w03_lab_Kahn_Laura.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Import libraries\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Read the CSV into a data frame\n",
    "df = pd.read_csv('imdb.csv', delimiter='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Year</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>!Next?</td>\n",
       "      <td>1994</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>#1 Single</td>\n",
       "      <td>2006</td>\n",
       "      <td>6.1</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Title  Year  Rating  Votes\n",
       "0     !Next?  1994     5.4      5\n",
       "1  #1 Single  2006     6.1     61"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#See the first two rows of data\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1994\n",
       "1    2006\n",
       "Name: Year, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Extract one column using dictionary-like expression\n",
    "df['Year'].head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Year</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1994</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Rating\n",
       "0  1994     5.4\n",
       "1  2006     6.1\n",
       "2  2013     7.1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Extract multiple columns\n",
    "df[['Year', 'Rating']].head(3)"
   ]
  },
  {
   "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>Title</th>\n",
       "      <th>Year</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>!Next?</td>\n",
       "      <td>1994</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>#1 Single</td>\n",
       "      <td>2006</td>\n",
       "      <td>6.1</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>#7DaysLater</td>\n",
       "      <td>2013</td>\n",
       "      <td>7.1</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>#Bikerlive</td>\n",
       "      <td>2014</td>\n",
       "      <td>6.8</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>#ByMySide</td>\n",
       "      <td>2012</td>\n",
       "      <td>5.5</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>#LawstinWoods</td>\n",
       "      <td>2013</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>#lovemilla</td>\n",
       "      <td>2013</td>\n",
       "      <td>6.7</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>#nitTWITS</td>\n",
       "      <td>2011</td>\n",
       "      <td>7.1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>$#*! My Dad Says</td>\n",
       "      <td>2010</td>\n",
       "      <td>6.3</td>\n",
       "      <td>4349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>$1,000,000 Chance of a Lifetime</td>\n",
       "      <td>1986</td>\n",
       "      <td>6.4</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Title  Year  Rating  Votes\n",
       "0                           !Next?  1994     5.4      5\n",
       "1                        #1 Single  2006     6.1     61\n",
       "2                      #7DaysLater  2013     7.1     14\n",
       "3                       #Bikerlive  2014     6.8     11\n",
       "4                        #ByMySide  2012     5.5     13\n",
       "5                    #LawstinWoods  2013     7.0      6\n",
       "6                       #lovemilla  2013     6.7     17\n",
       "7                        #nitTWITS  2011     7.1      9\n",
       "8                 $#*! My Dad Says  2010     6.3   4349\n",
       "9  $1,000,000 Chance of a Lifetime  1986     6.4     16"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Extract first 10 rows\n",
    "df[:10]"
   ]
  },
  {
   "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>Year</th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1994</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2011</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2010</td>\n",
       "      <td>4349</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Votes\n",
       "0  1994      5\n",
       "1  2006     61\n",
       "2  2013     14\n",
       "3  2014     11\n",
       "4  2012     13\n",
       "5  2013      6\n",
       "6  2013     17\n",
       "7  2011      9\n",
       "8  2010   4349"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Select rows and columns\n",
    "df[['Year', 'Votes']][:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Q1 - Calculate statistics for movie ratings from 1994\n",
    "#First we get data from only 1994\n",
    "geq = df['Year'] == 1994\n",
    "leq = df['Year'] == 1994\n",
    "movie_ninetyfour = df[geq & leq]"
   ]
  },
  {
   "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>Title</th>\n",
       "      <th>Year</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>!Next?</td>\n",
       "      <td>1994</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>10 + 2</td>\n",
       "      <td>1994</td>\n",
       "      <td>5.5</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>3000 scénarios contre un virus</td>\n",
       "      <td>1994</td>\n",
       "      <td>5.8</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>37 Grad</td>\n",
       "      <td>1994</td>\n",
       "      <td>6.6</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>704 Hauser</td>\n",
       "      <td>1994</td>\n",
       "      <td>4.9</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Title  Year  Rating  Votes\n",
       "0                            !Next?  1994     5.4      5\n",
       "80                           10 + 2  1994     5.5     22\n",
       "469  3000 scénarios contre un virus  1994     5.8     50\n",
       "483                         37 Grad  1994     6.6     11\n",
       "623                      704 Hauser  1994     4.9     37"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_ninetyfour.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.208550512445095 6.4\n"
     ]
    }
   ],
   "source": [
    "#Calulate mean and median\n",
    "print (movie_ninetyfour ['Rating'].mean(), movie_ninetyfour['Rating'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1    4.20\n",
       "0.9    7.96\n",
       "Name: Rating, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate the 10% and 90% quantiles for the 1994 movies\n",
    "movie_ninetyfour['Rating'].quantile([.1, .9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Q2 - Plot histogram of movie ratings from 2000-2014\n",
    "#First get the data from the years in this date range\n",
    "twothousand = df['Year'] >= 2000\n",
    "twothousandfourteen = df['Year'] <= 2014\n",
    "movie00to14 = df[twothousand & twothousandfourteen]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Title</th>\n",
       "      <th>Year</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>#1 Single</td>\n",
       "      <td>2006</td>\n",
       "      <td>6.1</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>#7DaysLater</td>\n",
       "      <td>2013</td>\n",
       "      <td>7.1</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>#Bikerlive</td>\n",
       "      <td>2014</td>\n",
       "      <td>6.8</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>#ByMySide</td>\n",
       "      <td>2012</td>\n",
       "      <td>5.5</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>#LawstinWoods</td>\n",
       "      <td>2013</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>#lovemilla</td>\n",
       "      <td>2013</td>\n",
       "      <td>6.7</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>#nitTWITS</td>\n",
       "      <td>2011</td>\n",
       "      <td>7.1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>$#*! My Dad Says</td>\n",
       "      <td>2010</td>\n",
       "      <td>6.3</td>\n",
       "      <td>4349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>$100 Taxi Ride</td>\n",
       "      <td>2001</td>\n",
       "      <td>6.8</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>$2 Bill</td>\n",
       "      <td>2002</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>$24 in 24</td>\n",
       "      <td>2012</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>$25 Million Dollar Hoax</td>\n",
       "      <td>2004</td>\n",
       "      <td>4.7</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>$40 a Day</td>\n",
       "      <td>2002</td>\n",
       "      <td>6.1</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>$5 Cover</td>\n",
       "      <td>2009</td>\n",
       "      <td>5.8</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>$5 Cover: Seattle</td>\n",
       "      <td>2009</td>\n",
       "      <td>7.2</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>$9.99</td>\n",
       "      <td>2003</td>\n",
       "      <td>7.7</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>'Conversations with My Wife'</td>\n",
       "      <td>2010</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>'Da Kink in My Hair</td>\n",
       "      <td>2007</td>\n",
       "      <td>4.3</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>'Orrible</td>\n",
       "      <td>2001</td>\n",
       "      <td>6.7</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>'The Making Of...'</td>\n",
       "      <td>2014</td>\n",
       "      <td>8.5</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Title  Year  Rating  Votes\n",
       "1                      #1 Single  2006     6.1     61\n",
       "2                    #7DaysLater  2013     7.1     14\n",
       "3                     #Bikerlive  2014     6.8     11\n",
       "4                      #ByMySide  2012     5.5     13\n",
       "5                  #LawstinWoods  2013     7.0      6\n",
       "6                     #lovemilla  2013     6.7     17\n",
       "7                      #nitTWITS  2011     7.1      9\n",
       "8               $#*! My Dad Says  2010     6.3   4349\n",
       "10                $100 Taxi Ride  2001     6.8     11\n",
       "12                       $2 Bill  2002     6.0     15\n",
       "13                     $24 in 24  2012     7.0     13\n",
       "14       $25 Million Dollar Hoax  2004     4.7     21\n",
       "15                     $40 a Day  2002     6.1    167\n",
       "16                      $5 Cover  2009     5.8     59\n",
       "17             $5 Cover: Seattle  2009     7.2     15\n",
       "18                         $9.99  2003     7.7     16\n",
       "21  'Conversations with My Wife'  2010     3.2      6\n",
       "22           'Da Kink in My Hair  2007     4.3    171\n",
       "24                      'Orrible  2001     6.7    107\n",
       "25            'The Making Of...'  2014     8.5     53"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie00to14.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x600fb70>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XAxERdx+3Xf5gzcxqaLq3slYlOZwOPEOzIP088ATwlxGxJ2tgZmZ9qhLTShHx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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x611d470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now let's plot a histogram of Ratings from the Movie Ratings \n",
    "#from 2000 to 2014.\n",
    "movie00to14['Rating'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  2.74000000e+02,   5.50000000e+02,   1.40500000e+03,\n",
       "          1.95800000e+03,   4.34000000e+03,   5.16500000e+03,\n",
       "          9.75500000e+03,   1.14520000e+04,   2.05740000e+04,\n",
       "          2.19260000e+04,   2.76360000e+04,   4.17550000e+04,\n",
       "          3.75340000e+04,   4.89360000e+04,   2.93760000e+04,\n",
       "          2.86830000e+04,   1.50620000e+04,   6.47600000e+03,\n",
       "          1.38000000e+02,   1.60000000e+01]),\n",
       " array([ 1.   ,  1.445,  1.89 ,  2.335,  2.78 ,  3.225,  3.67 ,  4.115,\n",
       "         4.56 ,  5.005,  5.45 ,  5.895,  6.34 ,  6.785,  7.23 ,  7.675,\n",
       "         8.12 ,  8.565,  9.01 ,  9.455,  9.9  ]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RqNxBMmKym1KGDEc0JIklYrJbxui+hzKaujEhpSssIiIalcQSERGNSmKJiIhGJbFERESj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\niIhGJbFERESjklgiIqJRSSwREdGo/w+gVP8Unhk2RAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb2b77f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Q3 Basic plotting of all movie Ratings using matplotlib\n",
    "#Add x and y-axis labels and title\n",
    "plt.title('Frequency of IMDB Movie Ratings')\n",
    "plt.xlabel('Movie Rating')\n",
    "plt.ylabel('Frequency')\n",
    "plt.hist(df['Rating'], bins=20, color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([   824.,   3363.,   9505.,  21207.,  42500.,  69391.,  86470.,\n",
       "         58059.,  21538.,    154.]),\n",
       " array([ 1.  ,  1.89,  2.78,  3.67,  4.56,  5.45,  6.34,  7.23,  8.12,\n",
       "         9.01,  9.9 ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WmwVcV5avB2aWkV6HAS8Hlpfmsg2SZpTO/DPbtplVlt9FNRggIiK6pNNNYS8B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\nsURERKOSWCIiolFJLBER0aj/DzryrCvnvgX9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdd80d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Q4: Basic Plotting with Seaborn\n",
    "#Rerun the histogram with 10 bins and color blue\n",
    "plt.title('Frequency of IMDB Movie Ratings')\n",
    "plt.xlabel('Movie Rating')\n",
    "plt.ylabel('Frequency')\n",
    "plt.hist(df['Rating'], bins=10, color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1241cb00>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OnTsByM3NZfLkyVgsFgYMGMCgQYPIz8+nqKiIyspKzznYiYmJnmVycnI864qLi/P6QjAi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\nK+PMmY61d95qDcFkMrV4PR0+JDRHdUA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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12160160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "#Plot the histogram with seaborn\n",
    "plt.title('Frequency of IMDB Movie Ratings')\n",
    "plt.xlabel('Movie Rating')\n",
    "plt.ylabel('Frequency')\n",
    "sns.distplot(df['Rating'], bins=10,kde=False, color='purple')"
   ]
  }
 ],
 "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": 2
}