{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "'''\n",
    "EXERCISE: \"Human Learning\" with iris data\n",
    "'''\n",
    "from sklearn.datasets import load_iris\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.datasets.base.Bunch"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the famous iris data\n",
    "iris = load_iris()\n",
    "type(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(iris.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.1,  3.5,  1.4,  0.2],\n",
       "       [ 4.9,  3. ,  1.4,  0.2],\n",
       "       [ 4.7,  3.2,  1.3,  0.2],\n",
       "       [ 4.6,  3.1,  1.5,  0.2],\n",
       "       [ 5. ,  3.6,  1.4,  0.2]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what do you think these attributes represent?\n",
    "iris.data[:5,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], \n",
       "      dtype='|S10')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# intro to numpy\n",
    "type(iris.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2    3\n",
       "0  5.1  3.5  1.4  0.2\n",
       "1  4.9  3.0  1.4  0.2\n",
       "2  4.7  3.2  1.3  0.2\n",
       "3  4.6  3.1  1.5  0.2\n",
       "4  5.0  3.6  1.4  0.2"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## PART 1: Read data into pandas and explore\n",
    "pd.DataFrame(iris.data).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal_length', 'sepal_width', 'petal_length', 'petal_width']"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names\n",
    "# the feature_names are a bit messy, let's \n",
    "# clean them up. remove the (cm)\n",
    "# at the end and replace any spaces with an underscore\n",
    "# create a list called \"features\" that \n",
    "# holds the cleaned column names\n",
    "features = [i.replace(' ','_')[:-5] for i in iris.feature_names] # <FILL IN>\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# read the iris data into pandas, with our refined column names\n",
    "df = pd.DataFrame(iris.data, columns=features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.1,  3.5,  1.4,  0.2],\n",
       "       [ 4.9,  3. ,  1.4,  0.2],\n",
       "       [ 4.7,  3.2,  1.3,  0.2],\n",
       "       [ 4.6,  3.1,  1.5,  0.2],\n",
       "       [ 5. ,  3.6,  1.4,  0.2],\n",
       "       [ 5.4,  3.9,  1.7,  0.4],\n",
       "       [ 4.6,  3.4,  1.4,  0.3],\n",
       "       [ 5. ,  3.4,  1.5,  0.2],\n",
       "       [ 4.4,  2.9,  1.4,  0.2],\n",
       "       [ 4.9,  3.1,  1.5,  0.1],\n",
       "       [ 5.4,  3.7,  1.5,  0.2],\n",
       "       [ 4.8,  3.4,  1.6,  0.2],\n",
       "       [ 4.8,  3. ,  1.4,  0.1],\n",
       "       [ 4.3,  3. ,  1.1,  0.1],\n",
       "       [ 5.8,  4. ,  1.2,  0.2],\n",
       "       [ 5.7,  4.4,  1.5,  0.4],\n",
       "       [ 5.4,  3.9,  1.3,  0.4],\n",
       "       [ 5.1,  3.5,  1.4,  0.3],\n",
       "       [ 5.7,  3.8,  1.7,  0.3],\n",
       "       [ 5.1,  3.8,  1.5,  0.3],\n",
       "       [ 5.4,  3.4,  1.7,  0.2],\n",
       "       [ 5.1,  3.7,  1.5,  0.4],\n",
       "       [ 4.6,  3.6,  1. ,  0.2],\n",
       "       [ 5.1,  3.3,  1.7,  0.5],\n",
       "       [ 4.8,  3.4,  1.9,  0.2],\n",
       "       [ 5. ,  3. ,  1.6,  0.2],\n",
       "       [ 5. ,  3.4,  1.6,  0.4],\n",
       "       [ 5.2,  3.5,  1.5,  0.2],\n",
       "       [ 5.2,  3.4,  1.4,  0.2],\n",
       "       [ 4.7,  3.2,  1.6,  0.2],\n",
       "       [ 4.8,  3.1,  1.6,  0.2],\n",
       "       [ 5.4,  3.4,  1.5,  0.4],\n",
       "       [ 5.2,  4.1,  1.5,  0.1],\n",
       "       [ 5.5,  4.2,  1.4,  0.2],\n",
       "       [ 4.9,  3.1,  1.5,  0.1],\n",
       "       [ 5. ,  3.2,  1.2,  0.2],\n",
       "       [ 5.5,  3.5,  1.3,  0.2],\n",
       "       [ 4.9,  3.1,  1.5,  0.1],\n",
       "       [ 4.4,  3. ,  1.3,  0.2],\n",
       "       [ 5.1,  3.4,  1.5,  0.2],\n",
       "       [ 5. ,  3.5,  1.3,  0.3],\n",
       "       [ 4.5,  2.3,  1.3,  0.3],\n",
       "       [ 4.4,  3.2,  1.3,  0.2],\n",
       "       [ 5. ,  3.5,  1.6,  0.6],\n",
       "       [ 5.1,  3.8,  1.9,  0.4],\n",
       "       [ 4.8,  3. ,  1.4,  0.3],\n",
       "       [ 5.1,  3.8,  1.6,  0.2],\n",
       "       [ 4.6,  3.2,  1.4,  0.2],\n",
       "       [ 5.3,  3.7,  1.5,  0.2],\n",
       "       [ 5. ,  3.3,  1.4,  0.2],\n",
       "       [ 7. ,  3.2,  4.7,  1.4],\n",
       "       [ 6.4,  3.2,  4.5,  1.5],\n",
       "       [ 6.9,  3.1,  4.9,  1.5],\n",
       "       [ 5.5,  2.3,  4. ,  1.3],\n",
       "       [ 6.5,  2.8,  4.6,  1.5],\n",
       "       [ 5.7,  2.8,  4.5,  1.3],\n",
       "       [ 6.3,  3.3,  4.7,  1.6],\n",
       "       [ 4.9,  2.4,  3.3,  1. ],\n",
       "       [ 6.6,  2.9,  4.6,  1.3],\n",
       "       [ 5.2,  2.7,  3.9,  1.4],\n",
       "       [ 5. ,  2. ,  3.5,  1. ],\n",
       "       [ 5.9,  3. ,  4.2,  1.5],\n",
       "       [ 6. ,  2.2,  4. ,  1. ],\n",
       "       [ 6.1,  2.9,  4.7,  1.4],\n",
       "       [ 5.6,  2.9,  3.6,  1.3],\n",
       "       [ 6.7,  3.1,  4.4,  1.4],\n",
       "       [ 5.6,  3. ,  4.5,  1.5],\n",
       "       [ 5.8,  2.7,  4.1,  1. ],\n",
       "       [ 6.2,  2.2,  4.5,  1.5],\n",
       "       [ 5.6,  2.5,  3.9,  1.1],\n",
       "       [ 5.9,  3.2,  4.8,  1.8],\n",
       "       [ 6.1,  2.8,  4. ,  1.3],\n",
       "       [ 6.3,  2.5,  4.9,  1.5],\n",
       "       [ 6.1,  2.8,  4.7,  1.2],\n",
       "       [ 6.4,  2.9,  4.3,  1.3],\n",
       "       [ 6.6,  3. ,  4.4,  1.4],\n",
       "       [ 6.8,  2.8,  4.8,  1.4],\n",
       "       [ 6.7,  3. ,  5. ,  1.7],\n",
       "       [ 6. ,  2.9,  4.5,  1.5],\n",
       "       [ 5.7,  2.6,  3.5,  1. ],\n",
       "       [ 5.5,  2.4,  3.8,  1.1],\n",
       "       [ 5.5,  2.4,  3.7,  1. ],\n",
       "       [ 5.8,  2.7,  3.9,  1.2],\n",
       "       [ 6. ,  2.7,  5.1,  1.6],\n",
       "       [ 5.4,  3. ,  4.5,  1.5],\n",
       "       [ 6. ,  3.4,  4.5,  1.6],\n",
       "       [ 6.7,  3.1,  4.7,  1.5],\n",
       "       [ 6.3,  2.3,  4.4,  1.3],\n",
       "       [ 5.6,  3. ,  4.1,  1.3],\n",
       "       [ 5.5,  2.5,  4. ,  1.3],\n",
       "       [ 5.5,  2.6,  4.4,  1.2],\n",
       "       [ 6.1,  3. ,  4.6,  1.4],\n",
       "       [ 5.8,  2.6,  4. ,  1.2],\n",
       "       [ 5. ,  2.3,  3.3,  1. ],\n",
       "       [ 5.6,  2.7,  4.2,  1.3],\n",
       "       [ 5.7,  3. ,  4.2,  1.2],\n",
       "       [ 5.7,  2.9,  4.2,  1.3],\n",
       "       [ 6.2,  2.9,  4.3,  1.3],\n",
       "       [ 5.1,  2.5,  3. ,  1.1],\n",
       "       [ 5.7,  2.8,  4.1,  1.3],\n",
       "       [ 6.3,  3.3,  6. ,  2.5],\n",
       "       [ 5.8,  2.7,  5.1,  1.9],\n",
       "       [ 7.1,  3. ,  5.9,  2.1],\n",
       "       [ 6.3,  2.9,  5.6,  1.8],\n",
       "       [ 6.5,  3. ,  5.8,  2.2],\n",
       "       [ 7.6,  3. ,  6.6,  2.1],\n",
       "       [ 4.9,  2.5,  4.5,  1.7],\n",
       "       [ 7.3,  2.9,  6.3,  1.8],\n",
       "       [ 6.7,  2.5,  5.8,  1.8],\n",
       "       [ 7.2,  3.6,  6.1,  2.5],\n",
       "       [ 6.5,  3.2,  5.1,  2. ],\n",
       "       [ 6.4,  2.7,  5.3,  1.9],\n",
       "       [ 6.8,  3. ,  5.5,  2.1],\n",
       "       [ 5.7,  2.5,  5. ,  2. ],\n",
       "       [ 5.8,  2.8,  5.1,  2.4],\n",
       "       [ 6.4,  3.2,  5.3,  2.3],\n",
       "       [ 6.5,  3. ,  5.5,  1.8],\n",
       "       [ 7.7,  3.8,  6.7,  2.2],\n",
       "       [ 7.7,  2.6,  6.9,  2.3],\n",
       "       [ 6. ,  2.2,  5. ,  1.5],\n",
       "       [ 6.9,  3.2,  5.7,  2.3],\n",
       "       [ 5.6,  2.8,  4.9,  2. ],\n",
       "       [ 7.7,  2.8,  6.7,  2. ],\n",
       "       [ 6.3,  2.7,  4.9,  1.8],\n",
       "       [ 6.7,  3.3,  5.7,  2.1],\n",
       "       [ 7.2,  3.2,  6. ,  1.8],\n",
       "       [ 6.2,  2.8,  4.8,  1.8],\n",
       "       [ 6.1,  3. ,  4.9,  1.8],\n",
       "       [ 6.4,  2.8,  5.6,  2.1],\n",
       "       [ 7.2,  3. ,  5.8,  1.6],\n",
       "       [ 7.4,  2.8,  6.1,  1.9],\n",
       "       [ 7.9,  3.8,  6.4,  2. ],\n",
       "       [ 6.4,  2.8,  5.6,  2.2],\n",
       "       [ 6.3,  2.8,  5.1,  1.5],\n",
       "       [ 6.1,  2.6,  5.6,  1.4],\n",
       "       [ 7.7,  3. ,  6.1,  2.3],\n",
       "       [ 6.3,  3.4,  5.6,  2.4],\n",
       "       [ 6.4,  3.1,  5.5,  1.8],\n",
       "       [ 6. ,  3. ,  4.8,  1.8],\n",
       "       [ 6.9,  3.1,  5.4,  2.1],\n",
       "       [ 6.7,  3.1,  5.6,  2.4],\n",
       "       [ 6.9,  3.1,  5.1,  2.3],\n",
       "       [ 5.8,  2.7,  5.1,  1.9],\n",
       "       [ 6.8,  3.2,  5.9,  2.3],\n",
       "       [ 6.7,  3.3,  5.7,  2.5],\n",
       "       [ 6.7,  3. ,  5.2,  2.3],\n",
       "       [ 6.3,  2.5,  5. ,  1.9],\n",
       "       [ 6.5,  3. ,  5.2,  2. ],\n",
       "       [ 6.2,  3.4,  5.4,  2.3],\n",
       "       [ 5.9,  3. ,  5.1,  1.8]])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n",
    "df.as_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nspecies ==  \\n['setosa',\\n 'setosa',\\n 'setosa',\\n 'setosa',\\n...\\n...\\n 'virginica',\\n 'virginica']\\n\\nHint: use the iris.target_names and iris.target arrays\\n\""
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# EXERCISEEE\n",
    "\n",
    "# create a list of species (should be 150 elements) \n",
    "# using iris.target and iris.target_names\n",
    "# resulting list should only have the words \"setosa\", \"versicolor\", and \"virginica\"\n",
    "'''\n",
    "species ==  \n",
    "['setosa',\n",
    " 'setosa',\n",
    " 'setosa',\n",
    " 'setosa',\n",
    "...\n",
    "...\n",
    " 'virginica',\n",
    " 'virginica']\n",
    "\n",
    "Hint: use the iris.target_names and iris.target arrays\n",
    "'''\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'setosa',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'versicolor',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica',\n",
       " 'virginica']"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ANSWERRRR\n",
    "\n",
    "\n",
    "species = [iris.target_names[i] for i in iris.target]\n",
    "list(iris.target_names[iris.target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add the species list as a new DataFrame column\n",
    "df['species'] = species\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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\">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\">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\">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",
       "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",
       "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",
       "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": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# explore data numerically, looking for differences between species\n",
    "# try grouping by species and check out the different predictors\n",
    "# explore data numerically, looking for differences between species\n",
    "df.describe()\n",
    "df.groupby('species').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "species\n",
       "setosa        5.006\n",
       "versicolor    5.936\n",
       "virginica     6.588\n",
       "Name: sepal_length, dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('species').sepal_length.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>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>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>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",
       "setosa             5.006        3.418         1.464        0.244\n",
       "versicolor         5.936        2.770         4.260        1.326\n",
       "virginica          6.588        2.974         5.552        2.026"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('species')['sepal_length', 'sepal_width', 'petal_length', 'petal_width'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">sepal_length</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sepal_width</th>\n",
       "      <th colspan=\"2\" halign=\"left\">petal_length</th>\n",
       "      <th colspan=\"2\" halign=\"left\">petal_width</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>amin</th>\n",
       "      <th>amax</th>\n",
       "      <th>amin</th>\n",
       "      <th>amax</th>\n",
       "      <th>amin</th>\n",
       "      <th>amax</th>\n",
       "      <th>amin</th>\n",
       "      <th>amax</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",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>4.3</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>versicolor</th>\n",
       "      <td>4.9</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>4.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3.8</td>\n",
       "      <td>4.5</td>\n",
       "      <td>6.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           sepal_length      sepal_width      petal_length      petal_width  \\\n",
       "                   amin amax        amin amax         amin amax        amin   \n",
       "species                                                                       \n",
       "setosa              4.3  5.8         2.3  4.4          1.0  1.9         0.1   \n",
       "versicolor          4.9  7.0         2.0  3.4          3.0  5.1         1.0   \n",
       "virginica           4.9  7.9         2.2  3.8          4.5  6.9         1.4   \n",
       "\n",
       "                 \n",
       "           amax  \n",
       "species          \n",
       "setosa      0.6  \n",
       "versicolor  1.8  \n",
       "virginica   2.5  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "agg is a new function we haven't seen yet. It will\n",
    "aggregate each column using specified lists of functions.\n",
    "We have been using some of its shortcuts but using\n",
    "agg allows us to put in many functions at a time\n",
    "\n",
    "df.groupby('species').agg(np.mean)\n",
    "==\n",
    "df.groupby('species').mean()\n",
    "\n",
    "BUT \n",
    "df.groupby('species').agg([np.min, np.max])\n",
    "\n",
    "doesn't have a short form\n",
    "'''\n",
    "\n",
    "df.groupby('species').agg([np.min, np.max])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x11d6018d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11d3b0a10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11d517110>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11d328390>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x126bbf810>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11d3a4c50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x126d6d2d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x12700e150>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x12706cdd0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1270fe0d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x127153e50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1271e1310>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x12718e790>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1272c5e10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x12745ffd0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1274cf750>]], dtype=object)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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EaPPrxMsvv9z8Oi4ujri4uE6orbhRSUhIICEhwdVqXDe2OgPlIgJ32YSsNziU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Pg4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\nryAIgiAIgjoRjlcQBEEQBEGd+B8jeRd/IvgKnwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d61ed10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_map = {\n",
    "    'versicolor': 'r',\n",
    "    'setosa': 'b',\n",
    "    'virginica': 'g'\n",
    "}\n",
    "colors = [color_map[s] for s in df['species']]\n",
    "pd.scatter_matrix(df, color=colors, figsize=(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11e1e1250>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tgf+0XRjeThw9QVG9d9K2rIPSeTI/QinVQCn1lFLqC6XUZ6qwNPJGHNayo30/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0AAAg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xiDaspGXxjZhmlFTvplnhThu0S3z4yC5eKKTH7G81AzSaAfaUcry7fi7/q3UZbzi4mUN2kYgY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\nZw0tMhqNRqM5a2iR0Wg0Gs1Z4/8DOBh90xuwF1QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e1eb7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# parallel coordinates help us visualize multivariate data\n",
    "\n",
    "from pandas.tools.plotting import parallel_coordinates\n",
    "parallel_coordinates(df, 'species')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'I have no clue'"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I used values in order to see all of the data at once\n",
    "# without .values, a dataframe is returned\n",
    "\n",
    "\n",
    "## PART 2: Write a function to predict the species for each observation\n",
    "\n",
    "\n",
    "### EXERCISE!!!!! ###\n",
    "\n",
    "# note data must be in order\n",
    "# ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']\n",
    "\n",
    "# define a function that takes in a row of data and returns a predicted species\n",
    "def classify_iris(data):\n",
    "    if False and False or False and False:\n",
    "        return 'rose'\n",
    "    elif False and (True or False):\n",
    "        return 'lily'\n",
    "    else:\n",
    "        return 'I have no clue'\n",
    "\n",
    "# example use\n",
    "classify_iris([3.4, 5.4, 7.5, 2.3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def test_my_function(func):\n",
    "    # make predictions and store as numpy array\n",
    "    preds = np.array([func(row) for row in df.values])\n",
    "\n",
    "\n",
    "    # calculate the accuracy of the predictions\n",
    "    return np.mean(preds == df.species.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_my_function(classify_iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# EXAMPLEE using only petal_width\n",
    "def sinan_classify_iris(data):\n",
    "    if data[3] < 3:\n",
    "        return 'setosa'\n",
    "    else:\n",
    "        return None\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.33333333333333331"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_my_function(sinan_classify_iris) # TRY AND BEAT ME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [sfdat26-env]",
   "language": "python",
   "name": "Python [sfdat26-env]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
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}