notebooks/07_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": 1,
   "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.  Name the dataframe iris."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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.  Name the dataframe iris\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (8, 6)\n",
    "plt.rcParams['font.size'] = 14\n",
    "\n",
    "col_names= ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']\n",
    "url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'\n",
    "iris = pd.read_csv(url, header=0, names=col_names, na_filter=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 2\n",
    "\n",
    "Gather some basic information about the data such as:\n",
    "* shape\n",
    "* head\n",
    "* data types of the columns\n",
    "* describe\n",
    "* counts of the values in the column species\n",
    "* count the nulls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(149, 5)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.9, 3.0, 1.4, 0.2, 'Iris-setosa'],\n",
       "       [4.7, 3.2, 1.3, 0.2, 'Iris-setosa'],\n",
       "       [4.6, 3.1, 1.5, 0.2, 'Iris-setosa'],\n",
       "       [5.0, 3.6, 1.4, 0.2, 'Iris-setosa'],\n",
       "       [5.4, 3.9, 1.7, 0.4, 'Iris-setosa'],\n",
       "       [4.6, 3.4, 1.4, 0.3, 'Iris-setosa'],\n",
       "       [5.0, 3.4, 1.5, 0.2, 'Iris-setosa'],\n",
       "       [4.4, 2.9, 1.4, 0.2, 'Iris-setosa'],\n",
       "       [4.9, 3.1, 1.5, 0.1, 'Iris-setosa'],\n",
       "       [5.4, 3.7, 1.5, 0.2, 'Iris-setosa'],\n",
       "       [4.8, 3.4, 1.6, 0.2, 'Iris-setosa'],\n",
       "       [4.8, 3.0, 1.4, 0.1, 'Iris-setosa'],\n",
       "       [4.3, 3.0, 1.1, 0.1, 'Iris-setosa'],\n",
       "       [5.8, 4.0, 1.2, 0.2, 'Iris-setosa'],\n",
       "       [5.7, 4.4, 1.5, 0.4, 'Iris-setosa'],\n",
       "       [5.4, 3.9, 1.3, 0.4, 'Iris-setosa'],\n",
       "       [5.1, 3.5, 1.4, 0.3, 'Iris-setosa'],\n",
       "       [5.7, 3.8, 1.7, 0.3, 'Iris-setosa'],\n",
       "       [5.1, 3.8, 1.5, 0.3, 'Iris-setosa'],\n",
       "       [5.4, 3.4, 1.7, 0.2, 'Iris-setosa'],\n",
       "       [5.1, 3.7, 1.5, 0.4, 'Iris-setosa'],\n",
       "       [4.6, 3.6, 1.0, 0.2, 'Iris-setosa'],\n",
       "       [5.1, 3.3, 1.7, 0.5, 'Iris-setosa'],\n",
       "       [4.8, 3.4, 1.9, 0.2, 'Iris-setosa'],\n",
       "       [5.0, 3.0, 1.6, 0.2, 'Iris-setosa'],\n",
       "       [5.0, 3.4, 1.6, 0.4, 'Iris-setosa'],\n",
       "       [5.2, 3.5, 1.5, 0.2, 'Iris-setosa'],\n",
       "       [5.2, 3.4, 1.4, 0.2, 'Iris-setosa'],\n",
       "       [4.7, 3.2, 1.6, 0.2, 'Iris-setosa'],\n",
       "       [4.8, 3.1, 1.6, 0.2, 'Iris-setosa'],\n",
       "       [5.4, 3.4, 1.5, 0.4, 'Iris-setosa'],\n",
       "       [5.2, 4.1, 1.5, 0.1, 'Iris-setosa'],\n",
       "       [5.5, 4.2, 1.4, 0.2, 'Iris-setosa'],\n",
       "       [4.9, 3.1, 1.5, 0.1, 'Iris-setosa'],\n",
       "       [5.0, 3.2, 1.2, 0.2, 'Iris-setosa'],\n",
       "       [5.5, 3.5, 1.3, 0.2, 'Iris-setosa'],\n",
       "       [4.9, 3.1, 1.5, 0.1, 'Iris-setosa'],\n",
       "       [4.4, 3.0, 1.3, 0.2, 'Iris-setosa'],\n",
       "       [5.1, 3.4, 1.5, 0.2, 'Iris-setosa'],\n",
       "       [5.0, 3.5, 1.3, 0.3, 'Iris-setosa'],\n",
       "       [4.5, 2.3, 1.3, 0.3, 'Iris-setosa'],\n",
       "       [4.4, 3.2, 1.3, 0.2, 'Iris-setosa'],\n",
       "       [5.0, 3.5, 1.6, 0.6, 'Iris-setosa'],\n",
       "       [5.1, 3.8, 1.9, 0.4, 'Iris-setosa'],\n",
       "       [4.8, 3.0, 1.4, 0.3, 'Iris-setosa'],\n",
       "       [5.1, 3.8, 1.6, 0.2, 'Iris-setosa'],\n",
       "       [4.6, 3.2, 1.4, 0.2, 'Iris-setosa'],\n",
       "       [5.3, 3.7, 1.5, 0.2, 'Iris-setosa'],\n",
       "       [5.0, 3.3, 1.4, 0.2, 'Iris-setosa'],\n",
       "       [7.0, 3.2, 4.7, 1.4, 'Iris-versicolor'],\n",
       "       [6.4, 3.2, 4.5, 1.5, 'Iris-versicolor'],\n",
       "       [6.9, 3.1, 4.9, 1.5, 'Iris-versicolor'],\n",
       "       [5.5, 2.3, 4.0, 1.3, 'Iris-versicolor'],\n",
       "       [6.5, 2.8, 4.6, 1.5, 'Iris-versicolor'],\n",
       "       [5.7, 2.8, 4.5, 1.3, 'Iris-versicolor'],\n",
       "       [6.3, 3.3, 4.7, 1.6, 'Iris-versicolor'],\n",
       "       [4.9, 2.4, 3.3, 1.0, 'Iris-versicolor'],\n",
       "       [6.6, 2.9, 4.6, 1.3, 'Iris-versicolor'],\n",
       "       [5.2, 2.7, 3.9, 1.4, 'Iris-versicolor'],\n",
       "       [5.0, 2.0, 3.5, 1.0, 'Iris-versicolor'],\n",
       "       [5.9, 3.0, 4.2, 1.5, 'Iris-versicolor'],\n",
       "       [6.0, 2.2, 4.0, 1.0, 'Iris-versicolor'],\n",
       "       [6.1, 2.9, 4.7, 1.4, 'Iris-versicolor'],\n",
       "       [5.6, 2.9, 3.6, 1.3, 'Iris-versicolor'],\n",
       "       [6.7, 3.1, 4.4, 1.4, 'Iris-versicolor'],\n",
       "       [5.6, 3.0, 4.5, 1.5, 'Iris-versicolor'],\n",
       "       [5.8, 2.7, 4.1, 1.0, 'Iris-versicolor'],\n",
       "       [6.2, 2.2, 4.5, 1.5, 'Iris-versicolor'],\n",
       "       [5.6, 2.5, 3.9, 1.1, 'Iris-versicolor'],\n",
       "       [5.9, 3.2, 4.8, 1.8, 'Iris-versicolor'],\n",
       "       [6.1, 2.8, 4.0, 1.3, 'Iris-versicolor'],\n",
       "       [6.3, 2.5, 4.9, 1.5, 'Iris-versicolor'],\n",
       "       [6.1, 2.8, 4.7, 1.2, 'Iris-versicolor'],\n",
       "       [6.4, 2.9, 4.3, 1.3, 'Iris-versicolor'],\n",
       "       [6.6, 3.0, 4.4, 1.4, 'Iris-versicolor'],\n",
       "       [6.8, 2.8, 4.8, 1.4, 'Iris-versicolor'],\n",
       "       [6.7, 3.0, 5.0, 1.7, 'Iris-versicolor'],\n",
       "       [6.0, 2.9, 4.5, 1.5, 'Iris-versicolor'],\n",
       "       [5.7, 2.6, 3.5, 1.0, 'Iris-versicolor'],\n",
       "       [5.5, 2.4, 3.8, 1.1, 'Iris-versicolor'],\n",
       "       [5.5, 2.4, 3.7, 1.0, 'Iris-versicolor'],\n",
       "       [5.8, 2.7, 3.9, 1.2, 'Iris-versicolor'],\n",
       "       [6.0, 2.7, 5.1, 1.6, 'Iris-versicolor'],\n",
       "       [5.4, 3.0, 4.5, 1.5, 'Iris-versicolor'],\n",
       "       [6.0, 3.4, 4.5, 1.6, 'Iris-versicolor'],\n",
       "       [6.7, 3.1, 4.7, 1.5, 'Iris-versicolor'],\n",
       "       [6.3, 2.3, 4.4, 1.3, 'Iris-versicolor'],\n",
       "       [5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'],\n",
       "       [5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'],\n",
       "       [5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'],\n",
       "       [6.1, 3.0, 4.6, 1.4, 'Iris-versicolor'],\n",
       "       [5.8, 2.6, 4.0, 1.2, 'Iris-versicolor'],\n",
       "       [5.0, 2.3, 3.3, 1.0, 'Iris-versicolor'],\n",
       "       [5.6, 2.7, 4.2, 1.3, 'Iris-versicolor'],\n",
       "       [5.7, 3.0, 4.2, 1.2, 'Iris-versicolor'],\n",
       "       [5.7, 2.9, 4.2, 1.3, 'Iris-versicolor'],\n",
       "       [6.2, 2.9, 4.3, 1.3, 'Iris-versicolor'],\n",
       "       [5.1, 2.5, 3.0, 1.1, 'Iris-versicolor'],\n",
       "       [5.7, 2.8, 4.1, 1.3, 'Iris-versicolor'],\n",
       "       [6.3, 3.3, 6.0, 2.5, 'Iris-virginica'],\n",
       "       [5.8, 2.7, 5.1, 1.9, 'Iris-virginica'],\n",
       "       [7.1, 3.0, 5.9, 2.1, 'Iris-virginica'],\n",
       "       [6.3, 2.9, 5.6, 1.8, 'Iris-virginica'],\n",
       "       [6.5, 3.0, 5.8, 2.2, 'Iris-virginica'],\n",
       "       [7.6, 3.0, 6.6, 2.1, 'Iris-virginica'],\n",
       "       [4.9, 2.5, 4.5, 1.7, 'Iris-virginica'],\n",
       "       [7.3, 2.9, 6.3, 1.8, 'Iris-virginica'],\n",
       "       [6.7, 2.5, 5.8, 1.8, 'Iris-virginica'],\n",
       "       [7.2, 3.6, 6.1, 2.5, 'Iris-virginica'],\n",
       "       [6.5, 3.2, 5.1, 2.0, 'Iris-virginica'],\n",
       "       [6.4, 2.7, 5.3, 1.9, 'Iris-virginica'],\n",
       "       [6.8, 3.0, 5.5, 2.1, 'Iris-virginica'],\n",
       "       [5.7, 2.5, 5.0, 2.0, 'Iris-virginica'],\n",
       "       [5.8, 2.8, 5.1, 2.4, 'Iris-virginica'],\n",
       "       [6.4, 3.2, 5.3, 2.3, 'Iris-virginica'],\n",
       "       [6.5, 3.0, 5.5, 1.8, 'Iris-virginica'],\n",
       "       [7.7, 3.8, 6.7, 2.2, 'Iris-virginica'],\n",
       "       [7.7, 2.6, 6.9, 2.3, 'Iris-virginica'],\n",
       "       [6.0, 2.2, 5.0, 1.5, 'Iris-virginica'],\n",
       "       [6.9, 3.2, 5.7, 2.3, 'Iris-virginica'],\n",
       "       [5.6, 2.8, 4.9, 2.0, 'Iris-virginica'],\n",
       "       [7.7, 2.8, 6.7, 2.0, 'Iris-virginica'],\n",
       "       [6.3, 2.7, 4.9, 1.8, 'Iris-virginica'],\n",
       "       [6.7, 3.3, 5.7, 2.1, 'Iris-virginica'],\n",
       "       [7.2, 3.2, 6.0, 1.8, 'Iris-virginica'],\n",
       "       [6.2, 2.8, 4.8, 1.8, 'Iris-virginica'],\n",
       "       [6.1, 3.0, 4.9, 1.8, 'Iris-virginica'],\n",
       "       [6.4, 2.8, 5.6, 2.1, 'Iris-virginica'],\n",
       "       [7.2, 3.0, 5.8, 1.6, 'Iris-virginica'],\n",
       "       [7.4, 2.8, 6.1, 1.9, 'Iris-virginica'],\n",
       "       [7.9, 3.8, 6.4, 2.0, 'Iris-virginica'],\n",
       "       [6.4, 2.8, 5.6, 2.2, 'Iris-virginica'],\n",
       "       [6.3, 2.8, 5.1, 1.5, 'Iris-virginica'],\n",
       "       [6.1, 2.6, 5.6, 1.4, 'Iris-virginica'],\n",
       "       [7.7, 3.0, 6.1, 2.3, 'Iris-virginica'],\n",
       "       [6.3, 3.4, 5.6, 2.4, 'Iris-virginica'],\n",
       "       [6.4, 3.1, 5.5, 1.8, 'Iris-virginica'],\n",
       "       [6.0, 3.0, 4.8, 1.8, 'Iris-virginica'],\n",
       "       [6.9, 3.1, 5.4, 2.1, 'Iris-virginica'],\n",
       "       [6.7, 3.1, 5.6, 2.4, 'Iris-virginica'],\n",
       "       [6.9, 3.1, 5.1, 2.3, 'Iris-virginica'],\n",
       "       [5.8, 2.7, 5.1, 1.9, 'Iris-virginica'],\n",
       "       [6.8, 3.2, 5.9, 2.3, 'Iris-virginica'],\n",
       "       [6.7, 3.3, 5.7, 2.5, 'Iris-virginica'],\n",
       "       [6.7, 3.0, 5.2, 2.3, 'Iris-virginica'],\n",
       "       [6.3, 2.5, 5.0, 1.9, 'Iris-virginica'],\n",
       "       [6.5, 3.0, 5.2, 2.0, 'Iris-virginica'],\n",
       "       [6.2, 3.4, 5.4, 2.3, 'Iris-virginica'],\n",
       "       [5.9, 3.0, 5.1, 1.8, 'Iris-virginica']], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<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>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>1</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>2</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>3</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>4</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width      species\n",
       "0           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "1           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "2           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "3           5.0          3.6           1.4          0.2  Iris-setosa\n",
       "4           5.4          3.9           1.7          0.4  Iris-setosa"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iris-versicolor    50\n",
       "Iris-virginica     50\n",
       "Iris-setosa        49\n",
       "Name: species, 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/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>149.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>149.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.848322</td>\n",
       "      <td>3.051007</td>\n",
       "      <td>3.774497</td>\n",
       "      <td>1.205369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828594</td>\n",
       "      <td>0.433499</td>\n",
       "      <td>1.759651</td>\n",
       "      <td>0.761292</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>4.400000</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    149.000000   149.000000    149.000000   149.000000\n",
       "mean       5.848322     3.051007      3.774497     1.205369\n",
       "std        0.828594     0.433499      1.759651     0.761292\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      4.400000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>119</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>149 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal_length sepal_width petal_length petal_width species\n",
       "0          False       False        False       False   False\n",
       "1          False       False        False       False   False\n",
       "2          False       False        False       False   False\n",
       "3          False       False        False       False   False\n",
       "4          False       False        False       False   False\n",
       "5          False       False        False       False   False\n",
       "6          False       False        False       False   False\n",
       "7          False       False        False       False   False\n",
       "8          False       False        False       False   False\n",
       "9          False       False        False       False   False\n",
       "10         False       False        False       False   False\n",
       "11         False       False        False       False   False\n",
       "12         False       False        False       False   False\n",
       "13         False       False        False       False   False\n",
       "14         False       False        False       False   False\n",
       "15         False       False        False       False   False\n",
       "16         False       False        False       False   False\n",
       "17         False       False        False       False   False\n",
       "18         False       False        False       False   False\n",
       "19         False       False        False       False   False\n",
       "20         False       False        False       False   False\n",
       "21         False       False        False       False   False\n",
       "22         False       False        False       False   False\n",
       "23         False       False        False       False   False\n",
       "24         False       False        False       False   False\n",
       "25         False       False        False       False   False\n",
       "26         False       False        False       False   False\n",
       "27         False       False        False       False   False\n",
       "28         False       False        False       False   False\n",
       "29         False       False        False       False   False\n",
       "..           ...         ...          ...         ...     ...\n",
       "119        False       False        False       False   False\n",
       "120        False       False        False       False   False\n",
       "121        False       False        False       False   False\n",
       "122        False       False        False       False   False\n",
       "123        False       False        False       False   False\n",
       "124        False       False        False       False   False\n",
       "125        False       False        False       False   False\n",
       "126        False       False        False       False   False\n",
       "127        False       False        False       False   False\n",
       "128        False       False        False       False   False\n",
       "129        False       False        False       False   False\n",
       "130        False       False        False       False   False\n",
       "131        False       False        False       False   False\n",
       "132        False       False        False       False   False\n",
       "133        False       False        False       False   False\n",
       "134        False       False        False       False   False\n",
       "135        False       False        False       False   False\n",
       "136        False       False        False       False   False\n",
       "137        False       False        False       False   False\n",
       "138        False       False        False       False   False\n",
       "139        False       False        False       False   False\n",
       "140        False       False        False       False   False\n",
       "141        False       False        False       False   False\n",
       "142        False       False        False       False   False\n",
       "143        False       False        False       False   False\n",
       "144        False       False        False       False   False\n",
       "145        False       False        False       False   False\n",
       "146        False       False        False       False   False\n",
       "147        False       False        False       False   False\n",
       "148        False       False        False       False   False\n",
       "\n",
       "[149 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.isnull()"
   ]
  },
  {
   "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": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<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>36</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>31</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>12</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>11</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>8</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>33</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>38</th>\n",
       "      <td>5.1</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>21</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>37</th>\n",
       "      <td>4.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</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>0</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>26</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>27</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>28</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>29</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>5.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>5.5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</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>19</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>41</th>\n",
       "      <td>4.4</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>45</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</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>10</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>9</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>7</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>6</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>47</th>\n",
       "      <td>5.3</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>48</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</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>120</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>121</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>109</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</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>130</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>112</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</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>101</th>\n",
       "      <td>7.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>7.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</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>127</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>103</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</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>116</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</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>142</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>140</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>117</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</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>134</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>147</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>114</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</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>135</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>113</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</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>99</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>149 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width         species\n",
       "36            4.9          3.1           1.5          0.1     Iris-setosa\n",
       "31            5.2          4.1           1.5          0.1     Iris-setosa\n",
       "12            4.3          3.0           1.1          0.1     Iris-setosa\n",
       "11            4.8          3.0           1.4          0.1     Iris-setosa\n",
       "8             4.9          3.1           1.5          0.1     Iris-setosa\n",
       "33            4.9          3.1           1.5          0.1     Iris-setosa\n",
       "38            5.1          3.4           1.5          0.2     Iris-setosa\n",
       "21            4.6          3.6           1.0          0.2     Iris-setosa\n",
       "37            4.4          3.0           1.3          0.2     Iris-setosa\n",
       "23            4.8          3.4           1.9          0.2     Iris-setosa\n",
       "0             4.9          3.0           1.4          0.2     Iris-setosa\n",
       "26            5.2          3.5           1.5          0.2     Iris-setosa\n",
       "27            5.2          3.4           1.4          0.2     Iris-setosa\n",
       "28            4.7          3.2           1.6          0.2     Iris-setosa\n",
       "29            4.8          3.1           1.6          0.2     Iris-setosa\n",
       "35            5.5          3.5           1.3          0.2     Iris-setosa\n",
       "32            5.5          4.2           1.4          0.2     Iris-setosa\n",
       "24            5.0          3.0           1.6          0.2     Iris-setosa\n",
       "19            5.4          3.4           1.7          0.2     Iris-setosa\n",
       "41            4.4          3.2           1.3          0.2     Iris-setosa\n",
       "45            5.1          3.8           1.6          0.2     Iris-setosa\n",
       "46            4.6          3.2           1.4          0.2     Iris-setosa\n",
       "13            5.8          4.0           1.2          0.2     Iris-setosa\n",
       "10            4.8          3.4           1.6          0.2     Iris-setosa\n",
       "9             5.4          3.7           1.5          0.2     Iris-setosa\n",
       "7             4.4          2.9           1.4          0.2     Iris-setosa\n",
       "6             5.0          3.4           1.5          0.2     Iris-setosa\n",
       "47            5.3          3.7           1.5          0.2     Iris-setosa\n",
       "48            5.0          3.3           1.4          0.2     Iris-setosa\n",
       "3             5.0          3.6           1.4          0.2     Iris-setosa\n",
       "..            ...          ...           ...          ...             ...\n",
       "145           6.3          2.5           5.0          1.9  Iris-virginica\n",
       "120           5.6          2.8           4.9          2.0  Iris-virginica\n",
       "121           7.7          2.8           6.7          2.0  Iris-virginica\n",
       "109           6.5          3.2           5.1          2.0  Iris-virginica\n",
       "146           6.5          3.0           5.2          2.0  Iris-virginica\n",
       "130           7.9          3.8           6.4          2.0  Iris-virginica\n",
       "112           5.7          2.5           5.0          2.0  Iris-virginica\n",
       "138           6.9          3.1           5.4          2.1  Iris-virginica\n",
       "101           7.1          3.0           5.9          2.1  Iris-virginica\n",
       "111           6.8          3.0           5.5          2.1  Iris-virginica\n",
       "104           7.6          3.0           6.6          2.1  Iris-virginica\n",
       "123           6.7          3.3           5.7          2.1  Iris-virginica\n",
       "127           6.4          2.8           5.6          2.1  Iris-virginica\n",
       "103           6.5          3.0           5.8          2.2  Iris-virginica\n",
       "131           6.4          2.8           5.6          2.2  Iris-virginica\n",
       "116           7.7          3.8           6.7          2.2  Iris-virginica\n",
       "144           6.7          3.0           5.2          2.3  Iris-virginica\n",
       "142           6.8          3.2           5.9          2.3  Iris-virginica\n",
       "140           6.9          3.1           5.1          2.3  Iris-virginica\n",
       "117           7.7          2.6           6.9          2.3  Iris-virginica\n",
       "119           6.9          3.2           5.7          2.3  Iris-virginica\n",
       "134           7.7          3.0           6.1          2.3  Iris-virginica\n",
       "147           6.2          3.4           5.4          2.3  Iris-virginica\n",
       "114           6.4          3.2           5.3          2.3  Iris-virginica\n",
       "139           6.7          3.1           5.6          2.4  Iris-virginica\n",
       "135           6.3          3.4           5.6          2.4  Iris-virginica\n",
       "113           5.8          2.8           5.1          2.4  Iris-virginica\n",
       "143           6.7          3.3           5.7          2.5  Iris-virginica\n",
       "99            6.3          3.3           6.0          2.5  Iris-virginica\n",
       "108           7.2          3.6           6.1          2.5  Iris-virginica\n",
       "\n",
       "[149 rows x 5 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the values in the petal_width column and display them\n",
    "\n",
    "iris.sort_values(by='petal_width')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### split-apply-combine"
   ]
  },
  {
   "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_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_length</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4.3</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.4</th>\n",
       "      <td>3.033333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.5</th>\n",
       "      <td>2.300000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.6</th>\n",
       "      <td>3.325000</td>\n",
       "      <td>1.325000</td>\n",
       "      <td>0.225000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.7</th>\n",
       "      <td>3.200000</td>\n",
       "      <td>1.450000</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.8</th>\n",
       "      <td>3.180000</td>\n",
       "      <td>1.580000</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.9</th>\n",
       "      <td>2.866667</td>\n",
       "      <td>2.283333</td>\n",
       "      <td>0.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>3.120000</td>\n",
       "      <td>1.840000</td>\n",
       "      <td>0.430000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.1</th>\n",
       "      <td>3.475000</td>\n",
       "      <td>1.762500</td>\n",
       "      <td>0.425000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.2</th>\n",
       "      <td>3.425000</td>\n",
       "      <td>2.075000</td>\n",
       "      <td>0.475000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.3</th>\n",
       "      <td>3.700000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.4</th>\n",
       "      <td>3.550000</td>\n",
       "      <td>2.033333</td>\n",
       "      <td>0.516667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.5</th>\n",
       "      <td>2.842857</td>\n",
       "      <td>3.228571</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.6</th>\n",
       "      <td>2.816667</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>1.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.7</th>\n",
       "      <td>3.100000</td>\n",
       "      <td>3.587500</td>\n",
       "      <td>1.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.8</th>\n",
       "      <td>2.885714</td>\n",
       "      <td>4.071429</td>\n",
       "      <td>1.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.9</th>\n",
       "      <td>3.066667</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>1.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>2.733333</td>\n",
       "      <td>4.650000</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.1</th>\n",
       "      <td>2.850000</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>1.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.2</th>\n",
       "      <td>2.825000</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>1.725000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.3</th>\n",
       "      <td>2.855556</td>\n",
       "      <td>5.133333</td>\n",
       "      <td>1.811111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.4</th>\n",
       "      <td>2.957143</td>\n",
       "      <td>5.157143</td>\n",
       "      <td>1.871429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.5</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.240000</td>\n",
       "      <td>1.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.6</th>\n",
       "      <td>2.950000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>1.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.7</th>\n",
       "      <td>3.050000</td>\n",
       "      <td>5.262500</td>\n",
       "      <td>1.962500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.8</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>1.933333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.9</th>\n",
       "      <td>3.125000</td>\n",
       "      <td>5.275000</td>\n",
       "      <td>2.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>3.200000</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>1.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.1</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>2.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.2</th>\n",
       "      <td>3.266667</td>\n",
       "      <td>5.966667</td>\n",
       "      <td>1.966667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.3</th>\n",
       "      <td>2.900000</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.4</th>\n",
       "      <td>2.800000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>1.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.6</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>2.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.7</th>\n",
       "      <td>3.050000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>2.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.9</th>\n",
       "      <td>3.800000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              sepal_width  petal_length  petal_width\n",
       "sepal_length                                        \n",
       "4.3              3.000000      1.100000     0.100000\n",
       "4.4              3.033333      1.333333     0.200000\n",
       "4.5              2.300000      1.300000     0.300000\n",
       "4.6              3.325000      1.325000     0.225000\n",
       "4.7              3.200000      1.450000     0.200000\n",
       "4.8              3.180000      1.580000     0.200000\n",
       "4.9              2.866667      2.283333     0.533333\n",
       "5.0              3.120000      1.840000     0.430000\n",
       "5.1              3.475000      1.762500     0.425000\n",
       "5.2              3.425000      2.075000     0.475000\n",
       "5.3              3.700000      1.500000     0.200000\n",
       "5.4              3.550000      2.033333     0.516667\n",
       "5.5              2.842857      3.228571     0.900000\n",
       "5.6              2.816667      4.200000     1.416667\n",
       "5.7              3.100000      3.587500     1.100000\n",
       "5.8              2.885714      4.071429     1.400000\n",
       "5.9              3.066667      4.700000     1.700000\n",
       "6.0              2.733333      4.650000     1.500000\n",
       "6.1              2.850000      4.750000     1.416667\n",
       "6.2              2.825000      4.750000     1.725000\n",
       "6.3              2.855556      5.133333     1.811111\n",
       "6.4              2.957143      5.157143     1.871429\n",
       "6.5              3.000000      5.240000     1.900000\n",
       "6.6              2.950000      4.500000     1.350000\n",
       "6.7              3.050000      5.262500     1.962500\n",
       "6.8              3.000000      5.400000     1.933333\n",
       "6.9              3.125000      5.275000     2.050000\n",
       "7.0              3.200000      4.700000     1.400000\n",
       "7.1              3.000000      5.900000     2.100000\n",
       "7.2              3.266667      5.966667     1.966667\n",
       "7.3              2.900000      6.300000     1.800000\n",
       "7.4              2.800000      6.100000     1.900000\n",
       "7.6              3.000000      6.600000     2.100000\n",
       "7.7              3.050000      6.600000     2.200000\n",
       "7.9              3.800000      6.400000     2.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the mean of sepal_length grouped by species\n",
    "\n",
    "iris.groupby('sepal_length').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>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>5.004082</td>\n",
       "      <td>3.416327</td>\n",
       "      <td>1.465306</td>\n",
       "      <td>0.244898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-versicolor</th>\n",
       "      <td>5.936000</td>\n",
       "      <td>2.770000</td>\n",
       "      <td>4.260000</td>\n",
       "      <td>1.326000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-virginica</th>\n",
       "      <td>6.588000</td>\n",
       "      <td>2.974000</td>\n",
       "      <td>5.552000</td>\n",
       "      <td>2.026000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 sepal_length  sepal_width  petal_length  petal_width\n",
       "species                                                              \n",
       "Iris-setosa          5.004082     3.416327      1.465306     0.244898\n",
       "Iris-versicolor      5.936000     2.770000      4.260000     1.326000\n",
       "Iris-virginica       6.588000     2.974000      5.552000     2.026000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the mean of all numeric columns grouped by species\n",
    "\n",
    "iris.groupby('species').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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></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>49.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>49.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.465306</td>\n",
       "      <td>0.244898</td>\n",
       "      <td>5.004082</td>\n",
       "      <td>3.416327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.175061</td>\n",
       "      <td>0.108130</td>\n",
       "      <td>0.355879</td>\n",
       "      <td>0.384787</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>3.100000</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>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>3.700000</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     49.000000    49.000000     49.000000    49.000000\n",
       "                mean       1.465306     0.244898      5.004082     3.416327\n",
       "                std        0.175061     0.108130      0.355879     0.384787\n",
       "                min        1.000000     0.100000      4.300000     2.300000\n",
       "                25%        1.400000     0.200000      4.800000     3.100000\n",
       "                50%        1.500000     0.200000      5.000000     3.400000\n",
       "                75%        1.600000     0.300000      5.200000     3.700000\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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the describe information for all numeric columns grouped by species\n",
    "iris.groupby('species').describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10f92f410>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fq6rvjKckSZI0aaMOAnzTpAqRJEndGfVhQJIkaQ6MOgbgRnYxEdCwqrrduiqSJEkTN+oY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112cf4310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a histogram of petal_width grouped by species\n",
    "iris.petal_width.plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "species\n",
       "Iris-setosa        Axes(0.125,0.125;0.775x0.775)\n",
       "Iris-versicolor    Axes(0.125,0.125;0.775x0.775)\n",
       "Iris-virginica     Axes(0.125,0.125;0.775x0.775)\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IlYz372dTWdT6TNWSjG1RKhYpLRnHx7Ps5yqyV1aXjsVn3L3yCnuaXbKEddGT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      "text/plain": [
       "<matplotlib.figure.Figure at 0x115889550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BwNPTCgtzAG7dohZtBw86jjY7dSpFhX/yCVAkU04yzzwDfPghIDLUQjS//07u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\n7hwEpvNevuNwsKWxk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      "text/plain": [
       "<matplotlib.figure.Figure at 0x115889410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pNRUDxczFAD2Y7hYmniYspZqsZNBTzZr0/5o1JU3G1apRsLtDIDS2b6fJ/dFH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h7RPXrAQ095xYXfZ9aEr0E1pxq7A3YLbeOwzQG35t4SW5eSgYFBvjytrERAXBx+8fREOpuN4MxM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\n/wHN6F40BprhkAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115bf45d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display a box plot of petal_width grouped by species\n",
    "iris.groupby('species').plot(kind='boxplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Display box plot of all numeric columns grouped by species\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Generate ascatter plot of petal_length vs petal_width colored by species\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Generate a scatter matrix of all features colored by species.  Make the figure size 12x10\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 4\n",
    "\n",
    "Decide on a set of rules that could be used to predict species based on iris measurements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Define a new feature that represents petal area (\"feature engineering\")\n",
    "petal_area= pd.petal_length. *pd.petal_width "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Display a describe of petal_area grouped by species\n",
    "\n",
    "iris.groupby('species').petal_area.describe().unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Display a box plot of petal_area grouped by species\n",
    "\n",
    "iris.boxplot(column='petal_area', by= 'species')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Predicting setosa will be straightforward since all our Iris-setosa pedal_areas are < 2 and the other Iris species have petal_areas larger than 2. But what about the petal_areas of Iris-versicolor and Iris-virginica? Some of their petal_area values overlap.\n",
    "Let's look at that overlap in more detail.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Show only dataframe rows 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",
    "- Rule to predict **virginica:**\n",
    "- Rule to 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Define a function that given a row of data, returns a predicted species_num (0/1/2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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    "# Print the first row\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "# Calculate the percentage of correct predictions\n"
   ]
  }
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