{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import pandas using the standard naming convention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grab the iris dataset in CSV format "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.system(\"curl https://raw.githubusercontent.com/pydata/pandas/master/pandas/tests/data/iris.csv > ~/iris.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the data into a data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('~/iris.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>SepalLength</th>\n",
       "      <th>SepalWidth</th>\n",
       "      <th>PetalLength</th>\n",
       "      <th>PetalWidth</th>\n",
       "      <th>Name</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>Iris-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>Iris-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>Iris-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>Iris-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>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     SepalLength  SepalWidth  PetalLength  PetalWidth            Name\n",
       "0            5.1         3.5          1.4         0.2     Iris-setosa\n",
       "1            4.9         3.0          1.4         0.2     Iris-setosa\n",
       "2            4.7         3.2          1.3         0.2     Iris-setosa\n",
       "3            4.6         3.1          1.5         0.2     Iris-setosa\n",
       "4            5.0         3.6          1.4         0.2     Iris-setosa\n",
       "5            5.4         3.9          1.7         0.4     Iris-setosa\n",
       "6            4.6         3.4          1.4         0.3     Iris-setosa\n",
       "7            5.0         3.4          1.5         0.2     Iris-setosa\n",
       "8            4.4         2.9          1.4         0.2     Iris-setosa\n",
       "9            4.9         3.1          1.5         0.1     Iris-setosa\n",
       "10           5.4         3.7          1.5         0.2     Iris-setosa\n",
       "11           4.8         3.4          1.6         0.2     Iris-setosa\n",
       "12           4.8         3.0          1.4         0.1     Iris-setosa\n",
       "13           4.3         3.0          1.1         0.1     Iris-setosa\n",
       "14           5.8         4.0          1.2         0.2     Iris-setosa\n",
       "15           5.7         4.4          1.5         0.4     Iris-setosa\n",
       "16           5.4         3.9          1.3         0.4     Iris-setosa\n",
       "17           5.1         3.5          1.4         0.3     Iris-setosa\n",
       "18           5.7         3.8          1.7         0.3     Iris-setosa\n",
       "19           5.1         3.8          1.5         0.3     Iris-setosa\n",
       "20           5.4         3.4          1.7         0.2     Iris-setosa\n",
       "21           5.1         3.7          1.5         0.4     Iris-setosa\n",
       "22           4.6         3.6          1.0         0.2     Iris-setosa\n",
       "23           5.1         3.3          1.7         0.5     Iris-setosa\n",
       "24           4.8         3.4          1.9         0.2     Iris-setosa\n",
       "25           5.0         3.0          1.6         0.2     Iris-setosa\n",
       "26           5.0         3.4          1.6         0.4     Iris-setosa\n",
       "27           5.2         3.5          1.5         0.2     Iris-setosa\n",
       "28           5.2         3.4          1.4         0.2     Iris-setosa\n",
       "29           4.7         3.2          1.6         0.2     Iris-setosa\n",
       "..           ...         ...          ...         ...             ...\n",
       "120          6.9         3.2          5.7         2.3  Iris-virginica\n",
       "121          5.6         2.8          4.9         2.0  Iris-virginica\n",
       "122          7.7         2.8          6.7         2.0  Iris-virginica\n",
       "123          6.3         2.7          4.9         1.8  Iris-virginica\n",
       "124          6.7         3.3          5.7         2.1  Iris-virginica\n",
       "125          7.2         3.2          6.0         1.8  Iris-virginica\n",
       "126          6.2         2.8          4.8         1.8  Iris-virginica\n",
       "127          6.1         3.0          4.9         1.8  Iris-virginica\n",
       "128          6.4         2.8          5.6         2.1  Iris-virginica\n",
       "129          7.2         3.0          5.8         1.6  Iris-virginica\n",
       "130          7.4         2.8          6.1         1.9  Iris-virginica\n",
       "131          7.9         3.8          6.4         2.0  Iris-virginica\n",
       "132          6.4         2.8          5.6         2.2  Iris-virginica\n",
       "133          6.3         2.8          5.1         1.5  Iris-virginica\n",
       "134          6.1         2.6          5.6         1.4  Iris-virginica\n",
       "135          7.7         3.0          6.1         2.3  Iris-virginica\n",
       "136          6.3         3.4          5.6         2.4  Iris-virginica\n",
       "137          6.4         3.1          5.5         1.8  Iris-virginica\n",
       "138          6.0         3.0          4.8         1.8  Iris-virginica\n",
       "139          6.9         3.1          5.4         2.1  Iris-virginica\n",
       "140          6.7         3.1          5.6         2.4  Iris-virginica\n",
       "141          6.9         3.1          5.1         2.3  Iris-virginica\n",
       "142          5.8         2.7          5.1         1.9  Iris-virginica\n",
       "143          6.8         3.2          5.9         2.3  Iris-virginica\n",
       "144          6.7         3.3          5.7         2.5  Iris-virginica\n",
       "145          6.7         3.0          5.2         2.3  Iris-virginica\n",
       "146          6.3         2.5          5.0         1.9  Iris-virginica\n",
       "147          6.5         3.0          5.2         2.0  Iris-virginica\n",
       "148          6.2         3.4          5.4         2.3  Iris-virginica\n",
       "149          5.9         3.0          5.1         1.8  Iris-virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "matplotlib.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot a few things"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x111458ef0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PPujJ2AAAwACWS8n58+f17rvvqrCwUBUVFQoPD1d6erqmTJmi66+/vm3crbfeqv/5n//R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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111458588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['SepalWidth'].plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117c412e8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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775EjRyorK0tLly5VZWWlcnJyTKwMAAD0VUiGkm9LS0vT0KFDVVNT02MoKS8v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\nlkAoAQAAlvB/zFPR7hzclX0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111237f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['PetalWidth'].plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['Diff'] = df['SepalWidth'] - df['PetalWidth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1186de588>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AKAEAAEYglAAAACMQSgAAgBEIJQAAwAiEEgAAYARCCQAAMAKhBAAAGIFQAgAA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\nAAAAIxBKAACAEQglAADACP8/t/Oiiz2jTVUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1186f2160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Diff'].plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "classmap = {}\n",
    "for idx, name in enumerate(df['Name'].unique()):\n",
    "    classmap[name] = float(idx)\n",
    "df['Label'] = df['Name'].apply(lambda x: classmap[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1187df908>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7aNrETYX9nMcOeRvZhYrJZIK/vz8AIDAwECaTCZWVlfbXo6KicP78+bZHSKqx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117c502e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x = 'SepalLength', y = 'SepalWidth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118cce5f8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CC7BYLM3xK5yQ9IwIIYQQBtRVYDUwTGNwNU1+fj5z5swJPl68eDEA559/PtOmTaOsrIzi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\nh5JkRAghhBAdSpIRIYQQQnSo/wc458LBJWgfAwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118cdacc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x = 'SepalLength', y = 'SepalWidth', c = 'Label', colormap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118f039b0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1112XUr+mpiYefPDBVnclepM+c6to0aJFvPXWW9x5550UFxd3eGxRUREHDx5M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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118fb3470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x = 'PetalLength', y = 'PetalWidth', c = 'Label', colormap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:anaconda3]",
   "language": "python",
   "name": "conda-env-anaconda3-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": 0
}