notebooks/06_human_learning_iris.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise: \"Human learning\" with iris data\n",
    "\n",
    "**Question:** Can you predict the species of an iris using petal and sepal measurements?\n",
    "\n",
    "1. Read the iris data into a Pandas DataFrame, including column names.\n",
    "2. Gather some basic information about the data.\n",
    "3. Use sorting, split-apply-combine, and/or visualization to look for differences between species.\n",
    "4. Write down a set of rules that could be used to predict species based on iris measurements.\n",
    "\n",
    "**BONUS:** Define a function that accepts a row of data and returns a predicted species. Then, use that function to make predictions for all existing rows of data, and check the accuracy of your predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# display plots in the notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# increase default figure and font sizes for easier viewing\n",
    "plt.rcParams['figure.figsize'] = (8, 6)\n",
    "plt.rcParams['font.size'] = 14"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 1\n",
    "\n",
    "Read the iris data into a pandas DataFrame, including column names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# define a list of column names (as strings)\n",
    "col_names = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']\n",
    "\n",
    "# define the URL from which to retrieve the data (as a string)\n",
    "url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'\n",
    "\n",
    "# retrieve the CSV file and add the column names\n",
    "iris = pd.read_csv(url, header=None, names=col_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 2\n",
    "\n",
    "Gather some basic information about the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width      species\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"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# observe first five rows of data\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length    float64\n",
       "sepal_width     float64\n",
       "petal_length    float64\n",
       "petal_width     float64\n",
       "species          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.054000      3.758667     1.198667\n",
       "std        0.828066     0.433594      1.764420     0.763161\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iris-setosa        50\n",
       "Iris-versicolor    50\n",
       "Iris-virginica     50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.species.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length    0\n",
       "sepal_width     0\n",
       "petal_length    0\n",
       "petal_width     0\n",
       "species         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 3\n",
    "\n",
    "Use sorting, split-apply-combine, and/or visualization to look for differences between species."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sorting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width      species\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"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# sort the DataFrame by petal_width\n",
    "iris.sort_values(by='petal_width', ascending=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>5.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.5</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>37</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>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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal_length  sepal_width  petal_length  petal_width      species\n",
       "32           5.2          4.1           1.5          0.1  Iris-setosa\n",
       "13           4.3          3.0           1.1          0.1  Iris-setosa\n",
       "37           4.9          3.1           1.5          0.1  Iris-setosa\n",
       "9            4.9          3.1           1.5          0.1  Iris-setosa\n",
       "12           4.8          3.0           1.4          0.1  Iris-setosa"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.2, 4.1, 1.5, 0.1, 'Iris-setosa'],\n",
       "       [4.3, 3.0, 1.1, 0.1, 'Iris-setosa'],\n",
       "       [4.9, 3.1, 1.5, 0.1, 'Iris-setosa'],\n",
       "       [4.9, 3.1, 1.5, 0.1, 'Iris-setosa'],\n",
       "       [4.8, 3.0, 1.4, 0.1, 'Iris-setosa']], dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort the DataFrame by petal_width and display the NumPy array\n",
    "iris.sort_values(by='petal_width', ascending=True).values[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### split-apply-combine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "species\n",
       "Iris-setosa        5.006\n",
       "Iris-versicolor    5.936\n",
       "Iris-virginica     6.588\n",
       "Name: sepal_length, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean of sepal_length grouped by species\n",
    "iris.groupby(by='species', axis=0).sepal_length.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Iris-setosa</th>\n",
       "      <td>5.006</td>\n",
       "      <td>3.418</td>\n",
       "      <td>1.464</td>\n",
       "      <td>0.244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-versicolor</th>\n",
       "      <td>5.936</td>\n",
       "      <td>2.770</td>\n",
       "      <td>4.260</td>\n",
       "      <td>1.326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-virginica</th>\n",
       "      <td>6.588</td>\n",
       "      <td>2.974</td>\n",
       "      <td>5.552</td>\n",
       "      <td>2.026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 sepal_length  sepal_width  petal_length  petal_width\n",
       "species                                                              \n",
       "Iris-setosa             5.006        3.418         1.464        0.244\n",
       "Iris-versicolor         5.936        2.770         4.260        1.326\n",
       "Iris-virginica          6.588        2.974         5.552        2.026"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean of all numeric columns grouped by species\n",
    "iris.groupby('species').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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></th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>species</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\">Iris-setosa</th>\n",
       "      <th>count</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.464000</td>\n",
       "      <td>0.244000</td>\n",
       "      <td>5.006000</td>\n",
       "      <td>3.418000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.173511</td>\n",
       "      <td>0.107210</td>\n",
       "      <td>0.352490</td>\n",
       "      <td>0.381024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.400000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>3.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.575000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>3.675000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>4.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">Iris-versicolor</th>\n",
       "      <th>count</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.260000</td>\n",
       "      <td>1.326000</td>\n",
       "      <td>5.936000</td>\n",
       "      <td>2.770000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.469911</td>\n",
       "      <td>0.197753</td>\n",
       "      <td>0.516171</td>\n",
       "      <td>0.313798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>2.525000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>2.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.600000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">Iris-virginica</th>\n",
       "      <th>count</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.552000</td>\n",
       "      <td>2.026000</td>\n",
       "      <td>6.588000</td>\n",
       "      <td>2.974000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.551895</td>\n",
       "      <td>0.274650</td>\n",
       "      <td>0.635880</td>\n",
       "      <td>0.322497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>2.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>6.225000</td>\n",
       "      <td>2.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.550000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.875000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>3.175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>3.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       petal_length  petal_width  sepal_length  sepal_width\n",
       "species                                                                    \n",
       "Iris-setosa     count     50.000000    50.000000     50.000000    50.000000\n",
       "                mean       1.464000     0.244000      5.006000     3.418000\n",
       "                std        0.173511     0.107210      0.352490     0.381024\n",
       "                min        1.000000     0.100000      4.300000     2.300000\n",
       "                25%        1.400000     0.200000      4.800000     3.125000\n",
       "                50%        1.500000     0.200000      5.000000     3.400000\n",
       "                75%        1.575000     0.300000      5.200000     3.675000\n",
       "                max        1.900000     0.600000      5.800000     4.400000\n",
       "Iris-versicolor count     50.000000    50.000000     50.000000    50.000000\n",
       "                mean       4.260000     1.326000      5.936000     2.770000\n",
       "                std        0.469911     0.197753      0.516171     0.313798\n",
       "                min        3.000000     1.000000      4.900000     2.000000\n",
       "                25%        4.000000     1.200000      5.600000     2.525000\n",
       "                50%        4.350000     1.300000      5.900000     2.800000\n",
       "                75%        4.600000     1.500000      6.300000     3.000000\n",
       "                max        5.100000     1.800000      7.000000     3.400000\n",
       "Iris-virginica  count     50.000000    50.000000     50.000000    50.000000\n",
       "                mean       5.552000     2.026000      6.588000     2.974000\n",
       "                std        0.551895     0.274650      0.635880     0.322497\n",
       "                min        4.500000     1.400000      4.900000     2.200000\n",
       "                25%        5.100000     1.800000      6.225000     2.800000\n",
       "                50%        5.550000     2.000000      6.500000     3.000000\n",
       "                75%        5.875000     2.300000      6.900000     3.175000\n",
       "                max        6.900000     2.500000      7.900000     3.800000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# description of all numeric columns grouped by species\n",
    "iris.groupby('species').describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000000011D60CF8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000011EE4EF0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000000122B4400>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000012319710>]], dtype=object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GKDsVmBoRa/oRWy+32Um9kgS8APiv8BvPzKzrfKbeQ5KmA0TEun4m9PFsU9Lm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\n5KRuZmZWEk7qZmZmJeGkbmZmVhL/H5Y7hkOs+E9cAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ce6cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram of petal_width grouped by species\n",
    "iris.hist(column='petal_width', by='species', sharex=True, sharey=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd136710>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uXODpEbFhbdnewG87A7kkSVp3U7nPfGNg22r2u8B/AF8DbsjMqyPiCGCnzNyr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\nyxnMJUlqOYO5JEkt9/8BoQqAnxLgxHUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd133518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# box plot of petal_width grouped by species\n",
    "iris.boxplot(column='petal_width', by='species')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000000157406A0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000015073F60>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000000159C4780>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000015A29908>]], dtype=object)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KSTrFpdy0/9IpLq3KEpOhLZhFRETKSG3MIgWiNmaRapgtr2cd+UtERKQ0On2c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\ngiAIWoi4MQdBEARBCxE35iAIgiBoIf4f5S4hlCXQCcsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15760a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# box plot of all numeric columns grouped by species\n",
    "iris.boxplot(by='species', rot=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# map species to a numeric value so that plots can be colored by species\n",
    "iris['species_num'] = iris.species.map({'Iris-setosa':0, 'Iris-versicolor':1, 'Iris-virginica':2})\n",
    "\n",
    "# alternative method\n",
    "iris['species_num'] = iris.species.factorize()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "      <th>species_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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",
       "      <td>0</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</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",
       "      <td>0</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal_length  sepal_width  petal_length  petal_width      species  \\\n",
       "13           4.3          3.0           1.1          0.1  Iris-setosa   \n",
       "12           4.8          3.0           1.4          0.1  Iris-setosa   \n",
       "37           4.9          3.1           1.5          0.1  Iris-setosa   \n",
       "9            4.9          3.1           1.5          0.1  Iris-setosa   \n",
       "34           4.9          3.1           1.5          0.1  Iris-setosa   \n",
       "\n",
       "    species_num  \n",
       "13            0  \n",
       "12            0  \n",
       "37            0  \n",
       "9             0  \n",
       "34            0  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16414f28>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\alsherman\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": 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NM3E1cjnJHmAwbBpZrh70MUGaN28e8+bNK5VrlfcufV+jt8AhwA0c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\nVQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1641f390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot of petal_length vs petal_width colored by species\n",
    "iris.plot(kind='scatter', x='petal_length', y='petal_width', c='species_num', colormap='brg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000000D60CC18>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000E4676A0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000E4902B0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000E5EBF28>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000000000E612F98>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000E6E5D30>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000E719908>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000E841DD8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000000000E9E2908>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000EA04D30>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000EB33CC0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000EB53C88>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000000000ECFEC18>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000EB5EB70>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000EE19D68>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000EF85AC8>]], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vXm1SoRSDwcCuHTsoys9nwa234u/v39nQJXr32Oxp9Ho9O3bvoKyyjBtn32jy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\nyszIuiUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc61f668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter matrix of all features colored by species\n",
    "pd.scatter_matrix(iris.drop('species_num', axis=1), c=iris.species_num, figsize=(12, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 4\n",
    "\n",
    "Write down a set of rules that could be used to predict species based on iris measurements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# define a new feature that represents petal area (\"feature engineering\")\n",
    "iris['petal_area'] = iris.petal_length * iris.petal_width"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>species</th>\n",
       "      <th></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>Iris-setosa</th>\n",
       "      <td>50</td>\n",
       "      <td>0.3628</td>\n",
       "      <td>0.183248</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.2650</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-versicolor</th>\n",
       "      <td>50</td>\n",
       "      <td>5.7204</td>\n",
       "      <td>1.368403</td>\n",
       "      <td>3.30</td>\n",
       "      <td>4.8600</td>\n",
       "      <td>5.615</td>\n",
       "      <td>6.75</td>\n",
       "      <td>8.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-virginica</th>\n",
       "      <td>50</td>\n",
       "      <td>11.2962</td>\n",
       "      <td>2.157412</td>\n",
       "      <td>7.50</td>\n",
       "      <td>9.7175</td>\n",
       "      <td>11.445</td>\n",
       "      <td>12.79</td>\n",
       "      <td>15.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count     mean       std   min     25%     50%    75%    max\n",
       "species                                                                      \n",
       "Iris-setosa         50   0.3628  0.183248  0.11  0.2650   0.300   0.42   0.96\n",
       "Iris-versicolor     50   5.7204  1.368403  3.30  4.8600   5.615   6.75   8.64\n",
       "Iris-virginica      50  11.2962  2.157412  7.50  9.7175  11.445  12.79  15.87"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# description of petal_area grouped by species\n",
    "iris.groupby('species').petal_area.describe().unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xeb27128>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fv9uvnFWF38+K48BtVgMLF6bu7E4s8+d3ruyFC7v9ypn1Hue4zWqgrnnoutbb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xda63a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# box plot of petal_area grouped by species\n",
    "iris.boxplot(column='petal_area', by='species')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "      <th>species_num</th>\n",
       "      <th>petal_area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>1</td>\n",
       "      <td>7.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>1</td>\n",
       "      <td>7.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>1</td>\n",
       "      <td>7.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>1</td>\n",
       "      <td>7.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>2</td>\n",
       "      <td>7.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>1</td>\n",
       "      <td>7.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>4.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>2</td>\n",
       "      <td>7.65</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",
       "      <td>2</td>\n",
       "      <td>7.65</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",
       "      <td>2</td>\n",
       "      <td>7.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>1</td>\n",
       "      <td>8.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>1</td>\n",
       "      <td>8.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>1</td>\n",
       "      <td>8.64</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",
       "      <td>2</td>\n",
       "      <td>8.64</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",
       "      <td>2</td>\n",
       "      <td>8.64</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",
       "      <td>2</td>\n",
       "      <td>8.82</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",
       "      <td>2</td>\n",
       "      <td>8.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width          species  \\\n",
       "86            6.7          3.1           4.7          1.5  Iris-versicolor   \n",
       "85            6.0          3.4           4.5          1.6  Iris-versicolor   \n",
       "52            6.9          3.1           4.9          1.5  Iris-versicolor   \n",
       "72            6.3          2.5           4.9          1.5  Iris-versicolor   \n",
       "119           6.0          2.2           5.0          1.5   Iris-virginica   \n",
       "56            6.3          3.3           4.7          1.6  Iris-versicolor   \n",
       "106           4.9          2.5           4.5          1.7   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",
       "83            6.0          2.7           5.1          1.6  Iris-versicolor   \n",
       "77            6.7          3.0           5.0          1.7  Iris-versicolor   \n",
       "70            5.9          3.2           4.8          1.8  Iris-versicolor   \n",
       "126           6.2          2.8           4.8          1.8   Iris-virginica   \n",
       "138           6.0          3.0           4.8          1.8   Iris-virginica   \n",
       "123           6.3          2.7           4.9          1.8   Iris-virginica   \n",
       "127           6.1          3.0           4.9          1.8   Iris-virginica   \n",
       "\n",
       "     species_num  petal_area  \n",
       "86             1        7.05  \n",
       "85             1        7.20  \n",
       "52             1        7.35  \n",
       "72             1        7.35  \n",
       "119            2        7.50  \n",
       "56             1        7.52  \n",
       "106            2        7.65  \n",
       "133            2        7.65  \n",
       "134            2        7.84  \n",
       "83             1        8.16  \n",
       "77             1        8.50  \n",
       "70             1        8.64  \n",
       "126            2        8.64  \n",
       "138            2        8.64  \n",
       "123            2        8.82  \n",
       "127            2        8.82  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# only show irises with a petal_area between 7 and 9\n",
    "iris[(iris.petal_area > 7) & (iris.petal_area < 9)].sort('petal_area')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "My set of rules for predicting species:\n",
    "\n",
    "- If petal_area is less than 2, predict **setosa**.\n",
    "- Else if petal_area is less than 7.4, predict **versicolor**.\n",
    "- Otherwise, predict **virginica**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus\n",
    "\n",
    "Define a function that accepts a row of data and returns a predicted species. Then, use that function to make predictions for all existing rows of data, and check the accuracy of your predictions."
   ]
  }
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