEItwAAADAG4RYAAADGINwCAADAGIRbAAAAGINwCwAAAGMQbgEAAGAM\nwi0AAACMQbgFAACAMQi3AAAAMEaJwu3q1avldruLjP/888965513rrYmAAAAoESCijvxxx9/1IUL\nFyRJiYmJql+/vqpWrVpozv79+zV9+nQ99thjpVokAAAAUBzFDrcbN27UqFGj5HA45PF41KtXryJz\nPB6P2rdvX6oFAgAAAMVV7HDbrVs3RUVFKT8/X/3799esWbN0/fXXe487HA5VqFBBt9xyyzUpFAAA\nAPg9xQ63knTbbbdJkhYtWqQWLVooKMinHwcAAACuqRKl01atWmnHjh3auXOnLl68KI/HU+j44MGD\nS6U4AAAAwBclCrdz587V7Nmz5XQ6ValSpULHHA4H4RYAAACWKFG4Xb58uZ577jk99dRTpV0PAABG\nyc/L1VdfHbS6DJ8FBgbI6QxTVla28vLyrS7HZ40b36rg4GCry4AFShRuz507py5dupR2LQAAGOf8\nme+14KPvVPk/P1ldit84d+qYpgyTYmPjrC4FFihRuG3RooV27dqlqKio0q4HAADjVA6/WVVq1Le6\nDMAvlCjcdunSRePHj9e+fftUp06dIrf9u3XrVirFAQAAAL4oUbh94YUXJOmyX7XrcDgItwAAALBE\nicLtwYOltzE+IyNDr7zyirZu3arQ0FDde++9GjZsmIKDg5Wenq4xY8Zo9+7dioqKUmJiotq2bVtq\n1wYAAIBZAqwuYOjQocrJydGyZcs0ffp0/etf/9LMmTMlSU8//bQiIyO1cuVKde3aVYMHD9aJEycs\nrhgAAAB2VaI7ty6XSw6H44rH169fX6zzpKWlac+ePfr8889VrVo1SQVhd8qUKWrXrp3S09P1/vvv\nKyQkRAkJCdqyZYuSk5P5HF0AAABcVonCbffu3QuF29zcXB05ckSbNm3S0KFDi32eiIgIvf32295g\ne8m5c+f0xRdfqHHjxgoJCfGOx8XFaffu3SUpGQAAAH6gROF2yJAhlx3/xz/+oX//+9/q379/sc5T\nuXLlQntoPR6PlixZojZt2ujkyZOKjIwsND88PFwZGRklKRkAAAB+oETh9kratWunV199tcQ/P2XK\nFB04cEDJyclauHBhkY8YCw4Oltvt9vm8gYGWby0uVy71y9/65m/PFwBMFhgYoKCgsn9d99f30KtR\n2r0q1XC7bt06VaxYsUQ/O3XqVC1evFivv/666tWrp5CQEJ09e7bQHLfbrdDQUJ/P7XSGlagmf+dv\nffO35wsAJnM6w1S1askySWldH9YotV8oO3/+vM6ePXvFLQu/Zfz48VqxYoWmTp2qDh06SJKqV6+u\nQ4cOFZqXmZmpiIgIn89fXr8X2yrl/fvESyorK9vqEgAApSQrK1unT58v8+v663vo1bjUs9JSKr9Q\nJknXXXedmjdvrtatW/t0rjlz5mjFihWaMWOGOnbs6B1v1qyZ3nrrLbndbu/2hJSUFLVs2dLnevPy\n8pWbywLzlb/1jRchADCH1e9hVl/fn5XqL5T5KjU1VUlJSXrqqacUGxurzMxM77FWrVqpZs2aGjVq\nlJ5++ml99tln2rt3ryZPnlwq1wYAAIB5Srzndt++fVqwYIG+/vprBQUFqV69eurfv7+aNm1a7HOs\nX79e+fn5SkpKUlJSkqSCT0xwOBw6cOCA5s6dqxdeeEE9e/bUzTffrLlz56pGjRolLRkAAACGK1G4\n3bZtmx5//HHdcsstatu2rfLz87Vz50498sgjevfddxUXF1es8yQkJCghIeGKx2+++WYtXry4JCUC\nAADAD5Uo3M6YMUM9e/bUSy+9VGj8pZde0uuvv04gBQAAgCVK9MFi+/fvV79+/YqM9+3bV/v27bvq\nogAAAICSKFG4rVq1qk6fPl1k/McffyzyxQsAAABAWSlRuL377rs1fvx4paamescOHTqkCRMmyOVy\nlVpxAAAAgC9KtOf22Wef1YABA9SlSxdVrlxZkpSVlaWGDRtqxIgRpVogAAAAUFw+h9vs7Gw5nU4l\nJydr06ZN+uabb3ThwgXdeuutateunQIC+C5lAAAAWMOnJLp27Vq5XC59+eWXCggIUPv27TVw4EDt\n2rVLI0aM0Pr1669VnQAAAMDvKna43bp1q0aMGKG7775b1atXL3Rs9OjRcrlcevbZZ7Vz585SLxIA\nAAAojmKH2/nz56tv376aOHGiIiIiCh2rW7euJk2apK5du3q/aQwAAAAoa8UOt/v371evXr1+c84j\njzyi/fv3X3VRAAAAQEkUO9zm5OQoNDT0N+dUqVJF2dnZV10UAAAAUBLFDrfR0dHatWvXb87ZuXOn\noqKirrooAAAAoCSKHW67du2qmTNnKiMj47LHMzIyNHPmTP3pT38qteIAAAAAXxT7c2779u2rdevW\nqUuXLurZs6diY2PldDp15swZ7dy5U6tWrVLt2rX1xBNPXMt6AQAAgCsqdrgNDAzUO++8o9dff10r\nV67UO++84z12ww036NFHH9WgQYN+d18uAAAAcK349A1lwcHBGjFihIYNG6bjx4/r7NmzqlatmmrV\nqiWHw3GtagQAAACKxeev35WkoKAgRUdHl3YtAAAAwFXx6et3AQAAADsj3AIAAMAYhFsAAAAYg3AL\nAAAAYxBuAQAAYAzCLQAAAIxBuAUAAIAxCLcAAAAwBuEWAAAAxiDcAgAAwBiEWwAAABiDcAsAAABj\nEG4BAABgDMItAAAAjEG4BQAAgDEItwAAADAG4RYAAADGINwCAADAGIRbAAAAGCPI6gJweSm7dmvb\n9hRLrh0Q6FBoWLAuZLuVn+expAYrnD93RlIlq8sAAABXgXBrU6s//lRH8xtZXYZfObN/i4Kq32Z1\nGQAA4CoQbm0qwOFQ0HWhVpfhVwIC+OcAAEB5x55bAAAAGINwCwAAAGPYKty63W7df//92r59u3ds\nwoQJiomJUcOGDb1/L1261MIqAQAAYFe22WTodrs1bNgwHTp0qNB4Wlqahg8fru7du3vHKlXiN9oB\nAABQlC3u3Kampqp3795KT0+/7LFGjRopPDzc+yckJMSCKgEAAGB3tgi327ZtU5s2bbRixQp5PL98\nrupPP/2kjIwM1a5d27riAAAAUG7YYltCnz59LjuelpYmh8OhpKQkbdy4UVWqVNGAAQPUrVu3Mq4Q\nAAAA5YEtwu2VpKWlKSAgQHXr1lV8fLy2bdumMWPGqFKlSurQoUOxzxMYaIsb1D5xOBxWl+B/Aug5\nAJgiMDBAQUFl//5/KXOUx+xhldLula3Dbbdu3eRyueR0OiVJt9xyi44cOaLly5f7FG6dzrBrVeI1\nExISJLmtrsK/BDoc8p8vGwYAszmdYapataKl14c1bB1uJXmD7SV16tTR1q1bfTpHVla28vLyS7Os\nay4nJ1fiRmKZyvN47LEJHQBw1bKysnX69Pkyv25gYICczrBymT2scqlnpcXW4XbWrFnatWuXFi5c\n6B07cOCAoqOjfTpPXl6+cnPL1wLzeDyE27KWz31bADCF1e/9Vl/fn9n6RtXdd9+t7du3a+HChTp+\n/LiWLVumDz/8UAMHDrS6NAAAANiQ7cLtr3+R6tZbb9WsWbO0evVq3X///Vq6dKmmTZumpk2bWlgh\nAAAA7Mp22xIOHDhQ6LHL5ZLL5bKoGgAAAJQntrtzCwAAAJQU4RYAAADGINwCAADAGIRbAAAAGINw\nCwAAAGMQbgEAAGAMwi0AAACMQbgFAACAMQi3AAAAMAbhFgAAAMYg3AIAAMAYhFsAAAAYg3ALAAAA\nYxBuAQAAYAzCLQAAAIxBuAUAAIAxCLcAAAAwBuEWAAAAxiDcAgAAwBiEWwAAABiDcAsAAABjEG4B\nAABgDMItAAAAjEG4BQAAgDEItwAAADAG4RYAAADGINwCAADAGIRbAAAAGINwCwAAAGMQbgEAAGAM\nwi0AAACMQbgFAACAMQi3AAAAMAbhFgAAAMYg3AIAAMAYhFsAAAAYg3ALAAAAYxBuAQAAYAzCLQAA\nAIxBuAUAAIAxCLcAAAAwBuEWAAAAxrBVuHW73br//vu1fft271h6eroGDBig2NhYdenSRZ9//rmF\nFQIAAMDObBNu3W63hg0bpkOHDhUaf+aZZxQZGamVK1eqa9euGjx4sE6cOGFRlQAAALAzW4Tb1NRU\n9e7dW+np6YXGt2zZouPHj+vll19WnTp1lJCQoObNmys5OdmiSgEAAGBntgi327ZtU5s2bbRixQp5\nPB7v+J49e9S4cWOFhIR4x+Li4rR7924rygQAAIDNBVldgCT16dPnsuMnT55UZGRkobHw8HBlZGSU\nRVkAAAAoZ2wRbq8kOztbwcHBhcaCg4Pldrt9Ok9goC1uUPvE4XBYXYL/CaDnAGCKwMAABQWV/fv/\npcxRHrOHVUq7V7YOtyEhITp79myhMbfbrdDQUJ/O43SGlWZZZSIkJEjyLcPjKgU6HPL8/jQAQDng\ndIapatWKll4f1rB1uK1evXqRT0/IzMxURESET+fJyspWXl5+aZZ2zeXk5ErcSCxTeR6PPTahAwCu\nWlZWtk6fPl/m1w0MDJDTGVYus4dVLvWstNg63DZr1kxvvfWW3G63d3tCSkqKWrZs6dN58vLylZtb\nvhaYx+Mh3Ja1fO7bAoAprH7vt/r6/szWN6patWqlmjVratSoUTp06JDmz5+vvXv3qlevXlaXBgAA\nABuyXbj99S9SBQQEaN68eTp58qR69uypNWvWaO7cuapRo4aFFQIAAMCubLct4cCBA4Ue16pVS4sX\nL7aoGgAAAJQntrtzCwAAAJQU4RYAAADGINwCAADAGIRbAAAAGINwCwAAAGMQbgEAAGAMwi0AAACM\nQbgFAACAMQi3AAAAMAbhFgAAAMYg3AIAAMAYhFsAAAAYg3ALAAAAYxBuAQAAYAzCLQAAAIxBuAUA\nAIAxCLcAAAAwBuEWAAAAxiDcAgAAwBiEWwAAABiDcAsAAABjEG4BAABgDMItAAAAjEG4BQAAgDEI\ntwAAADBGkNUFAAAAlKb8vFx99dVBS64dGBggpzNMWVnZysvLt6QGqzRufKuCg4OtLoNwCwAAzHL+\nzPda8NF3qvyfn6wuxW+cO3VMU4ZJsbFxVpdCuAUAAOapHH6zqtSob3UZsAB7bgEAAGAMwi0AAACM\nQbgFAACAMQi3AAAAMAbhFgAAAMYg3AIAAMAYhFsAAAAYg3ALAAAAYxBuAQAAYAzCLQAAAIxBuAUA\nAIAxCLcAAAAwBuEWAAAAxrB9uP30008VExOjhg0bev/+61//anVZAAAAsKEgqwv4PYcOHZLL5dKE\nCRPk8XgkSSEhIRZXBQAAADuyfbhNTU1V/fr1Va1aNatLAQAAgM3ZfltCamqqoqOjrS4DAAAA5YDt\nw+3hw4e1adMmde7cWR07dtS0adN08eJFq8sCAACADdl6W8J3332nCxcuKCQkRDNnzlR6eromTJig\nnJwcjR6syO+SAAATXElEQVQ9utjnCQy0fYYvwuFwWF2C/wmg5wAAlFRgYICCgnzPXKWd02wdbm+8\n8UZt3bpVTqdTkhQTE6P8/HyNGDFCiYmJxQ6ATmfYtSzzmggJCZLcVlfhXwIdDnmsLgIAgHLK6QxT\n1aoVrS7D3uFWkjfYXlK3bl3l5OTozJkzqlq1arHOkZWVrby8/GtR3jWTk5MrcSOxTOV5PPbfpwMA\ngE1lZWXr9OnzPv9cYGBAqd6ItHW43bx5s/72t79p48aN3o//2r9/v6pUqVLsYCtJeXn5ys0tX+HW\n4/EQbstaPvdtAQAoKbvkLVvfqIqNjVVYWJheeOEFHT58WBs2bNDUqVP15JNPWl0aAAAAbMjWd24r\nVqyoBQsWaOLEierVq5cqVqyohx9+WI8//rjVpQEAAMCGbB1upYI9tgsWLLC6DAAAAJQDtt6WAAAA\nAPiCcAsAAABjEG4BAABgDMItAAAAjEG4BQAAgDEItwAAADAG4RYAAADGINwCAADAGIRbAAAAGINw\nCwAAAGMQbgEAAGAMwi0AAACMQbgFAACAMQi3AAAAMAbhFgAAAMYg3AIAAMAYhFsAAAAYg3ALAAAA\nYxBuAQAAYAzCLQAAAIxBuAUAAIAxCLcAAAAwBuEWAAAAxiDcAgAAwBiEWwAAABiDcAsAAABjEG4B\nAABgDMItAAAAjEG4BQAAgDEItwAAADAG4RYAAADGINwCAADAGIRbAAAAGINwCwAAAGMQbgEAAGAM\nwi0AAACMQbgFAACAMQi3AAAAMAbhFgAAAMYg3AIAAMAYhFsAAAAYw/bh1u12a/To0brtttvUrl07\nLVy40OqSAAAAYFNBVhfwe1599VXt379fixcvVnp6ukaOHKmoqCh16tTJ6tIAAABgM7a+c5udna3k\n5GT9/e9/V0xMjDp06KCBAwdqyZIlVpcGAAAAG7J1uD148KDy8vLUvHlz71hcXJz27NljYVUAAACw\nK1uH25MnT6pKlSoKCvpl90R4eLhycnJ0+vRpCysDAACAHdl6z212draCg4MLjV167Ha7i32ewEBb\nZ/jLcjgcVpfgfwIcOnfqmNVV+JWfz56Q5LG6DL9Cz8sePS979LzsnTt1TIGBrRQU5HvmKu2cZutw\nGxISUiTEXnocFhZW7PM4ncWfaxdzp4+1ugQ/9IDVBQAAgKtk61ua1atX15kzZ5Sfn+8dy8zMVGho\nqJxOp4WVAQAAwI5sHW4bNmyooKAg7d692zu2Y8cONWnSxMKqAAAAYFe2DrehoaF64IEHNHbsWO3d\nu1effvqpFi5cqP79+1tdGgAAAGzI4fF4bL3j+sKFC3rppZe0bt06Va5cWQMHDlR8fLzVZQEAAMCG\nbB9uAQAAgOKy9bYEAAAAwBeEWwAAABiDcAsAAABjEG4BAABgDMItAAAAjGFUuE1ISFBiYuIVj+/f\nv1+9e/dW8+bN9eCDD+rLL78sw+rs6fd6NmjQIMXExKhhw4bevzds2FCGFdrHp59+WqQXf/3rXy87\nl7X2C1/6xnor4Ha79dJLL6lVq1a68847NWPGjCvOZa0V8KVnrLMCq1atKtKHmJgYNWrU6LLzWWsF\nfO0b663AiRMn9Je//EVxcXG655579O67715x7lWvNY8h1q5d62nQoIFn1KhRlz3+888/e9q2beuZ\nMmWKJzU11TNhwgRP27ZtPdnZ2WVcqX38Xs88Ho+nU6dOnrVr13oyMzO9f9xudxlWaR9JSUmeQYMG\neU6dOuXtxblz54rMY60VVty+eTyst0vGjBnj6dy5s2fv3r2eLVu2eG6//XbPihUrisxjrf2iuD3z\neFhnl+Tk5BTqwffff+/p1KmTZ/LkyUXmstZ+4UvfPB7W2yW9e/f2DBs2zHP06FHPp59+6mnevLnn\nf//3f4vMK421ZkS4PXPmjKd9+/aeBx988IpB7f333/d06NCh0FinTp08q1atKosSbac4PcvJyfE0\natTIc+TIkTKuzp6GDx/umT59+u/OY60VVty+sd4KnDlzxtO4cWPP9u3bvWPz58/3jB49ushc1loB\nX3rGOruyN954w9OpU6fLBi/W2pX9Vt9YbwXOnj3radCggeebb77xjg0ZMsQzfvz4InNLY60ZsS3h\n1Vdf1QMPPKC6detecc6ePXsUFxdXaKxFixbatWvXtS7PlorTs8OHD8vhcKhWrVplWJl9paamKjo6\n+nfnsdYKK27fWG8FUlJSVLlyZbVs2dI79uSTT+qVV14pMpe1VsCXnrHOLu/s2bN6++23NXz4cF13\n3XVFjrPWLu/3+sZ6KxAaGqqwsDCtXLlSubm5SktL086dOy+7laM01lq5D7dbtmxRSkqKnnnmmd+c\n98MPPygyMrLQWHh4uDIyMq5lebZU3J6lpqaqUqVKev7553XnnXfqwQcf1MaNG8uoSvs5fPiwNm3a\npM6dO6tjx46aNm2aLl68WGQea62w4vaN9Vbg+PHjioqK0urVq3XvvfeqQ4cOmjdvnjyX+TJJ1loB\nX3rGOru8ZcuWqXr16urYseNlj7PWLu/3+sZ6KxAcHKwXX3xR//jHP9SsWTPdd999+uMf/6gePXoU\nmVsaa61ch1u3261x48Zp7NixCg4O/s25Fy5cKDInODhYbrf7WpZoO770LC0tTTk5OWrXrp0WLFig\n9u3ba9CgQX75SwTfffedLly4oJCQEM2cOVMjR47UmjVrNHXq1CJzWWu/8KVvrLcCP//8s44cOaL3\n3ntPkydP1qhRo7R48eLL/vIFa62ALz1jnV1ecnKy4uPjr3ictXZ5v9c31tsvUlNT5XK59P7772vy\n5Mlat26d1q5dW2Reaay1oKuu1kKzZ89WkyZNdMcdd/zu3JCQkCKNcbvdCg0NvVbl2ZIvPRs8eLD6\n9++vypUrS5IaNGigffv2acWKFXr55Zevdam2cuONN2rr1q1yOp2SpJiYGOXn52vEiBFKTEyUw+Hw\nzmWt/cKXvrHeCgQGBur8+fOaPn26atSoIUn69ttvtXz5cj322GOF5rLWCvjSM9ZZUXv27FFGRobu\nu+++K85hrRVVnL6x3gps2bJFycnJ2rhxo4KDg9WoUSOdOHFCSUlJ6tKlS6G5pbHWynW4/fjjj3Xq\n1CnFxsZKkvd/da5bt047d+4sNLd69eo6efJkobHMzExFRESUTbE24UvPJHn/QV5St25dpaamXvtC\nbehSQLukbt26ysnJ0ZkzZ1S1alXvOGutsOL2TWK9SVJkZKRCQkK8IU2SoqOjdeLEiSJzWWsFfOmZ\nxDr7vzZv3qzbbrutSF9+jbVWVHH6JrHeJOnLL79U7dq1C92Rbdiwod58880ic0tjrZXrbQlLlizR\nmjVr9OGHH+rDDz+Uy+WSy+XSBx98UGRus2bNimxG3rlzp5o3b15W5dqCLz1LTEzU6NGjC40dPHiw\nWL8cZJrNmzerdevWysnJ8Y7t379fVapUKRLQWGu/8KVvrLcCzZo1U05Ojo4ePeodS01NVVRU1GXn\nstZ86xnrrKg9e/aoRYsWvzmHtVZUcfrGeisQGRmpo0ePKjc31zuWlpamm266qcjc0lhr5Trc1qxZ\nU7Vq1fL+qVixoipWrOj9rcTMzEzvm2rnzp117tw5TZw4UampqZowYYKys7N17733WvkUypwvPXO5\nXFqzZo1Wr16tY8eOac6cOdq5c+dv7i8yVWxsrMLCwvTCCy/o8OHD2rBhg6ZOnaonn3xSEmvtSnzp\nG+utQHR0tNq3b69Ro0bp4MGD2rRpk9566y098sgjklhrl+NLz1hnRX399deX/eQc1tpvK07fWG8F\nXC6XgoKC9Pe//11HjhzRZ599pjfffFP9+vWTdA3WWkk/s8yORo0aVegzWxs0aFDoc9H27Nnj6d69\nu6dZs2ae3r17ew4cOGBFmbbyez17//33PZ06dfI0bdrU06NHD8+OHTusKNMWDh065Hn88cc9LVq0\n8LRr184zd+5c7zHW2pX50jfWW4Fz5855Ro4c6WnRooWnbdu2nnnz5nmPsdYuz5eesc4Ka9asmWfz\n5s1Fxllrv624fWO9Fbj0XtCyZUtPp06dPIsWLfIeK+215vB4LvNZKQAAAEA5VK63JQAAAAC/RrgF\nAACAMQi3AAAAMAbhFgAAAMYg3AIAAMAYhFsAAAAYg3ALAAAAYxBuAQAAYAzCLQAAAIxBuAUAG4qP\nj1diYuIVj8fExGj16tVlWJH0/fff6+OPP/Y+drlcmjNnTpnWAAC/h3ALACiWkSNHatOmTVaXAQC/\niXALACgWj8djdQkA8LsItwDwf2zYsEE9e/ZU8+bNdccddygxMVFZWVne46mpqUpISFBsbKzuvPNO\nDR8+XJmZmd7j8fHxmjhxov72t7+pefPmat++vebPn1/oGp9++ql69+6t2NhYNW3aVD169NDmzZtL\nXPO//vUv9ejRQ82aNVOnTp00c+ZMud1u7/GYmBitXLlSAwYMULNmzXTnnXdq7ty5hc6xZs0a3Xff\nfWratKkeeughLVq0SDExMd7ntH37dq1atUr33HOP92d++OEHDRkyRLGxsbr99ts1efJkQjAASxFu\nAeBXTp8+rSFDhujBBx/UJ598orlz52rHjh2aOnWqJCkjI0OPPvqooqOjtWrVKs2fP18//fSTHnro\nIV24cMF7nuXLl+v666/XqlWr9Nxzz2nevHl6++23JUlffvmlhg4dqvvvv19r167Ve++9p/DwcI0c\nOVK5ubk+17xx40Y999xz6tOnjz766CONGzdOn3zyiUaOHFlo3pQpU9SzZ099/PHHio+P1+zZs7Vj\nxw5JBeF41KhR6t27t9asWaMePXpo2rRpcjgckqQ5c+aoefPmuu+++7Ry5UrvOf/5z3+qdevWWrt2\nrUaMGKF33nlHq1at8vk5AEBpIdwCwK9kZGTo4sWLqlmzpmrUqKHY2Fi98cYb6tu3r6SC0FqzZk0l\nJiaqdu3aatSokWbMmKFTp07pk08+8Z6nTp06evHFFxUdHa1u3bopPj5eixYtkiQFBgbqxRdfVHx8\nvKKiohQTE6P4+Hj9+OOPOnXqlM81v/nmm3rooYf04IMP6qabbtIdd9yhcePG6b//+7/13Xffeed1\n795dXbp0UVRUlJ566ik5nU7t3LlTkvRf//Vf+tOf/qTHHntMf/jDH/TQQw+pT58+3p+9/vrrdd11\n1ykkJERVqlTxjnfu3Fl9+/ZVVFSUevTooQYNGmjfvn0+PwcAKC1BVhcAAHYSExOjP//5z3rqqacU\nERGhtm3b6q677lLHjh0lSQcOHNA333yj2NjYQj/ndruVlpbmfdyqVatCx2NjY/X222/rzJkziomJ\n0fXXX6+33npLaWlpOnr0qA4cOCBJysvL87nm/fv3a+/evXrvvfcKjQcEBCg1NVU33nijpILA/WuV\nKlXSxYsXJRXcTe7cuXOh4y1bttS77777m9f+wx/+UOix0+ksdAcbAMoa4RYA/o/XXntNgwcP1saN\nG/Xvf/9bzz//vFq2bKmFCxcqPz9frVu31rhx44r8XOXKlb3/HRRU+OU1Pz9fUsFd223btmngwIG6\n6667FBcXp65du+rnn3/W4MGDS1Rvfn6+Bg4cqO7duxc5FhER4f3v4ODgIscv7Y8NCgry1uiLgICi\n/wOQPbcArMS2BAD4lT179mjSpEmqXbu2+vXrpzfeeEMTJ07Uf/7zH/3444+qX7++0tLSVKNGDdWq\nVUu1atWS0+nUK6+8oq+//tp7nv/7v+ZTUlJ00003qXLlylq4cKFuv/12zZo1S/3791ebNm282wdK\nEgzr16+vw4cPe+upVauWvvvuO7366qs6f/58sc7RoEEDffHFF4XGdu3aVejxpf23AGBnhFsA+JWK\nFStq6dKleu2113Ts2DF9/fXX+vjjj1W7dm1Vq1ZNjzzyiM6dO6fhw4fr4MGDOnjwoJ599lnt27dP\n9evX955nx44dmjNnjo4ePark5GQtX75cTz75pCSpZs2a+uqrr5SSkqJvv/1WK1eu1KxZsySp0Ccc\nFNeTTz6pdevWae7cuTpy5Ii2bNmixMREnT9/XuHh4cU6R0JCgj755BO98847Onr0qFauXKmlS5cW\nmlOhQgV9++23ysjI8LlGACgrhFsA+JW6detq7ty52rp1q7p166ZHH31UQUFB3o/yuummm7RkyRKd\nP39ejzzyiPr166eQkBC9++67qlq1qvc899xzj1JTU9W1a1fNnz9fo0ePVu/evSVJQ4cOVbNmzTRo\n0CB1795dycnJmjRpkkJDQ7V3795i1fnru6idO3fWjBkztH79enXt2lUjRozQH//4R82ePfuy8y83\n1q5dO7388statmyZ7r//fiUnJ6tPnz667rrrvHP69Omjr7/+Wl27dlV+fj53cgHYksPD5igAKFXx\n8fG66aabNGnSJKtLKbbt27frhhtuUHR0tHfsjTfe0D//+U/9z//8j4WVAYBvuHMLANDmzZv1xBNP\naOvWrfr++++1fv16LVq0SA888IDVpQGAT/i0BACABg8erOzsbI0cOVI//vijatasqQEDBmjgwIFW\nlwYAPmFbAgAAAIzBtgQAAAAYg3ALAAAAYxBuAQAAYAzCLQAAAIxBuAUAAIAxCLcAAAAwBuEWAAAA\nxiDcAgAAwBj/D1nGcloQGRV+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29f41a60f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# by default matplotlib creates 10 bins, we can customize it 2 ways\n",
    "# no of bins\n",
    "\n",
    "# plotting the sepal length ( sns style)\n",
    "sepal = data[:,0]\n",
    "\n",
    "sns.set()\n",
    "plt.hist(sepal, bins=6)\n",
    "plt.xlabel(\"sepal length\")\n",
    "plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x29f41cfae10>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AwEoUWQAAAFiJIgsAAAArUWQBAABgJYosAAAArESRBQAAgJUosgAAALASRRYA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29f41cec5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bins details\n",
    "\n",
    "# plotting the sepal length ( sns style)\n",
    "sepal = data[:,0]\n",
    "\n",
    "sns.set()\n",
    "plt.hist(sepal, bins=[ x for x in range(0, 10)])\n",
    "plt.xlabel(\"sepal length\")\n",
    "plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "  species  \n",
       "0  Setosa  \n",
       "1  Setosa  \n",
       "2  Setosa  \n",
       "3  Setosa  \n",
       "4  Setosa  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "iris = pd.DataFrame(data, columns=col)\n",
    "iris['species'] = ['Setosa' if x==0 else 'Versicolour' if x==1 else 'Virginica' for x in irisds['target']]\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Bee Sworm plot **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x29f42f40b00>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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R9/8OMo3W06XRFfCapQVEREREXak5/wwqV7/fGmIBQBRhzDyMyjX/8WxhdMUYZImIiKhH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\nEEIIIdolSWSFEEIIIUS79D/RH8/Kz6euIAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29f42fa0860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "sns.swarmplot(x=\"species\", y='sepal length (cm)', data=iris)\n",
    "plt.xlabel(\"species\")\n",
    "plt.ylabel(\"sepal length (cm)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Empirical cumulative distribution function (ECDF) **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def ecdf(data):\n",
    "    n = len(data)\n",
    "    x = np.sort(data)\n",
    "    y = np.arange(1, n+1)/n\n",
    "    return x,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x29f430862e8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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l8ACshtAJABZEOTwAqyF0AoAFUQ4PwGq4kQgALIhyeABWw04nAAAADEfoBAAAgOG4vA4A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\nAIb7/1XOnM0ePhe2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29f42fb8e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting\n",
    "\n",
    "x, y = ecdf(iris[iris['species']=='Setosa']['sepal length (cm)'])\n",
    "plt.plot(x, y, marker='.', linestyle='none')\n",
    "plt.xlabel(\"sepal length (cm)\")\n",
    "plt.xlabel(\"ecdf\")\n",
    "plt.margins(0.02)\n",
    "plt.legend(('Setosa'), loc='upper left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x29f430f5860>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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s0B133KGFCxdq4MCBWrlypW666Sb985//VEhIiGHfAGBXkV2DNTM9xexpAG4i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\njtAJAAAA0xE6AQAAYLpfMASr16I5NHAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29f43054438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting all species\n",
    "x, y = ecdf(iris[iris['species']=='Setosa']['sepal length (cm)'])\n",
    "plt.plot(x, y, marker='.', linestyle='none')\n",
    "\n",
    "x, y = ecdf(iris[iris['species']=='Versicolour']['sepal length (cm)'])\n",
    "plt.plot(x, y, marker='.', linestyle='none')\n",
    "\n",
    "x, y = ecdf(iris[iris['species']=='Virginica']['sepal length (cm)'])\n",
    "plt.plot(x, y, marker='.', linestyle='none')\n",
    "\n",
    "plt.xlabel(\"sepal length (cm)\")\n",
    "plt.xlabel(\"ecdf\")\n",
    "plt.margins(0.02)\n",
    "plt.legend(('Setosa','Versicolour', 'Virginica'), loc='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary Statistics "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**mean** - avg value but it is affected by outliers values\n",
    "\n",
    "\n",
    "$$ mean = \\frac{1}{n}\\Sigma_{i=1}^n {i_n} $$\n",
    "$$ mean = \\frac{1}{n}\\sum_{n=1}^n {i_n} $$\n",
    "\n",
    "**median** - middile value in values, doesn't affected by diversity of values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean :  6.587999999999998\n",
      "Median :  6.5\n"
     ]
    }
   ],
   "source": [
    "# checking out what is avg and median sepal length (cm) for Virginica species\n",
    "\n",
    "print(\"Mean : \",np.mean(iris[iris['species']=='Virginica']['sepal length (cm)']))\n",
    "print(\"Median : \",np.median(iris[iris['species']=='Virginica']['sepal length (cm)']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# creating variables\n",
    "virgin = iris[iris['species']=='Virginica']\n",
    "setosa = iris[iris['species']=='Setosa']\n",
    "versi = iris[iris['species']=='Versicolour']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25, 50, 70 percentile [ 6.225  6.5    6.9  ]\n"
     ]
    }
   ],
   "source": [
    "# getting percentile 25, 50, 75\n",
    "\n",
    "print(\"25, 50, 70 percentile\", np.percentile(virgin['sepal length (cm)'], [25, 50, 75]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    for repesenting the percentile and checking the outliers, we use boxplot\n",
    "    in box plot, box starts from 25% , median 50% and end at 75%\n",
    "    whiskers are ususllay represent the 1.5x range of box values(25-75)\n",
    "    If there is any points after the whiskers are called outliers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pYcOGKigoUExMjGJjY+VwXL10hoWVv+Y6RRUSEuSR7QAlISQkSKGh5e2OgRJU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\nTAEAAGAEiikAAACM8P8AVgy/tIr0FbQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29f4305f518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sns.boxplot(x='species', y='sepal length (cm)', data=virgin)\n",
    "#sns.boxplot(x='species', y='sepal length (cm)', data=setosa)\n",
    "#sns.boxplot(x='species', y='sepal length (cm)', data=versi)\n",
    "\n",
    "sns.boxplot(x='species', y='sepal length (cm)', data=iris)\n",
    "plt.margins(0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Variance** - it is the sum of squared distance of data-point from mean data-point\n",
    "\n",
    "if x(bar) is mean\n",
    "\n",
    "$$ variance = \\frac{1}{n}{\\sum_{i=1}^{n}(x_i - \\bar{x})^2} $$\n",
    "\n",
    "** Standard Variance ** - square root of variance\n",
    "\n",
    "$$ std =\\sqrt{\\frac{1}{n}{\\sum_{i=1}^{n}(x_i - \\bar{x})^2}} $$\n",
    "\n",
    "higher std value represents the more diverse data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2611040000000001"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var = np.var(versi['sepal length (cm)'])\n",
    "var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.510983365678 0.5109833656783752\n"
     ]
    }
   ],
   "source": [
    "std1 = np.sqrt(var)\n",
    "std2 = np.std(versi['sepal length (cm)'])\n",
    "print(std1, std2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** covariance ** - how two datapoints are related with each other\n",
    "Or A measure of how two quantities vary together\n",
    "\n",
    "$$ cov = \\frac{1}{n}{\\sum{(x-\\bar{x})}{(y-\\bar{y})}} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Pearson correlation coefficient ** - \n",
    "\n",
    "$$ \\rho = \\frac{\\frac{1}{n}{\\sum{(x-\\bar{x})}{(y-\\bar{y})}}}{std(x) std(y)} $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HR40bN1anTp3UrFkzV9cHAAAAXJbDQfbixYt66623lJSUpIyMDNWtW1fXXXedysvLdfbs\nWZWXl6tt27a6//77NWzYMNWpU8eddQMAAKCWcyjIHjt2TDNmzJCfn5+GDx+uW2+9Va1atZKPz/dX\nJpSXlys9PV179+7VmjVrtHr1aj377LPq1KmTW4sHAABA7eVQkP3zn/+sp556Srfccstlt/v4+KhD\nhw7q0KGDHnjgAX344YeaM2eO3njjDZcWCwAAAPzAoSC7ceNGWSwWh3fau3dv9erV66qLAgAAAH6J\nQ3ctcCbEXsvXAAAAAI5y+q4Fhw8f1p///GdlZGSorKys0va0tDSXFAYAAABUxekg+6c//Un16tVT\nYmKi6tev746aAAAAgF/kdJDNysrS5s2b1bZtW3fUAwAqOFeqpOQ0ZWUXqmVEsBLioxUS4Od1+zRJ\nwblSrdmZrhPZRWoREaTxQ6K88pia0ifqBLyD05/s1bFjR3399dfuqAUAJElJyWk6ZM3VmaJSHbLm\nKin52i9Zcsc+TZKUnKaDGTnKKzivgxk5XntMTekTdQLewekZ2dmzZ+v3v/+9Dh06pObNm9vvJfuD\nYcOGuaw4ALVTVnZhlcvesk+TmHJMTekTdQLewekgu2PHDmVlZWn58uWVtlksFoIsgGvWMiJYZ4py\nKyx74z5NYsoxNaVP1Al4B4vNZrM58wXdu3dXQkKC7r//fvn7+7urLrc5ffrH/436+vooNDRQ+fnF\nunix3INVoSr0yfu5uke1+dpLd+EaWddyV50mnEu1HT+Tqkd4uGP/6XI6yHbr1k1btmxR8+bNr6ow\nTyPImoc+eT96ZAb65P3okfejR9XD0SDr9Ju97rzzTq1fv97pggAAAABXcvoa2cLCQiUnJ+vtt99W\n8+bN5etbcRevvfaay4oDAAAArsTpIOvj46OhQ4e6oxYAAADAYU4H2fnz50uSysrKVLduXUlSdna2\nIiIiXFsZAAAAUAWnr5HNy8vTuHHjtHTpUvu64cOH64EHHtDZs2ddWhwAAABwJU4H2blz56qkpETx\n8fH2datWrVJhYaGeffZZlxYHAAAAXInTQXbPnj2aM2eO2rVrZ1930003aebMmfr3v//tytoAAACA\nK3I6yF66dEmXu/Vs3bp1VVJS4pKiAAAAgF/idJDt1q2blixZoqKiIvu6oqIivfjii+rWrZtLiwMA\nAACuxOm7FiQmJuree+9Vv3791KpVK0nSl19+qQYNGmj16tWurg8AAAC4LKeDbIsWLbRjxw4lJycr\nIyNDvr6+uueeezR06FDVr1/fHTUCAAAAlTgUZEtKSuTv729fDg4O1t133+3U1wAAAACu5NA1snfd\ndZe2bdt22Td5/VxZWZneeOMNDR8+/JqLAwAAAK7EoRnZ1atX66mnntJzzz2nwYMHq3///mrfvr0a\nNmyo8vJy5eXl6dixY9q7d6+Sk5PVrl07rVq1yt21AwAAoBZzKMjecMMNeuWVV7R3714lJSXpf//3\nf3Xx4sUKY/z8/NSzZ08tXrxYvXr1ckuxAAAAwA+cerNXjx491KNHD5WUlOjYsWPKycmRj4+PwsPD\nFRUVxTWxALzWNznFWrg+VYUlZQr2r6tpY7rohkaBni7LaBxT1yo4V6o1O9N1IrtILSKCNH5IlEIC\n/DxdVrUoOFeqpOQ0ZWUXqmVEsBLio2vNc8e1cfo+spLk7++vm2++Wb/5zW902223qXPnzoRYAF5t\n4fpUFZwrk80mFZwr08L1qZ4uyXgcU9dKSk7TwYwc5RWc18GMHCUlp3m6pGqTlJymQ9ZcnSkq1SFr\nbq167rg2VxVkAcA0hSVlVS7DeRxT18rKLqxyuSarzc8d14YgC6BWCPavW+UynMcxda2WEcFVLtdk\ntfm549oQZAHUCtPGdFFIQF1ZLFJIwPfXc+LacExdKyE+WrFtG6lhSH3Ftm2khPhoT5dUbRLioxUT\nGaYGQX6KiQyrVc8d18Zic+TmsDXI6dM//rrC19dHoaGBys8v1sWL5R6sClWhT96PHpmBPnk/euT9\n6FH1CA93bFbe6Y+olaR9+/bp6NGjOn/+fKUPSXjkkUeuZpcAAACAU5wOsitXrtSSJUsUHBys4OCK\nadlisRBkAQAAUC2cDrJr167Vo48+qkmTJrmjHgAAAMAhTr/Z68yZMxo6dKg7agEAAAAc5nSQ7dq1\nqw4cOOCOWgAAAACHOXRpwbZt2+z//tWvfqVZs2YpIyNDLVu2VJ06dSqMHTZsmGsrBAAAAC7DoSA7\nY8aMSutWrlxZaZ3FYiHIAgAAoFo4FGTT09PdXQcAAADgFKevkR03bpwKCyt/BnJubi6zsQAAAKg2\nDs3I7t69W0eOHJEkffLJJ1q+fLkCAgIqjMnKytLXX3/t+goBAACAy3AoyDZt2lSzZ8+WzWaTxWLR\njh075OPz42SuxWJRQECApk2b5rZCAQAAgJ9yKMjeeOONev/99yVJcXFx2rx5sxo2bOjWwgAAAICq\nOP3JXv/85z/dUQcAAADgFIeC7Lhx4xze4WuvvXbVxQAAAACOcuiuBU2bNrX/adSokT755BMVFhYq\nMjJS7du3V1lZmVJSUtSmTRt31wsAAABIcnBGdv78+fZ/JyYmavz48ZU+JOGFF16Q1Wp1bXUAAADA\nFTh9jeyuXbu0devWSuuHDRvGfWRRaxWcK1VScpqysgvVMiJYCfHRCgnw83RZxjLleH6TU6yF61NV\nWFKmYP+6mjami25oFOjpsi6r4Fyp1uxM14nsIrWICNL4IVFeeUwBwBlOfyBCSEiIPvvss0rr9+/f\nr7CwMJcUBZgmKTlNh6y5OlNUqkPWXCUlp3m6JKOZcjwXrk9Vwbky2WxSwbkyLVyf6umSrigpOU0H\nM3KUV3BeBzNyvPaYAoAznJ6RHT16tJ5++mlZrVZ17NhR5eXlSk1N1bp16zR16lR31Ah4vazswiqX\n4RxTjmdhSVmVy97ElGMKAM5wOsj+/ve/V506dbR27VotW7ZMktSkSRNNmzZNY8aMcXmBgAlaRgTr\nTFFuhWVcPVOOZ7B/XRWcK6uw7K1MOaYA4AyLzWazXe0X5+fny2KxqEGDBq6sya1On/5xFsLX10eh\noYHKzy/WxYvlHqwKVTGhT6Zc0+kuru6RKceTa2ThaiZ8v6vt6FH1CA937D/bDgXZbdu26fbbb5ef\nn5+2bdtW5Vhvf8MXQdY89Mn70SMz0CfvR4+8Hz2qHo4GWYcuLZgxY4b69u2rsLCwSrfd+imLxeL1\nQRYAAAA1g0NBNj09/bL/BgAAADzF6dtvPfDAA1q3bp1OnTrljnoAAAAAhzgdZNu1a6e1a9fq17/+\ntUaMGKGlS5cySwsAAIBq53SQnTFjhnbu3Kl33nlHw4YN04EDBzRq1CjFxcVp7ty57qgRAAAAqMTp\nIPuDFi1aKD4+XiNGjNCgQYN06tQprV271pW1AQAAAFfk9Aci/OMf/9C+ffu0b98+Wa1WNW7cWD17\n9tS8efPUq1cvd9QIAAAAVOJ0kJ0yZYp8fHw0YMAALViwQB07dnRHXQAAAECVnA6yS5cu1ccff6wP\nP/xQd999tzp16qSePXuqZ8+eio2Nla+v07sEAAAAnOZ06hw4cKAGDhwoSfr222/10Ucf6eOPP9bq\n1avl4+OjAwcOuLxIAAAA4Oeuevo0OztbH3/8sT766CN99NFH8vHxUe/evV1ZGwAAAHBFTgfZuXPn\n6qOPPlJmZqYaN26sX//611qwYIF69uwpPz8/d9QIAAAAVOJ0kE1JSdGQIUMUFxenDh06uKMmAAAA\n4Bc5HWS3bNnijjoAAAAAp1z1ByIAAAAAnkSQBQAAgJG46SuAa1JwrlRrdqbrRHaRWkQEafyQKIUE\neN8bPwvOlSopOU1Z2YVqGRGshPjoa67THfsEADiOGVkA1yQpOU0HM3KUV3BeBzNylJSc5umSLisp\nOU2HrLk6U1SqQ9Zcl9Tpjn0CABzn0IxsYmKiwzucP3/+VRcDwDxZ2YVVLnsLd9RpynMHgJrKoSB7\n8uRJd9cBwFAtI4J1pii3wrI3ckedpjx3AKipLDabzebpIqrT6dM/zpj4+vooNDRQ+fnFunix3INV\noSr0ybtxjaw518hyLnk/euT96FH1CA93bGLgqoLsxYsXlZubq0uXLkmSbDabSktLdeTIEd1xxx3O\n7q5aEWTNQ5+8Hz0yA33yfvTI+9Gj6uFokHX6rgV79uzR9OnTlZeXV2lb/fr1vT7IAgAAoGZw+q4F\nS5YsUYcOHfTXv/5V9evX19KlS/XEE08oKChIixYtckeNAAAAQCVOz8h+8cUXmjdvnqKiohQdHa2A\ngACNHTtWAQEBeuWVVzRw4EB31AkAAABU4PSMbJ06dRQc/P11Cy1bttTnn38uSerRo4esVqtrqwMA\nAACuwOkg27ZtW/3zn/+UJLVp00YpKSmSpFOnTrm2MgAAAKAKTl9aMGHCBE2ZMkV169bVb3/7W730\n0kuaMGGC/vvf/6pHjx7uqBEAAACoxOkZ2YEDB+qNN95QbGysmjRpotWrV6tOnToaMGCAZs+e7Y4a\nAQAAgEqcnpGVpJtuukmSdObMGUVHR2v58uUuLQoAAAD4JU7PyErS6tWr1a9fP/Xs2VO33HKLBg0a\npE2bNrm6NgAAAOCKnJ6RXblypV5++WWNHTtWnTt3Vnl5uVJSUjRv3jxJ0qhRo1xeJAAAAPBzTgfZ\ndevWadasWRo2bJh93cCBAxUZGamVK1cSZAEAAFAtnL604OzZs4qJiam0vlu3bsrOznZJUQAAAMAv\ncTrIDhgwQK+//nql9du3b1dcXJzTBWRnZ2vKlCnq3r27+vfvrwULFqi0tPSyYydNmmT/RLEf/t69\ne7fTjwkAAADzOX1pQVhYmDZs2KCUlBTdcsst8vX11dGjR7V//34NGDBAiYmJ9rHz58//xf1NmTJF\nDRo00Pr163XmzBk98cQTqlOnjqZOnVppbGZmphYvXlzhfrUhISHOPgUAAADUAE4H2bS0NMXGxkqS\n0tPT7etvvvlmnT17VmfPnnV4X5mZmTp8+LA+/PBDNWzYUNL3wXbhwoWVgmxpaalOnjypjh07Kiws\nzNmyAQAAUMM4HWQvd1nB1QoPD9fq1avtIVaSbDabCgsLK409fvy4LBaLmjdv7rLHBwAAgLmu6gMR\nzp8/r127dikzM1MPPPCAPv/8c7Vt21ahoaFO7Sc4OFi9e/e2L9tsNq1du1a9evWqNNZqtSooKEhT\np07Vvn371KRJE02ePFn9+vW7mqeAWqzgXKmSktOUlV2olhHBSoiPVkiAn6fLMlbBuVKt2ZmuE9lF\nahERpPFDoq75eNIjuBKvJ6DmcjrI5uTkaPTo0crNzVVpaalGjhypv/3tbzp69KheffVVRUZGXnUx\nCxcuVHp6ut58881K2zIzM3XhwgX17dtXEyZM0LvvvqtJkyZp06ZN9k8ac4SPj0U+PhZJUp06PhX+\nhndydZ/W7EzXIWuuJOlMUa7W7EzX46NjXbLv2mjNznQdzMiRJOUVnNcaXfvxpEfuUVu/55n0eqqt\nPTIJPfIuTgfZBQsWqG3btnr77bftM6fPPvus/vCHP2jRokVasWLFVRWyaNEivf7663rhhRcuG4Yf\neeQR3X///QoODpYktW/fXkePHtXGjRs1e/Zshx+nYcNAWSyWCutCQvyvqmZUL1f16UR2UaXl0NBA\nl+y7NnLH8aRH7lXbvueZ+HqqbT0yET3yDk4H2b1792rlypXy9/+xgdddd52mT5+ucePGXVURc+bM\n0caNG7Vo0SINHDjwiuN+CLE/iIyMlNVqdeqx8vKKK8zIhoT4q6CgRJculTtfOKqFq/vUIiJIeQXn\nKyzn5xdf835rK3ccT3rkHrX1e55Jr6fa2iOT0KPq4eh/Np0OssXFxQoICLjstosXLzq7Oy1dulQb\nN27U888/r0GDBl1xXGJioiwWi/2jcKXv75rQrl07px6vvNym8nJbhXWXLpXr4kVejN7OVX0aPyRK\nSeU/Xi83fkgU/b8G44dEaY0qXiN7rceTHrlXbfueZ+Lrqbb1yET0yDs4HWS7deumDRs2VLhfbFlZ\nmZYvX64uXbo4tS+r1arly5dr4sSJ6ty5s3JycuzbGjVqpJycHAUHB6tevXqKi4vT448/rltuuUVd\nunTRW2+1XAogAAAcFUlEQVS9pdTUVM2ZM8fZp4BaLiTAT4+OrPzpdLg6IQF+enx0rEJDA5WfX+yS\nb+z0CK7E6wmouSw2m832y8N+ZLVade+996pJkybKyMhQ9+7dlZmZqcLCQq1du1ZRUVEO72vlypV6\n/vnnK6yz2WyyWCxKS0tTVFSUFixYoGHDhkmSNm/erFWrVunUqVO68cYb9cQTT6hr167OlK/Tp3+8\ntZevr49Lf/jCPeiT96NHZqBP3o8eeT96VD3Cw4N/eZCuIshK33+s7IYNG5SWlqby8nK1bdtWY8aM\nUbNmzZwutLoRZM1Dn7wfPTIDffJ+9Mj70aPq4WiQvar7yEZEROgPf/iDfTkvL6/ChxoAAAAA7ub0\nTdAKCgr01FNP6b///a8uXbqkhIQE9e7dW0OGDNFXX33ljhoBAACASpwOsvPnz9fevXvl6+urd999\nV/v379fChQvVqlUrLVy40B01AgAAAJU4fWnB7t27tWzZMkVGRmrVqlXq3bu3hg4dqvbt2+vee+91\nR40AAABAJU7PyJ47d05NmjSRJH344Yf2T/eqX7++Ll265NrqAAAAgCtwekY2MjJS//73v9WkSROd\nPn1a/fr1kyRt2rTpsh8tCwAAALiD00F2ypQpmjx5ssrKyvTb3/5WrVq10vz587Vu3TotW7bMHTUC\nAAAAlTgdZPv376/du3crOzvb/uEH8fHxGjVqFDOyAAAAqDZXdR/Z0NBQhYaG2pc7derksoIAAAAA\nRzj9Zi8AAADAGxBkAQAAYCSCLAAAAIxEkAUAAICRrurNXsDlFJwrVVJymrKyC9UyIlgJ8dEKCfDz\ndFlws29yirVwQ6oKz5UpOKCupt3TRTc0CvR0WQCAWoAZWbhMUnKaDllzdaaoVIesuUpKTvN0SagG\nC9enqqC4TDabVFBcpoXrUz1dEgCgliDIwmWysgurXEbNVFhSVuUyAADuQpCFy7SMCK5yGTVTsH/d\nKpcBAHAXgixcJiE+WjGRYWoQ5KeYyDAlxEd7uiRUg2ljuigksK4sFikksK6mjeni6ZIAALUEb/aC\ny4QE+OnRkTGeLgPV7IZGgVr6WH+FhgYqP79YFy+We7okAEAtwYwsAAAAjESQBQAAgJEIsgAAADAS\nQRYAAABGIsgCAADASARZAAAAGIkgCwAAACMRZAEAAGAkgiwAAACMRJAFAACAkQiyAAAAMBJBFgAA\nAEYiyAIAAMBIBFkAAAAYiSALAAAAIxFkAQAAYCRfTxcAVKXgXKnW7EzXiewitYgI0vghUQoJ8PN0\nWfgJegRvV3CuVEnJacrKLlTLiGAlxEfzGgVqCGZk4dWSktN0MCNHeQXndTAjR0nJaZ4uCT9Dj+Dt\nkpLTdMiaqzNFpTpkzeU1CtQgBFl4tazswiqX4Xn0CN6O1yhQcxFk4dVaRgRXuQzPo0fwdrxGgZqL\nIAuvlhAfrdi2jdQwpL5i2zZSQny0p0vCz9AjeLuE+GjFRIapQZCfYiLDeI0CNYjFZrPZPF1EdTp9\n+sdfKfn6+ig0NFD5+cW6eLHcg1WhKvTJ+9EjM9An70ePvB89qh7h4Y795oQZWQAAABiJIAsAAAAj\nEWQBAABgJIIsAAAAjESQBQAAgJEIsgAAADASQRYAAABGIsgCAADASARZAAAAGIkgCwAAACMRZAEA\nAGAkgiwAAACMRJAFAACAkQiyAAAAMBJBFgAAAEYiyAIAAMBIvp4uAADgfgXnSrVmZ7pOZBepRUSQ\nxg+JUkiAn6fLqqTgXKmSktOUlV2olhHBSoiP9so6AXgHZmQBoBZISk7TwYwc5RWc18GMHCUlp3m6\npMtKSk7TIWuuzhSV6pA112vrBOAdCLIAUAtkZRdWuewtTKkTgHcgyAJALdAyIrjKZW9hSp0AvANB\nFgBqgYT4aMW2baSGIfUV27aREuKjPV3SZSXERysmMkwNgvwUExnmtXUC8A682QsAaoGQAD89PjpW\noaGBys8v1sWL5Z4u6bJCAvz06MgYT5cBwBDMyAIAAMBIBFkAAAAYiSALAAAAIxFkAQAAYCSCLAAA\nAIxEkAUAAICRCLIAAAAwEkEWAAAARiLIAgAAwEgEWQAAABiJIAsAAAAjEWQBAABgJIIsAAAAjESQ\nBQAAgJEIsgAAADASQRYAAABGIsgCAADASARZAAAAGIkgCwAAACMRZAEAAGAkgiwAAACMRJAFAACA\nkQiyAAAAMBJBFgAAAEYiyAIAAMBIBFkAAAAYiSALAAAAIxFkAQAAYCSCLAAAAIxEkAUAAICRCLIA\nAAAwEkEWAAAARiLIAgAAwEgeD7LZ2dmaMmWKunfvrv79+2vBggUqLS297NjPPvtMo0aNUmxsrEaO\nHKljx45Vc7UAAADwFh4PslOmTNGFCxe0fv16LVmyRP/617/04osvVhpXUlKiCRMmqFu3btqyZYti\nY2M1ceJEnT9/3gNVAwAAwNM8GmQzMzN1+PBhzZ8/X5GRkerataumTJmit99+u9LY5ORk+fv7a+rU\nqWrTpo2efPJJBQYGateuXR6oHAAAAJ7m0SAbHh6u1atXq2HDhvZ1NptNhYWFlcYePnxYXbt2rbCu\nS5cuOnDggNvrBAAAgPfx9eSDBwcHq3fv3vZlm82mtWvXqlevXpXGfvfdd2rXrl2FdWFhYfriiy/c\nXic8p+BcqdbsTNeJ7CK1iAjS+CFRCgnw83RZAADAC3g0yP7cwoULlZ6erjfffLPStvPnz8vPr2KA\n8fPzu+Ibw67Ex8ciHx+LJKlOHZ8Kf8P7rNmZroMZOZKkvILzWqN0PT461sNV4ec4l8xAn7wfPfJ+\n9Mi7eE2QXbRokV5//XW98MILioyMrLS9Xr16lUJraWmp6tev79TjNGwYKIvFUmFdSIi/8wWjWpzI\nLqq0HBoa6KFq8Es4l8xAn7wfPfJ+9Mg7eEWQnTNnjjZu3KhFixZp4MCBlx0TERGh06dPV1iXk5Oj\n8PBwpx4rL6+4woxsSIi/CgpKdOlS+dUVD7dqERGkvILzFZbz84s9WBEuh3PJDPTJ+9Ej70ePqoej\nk1YeD7JLly7Vxo0b9fzzz2vQoEFXHBcTE6NVq1ZVWJeamqpJkyY59Xjl5TaVl9sqrLt0qVwXL/Ji\n9Ebjh0RpjSpeI0uvvBfnkhnok/ejR96PHnkHjwZZq9Wq5cuXa+LEiercubNycnLs2xo1aqScnBwF\nBwerXr16Gjx4sJYsWaJ58+Zp9OjR2rBhg0pKSjRkyBAPPgO4W0iAnx4fHavQ0EDl5xfzTQMAANh5\n9Erl999/X+Xl5Vq+fLn69u2rvn37qk+fPurbt68kqU+fPtq5c6ckKSgoSCtWrND+/ft111136ciR\nI1q1apXT18gCAACgZrDYbDbbLw+rOU6f/vEetb6+Psz0GYA+eT96ZAb65P3okfejR9UjPDzYoXHc\nOwIAAABGIsgCAADASARZAAAAGIkgCwAAACMRZAEAAGAkgiwAAACMRJAFAACAkQiyAAAAMBJBFgAA\nAEYiyAIAAMBIBFkAAAAYiSALAAAAIxFkAQAAYCSCLAAAAIxEkAUAAICRCLIAAAAwEkEWAAAARiLI\nAgAAwEgEWQAAABiJIAsAAAAjEWQBAABgJIIsAAAAjESQBQAAgJEIsgAAADASQRYAAABGIsgCAADA\nSARZAAAAGIkgCwAAACMRZAEAAGAkgiwAAACMRJAFAACAkQiyAAAAMBJBFgAAAEYiyAIAAMBIBFkA\nAAAYiSALAAAAIxFkAQAAYCSCLAAAAIxEkAUAAICRCLIAAAAwEkEWAAAARiLIAgAAwEgEWQAAABiJ\nIAsAAAAjEWQBAABgJIIsAAAAjESQBQAAgJEIsgAAADASQRYAAABGIsgCAADASARZAAAAGIkgCwAA\nACMRZAEAAGAkgiwAAACMRJAFAACAkSw2m83m6SIAAAAAZzEjCwAAACMRZAEAAGAkgiwAAACMRJAF\nAACAkQiyAAAAMBJBFgAAAEYiyAIAAMBIBFkAAAAYiSALAAAAIxFkAQAAYKRaFWQnTJigxMTEK26f\nNGmSoqKiFB0dbf979+7d1Vhh7fXee+9VOvaPPvroZcd+9tlnGjVqlGJjYzVy5EgdO3asmqutnZzp\nEeeSZ5SWlurPf/6zbrnlFvXp00fPP//8FcdyHnmOM33iXKp+W7durXTMo6Ki1KFDh8uO51zyLF9P\nF1BdkpOT9cEHH2j48OFXHJOZmanFixerR48e9nUhISHVUV6t98UXXyguLk7PPPOMbDabJKlevXqV\nxpWUlGjChAm68847tWDBAm3YsEETJ07Ue++9p/r161d32bWKoz2SOJc85ZlnntEnn3yiv/3tbyoq\nKtJjjz2mpk2batSoURXGcR55lqN9kjiXPCE+Pl79+vWzL5eVlen+++9XXFxcpbGcS55XK2Zkz549\nq0WLFqlTp05XHFNaWqqTJ0+qY8eOCgsLs/+pW7duNVZae1mtVrVt21YNGza0H/ugoKBK45KTk+Xv\n76+pU6eqTZs2evLJJxUYGKhdu3Z5oOraxdEecS55xtmzZ7VlyxY988wz6tixo3r06KEHHnhAhw4d\nqjSW88hznOkT55Jn+Pn5VTjef//73yVJjz/+eKWxnEueVyuC7LPPPqs777xTkZGRVxxz/PhxWSwW\nNW/evBorww+sVqtat279i+MOHz6srl27VljXpUsXHThwwF2l4f9ztEecS56RkpKi4OBg3XzzzfZ1\nDz30kObOnVtpLOeR5zjTJ84lzzt79qxWr16tP/7xj5f9DwTnkufV+CD78ccfKyUlRf/7v/9b5Tir\n1aqgoCBNnTpVffr00ciRI/XBBx9UU5U4fvy4/vOf/2jw4MEaNGiQFi9erLKyskrjvvvuOzVu3LjC\nurCwMGVnZ1dXqbWWoz3iXPKMr776Sk2bNtW2bds0ZMgQDRw4UC+//LL9MpCf4jzyHGf6xLnkeevX\nr1dERIQGDRp02e2cS55Xo4NsaWmpZs2apZkzZ8rPz6/KsZmZmbpw4YL69u2rV155Rf3799ekSZO4\naLsafPPNNzp//rzq1aunF198UdOnT9f27du1aNGiSmPPnz9fqZd+fn4qLS2trnJrJWd6xLnkGefO\nndOXX36pTZs2acGCBZoxY4Zef/11vfrqq5XGch55jjN94lzyvM2bN2vs2LFX3M655Hk1+s1eL730\nkjp27KhevXr94thHHnlE999/v4KDgyVJ7du319GjR7Vx40bNnj3b3aXWajfccIP27dtnfwNDVFSU\nysvLNW3aNCUmJspisdjH1qtXr9I3iNLSUi6qdzNnesS55Bl16tRRcXGxlixZouuvv16S9PXXX2vD\nhg0aP358hbGcR57jTJ84lzzr8OHDys7O1u23337FMZxLnlejg+yOHTuUm5urzp07S5L916DvvPOO\nUlNTK43/4ZvFDyIjI2W1Wt1fKCq9CzcyMlIXLlzQmTNnFBoaal8fERGh06dPVxibk5Oj8PDwaqmz\nNnO0RxLnkic0btxY9erVs4cjSWrdurVOnTpVaSznkec40yeJc8mT9uzZo27dulXqwU9xLnlejb60\nYO3atdq+fbveeustvfXWW4qLi1NcXJz9HYg/lZiYqCeeeKLCuvT0dIfe3IJrs2fPHnXv3l0XLlyw\nr/vss8/UoEGDSgEpJiam0kX0qampio2NrZZaaytnesS55BkxMTG6cOGCsrKy7OusVquaNm162bGc\nR57hTJ84lzzr8OHD6tKlS5VjOJc8r0YH2SZNmqh58+b2P4GBgQoMDLS/AzQnJ8f+gzkuLk7bt2/X\ntm3bdOLECS1dulSpqalVXhsD1+jcubP8/f315JNP6vjx49q9e7cWLVqkhx56SFLFPg0ePFiFhYWa\nN2+erFarnnnmGZWUlGjIkCGefAo1njM94lzyjNatW6t///6aMWOG0tPT9Z///EerVq3SmDFjJHEe\neQtn+sS55Fmff/75Ze92xLnkZWy1yIwZM2wzZsywL7dv3962detW+/Ibb7xhu+2222ydOnWyjRgx\nwrZ//35PlFkrffHFF7YHHnjA1qVLF1vfvn1ty5Yts2/7eZ8OHz5sGz58uC0mJsY2atQoW1pamidK\nrnWc6RHnkmcUFhbapk+fbuvSpYutd+/etpdfftm+jfPIezjTJ84lz4mJibHt2bOn0nrOJe9isdku\nc88PAAAAwMvV6EsLAAAAUHMRZAEAAGAkgiwAAACMRJAFAACAkQiyAAAAMBJBFgAAAEYiyAIAAMBI\nBFkAAAAYiSALAAAAIxFkAcCNxo4dq8TExCtuj4qK0rZt26qxIunbb7/Vjh077MtxcXFaunSp0/sp\nKirS7bffruzsbFeWZzd58mS99957btk3gJqBIAsAtcz06dP1n//855r3s3DhQsXHxysiIsIFVVU2\ndepUzZkzRwUFBW7ZPwDzEWQBoJax2WzXvI+srCy99dZbGjdunAsqurwWLVooJiZGr776qtseA4DZ\nCLIAaqzdu3frrrvuUmxsrHr16qXExMQKs3tWq1UTJkxQ586d1adPH/3xj39UTk6OffvYsWM1b948\n/d///Z9iY2PVv39/rVy5ssJjvPfeexo1apQ6d+6sTp06acSIEdqzZ89V1/yvf/1LI0aMUExMjG67\n7Ta9+OKLKi0ttW+PiorSm2++qYSEBMXExKhPnz5atmxZhX1s375dt99+uzp16qTRo0frtddeU1RU\nlP05ffrpp9q6dasGDBhg/5rvvvtOkydPVufOndWjRw8tWLCgysD76quvqkePHgoODravy83N1bRp\n09SjRw/dfPPNevjhh3XixAlJ0tKlS5WQkKBly5apd+/e6tKli55++mmdOnVKDz/8sGJjY3Xbbbdp\n9+7dFR4nPj5e69atq3AMAOAHBFkANVJ+fr4mT56skSNHateuXVq2bJn279+vRYsWSZKys7N17733\nqnXr1tq6datWrlypoqIijR49WufPn7fvZ8OGDbruuuu0detWPfbYY3r55Ze1evVqSdKxY8c0ZcoU\nDR06VG+//bY2bdqksLAwTZ8+XRcvXnS65g8++ECPPfaY7rnnHiUnJ2vWrFnatWuXpk+fXmHcwoUL\nddddd2nHjh0aO3asXnrpJe3fv1/S90F4xowZGjVqlLZv364RI0Zo8eLFslgskr4PlLGxsbr99tv1\n5ptv2ve5ZcsWde/eXW+//bamTZumNWvWaOvWrVes9f3331f//v3ty5cuXVJCQoIyMzO1YsUKbdq0\nSeXl5XrooYfsgfjTTz/V8ePHtX79ej311FPatGmT/ud//kfx8fHasmWL2rRpU+l64v79+6ugoEAp\nKSlOH08ANR9BFkCNlJ2drbKyMjVp0kTXX3+9OnfurBUrVui+++6T9H1AbdKkiRITE9WqVSt16NBB\nzz//vHJzc7Vr1y77ftq0aaOnn35arVu31rBhwzR27Fi99tprkqQ6dero6aef1tixY9W0aVNFRUVp\n7NixysvLU25urtM1//Wvf9Xo0aM1cuRINWvWTL169dKsWbO0c+dOffPNN/Zxw4cP129/+1s1bdpU\nEydOVEhIiFJTUyVJf/vb3/Sb3/xG48ePV8uWLTV69Gjdc8899q+97rrrVLduXdWrV08NGjSwrx88\neLDuu+8+NW3aVCNGjFD79u119OjRy9Z56tQpZWdnq127dvZ1H330kTIyMrRkyRLFxsaqTZs2euaZ\nZzRgwACdOXPGPm7OnDlq2bKlhg8frtDQUPXq1UtDhw5VmzZtNGbMGOXn51eYFa9fv76aNWumgwcP\nOn08AdR8vp4uAADcISoqSvHx8Zo4caLCw8PVu3dv3XrrrRo0aJAkKS0tTRkZGercuXOFrystLVVm\nZqZ9+ZZbbqmwvXPnzlq9erXOnDmjqKgoXXfddVq1apUyMzOVlZWltLQ0Sd/PUDrrs88+05EjR7Rp\n06YK6318fGS1WnXDDTdI+j5c/1RQUJDKysokfT9LPHjw4Arbb7755l+8zrRly5YVlkNCQirMTP/U\n6dOnZbFYFBYWZl+XkZGhkJAQtWjRwr6ucePGmjZtmn05LCxM/v7+9mV/f381b97cvly/fn1JqnQZ\nQcOGDSuEWwD4AUEWQI313HPP6ZFHHtEHH3ygjz76SFOnTtXNN9+spKQklZeXq3v37po1a1alr/vp\ndZ++vhW/TZaXl0v6fjb2k08+0e9+9zvdeuut6tq1q+644w6dO3dOjzzyyFXVW15ert/97ncaPnx4\npW3h4eH2f/v5+VXa/sOv7319fe01OsPHp/Iv6K50jewPY38a1n9+nC7ncmN+uOShKpcuXXJoHIDa\nh0sLANRIhw8f1vz589WqVSuNGzdOK1as0Lx587R3717l5eWpbdu2yszM1PXXX6/mzZurefPmCgkJ\n0dy5c/X555/b9/PzX6+npKSoWbNmCg4OVlJSknr06KG//OUvuv/++9WzZ0/7JQBXc2eAtm3b6vjx\n4/Z6mjdvrm+++UbPPvusiouLHdpH+/btdejQoQrrDhw4UGH5WkNheHi4bDab8vPz7etuvPFGFRQU\n6KuvvrKvy8vLU48ePXT48OFrerzc3Fw1btz4mvYBoGYiyAKokQIDA7Vu3To999xzOnHihD7//HPt\n2LFDrVq1UsOGDTVmzBgVFhbqj3/8o9LT05Wenq4//OEPOnr0qNq2bWvfz/79+7V06VJlZWVp8+bN\n2rBhgx566CFJUpMmTfTf//5XKSkp+vrrr/Xmm2/qL3/5i6TKvx53xEMPPaR33nlHy5Yt05dffqmP\nP/5YiYmJKi4urvBr/KpMmDBBu3bt0po1a5SVlaU333xT69atqzAmICBAX3/99VV/kEHjxo3VpEkT\nHTt2zL6uZ8+euummmzRt2jQdPnxYGRkZmj59uho1aqSbbrrJ4X3//D8AZ86c0bfffquYmJirqhVA\nzUaQBVAjRUZGatmyZdq3b5+GDRume++9V76+vvbbZzVr1kxr165VcXGxxowZo3HjxqlevXp69dVX\nFRoaat/PgAEDZLVadccdd2jlypV64oknNGrUKEnSlClTFBMTo0mTJmn48OHavHmz5s+fr/r16+vI\nkSMO1fnT2dHBgwfr+eef1/vvv6877rhD06ZNU79+/fTSSy9ddvzl1vXt21ezZ8/W+vXrNXToUG3e\nvFn33HOP6tatax9zzz336PPPP9cdd9yh8vLyq5qhHTBggPbu3VuhhuXLl6tJkyZ68MEHde+998rf\n31+rVq1SnTp1fvG5X2ndvn37FBISom7dujldI4Caz2JzxZ2xAaAGGjt2rJo1a6b58+d7uhSHffrp\np2rUqJFat25tX7dixQpt2bJF//jHP1z2OMePH9ewYcP0z3/+0+HZ4qvx8MMPq0OHDpoyZYrbHgOA\nuZiRBYAaZM+ePXrwwQe1b98+ffvtt3r//ff12muv6c4773Tp47Ru3Vrx8fH2W5G5g9Vq1dGjRzV+\n/Hi3PQYAs3HXAgCoQR555BGVlJRo+vTpysvLU5MmTZSQkKDf/e53Ln+sHz54YcyYMYqIiHD5/hcv\nXqyZM2cqJCTE5fsGUDNwaQEAAACMxKUFAAAAMBJBFgAAAEYiyAIAAMBIBFkAAAAYiSALAAAAIxFk\nAQAAYCSCLAAAAIxEkAUAAICR/h8LipN1yR/WywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29f43131cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the scatter plot\n",
    "plt.plot(versi['sepal length (cm)'], versi['sepal width (cm)'], marker='.',linestyle='none')\n",
    "plt.xlabel('sepal length (cm)')\n",
    "plt.ylabel(\"sepal width (cm)\")\n",
    "plt.margins(0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AALVo0SIUNQIAAAB+Ag6yeXl5uuaaa5ScnKwePXqEoiYAAADguAIOsi+//HIo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\nFgAAAJ5EkAUAAIAnEWQBAADgST8AM9jeyyH4MmYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29f437f0eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(versi['sepal length (cm)' ], versi['sepal width (cm)'])\n",
    "plt.xlabel('sepal length (cm)')\n",
    "plt.ylabel(\"sepal width (cm)\")\n",
    "plt.margins(0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.26643265  0.08518367]\n",
      " [ 0.08518367  0.09846939]]\n"
     ]
    }
   ],
   "source": [
    "## Calculating the covariance\n",
    "cov_mat = np.cov(versi['sepal length (cm)'], versi['sepal width (cm)'])\n",
    "print(cov_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.          0.52591072]\n",
      " [ 0.52591072  1.        ]]\n",
      "0.525910717283\n"
     ]
    }
   ],
   "source": [
    "## Calculating the corelation coefficient\n",
    "coef = np.corrcoef(versi['sepal length (cm)'], versi['sepal width (cm)'])\n",
    "print(coef)\n",
    "print(coef[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5109833656783752 0.31064449134018135\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 1.67848332,  0.53664359],\n",
       "       [ 0.53664359,  0.62034148]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's calculate the corelation coefficient in math\n",
    "std_len = np.std(versi['sepal length (cm)'])\n",
    "std_wid = np.std(versi['sepal width (cm)'])\n",
    "print(std_len, std_wid)\n",
    "\n",
    "coeff = cov_mat/(std_len*std_wid)\n",
    "coeff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}