homework 7_foundations_shengying.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"07-hw-animals.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied (use --upgrade to upgrade): matplotlib in /Users/sz2472/.virtualenvs/pandas/lib/python3.5/site-packages\n",
      "Requirement already satisfied (use --upgrade to upgrade): python-dateutil in /Users/sz2472/.virtualenvs/pandas/lib/python3.5/site-packages (from matplotlib)\n",
      "Requirement already satisfied (use --upgrade to upgrade): pytz in /Users/sz2472/.virtualenvs/pandas/lib/python3.5/site-packages (from matplotlib)\n",
      "Requirement already satisfied (use --upgrade to upgrade): cycler in /Users/sz2472/.virtualenvs/pandas/lib/python3.5/site-packages (from matplotlib)\n",
      "Requirement already satisfied (use --upgrade to upgrade): numpy>=1.6 in /Users/sz2472/.virtualenvs/pandas/lib/python3.5/site-packages (from matplotlib)\n",
      "Requirement already satisfied (use --upgrade to upgrade): pyparsing!=2.0.0,!=2.0.4,>=1.5.6 in /Users/sz2472/.virtualenvs/pandas/lib/python3.5/site-packages (from matplotlib)\n",
      "Requirement already satisfied (use --upgrade to upgrade): six>=1.5 in /Users/sz2472/.virtualenvs/pandas/lib/python3.5/site-packages (from python-dateutil->matplotlib)\n"
     ]
    }
   ],
   "source": [
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>animal</th>\n",
       "      <th>name</th>\n",
       "      <th>length</th>\n",
       "      <th>inches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cat</td>\n",
       "      <td>Anne</td>\n",
       "      <td>35</td>\n",
       "      <td>13.779528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cat</td>\n",
       "      <td>Bob</td>\n",
       "      <td>45</td>\n",
       "      <td>17.716535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dog</td>\n",
       "      <td>Egglesburg</td>\n",
       "      <td>65</td>\n",
       "      <td>25.590551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dog</td>\n",
       "      <td>Devon</td>\n",
       "      <td>50</td>\n",
       "      <td>19.685039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cat</td>\n",
       "      <td>Charlie</td>\n",
       "      <td>32</td>\n",
       "      <td>12.598425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dog</td>\n",
       "      <td>Fontaine</td>\n",
       "      <td>35</td>\n",
       "      <td>13.779528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  animal        name  length     inches\n",
       "0    cat        Anne      35  13.779528\n",
       "1    cat         Bob      45  17.716535\n",
       "2    dog  Egglesburg      65  25.590551\n",
       "3    dog       Devon      50  19.685039\n",
       "4    cat     Charlie      32  12.598425\n",
       "5    dog    Fontaine      35  13.779528"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['animal', 'name', 'length', 'inches'], dtype='object')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### display the first three animals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>animal</th>\n",
       "      <th>name</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cat</td>\n",
       "      <td>Anne</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cat</td>\n",
       "      <td>Bob</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dog</td>\n",
       "      <td>Egglesburg</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  animal        name  length\n",
       "0    cat        Anne      35\n",
       "1    cat         Bob      45\n",
       "2    dog  Egglesburg      65"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_to_show = ['animal','name','length']\n",
    "df[columns_to_show][0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sort the animals to see the 3 longest animals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>animal</th>\n",
       "      <th>name</th>\n",
       "      <th>length</th>\n",
       "      <th>inches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dog</td>\n",
       "      <td>Egglesburg</td>\n",
       "      <td>65</td>\n",
       "      <td>25.590551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dog</td>\n",
       "      <td>Devon</td>\n",
       "      <td>50</td>\n",
       "      <td>19.685039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cat</td>\n",
       "      <td>Bob</td>\n",
       "      <td>45</td>\n",
       "      <td>17.716535</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  animal        name  length     inches\n",
       "2    dog  Egglesburg      65  25.590551\n",
       "3    dog       Devon      50  19.685039\n",
       "1    cat         Bob      45  17.716535"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='length', ascending=False).head(3) #sort_values, not sort, and sort by length column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What are the counts of the different values of the \"animal\" column? a.k.a. how many cats and how many dogs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dog    3\n",
       "cat    3\n",
       "Name: animal, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['animal'].value_counts() #value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Only select the dogs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "is_dog = df['animal']=='dog'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>animal</th>\n",
       "      <th>name</th>\n",
       "      <th>length</th>\n",
       "      <th>inches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dog</td>\n",
       "      <td>Egglesburg</td>\n",
       "      <td>65</td>\n",
       "      <td>25.590551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dog</td>\n",
       "      <td>Devon</td>\n",
       "      <td>50</td>\n",
       "      <td>19.685039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dog</td>\n",
       "      <td>Fontaine</td>\n",
       "      <td>35</td>\n",
       "      <td>13.779528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  animal        name  length     inches\n",
       "2    dog  Egglesburg      65  25.590551\n",
       "3    dog       Devon      50  19.685039\n",
       "5    dog    Fontaine      35  13.779528"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['animal'] == 'dog'] #df['animal]==dog will only give a list of true or false, if you want the rows back, you have to put a df[] to put the is_dog into a data frame "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display all of the animals that are greater than 40 cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>animal</th>\n",
       "      <th>name</th>\n",
       "      <th>length</th>\n",
       "      <th>inches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cat</td>\n",
       "      <td>Bob</td>\n",
       "      <td>45</td>\n",
       "      <td>17.716535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dog</td>\n",
       "      <td>Egglesburg</td>\n",
       "      <td>65</td>\n",
       "      <td>25.590551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dog</td>\n",
       "      <td>Devon</td>\n",
       "      <td>50</td>\n",
       "      <td>19.685039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  animal        name  length     inches\n",
       "1    cat         Bob      45  17.716535\n",
       "2    dog  Egglesburg      65  25.590551\n",
       "3    dog       Devon      50  19.685039"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['length'] > 40]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 'length' is the animal's length in cm. Create a new column called inches that is the length in inches"
   ]
  },
  {
   "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>animal</th>\n",
       "      <th>name</th>\n",
       "      <th>length</th>\n",
       "      <th>inches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cat</td>\n",
       "      <td>Anne</td>\n",
       "      <td>35</td>\n",
       "      <td>13.779528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cat</td>\n",
       "      <td>Bob</td>\n",
       "      <td>45</td>\n",
       "      <td>17.716535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dog</td>\n",
       "      <td>Egglesburg</td>\n",
       "      <td>65</td>\n",
       "      <td>25.590551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dog</td>\n",
       "      <td>Devon</td>\n",
       "      <td>50</td>\n",
       "      <td>19.685039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cat</td>\n",
       "      <td>Charlie</td>\n",
       "      <td>32</td>\n",
       "      <td>12.598425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dog</td>\n",
       "      <td>Fontaine</td>\n",
       "      <td>35</td>\n",
       "      <td>13.779528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  animal        name  length     inches\n",
       "0    cat        Anne      35  13.779528\n",
       "1    cat         Bob      45  17.716535\n",
       "2    dog  Egglesburg      65  25.590551\n",
       "3    dog       Devon      50  19.685039\n",
       "4    cat     Charlie      32  12.598425\n",
       "5    dog    Fontaine      35  13.779528"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['inches']=df['length']/2.54\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the cats to a separate variable called \"cats.\" Save the dogs to a separate variable called \"dogs.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dogs=df['animal'] == 'dog'\n",
    "cats=df['animal'] == 'cat'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display all of the animals that are cats and above 12 inches long. First do it using the \"cats\" variable, then do it using your normal dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>animal</th>\n",
       "      <th>name</th>\n",
       "      <th>length</th>\n",
       "      <th>inches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cat</td>\n",
       "      <td>Anne</td>\n",
       "      <td>35</td>\n",
       "      <td>13.779528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cat</td>\n",
       "      <td>Bob</td>\n",
       "      <td>45</td>\n",
       "      <td>17.716535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cat</td>\n",
       "      <td>Charlie</td>\n",
       "      <td>32</td>\n",
       "      <td>12.598425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  animal     name  length     inches\n",
       "0    cat     Anne      35  13.779528\n",
       "1    cat      Bob      45  17.716535\n",
       "4    cat  Charlie      32  12.598425"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['animal'] =='cat') & (df['inches']>12)] #panda only recognizes the conditions in parentaces\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What's the mean length of a cat?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>length</th>\n",
       "      <th>inches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>37.333333</td>\n",
       "      <td>14.698163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.806859</td>\n",
       "      <td>2.679866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>12.598425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.500000</td>\n",
       "      <td>13.188976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>13.779528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>15.748031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>17.716535</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          length     inches\n",
       "count   3.000000   3.000000\n",
       "mean   37.333333  14.698163\n",
       "std     6.806859   2.679866\n",
       "min    32.000000  12.598425\n",
       "25%    33.500000  13.188976\n",
       "50%    35.000000  13.779528\n",
       "75%    40.000000  15.748031\n",
       "max    45.000000  17.716535"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[cats].describe()\n",
    "#the mean length of a cat is 37.33333"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What's the mean length of a dog?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>length</th>\n",
       "      <th>inches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>50.0</td>\n",
       "      <td>19.685039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15.0</td>\n",
       "      <td>5.905512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>35.0</td>\n",
       "      <td>13.779528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>42.5</td>\n",
       "      <td>16.732283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50.0</td>\n",
       "      <td>19.685039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>57.5</td>\n",
       "      <td>22.637795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>65.0</td>\n",
       "      <td>25.590551</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       length     inches\n",
       "count     3.0   3.000000\n",
       "mean     50.0  19.685039\n",
       "std      15.0   5.905512\n",
       "min      35.0  13.779528\n",
       "25%      42.5  16.732283\n",
       "50%      50.0  19.685039\n",
       "75%      57.5  22.637795\n",
       "max      65.0  25.590551"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[dogs].describe()\n",
    "# the mean length of a dog is 50inches"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use groupby to accomplish both of the above tasks at once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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></th>\n",
       "      <th>length</th>\n",
       "      <th>inches</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>animal</th>\n",
       "      <th>animal</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">False</th>\n",
       "      <th rowspan=\"8\" valign=\"top\">True</th>\n",
       "      <th>count</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>19.685039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>5.905512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>13.779528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>42.500000</td>\n",
       "      <td>16.732283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>19.685039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>57.500000</td>\n",
       "      <td>22.637795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>65.000000</td>\n",
       "      <td>25.590551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">True</th>\n",
       "      <th rowspan=\"8\" valign=\"top\">False</th>\n",
       "      <th>count</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>37.333333</td>\n",
       "      <td>14.698163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.806859</td>\n",
       "      <td>2.679866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>12.598425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.500000</td>\n",
       "      <td>13.188976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>13.779528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>15.748031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>17.716535</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        length     inches\n",
       "animal animal                            \n",
       "False  True   count   3.000000   3.000000\n",
       "              mean   50.000000  19.685039\n",
       "              std    15.000000   5.905512\n",
       "              min    35.000000  13.779528\n",
       "              25%    42.500000  16.732283\n",
       "              50%    50.000000  19.685039\n",
       "              75%    57.500000  22.637795\n",
       "              max    65.000000  25.590551\n",
       "True   False  count   3.000000   3.000000\n",
       "              mean   37.333333  14.698163\n",
       "              std     6.806859   2.679866\n",
       "              min    32.000000  12.598425\n",
       "              25%    33.500000  13.188976\n",
       "              50%    35.000000  13.779528\n",
       "              75%    40.000000  15.748031\n",
       "              max    45.000000  17.716535"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([cats,dogs]).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make a histogram of the length of dogs. I apologize that it is so boring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x109ad6e48>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rUs3IyEjqeGRkZFKNJMViMcVisdRxc3Oz1v/nv0+r8fkiGAzOdQuu8vJ8y5dLv62umOs2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109987f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_dog_data = df[df['animal'] == 'dog']\n",
    "new_dog_data['length'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Change your graphing style to be something else (anything else!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10a3481d0>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JWZY1ac3IyEjmeGRkZNIaSYrH44rH45nj1tZWrdjwwIwKXyhCodB8l+AqT/e3cqX02on5\nrsI1np6dXOxv5Upp3fw/L7q7uzO3o9GootHoVdfnvIxTU1OjwcFBDQ0NKZVKqbe3V3V1dVlr6urq\n9Kc//UmS9N5772nJkiUqLS2dtFc0GlVra2vmn88X60X0t7B5uT8v9yaZ0d/nszRX0Et5nNn7/X61\ntbWpvb1djuOosbFRkUhEPT098vl8isViuuWWW9Tf369t27Zp8eLF2rRpU0EaAgAURl7X7Gtra9XR\n0ZF1X3Nzc9ZxW1tb4aoCABTUvH6CNp+XHgsZ/S1sXu7Py71J9DcVn+M4jgu1AACuIXw3DgAYgLAH\nAAPM2YeqLl++rH379imVSimVSqmurk7r16/XK6+8otdff13Lli2TJN17772qra2dq7IKyrZt7d69\nW5ZlaefOnfrkk0/03HPPaWhoSMuXL9fDDz+s4uLi+S5zxmzb1q5duxQOh7Vz505PzW7Lli0qLi6W\nz+dTIBDQU0895an5TdWfl+Y3Pj6uX/ziF/roo4/k8/m0adMmVVRUeGJ+U/U2MDAw7dnN6TX7iYkJ\nLVq0SLZt6yc/+Ynuu+8+nTx5Ul/4whd01113zVUZrvn973+vDz74QBcvXtTOnTv18ssvKxQK6bvf\n/a6OHj2qf/3rX9qwYcN8lzlj/9nfK6+84pnZbd26VU8//bSWLl2auc9L85uqPy/N74UXXtCXv/xl\nNTQ0KJ1Oa2JiQr/97W89Mb+penvttdemPbs5vYyzaNEiSf8+y7dtO/PE88J7xCMjI+rv71dTU1Pm\nvhMnTmS+IO6OO+6Y9AVyC8lU/UnemJ307z7+sxcvzW+q/j67f6EbHx/Xu+++q4aGBklSIBBQcXGx\nJ+Z3pd6k6c9uTr8b57PLAOfOnVNzc7MikYgk6dixY/rzn/+s6upq/fCHP1yQL7WOHDmi++67T+Pj\n45n7zp8/n/kkcWlpqc6fPz9f5c3aVP1J3pidJPl8PrW3t8vv9ysWi6mpqclT8/t8f01NTYrFYpK8\nMb9//OMfCoVCOnTokD788EN98Ytf1P333++J+V2pN2n6s5vTsPf7/frZz36m8fFxPfnkk3rnnXf0\n7W9/Wz/4wQ/k8/n0m9/8RkeOHFlwn8B96623tGzZMlVVVWV9989/8vl8c1hV4VypPy/M7jNPPPGE\nysrKdOHCBbW3t2vlypWT1izU+UnZ/T3xxBOKRCKemZ9t2zp9+rTa2tpUXV2trq4uHT16dNK6hTi/\nK/V25513Tnt28/K3cYqLi3XzzTfr1KlTKikpyQyhqalJp06dmo+SZuXdd9/ViRMntHXrVnV0dOjt\nt9/WgQMHVFpamvle/9HR0cybKQvNVP0dPHjQE7P7TFlZmSSppKREa9euVSKR8Mz8pOz+vva1rymR\nSHhmfpZlKRwOq7q6WpJ022236fTp056Y35V6m8ns5izsL1y4kLkE8Omnn+rkyZOqqqrK+p+cvPnm\nm1q1atVclVQw69ev14svvqiDBw9qx44d+spXvqJt27bp1ltv1fHjxyVJx48fn/QFcgvFVP1t3brV\nE7OT/v0XBy5duiRJunTpkv7yl7/o+uuv98z8pupv1apVnplfaWmpwuGwPv74Y0nSyZMnFYlEPDG/\nK/U2k9nN2WWc0dFRvfDCC5k3itatW6evfvWrOnjwoM6cOSOfz6fy8nJt3LhxrkpyXUtLi37+85/r\nj3/8o8rLy/Xwww/Pd0kF9fLLL3tidufPn9czzzwjn8+ndDqtdevWac2aNaqurvbE/K7Un5f+7P3o\nRz/SgQMHlEqldN1112nz5s2ybdsT85uqt8OHD097dnxdAgAYgE/QAoABCHsAMABhDwAGIOwBwACE\nPQAYgLAHAAMQ9gBgAMIeAAzw/22Ag7FyQfe3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a3df080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('ggplot')\n",
    "new_dog_data['length'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Make a horizontal bar graph of the length of the animals, with their name as the label (look at the billionaires notebook I put on Slack!)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10a61c7b8>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TydTg9qopNUmSnLfLysqwdu1a52yrZ8+e2LlzJ0wmE0RRxNWrV9GuXTvodDr069cPu3fv\nxvnz55GYmAgA6Nq1K/bu3QuTyYSCggKUlJTAaDTi/PnzLudx5c0nIs/ncEgoLy9XOobb9Hq91+bX\n6/WNnhCoptSqq6uRlJRU65L+kSNHAgCGDBmC4uJiJCUlQZIkBAUFYc6cOQDuFN7KlSvRu3dviKII\nAHjuueewZs0azJ49G6IoIjExsdYMrQY/LkBE5FkE6e4pDskqb0Ss0hGIqJH8/2cprOF/UjqG27x5\npmY0Ghu9j2qvfiQiopanUcuP+fn5OHDgAK5fv44JEyYgPz8fNpsNnTt3lisfERGRy1yeqR04cADz\n589HaWkp9u7dCwCorKzExo0bZQtHRETUGC7P1DIyMjBv3jyEh4fjwIEDAIDOnTvj4sWLcmUjIiJq\nFJdnajdu3KizzCgIAq8AJCIij+HyTC0iIgI//vgjBg0a5Hxs//79iIqKkiWYGumSzUpHcIsoamG3\n25SO4TbmV5a359cEhyodgRrB5Uv68/PzsWDBAoSGhuKXX35xfjB53rx56NChg9w5VaHmq7a8jTdf\nEgwwv9KYX1nenN+dS/pdnql17NgRy5cvx5EjRxATE4N27dohJibG+T2IRERESmvUJf2+vr7o37+/\nXFmIiIgeiMulVlJSgm3btuHixYuorKys9dyKFSuaPBgREVFjuVxqaWlpMBqNSEhIgE6nkzMTERGR\nW1wutZoLRTQafrMWERF5JpcbKiYmBtnZ2XJmISIieiAuz9Ref/11zJs3Dw899BCCgoJqPTdlypQm\nD0ZERNRYLpfa6tWrIYoiOnbsyHNqRETkkVwutVOnTuGzzz6Dv7+/nHmIiIjc5vI5tc6dO3vtp9KJ\niKhlcHmmZjKZkJKSgsGDB9c5p/b00083eTAiIqLGcrnUzpw5A4PBgBMnTtR5jqVGRESewOVSmz9/\nvpw5iIiIHlijvvuxhiRJuPvL/fmBbCIi8gQul1ppaSnWrVuH06dP49atW7We27p1a5MHIyIiaiyX\np1iff/45tFot3n//ffj5+cFsNiM2NhZvvfWWnPmIiIhc5nKpnT17FpMnT0Z4eDgEQUB4eDgmT56M\nH374Qc58RERELnN5+VGj0UAURQBAYGAgysrK4O/vj9LSUtnCqY2Ye1rpCG6pErUQ7TalY7iN+ZXF\n/MqSNb8hBPa2wfKM7SaXSy0qKgrHjh1DXFwcevbsiWXLlkGn0yEyMlLOfKpiXZykdAQioiajSzYD\n3lpq06ZNc17xOH78eOzYsQOVlZUYMWKEbOGIiIgaw+VS8/X1xZ49e2r98rUkSdi8eTOmTp0qW0Ai\nIiJXuVxq6enpuHTpEmJiYup8TZYrxowZg/DwcEiSBEEQ0L9/f7zwwguNHgcAxo0bh40bNzZ6v8TE\nRJjNZrRq1cqt1yUiIs/mcqkdP34c6enpCAwMdOuFaj4G0BQEQWjW/QDA4XDwQ+ZERB7O5VILDg5G\ndXW12y909zeQ3O3o0aPYtGkT/Pz8EB0djcLCQiQnJ6OsrAwff/wxfvvtN/zpT3/CyZMn651lff/9\n9zhw4ABsNhvi4uIwevRoVFVVYdmyZSgtLYXD4cBLL72Efv36QZIkbN++HVlZWfD19cX06dPRvn17\nrFq1CjExMejTpw+A32eC2dnZ2Lp1KwIDA1FQUIDly5fj66+/xr59+xAUFASDwYDIyEiMHDnS7feF\niIiajsulNnDgQCxZsgTDhg1DmzZtaj3XvXv3++5vtVqRlJTkXH4cNWoUYmNjsWbNGnz44YcIDg7G\nihUrnLOpr7/+Gt27d8eoUaOQlZWF//znP3XGPHHiBK5du4ZFixZBkiSYzWbk5OTgxo0bMBgMSE5O\nBgDcvn3buU+rVq2QmpqKH3/8EevXr3duc7e7Z3QXLlxAWloagoODkZubi0OHDiE1NRU2mw1JSUm8\n+pOIyIO4XGr//Oc/AQBfffVVrccFQUB6evp99/f19a2z/Hjx4kU89NBDCA6+c0lofHw8du3aBQDI\nycnBnDlzAACPP/54vefBjh8/jhMnTjjLsqqqClevXkXXrl2xadMmbNmyBb169ULXrl2d+/Tv39/5\nWl9++eV9c0dFRTnznTlzBrGxsdBqtdBqtYiJibnv/kRE1HxcLrWVK1fKEuBey5KubCdJEkaNGoWh\nQ4fWec5sNuPYsWP429/+hh49euCll14CUHsWVnNbo9E4x5ckCTbb7x9U9PX1df1g7mKxWGCxWJz3\nExIS3BqHiMhTiaIWAXq9rK+RkZHhvG0ymWAymRrc3q1v6XdHfaVkNBpRVFSEkpISBAcHIzMz0/lc\nly5dkJmZiRdeeAHHjx+v9SXKNWM9/vjj2Lp1KwYMGAA/Pz+UlpZCq9XCbrejVatWGDBgAAICArB7\n927nvjVj7t+/H9HR0QCA0NBQ5Obmom/fvjh06FCtUrtbly5dsGbNGowaNQp2ux1HjhzBM888U++2\nrrz5RETezG63oby8XLbx9Xp9oycEzVZq1dXVtc6p9ezZE2PHjsWECROQkpICPz8/REZGOmdPo0eP\nxooVK7B3715ER0ejTZs28PPzA/D7DOuxxx5Dfn4+5s2bBwDw9/fHtGnTcPXqVWzevBmCIECr1Tq/\ndFkQBNy6dQtz5syBj48P3n77bQDAkCFD8NFHH+Hdd99Fz549na/zR5GRkYiNjcWcOXMQFBSEzp07\nIyAgQNb3jYiIXCdIrq7/yaSystJZImvXroXRaMTw4cNhs9mg0Wig0Whw9uxZrFu3rsk+EvAgavJa\nrVbMnz8fkyZNQnh4uEv75o2IlTccEVEz0iWbYY98VLbxjUZjo/dptpnavezatQv//e9/YbPZ8Mgj\njzjPj5WUlGDZsmVwOBzw8fHBpEmTFE56x+eff44rV66guroagwcPdrnQiIhIforP1FoSztSISE08\ncabGr8ggIiLVYKkREZFqsNSIiEg1FL9QpCXRJSt/9aY7RFELuxf/8i/zK4v5lSVrfkOIPOM+AJZa\nM5LzhKqcAvR6WT9gKTfmVxbzK8vb8zcWlx+JiEg1WGpERKQaLDUiIlINlhoREakGS42IiFSDpUZE\nRKrBUiMiItVgqRERkWqw1IiISDVYakREpBosNSIiUg2WGhERqQZLjYiIVIOlRkREqsFSIyIi1WCp\nERGRarDUiIhINfjL181IzD2tdAS3VIlaiF78c/bMryzZ8xtCYG8bLN/45FVYas3IujhJ6QhEqqNL\nNgMsNfp/XH4kIiLVYKkREZFqsNSIiEg1WvQ5tTFjxiA8PBwOhwOiKOKNN95AdHT0PbfPzs7G999/\nj+Tk5GZMSURErmrRpebn5wez2QwAOH78OLZs2YL//d//bXAfQRCaIRkREbmjRZeaJEnO2xUVFWjV\nqpXz/qZNm5CVlQWNRoMXX3wR/fv3d263ePFiXLt2Dd27d8eECROaPTcREdWvRZea1WpFUlISrFYr\nrl+/jvfffx8A8NNPP+Hy5ctYunQpbty4gblz56Jbt24AgNzcXCxbtgzBwcFISUnBTz/9hD59+ih5\nGERE9P9adKn5+vo6lx/Pnj2L9PR0LF26FDk5OYiPjwcABAUFoVu3bsjNzYW/vz+ioqIQEhICAIiP\nj0dOTk69pWaxWGCxWJz3ExISmuGIiFoeUdQiQK+XbXydTge9jOPLzdvzZ2RkOG+bTCaYTKYGt2/R\npXa36OholJWVoaysrFH73escmytvPhE9OLvdhvLyctnG1+v1so4vN2/Or9frGz0haNGX9N99Ti0/\nPx+SJEGv1+PRRx9FZmYmHA4HysrKcPr0aURFRQEAzp07h+LiYjgcDmRmZqJr165KxScioj9o0TO1\n6upqJCUlOctt6tSpEAQBcXFxOHv2LObMmQONRoPXXnsNQUFByM/PR1RUFNatW4fCwkKYTCbExcUp\nfBRERFRDkO6erpCs8kbEKh2BSHV0yWbYIx+VbXxvXr4DvDu/0Whs9D4tevmRiIjUhaVGRESqwVIj\nIiLVYKkREZFqtOirH5ubLtmsdAS3iKIWdi/+5WXmV5bs+Q0h8o1NXoel1ozkvEJLTgFefPUUwPxK\n8/b85F24/EhERKrBUiMiItVgqRERkWqw1IiISDVYakREpBosNSIiUg2WGhERqQZLjYiIVIOlRkRE\nqsFSIyIi1WCpERGRarDUiIhINVhqRESkGiw1IiJSDZYaERGpBkuNiIhUg6VGRESqwV++bkZi7mml\nI7ilStRCtNuUjuE25r8PQwjsbYPlG5+oGbHUmpF1cZLSEYjq0CWbAZYaqQSXH4mISDVYakREpBos\nNSIiUo0WU2o///wzxowZg4KCAqWjEBGRTFpMqWVmZqJXr17Yv3+/0lGIiEgmLeLqx8rKSvzyyy/4\n4IMPkJKSgtGjRyM7Oxvbtm2DXq9HXl4eIiIiMG3aNABAYmIiBg0ahCNHjsDhcGDGjBkwGo2oqqrC\nF198gStXrsBms2H06NGIjY1V+OiIiKhGi5ipHT58GD179kRwcDBat26NCxcuAAAuXryI119/HWlp\naSgsLMSZM2ec+wQFBcFsNuOZZ57Bjh07AADffvstevTogZSUFMyfPx+bNm2C1WpV5JiIiKiuFjFT\n27dvH0aOHAkA6NevH/bt24eYmBhERUWhbdu2AIDw8HAUFxejS5cuAIC4uDgAQEREBH7++WcAwIkT\nJ3DkyBF8//33AACbzYaSkhIYjcY6r2mxWGCxWJz3ExIS5DtAogcgiloE6PWyja/T6aCXcXy5Mb+y\nMjIynLdNJhNMJlOD26u+1G7evAmLxYK8vDwIggCHwwFBENCrVy9otb8fvkajgd1ud9738fGp87gk\nSZg1axY6dOhw39d15c0n8gR2uw3l5eWyja/X62UdX27Mrxy9Xt/oCYHqlx8PHjyIgQMHYuXKlUhP\nT8eqVasQGhqK06cb/5VVPXv2xD/+8Q/n/YsXLzZhUiIielCqL7XMzEznUmKNuLg4ZGZmQhCEeve5\n1+MvvfQSbDYbZs+ejVmzZmHr1q1NnpeIiNwnSJIkKR2ipcgbwSslyfPoks2wRz4q2/jevPwFML+S\n6rte4X5UP1MjIqKWg6VGRESqwVIjIiLVYKkREZFqsNSIiEg1VP/ha0+iSzYrHcEtoqiF3W5TOobb\nmP8+DCHyjU3UzFhqzUjOy6blFODFlwQDzE/UknD5kYiIVIOlRkREqsFSIyIi1WCpERGRarDUiIhI\nNVhqRESkGiw1IiJSDf70DBERqQZnas0kIyND6Qhu8+bsAPMrjfmV5c353cnOUiMiItVgqRERkWqw\n1JqJyWRSOoLbvDk7wPxKY35leXN+d7LzQhEiIlINztSIiEg1WGpERKQa/D01mWVlZWHDhg2QJAlP\nPfUURo0apXSkBq1evRpHjx5FUFAQUlNTAQA3b97E8uXLUVxcjNDQUMyYMQMBAQEKJ63fr7/+ivT0\ndNy4cQOCIGDIkCEYPny4VxxDdXU15s+fD5vNBpvNhtjYWIwdO9Yrst/N4XBg7ty5MBgMSEpK8qr8\niYmJCAgIgCAIEEURixYt8qr8FRUV+PTTT5GXlwdBEDB58mR06NDBK/IXFBRg+fLlEAQBkiShsLAQ\nY8aMwcCBAxuXXyLZ2O12aerUqVJRUZFUXV0tzZ49W7py5YrSsRp0+vRp6cKFC9KsWbOcj23atEna\nvn27JEmS9N1330mbN29WKt59/fbbb9KFCxckSZKk27dvS9OnT5euXLniNcdQWVkpSdKdPzvvvfee\ndPr0aa/JXmPHjh3SihUrpMWLF0uS5F1/fhITE6Xy8vJaj3lT/vT0dGn37t2SJEmSzWaTbt265VX5\na9jtdmnixIlScXFxo/Nz+VFG586dQ4cOHRASEgKtVov4+HgcOnRI6VgN6tq1KwIDA2s9dvjwYQwa\nNAgAMHjwYI8+hjZt2iA8PBwA4Ofnh44dO+LXX3/1mmPw9fUFcGfW5nA40KpVK6/JDtyZKR87dgxD\nhgxxPuZN+SVJgvSHa+e8JX9FRQVycnLw1FNPAQBEUURAQIDX5L/byZMn0b59ewQHBzc6P5cfZVRa\nWop27do57xsMBpw7d07BRO65ceMG2rRpA+BOady4cUPhRK4pKirCpUuXEB0d7TXH4HA4kJycjMLC\nQjzzzDO+kGpKAAADAklEQVQICwvzmuwA8OWXX+K1115DRUWF8zFvyi8IAhYsWACNRoOhQ4diyJAh\nXpO/qKgIer0eq1atwqVLlxAREYHx48d7Tf67ZWZmYsCAAQAa/+eHpUaNJgiC0hHuq7KyEmlpaRg/\nfjz8/PzqPO+px6DRaPDRRx+hoqICKSkpsFgsdbbx1Ow152LDw8PrzV3DU/MDwIcffoi2bduirKwM\nCxYsgNForLONp+Z3OBy4cOEC3nzzTURGRmLDhg3Yvn17ne08NX8Nm82Gw4cP45VXXqn3+fvlZ6nJ\nyGAwoKSkxHm/tLQUBoNBwUTuadOmDa5fv+78Z1BQkNKRGmS327F06VIMHDgQvXv3BuB9xxAQEIAn\nnngCubm5XpM9JycHhw8fxrFjx2C1WnH79m188sknXpMfANq2bQsAaN26NXr37o1z5855TX6DwYB2\n7dohMjISANC3b19s377da/LXyMrKQkREBFq3bg2g8f/t8pyajKKionDt2jUUFxfDZrNh//79iI2N\nVTrWff3xvEJMTAz27NkDANizZ4/HH8Pq1asRFhaG4cOHOx/zhmMoKytzLttZrVacPHkSjzzyiFdk\nB4CxY8di9erVSE9PxzvvvIPu3btj2rRpXpO/qqoKlZWVAO7M9E+cOIFOnTp5Tf42bdqgXbt2KCgo\nAHDnvFRYWJjX5K+xb98+xMfHO+83Nj+/UURmWVlZWL9+PSRJwtNPP+3xl/SvWLEC2dnZKC8vR1BQ\nEBISEtC7d28sW7YMJSUlCAkJwYwZM+pcTOIpcnJyMH/+fHTq1AmCIEAQBLz88suIiory+GO4fPky\nVq5c6fxLxZNPPonnn38eN2/e9Pjsf5SdnY0dO3Y4L+n3hvxFRUVYsmQJBEGA3W7Hk08+iVGjRnlN\nfgC4ePEiPvvsM9hsNrRv3x5TpkyBw+HwmvxVVVWYMmUK0tPT4e/vDwCNfv9ZakREpBpcfiQiItVg\nqRERkWqw1IiISDVYakREpBosNSIiUg2WGhERqQZLjYiIVIOlRkREqvF/fY3fn+jZox4AAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a819940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='barh', x='name',y='length') #length='false' remove the length #barh: a hornizontal bar chat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make a sorted horizontal bar graph of the cats, with the larger cats on top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10aa9eac8>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ab21dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['animal']== 'cat'].sort_values(by='length').plot(kind='barh', x='name', y='length')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}