Project - Data Cleaning and Analysis Star Wars Movies Survey.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project - Star Wars Movies Survey\n",
    "We will analyze data on the Star Wars movies. The data is based on a survey conducted by  [FiveThirtyEight](https://fivethirtyeight.com/), of Star Wars fans using the online tool SurveyMonkey. Survey results can be found [here](https://github.com/fivethirtyeight/data/tree/master/star-wars-survey). \n",
    "For the `star_wars.csv` file, we need to specify the `encoding=\"ISO-8859-1\"` because the data set has some characters that aren't in Python's default `utf-8 encoding`. Information on character encodings is available [here](https://www.joelonsoftware.com/2003/10/08/the-absolute-minimum-every-software-developer-absolutely-positively-must-know-about-unicode-and-character-sets-no-excuses/). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Data \n",
    "Data has following columns:\n",
    "The data has several columns, including:\n",
    "\n",
    "`RespondentID` - An anonymized ID for the respondent (person taking the survey)\n",
    "`Gender` - The respondent's gender\n",
    "`Age` - The respondent's age\n",
    "`Household Income` - The respondent's income\n",
    "`Education` - The respondent's education level\n",
    "`Location (Census Region)` - The respondent's location\n",
    "`Have you seen any of the 6 films in the Star Wars franchise?` - Has a Yes or No response\n",
    "`Do you consider yourself to be a fan of the Star Wars film franchise?` - Has a Yes or No response\n",
    "\n",
    "There are several other columns containing answers to questions aabout the Star Wars movies. For some questions, the respondent had to check one or more boxes. This type of data is difficult to represent in columnar format. As a result, this data set needs a lot of cleaning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preliminary Exploration of Data and Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1187 entries, 0 to 1186\n",
      "Data columns (total 38 columns):\n",
      " #   Column                                                                                                                                         Non-Null Count  Dtype  \n",
      "---  ------                                                                                                                                         --------------  -----  \n",
      " 0   RespondentID                                                                                                                                   1186 non-null   float64\n",
      " 1   Have you seen any of the 6 films in the Star Wars franchise?                                                                                   1187 non-null   object \n",
      " 2   Do you consider yourself to be a fan of the Star Wars film franchise?                                                                          837 non-null    object \n",
      " 3   Which of the following Star Wars films have you seen? Please select all that apply.                                                            674 non-null    object \n",
      " 4   Unnamed: 4                                                                                                                                     572 non-null    object \n",
      " 5   Unnamed: 5                                                                                                                                     551 non-null    object \n",
      " 6   Unnamed: 6                                                                                                                                     608 non-null    object \n",
      " 7   Unnamed: 7                                                                                                                                     759 non-null    object \n",
      " 8   Unnamed: 8                                                                                                                                     739 non-null    object \n",
      " 9   Please rank the Star Wars films in order of preference with 1 being your favorite film in the franchise and 6 being your least favorite film.  836 non-null    object \n",
      " 10  Unnamed: 10                                                                                                                                    837 non-null    object \n",
      " 11  Unnamed: 11                                                                                                                                    836 non-null    object \n",
      " 12  Unnamed: 12                                                                                                                                    837 non-null    object \n",
      " 13  Unnamed: 13                                                                                                                                    837 non-null    object \n",
      " 14  Unnamed: 14                                                                                                                                    837 non-null    object \n",
      " 15  Please state whether you view the following characters favorably, unfavorably, or are unfamiliar with him/her.                                 830 non-null    object \n",
      " 16  Unnamed: 16                                                                                                                                    832 non-null    object \n",
      " 17  Unnamed: 17                                                                                                                                    832 non-null    object \n",
      " 18  Unnamed: 18                                                                                                                                    824 non-null    object \n",
      " 19  Unnamed: 19                                                                                                                                    826 non-null    object \n",
      " 20  Unnamed: 20                                                                                                                                    815 non-null    object \n",
      " 21  Unnamed: 21                                                                                                                                    827 non-null    object \n",
      " 22  Unnamed: 22                                                                                                                                    821 non-null    object \n",
      " 23  Unnamed: 23                                                                                                                                    813 non-null    object \n",
      " 24  Unnamed: 24                                                                                                                                    828 non-null    object \n",
      " 25  Unnamed: 25                                                                                                                                    831 non-null    object \n",
      " 26  Unnamed: 26                                                                                                                                    822 non-null    object \n",
      " 27  Unnamed: 27                                                                                                                                    815 non-null    object \n",
      " 28  Unnamed: 28                                                                                                                                    827 non-null    object \n",
      " 29  Which character shot first?                                                                                                                    829 non-null    object \n",
      " 30  Are you familiar with the Expanded Universe?                                                                                                   829 non-null    object \n",
      " 31  Do you consider yourself to be a fan of the Expanded Universe?ÂÃ¦                                                                             214 non-null    object \n",
      " 32  Do you consider yourself to be a fan of the Star Trek franchise?                                                                               1069 non-null   object \n",
      " 33  Gender                                                                                                                                         1047 non-null   object \n",
      " 34  Age                                                                                                                                            1047 non-null   object \n",
      " 35  Household Income                                                                                                                               859 non-null    object \n",
      " 36  Education                                                                                                                                      1037 non-null   object \n",
      " 37  Location (Census Region)                                                                                                                       1044 non-null   object \n",
      "dtypes: float64(1), object(37)\n",
      "memory usage: 352.5+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "star_wars = pd.read_csv(\"star_wars.csv\", encoding=\"ISO-8859-1\")\n",
    "print(star_wars.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['RespondentID',\n",
      "       'Have you seen any of the 6 films in the Star Wars franchise?',\n",
      "       'Do you consider yourself to be a fan of the Star Wars film franchise?',\n",
      "       'Which of the following Star Wars films have you seen? Please select all that apply.',\n",
      "       'Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8',\n",
      "       'Please rank the Star Wars films in order of preference with 1 being your favorite film in the franchise and 6 being your least favorite film.',\n",
      "       'Unnamed: 10', 'Unnamed: 11', 'Unnamed: 12', 'Unnamed: 13',\n",
      "       'Unnamed: 14',\n",
      "       'Please state whether you view the following characters favorably, unfavorably, or are unfamiliar with him/her.',\n",
      "       'Unnamed: 16', 'Unnamed: 17', 'Unnamed: 18', 'Unnamed: 19',\n",
      "       'Unnamed: 20', 'Unnamed: 21', 'Unnamed: 22', 'Unnamed: 23',\n",
      "       'Unnamed: 24', 'Unnamed: 25', 'Unnamed: 26', 'Unnamed: 27',\n",
      "       'Unnamed: 28', 'Which character shot first?',\n",
      "       'Are you familiar with the Expanded Universe?',\n",
      "       'Do you consider yourself to be a fan of the Expanded Universe?ÂÃ¦',\n",
      "       'Do you consider yourself to be a fan of the Star Trek franchise?',\n",
      "       'Gender', 'Age', 'Household Income', 'Education',\n",
      "       'Location (Census Region)'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(star_wars.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                    Unnamed: 4  \\\n",
      "0  Star Wars: Episode II  Attack of the Clones   \n",
      "1  Star Wars: Episode II  Attack of the Clones   \n",
      "2                                          NaN   \n",
      "3  Star Wars: Episode II  Attack of the Clones   \n",
      "4  Star Wars: Episode II  Attack of the Clones   \n",
      "5  Star Wars: Episode II  Attack of the Clones   \n",
      "6  Star Wars: Episode II  Attack of the Clones   \n",
      "7  Star Wars: Episode II  Attack of the Clones   \n",
      "8  Star Wars: Episode II  Attack of the Clones   \n",
      "9  Star Wars: Episode II  Attack of the Clones   \n",
      "\n",
      "                                    Unnamed: 5  \\\n",
      "0  Star Wars: Episode III  Revenge of the Sith   \n",
      "1  Star Wars: Episode III  Revenge of the Sith   \n",
      "2                                          NaN   \n",
      "3  Star Wars: Episode III  Revenge of the Sith   \n",
      "4  Star Wars: Episode III  Revenge of the Sith   \n",
      "5  Star Wars: Episode III  Revenge of the Sith   \n",
      "6  Star Wars: Episode III  Revenge of the Sith   \n",
      "7  Star Wars: Episode III  Revenge of the Sith   \n",
      "8  Star Wars: Episode III  Revenge of the Sith   \n",
      "9  Star Wars: Episode III  Revenge of the Sith   \n",
      "\n",
      "                          Unnamed: 6  \\\n",
      "0  Star Wars: Episode IV  A New Hope   \n",
      "1  Star Wars: Episode IV  A New Hope   \n",
      "2                                NaN   \n",
      "3                                NaN   \n",
      "4  Star Wars: Episode IV  A New Hope   \n",
      "5  Star Wars: Episode IV  A New Hope   \n",
      "6  Star Wars: Episode IV  A New Hope   \n",
      "7  Star Wars: Episode IV  A New Hope   \n",
      "8  Star Wars: Episode IV  A New Hope   \n",
      "9  Star Wars: Episode IV  A New Hope   \n",
      "\n",
      "                                     Unnamed: 7  \\\n",
      "0  Star Wars: Episode V The Empire Strikes Back   \n",
      "1  Star Wars: Episode V The Empire Strikes Back   \n",
      "2                                           NaN   \n",
      "3                                           NaN   \n",
      "4  Star Wars: Episode V The Empire Strikes Back   \n",
      "5  Star Wars: Episode V The Empire Strikes Back   \n",
      "6  Star Wars: Episode V The Empire Strikes Back   \n",
      "7  Star Wars: Episode V The Empire Strikes Back   \n",
      "8  Star Wars: Episode V The Empire Strikes Back   \n",
      "9  Star Wars: Episode V The Empire Strikes Back   \n",
      "\n",
      "                                 Unnamed: 8  \n",
      "0  Star Wars: Episode VI Return of the Jedi  \n",
      "1  Star Wars: Episode VI Return of the Jedi  \n",
      "2                                       NaN  \n",
      "3                                       NaN  \n",
      "4  Star Wars: Episode VI Return of the Jedi  \n",
      "5  Star Wars: Episode VI Return of the Jedi  \n",
      "6  Star Wars: Episode VI Return of the Jedi  \n",
      "7  Star Wars: Episode VI Return of the Jedi  \n",
      "8  Star Wars: Episode VI Return of the Jedi  \n",
      "9  Star Wars: Episode VI Return of the Jedi  \n"
     ]
    }
   ],
   "source": [
    "print(star_wars.loc[:,'Unnamed: 4':'Unnamed: 8'].head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                   Unnamed: 10  \\\n",
      "0  Star Wars: Episode II  Attack of the Clones   \n",
      "1                                            2   \n",
      "2                                          NaN   \n",
      "3                                            2   \n",
      "4                                            6   \n",
      "5                                            4   \n",
      "6                                            4   \n",
      "7                                            5   \n",
      "8                                            5   \n",
      "9                                            4   \n",
      "\n",
      "                                   Unnamed: 11  \\\n",
      "0  Star Wars: Episode III  Revenge of the Sith   \n",
      "1                                            1   \n",
      "2                                          NaN   \n",
      "3                                            3   \n",
      "4                                            1   \n",
      "5                                            6   \n",
      "6                                            3   \n",
      "7                                            4   \n",
      "8                                            6   \n",
      "9                                            6   \n",
      "\n",
      "                         Unnamed: 12  \\\n",
      "0  Star Wars: Episode IV  A New Hope   \n",
      "1                                  4   \n",
      "2                                NaN   \n",
      "3                                  4   \n",
      "4                                  2   \n",
      "5                                  2   \n",
      "6                                  6   \n",
      "7                                  3   \n",
      "8                                  3   \n",
      "9                                  2   \n",
      "\n",
      "                                    Unnamed: 13  \\\n",
      "0  Star Wars: Episode V The Empire Strikes Back   \n",
      "1                                             5   \n",
      "2                                           NaN   \n",
      "3                                             5   \n",
      "4                                             4   \n",
      "5                                             1   \n",
      "6                                             5   \n",
      "7                                             1   \n",
      "8                                             2   \n",
      "9                                             1   \n",
      "\n",
      "                                Unnamed: 14  \n",
      "0  Star Wars: Episode VI Return of the Jedi  \n",
      "1                                         6  \n",
      "2                                       NaN  \n",
      "3                                         6  \n",
      "4                                         3  \n",
      "5                                         3  \n",
      "6                                         2  \n",
      "7                                         2  \n",
      "8                                         1  \n",
      "9                                         3  \n"
     ]
    }
   ],
   "source": [
    "print(star_wars.loc[:,'Unnamed: 10':'Unnamed: 14'].head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Unnamed: 16           Unnamed: 17  \\\n",
      "0        Luke Skywalker  Princess Leia Organa   \n",
      "1        Very favorably        Very favorably   \n",
      "2                   NaN                   NaN   \n",
      "3    Somewhat favorably    Somewhat favorably   \n",
      "4        Very favorably        Very favorably   \n",
      "5    Somewhat favorably    Somewhat favorably   \n",
      "6        Very favorably        Very favorably   \n",
      "7        Very favorably    Somewhat favorably   \n",
      "8    Somewhat favorably        Very favorably   \n",
      "9  Somewhat unfavorably    Somewhat favorably   \n",
      "\n",
      "                                   Unnamed: 18         Unnamed: 19  \\\n",
      "0                             Anakin Skywalker      Obi Wan Kenobi   \n",
      "1                               Very favorably      Very favorably   \n",
      "2                                          NaN                 NaN   \n",
      "3                           Somewhat favorably  Somewhat favorably   \n",
      "4                               Very favorably      Very favorably   \n",
      "5                         Somewhat unfavorably      Very favorably   \n",
      "6                               Very favorably      Very favorably   \n",
      "7                           Somewhat favorably      Very favorably   \n",
      "8  Neither favorably nor unfavorably (neutral)      Very favorably   \n",
      "9                           Somewhat favorably  Somewhat favorably   \n",
      "\n",
      "                                   Unnamed: 20           Unnamed: 21  \\\n",
      "0                            Emperor Palpatine           Darth Vader   \n",
      "1                               Very favorably        Very favorably   \n",
      "2                                          NaN                   NaN   \n",
      "3                             Unfamiliar (N/A)      Unfamiliar (N/A)   \n",
      "4                           Somewhat favorably        Very favorably   \n",
      "5                             Very unfavorably    Somewhat favorably   \n",
      "6  Neither favorably nor unfavorably (neutral)        Very favorably   \n",
      "7                               Very favorably        Very favorably   \n",
      "8                             Very unfavorably  Somewhat unfavorably   \n",
      "9                               Very favorably        Very favorably   \n",
      "\n",
      "                                   Unnamed: 22           Unnamed: 23  \\\n",
      "0                             Lando Calrissian             Boba Fett   \n",
      "1                             Unfamiliar (N/A)      Unfamiliar (N/A)   \n",
      "2                                          NaN                   NaN   \n",
      "3                             Unfamiliar (N/A)      Unfamiliar (N/A)   \n",
      "4                           Somewhat favorably  Somewhat unfavorably   \n",
      "5  Neither favorably nor unfavorably (neutral)        Very favorably   \n",
      "6  Neither favorably nor unfavorably (neutral)    Somewhat favorably   \n",
      "7                               Very favorably        Very favorably   \n",
      "8  Neither favorably nor unfavorably (neutral)    Somewhat favorably   \n",
      "9                               Very favorably        Very favorably   \n",
      "\n",
      "                                   Unnamed: 24         Unnamed: 25  \\\n",
      "0                                        C-3P0               R2 D2   \n",
      "1                               Very favorably      Very favorably   \n",
      "2                                          NaN                 NaN   \n",
      "3                             Unfamiliar (N/A)    Unfamiliar (N/A)   \n",
      "4                               Very favorably      Very favorably   \n",
      "5                           Somewhat favorably  Somewhat favorably   \n",
      "6                           Somewhat favorably  Somewhat favorably   \n",
      "7                           Somewhat favorably      Very favorably   \n",
      "8                           Somewhat favorably  Somewhat favorably   \n",
      "9  Neither favorably nor unfavorably (neutral)  Somewhat favorably   \n",
      "\n",
      "            Unnamed: 26                                  Unnamed: 27  \\\n",
      "0         Jar Jar Binks                                Padme Amidala   \n",
      "1        Very favorably                               Very favorably   \n",
      "2                   NaN                                          NaN   \n",
      "3      Unfamiliar (N/A)                             Unfamiliar (N/A)   \n",
      "4        Very favorably                               Very favorably   \n",
      "5      Very unfavorably                           Somewhat favorably   \n",
      "6    Somewhat favorably  Neither favorably nor unfavorably (neutral)   \n",
      "7  Somewhat unfavorably                           Somewhat favorably   \n",
      "8      Very unfavorably                         Somewhat unfavorably   \n",
      "9      Very unfavorably                         Somewhat unfavorably   \n",
      "\n",
      "          Unnamed: 28  \n",
      "0                Yoda  \n",
      "1      Very favorably  \n",
      "2                 NaN  \n",
      "3    Unfamiliar (N/A)  \n",
      "4      Very favorably  \n",
      "5  Somewhat favorably  \n",
      "6      Very favorably  \n",
      "7      Very favorably  \n",
      "8      Very favorably  \n",
      "9  Somewhat favorably  \n"
     ]
    }
   ],
   "source": [
    "print(star_wars.loc[:,'Unnamed: 16':'Unnamed: 28'].head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   RespondentID Have you seen any of the 6 films in the Star Wars franchise?  \\\n",
      "0           NaN                                           Response             \n",
      "1  3.292880e+09                                                Yes             \n",
      "2  3.292880e+09                                                 No             \n",
      "3  3.292765e+09                                                Yes             \n",
      "4  3.292763e+09                                                Yes             \n",
      "5  3.292731e+09                                                Yes             \n",
      "6  3.292719e+09                                                Yes             \n",
      "7  3.292685e+09                                                Yes             \n",
      "8  3.292664e+09                                                Yes             \n",
      "9  3.292654e+09                                                Yes             \n",
      "\n",
      "  Do you consider yourself to be a fan of the Star Wars film franchise?  \\\n",
      "0                                           Response                      \n",
      "1                                                Yes                      \n",
      "2                                                NaN                      \n",
      "3                                                 No                      \n",
      "4                                                Yes                      \n",
      "5                                                Yes                      \n",
      "6                                                Yes                      \n",
      "7                                                Yes                      \n",
      "8                                                Yes                      \n",
      "9                                                Yes                      \n",
      "\n",
      "  Which of the following Star Wars films have you seen? Please select all that apply.  \\\n",
      "0           Star Wars: Episode I  The Phantom Menace                                    \n",
      "1           Star Wars: Episode I  The Phantom Menace                                    \n",
      "2                                                NaN                                    \n",
      "3           Star Wars: Episode I  The Phantom Menace                                    \n",
      "4           Star Wars: Episode I  The Phantom Menace                                    \n",
      "5           Star Wars: Episode I  The Phantom Menace                                    \n",
      "6           Star Wars: Episode I  The Phantom Menace                                    \n",
      "7           Star Wars: Episode I  The Phantom Menace                                    \n",
      "8           Star Wars: Episode I  The Phantom Menace                                    \n",
      "9           Star Wars: Episode I  The Phantom Menace                                    \n",
      "\n",
      "                                    Unnamed: 4  \\\n",
      "0  Star Wars: Episode II  Attack of the Clones   \n",
      "1  Star Wars: Episode II  Attack of the Clones   \n",
      "2                                          NaN   \n",
      "3  Star Wars: Episode II  Attack of the Clones   \n",
      "4  Star Wars: Episode II  Attack of the Clones   \n",
      "5  Star Wars: Episode II  Attack of the Clones   \n",
      "6  Star Wars: Episode II  Attack of the Clones   \n",
      "7  Star Wars: Episode II  Attack of the Clones   \n",
      "8  Star Wars: Episode II  Attack of the Clones   \n",
      "9  Star Wars: Episode II  Attack of the Clones   \n",
      "\n",
      "                                    Unnamed: 5  \\\n",
      "0  Star Wars: Episode III  Revenge of the Sith   \n",
      "1  Star Wars: Episode III  Revenge of the Sith   \n",
      "2                                          NaN   \n",
      "3  Star Wars: Episode III  Revenge of the Sith   \n",
      "4  Star Wars: Episode III  Revenge of the Sith   \n",
      "5  Star Wars: Episode III  Revenge of the Sith   \n",
      "6  Star Wars: Episode III  Revenge of the Sith   \n",
      "7  Star Wars: Episode III  Revenge of the Sith   \n",
      "8  Star Wars: Episode III  Revenge of the Sith   \n",
      "9  Star Wars: Episode III  Revenge of the Sith   \n",
      "\n",
      "                          Unnamed: 6  \\\n",
      "0  Star Wars: Episode IV  A New Hope   \n",
      "1  Star Wars: Episode IV  A New Hope   \n",
      "2                                NaN   \n",
      "3                                NaN   \n",
      "4  Star Wars: Episode IV  A New Hope   \n",
      "5  Star Wars: Episode IV  A New Hope   \n",
      "6  Star Wars: Episode IV  A New Hope   \n",
      "7  Star Wars: Episode IV  A New Hope   \n",
      "8  Star Wars: Episode IV  A New Hope   \n",
      "9  Star Wars: Episode IV  A New Hope   \n",
      "\n",
      "                                     Unnamed: 7  \\\n",
      "0  Star Wars: Episode V The Empire Strikes Back   \n",
      "1  Star Wars: Episode V The Empire Strikes Back   \n",
      "2                                           NaN   \n",
      "3                                           NaN   \n",
      "4  Star Wars: Episode V The Empire Strikes Back   \n",
      "5  Star Wars: Episode V The Empire Strikes Back   \n",
      "6  Star Wars: Episode V The Empire Strikes Back   \n",
      "7  Star Wars: Episode V The Empire Strikes Back   \n",
      "8  Star Wars: Episode V The Empire Strikes Back   \n",
      "9  Star Wars: Episode V The Empire Strikes Back   \n",
      "\n",
      "                                 Unnamed: 8  \\\n",
      "0  Star Wars: Episode VI Return of the Jedi   \n",
      "1  Star Wars: Episode VI Return of the Jedi   \n",
      "2                                       NaN   \n",
      "3                                       NaN   \n",
      "4  Star Wars: Episode VI Return of the Jedi   \n",
      "5  Star Wars: Episode VI Return of the Jedi   \n",
      "6  Star Wars: Episode VI Return of the Jedi   \n",
      "7  Star Wars: Episode VI Return of the Jedi   \n",
      "8  Star Wars: Episode VI Return of the Jedi   \n",
      "9  Star Wars: Episode VI Return of the Jedi   \n",
      "\n",
      "  Please rank the Star Wars films in order of preference with 1 being your favorite film in the franchise and 6 being your least favorite film.  \\\n",
      "0           Star Wars: Episode I  The Phantom Menace                                                                                              \n",
      "1                                                  3                                                                                              \n",
      "2                                                NaN                                                                                              \n",
      "3                                                  1                                                                                              \n",
      "4                                                  5                                                                                              \n",
      "5                                                  5                                                                                              \n",
      "6                                                  1                                                                                              \n",
      "7                                                  6                                                                                              \n",
      "8                                                  4                                                                                              \n",
      "9                                                  5                                                                                              \n",
      "\n",
      "   ...         Unnamed: 28       Which character shot first?  \\\n",
      "0  ...                Yoda                          Response   \n",
      "1  ...      Very favorably  I don't understand this question   \n",
      "2  ...                 NaN                               NaN   \n",
      "3  ...    Unfamiliar (N/A)  I don't understand this question   \n",
      "4  ...      Very favorably  I don't understand this question   \n",
      "5  ...  Somewhat favorably                            Greedo   \n",
      "6  ...      Very favorably                               Han   \n",
      "7  ...      Very favorably                               Han   \n",
      "8  ...      Very favorably                               Han   \n",
      "9  ...  Somewhat favorably                               Han   \n",
      "\n",
      "  Are you familiar with the Expanded Universe?  \\\n",
      "0                                     Response   \n",
      "1                                          Yes   \n",
      "2                                          NaN   \n",
      "3                                           No   \n",
      "4                                           No   \n",
      "5                                          Yes   \n",
      "6                                          Yes   \n",
      "7                                          Yes   \n",
      "8                                           No   \n",
      "9                                           No   \n",
      "\n",
      "  Do you consider yourself to be a fan of the Expanded Universe?ÂÃ¦  \\\n",
      "0                                           Response                   \n",
      "1                                                 No                   \n",
      "2                                                NaN                   \n",
      "3                                                NaN                   \n",
      "4                                                NaN                   \n",
      "5                                                 No                   \n",
      "6                                                 No                   \n",
      "7                                                 No                   \n",
      "8                                                NaN                   \n",
      "9                                                NaN                   \n",
      "\n",
      "  Do you consider yourself to be a fan of the Star Trek franchise?    Gender  \\\n",
      "0                                           Response                Response   \n",
      "1                                                 No                    Male   \n",
      "2                                                Yes                    Male   \n",
      "3                                                 No                    Male   \n",
      "4                                                Yes                    Male   \n",
      "5                                                 No                    Male   \n",
      "6                                                Yes                    Male   \n",
      "7                                                 No                    Male   \n",
      "8                                                Yes                    Male   \n",
      "9                                                 No                    Male   \n",
      "\n",
      "        Age     Household Income                         Education  \\\n",
      "0  Response             Response                          Response   \n",
      "1     18-29                  NaN                High school degree   \n",
      "2     18-29         $0 - $24,999                   Bachelor degree   \n",
      "3     18-29         $0 - $24,999                High school degree   \n",
      "4     18-29  $100,000 - $149,999  Some college or Associate degree   \n",
      "5     18-29  $100,000 - $149,999  Some college or Associate degree   \n",
      "6     18-29    $25,000 - $49,999                   Bachelor degree   \n",
      "7     18-29                  NaN                High school degree   \n",
      "8     18-29                  NaN                High school degree   \n",
      "9     18-29         $0 - $24,999  Some college or Associate degree   \n",
      "\n",
      "  Location (Census Region)  \n",
      "0                 Response  \n",
      "1           South Atlantic  \n",
      "2       West South Central  \n",
      "3       West North Central  \n",
      "4       West North Central  \n",
      "5       West North Central  \n",
      "6          Middle Atlantic  \n",
      "7       East North Central  \n",
      "8           South Atlantic  \n",
      "9           South Atlantic  \n",
      "\n",
      "[10 rows x 38 columns]\n"
     ]
    }
   ],
   "source": [
    "print(star_wars.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1186 entries, 1 to 1186\n",
      "Data columns (total 38 columns):\n",
      " #   Column                                                                                                                                         Non-Null Count  Dtype  \n",
      "---  ------                                                                                                                                         --------------  -----  \n",
      " 0   RespondentID                                                                                                                                   1186 non-null   float64\n",
      " 1   Have you seen any of the 6 films in the Star Wars franchise?                                                                                   1186 non-null   object \n",
      " 2   Do you consider yourself to be a fan of the Star Wars film franchise?                                                                          836 non-null    object \n",
      " 3   Which of the following Star Wars films have you seen? Please select all that apply.                                                            673 non-null    object \n",
      " 4   Unnamed: 4                                                                                                                                     571 non-null    object \n",
      " 5   Unnamed: 5                                                                                                                                     550 non-null    object \n",
      " 6   Unnamed: 6                                                                                                                                     607 non-null    object \n",
      " 7   Unnamed: 7                                                                                                                                     758 non-null    object \n",
      " 8   Unnamed: 8                                                                                                                                     738 non-null    object \n",
      " 9   Please rank the Star Wars films in order of preference with 1 being your favorite film in the franchise and 6 being your least favorite film.  835 non-null    object \n",
      " 10  Unnamed: 10                                                                                                                                    836 non-null    object \n",
      " 11  Unnamed: 11                                                                                                                                    835 non-null    object \n",
      " 12  Unnamed: 12                                                                                                                                    836 non-null    object \n",
      " 13  Unnamed: 13                                                                                                                                    836 non-null    object \n",
      " 14  Unnamed: 14                                                                                                                                    836 non-null    object \n",
      " 15  Please state whether you view the following characters favorably, unfavorably, or are unfamiliar with him/her.                                 829 non-null    object \n",
      " 16  Unnamed: 16                                                                                                                                    831 non-null    object \n",
      " 17  Unnamed: 17                                                                                                                                    831 non-null    object \n",
      " 18  Unnamed: 18                                                                                                                                    823 non-null    object \n",
      " 19  Unnamed: 19                                                                                                                                    825 non-null    object \n",
      " 20  Unnamed: 20                                                                                                                                    814 non-null    object \n",
      " 21  Unnamed: 21                                                                                                                                    826 non-null    object \n",
      " 22  Unnamed: 22                                                                                                                                    820 non-null    object \n",
      " 23  Unnamed: 23                                                                                                                                    812 non-null    object \n",
      " 24  Unnamed: 24                                                                                                                                    827 non-null    object \n",
      " 25  Unnamed: 25                                                                                                                                    830 non-null    object \n",
      " 26  Unnamed: 26                                                                                                                                    821 non-null    object \n",
      " 27  Unnamed: 27                                                                                                                                    814 non-null    object \n",
      " 28  Unnamed: 28                                                                                                                                    826 non-null    object \n",
      " 29  Which character shot first?                                                                                                                    828 non-null    object \n",
      " 30  Are you familiar with the Expanded Universe?                                                                                                   828 non-null    object \n",
      " 31  Do you consider yourself to be a fan of the Expanded Universe?ÂÃ¦                                                                             213 non-null    object \n",
      " 32  Do you consider yourself to be a fan of the Star Trek franchise?                                                                               1068 non-null   object \n",
      " 33  Gender                                                                                                                                         1046 non-null   object \n",
      " 34  Age                                                                                                                                            1046 non-null   object \n",
      " 35  Household Income                                                                                                                               858 non-null    object \n",
      " 36  Education                                                                                                                                      1036 non-null   object \n",
      " 37  Location (Census Region)                                                                                                                       1043 non-null   object \n",
      "dtypes: float64(1), object(37)\n",
      "memory usage: 361.4+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Removing null entries from 'RespondentID' column\n",
    "star_wars=star_wars[star_wars['RespondentID'].notnull()]\n",
    "print(star_wars.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1     True\n",
      "2    False\n",
      "3     True\n",
      "4     True\n",
      "5     True\n",
      "Name: Have you seen any of the 6 films in the Star Wars franchise?, dtype: bool\n",
      "True     552\n",
      "True     350\n",
      "False    284\n",
      "Name: Do you consider yourself to be a fan of the Star Wars film franchise?, dtype: int64\n",
      "1      True\n",
      "2       NaN\n",
      "3     False\n",
      "4      True\n",
      "5      True\n",
      "6      True\n",
      "7      True\n",
      "8      True\n",
      "9      True\n",
      "10    False\n",
      "Name: Do you consider yourself to be a fan of the Star Wars film franchise?, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Converting Yes/N0/NAN columns to boolean values\n",
    "def bool(x):\n",
    "    if x=='Yes':\n",
    "        return True\n",
    "    elif x=='No':\n",
    "        return False\n",
    "    else:\n",
    "        return np.nan\n",
    "star_wars['Have you seen any of the 6 films in the Star Wars franchise?']=star_wars['Have you seen any of the 6 films in the Star Wars franchise?'].apply(bool)    \n",
    "print(star_wars['Have you seen any of the 6 films in the Star Wars franchise?'].head())\n",
    "star_wars['Do you consider yourself to be a fan of the Star Wars film franchise?']=star_wars['Do you consider yourself to be a fan of the Star Wars film franchise?'].apply(bool)    \n",
    "print(star_wars['Do you consider yourself to be a fan of the Star Wars film franchise?'].value_counts(dropna=False))\n",
    "print(star_wars['Do you consider yourself to be a fan of the Star Wars film franchise?'].head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Renaming and Cleaning Checkbox Columns\n",
    "- We will convert columlns with checkbox entries to Boolean values ('True'or 'False') using a simple regex.\n",
    "- Alternately,we can use the `pd.Seies.map()` [method](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.map.html), or `pd.Series.apply()` [method](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.apply.html#pandas.Series.apply). We can pass NAN values as such using the argument `na_action`, but unlike previous columns we will instead convert themn to 'False'as NAN indicates that the respondent did not watch that movie.\n",
    "- We will also change names of columns to a briefer nomenclature using the `pandas.DataFrame.rename()` [method](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rename.html). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['RespondentID',\n",
      "       'Have you seen any of the 6 films in the Star Wars franchise?',\n",
      "       'Do you consider yourself to be a fan of the Star Wars film franchise?',\n",
      "       'seen_1', 'seen_2', 'seen_3', 'seen_4', 'seen_5', 'seen_6',\n",
      "       'Please rank the Star Wars films in order of preference with 1 being your favorite film in the franchise and 6 being your least favorite film.',\n",
      "       'Unnamed: 10', 'Unnamed: 11', 'Unnamed: 12', 'Unnamed: 13',\n",
      "       'Unnamed: 14',\n",
      "       'Please state whether you view the following characters favorably, unfavorably, or are unfamiliar with him/her.',\n",
      "       'Unnamed: 16', 'Unnamed: 17', 'Unnamed: 18', 'Unnamed: 19',\n",
      "       'Unnamed: 20', 'Unnamed: 21', 'Unnamed: 22', 'Unnamed: 23',\n",
      "       'Unnamed: 24', 'Unnamed: 25', 'Unnamed: 26', 'Unnamed: 27',\n",
      "       'Unnamed: 28', 'Which character shot first?',\n",
      "       'Are you familiar with the Expanded Universe?',\n",
      "       'Do you consider yourself to be a fan of the Expanded Universe?ÂÃ¦',\n",
      "       'Do you consider yourself to be a fan of the Star Trek franchise?',\n",
      "       'Gender', 'Age', 'Household Income', 'Education',\n",
      "       'Location (Census Region)'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Rename Columns\n",
    "cols={'Which of the following Star Wars films have you seen? Please select all that apply.':'seen_1','Unnamed: 4':'seen_2','Unnamed: 5':'seen_3','Unnamed: 6':'seen_4','Unnamed: 7':'seen_5','Unnamed: 8':'seen_6'}\n",
    "star_wars=star_wars.rename(columns=cols)\n",
    "print(star_wars.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True     607\n",
      "False    579\n",
      "Name: seen_4, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Cleaning and converting Checkbox Columns to Boolean\n",
    "pattern=r'.*'\n",
    "for col in star_wars.columns[3:9]:\n",
    "    star_wars[col]=star_wars[col].str.contains(pattern,na=False,regex=True)\n",
    "star_wars.set_index('RespondentID',inplace=True)\n",
    "star_wars.reset_index(inplace=True)  \n",
    "print(star_wars['seen_4'].value_counts(dropna=False)) # checking a random column\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cleaning and converting Checkbox Columns to Boolean- Alternate method\n",
    "#  movie_mapping = {\n",
    "#     \"Star Wars: Episode I  The Phantom Menace\": True,\n",
    "#     np.nan: False,\n",
    "#     \"Star Wars: Episode II  Attack of the Clones\": True,\n",
    "#     \"Star Wars: Episode III  Revenge of the Sith\": True,\n",
    "#     \"Star Wars: Episode IV  A New Hope\": True,\n",
    "#     \"Star Wars: Episode V The Empire Strikes Back\": True,\n",
    "#     \"Star Wars: Episode VI Return of the Jedi\": True\n",
    "# }\n",
    "\n",
    "# for col in star_wars.columns[3:9]:\n",
    "#     star_wars[col] = star_wars[col].map(movie_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1186 entries, 0 to 1185\n",
      "Data columns (total 38 columns):\n",
      " #   Column                                                                                                                                         Non-Null Count  Dtype  \n",
      "---  ------                                                                                                                                         --------------  -----  \n",
      " 0   RespondentID                                                                                                                                   1186 non-null   float64\n",
      " 1   Have you seen any of the 6 films in the Star Wars franchise?                                                                                   1186 non-null   bool   \n",
      " 2   Do you consider yourself to be a fan of the Star Wars film franchise?                                                                          836 non-null    object \n",
      " 3   seen_1                                                                                                                                         1186 non-null   bool   \n",
      " 4   seen_2                                                                                                                                         1186 non-null   bool   \n",
      " 5   seen_3                                                                                                                                         1186 non-null   bool   \n",
      " 6   seen_4                                                                                                                                         1186 non-null   bool   \n",
      " 7   seen_5                                                                                                                                         1186 non-null   bool   \n",
      " 8   seen_6                                                                                                                                         1186 non-null   bool   \n",
      " 9   Please rank the Star Wars films in order of preference with 1 being your favorite film in the franchise and 6 being your least favorite film.  835 non-null    object \n",
      " 10  Unnamed: 10                                                                                                                                    836 non-null    object \n",
      " 11  Unnamed: 11                                                                                                                                    835 non-null    object \n",
      " 12  Unnamed: 12                                                                                                                                    836 non-null    object \n",
      " 13  Unnamed: 13                                                                                                                                    836 non-null    object \n",
      " 14  Unnamed: 14                                                                                                                                    836 non-null    object \n",
      " 15  Please state whether you view the following characters favorably, unfavorably, or are unfamiliar with him/her.                                 829 non-null    object \n",
      " 16  Unnamed: 16                                                                                                                                    831 non-null    object \n",
      " 17  Unnamed: 17                                                                                                                                    831 non-null    object \n",
      " 18  Unnamed: 18                                                                                                                                    823 non-null    object \n",
      " 19  Unnamed: 19                                                                                                                                    825 non-null    object \n",
      " 20  Unnamed: 20                                                                                                                                    814 non-null    object \n",
      " 21  Unnamed: 21                                                                                                                                    826 non-null    object \n",
      " 22  Unnamed: 22                                                                                                                                    820 non-null    object \n",
      " 23  Unnamed: 23                                                                                                                                    812 non-null    object \n",
      " 24  Unnamed: 24                                                                                                                                    827 non-null    object \n",
      " 25  Unnamed: 25                                                                                                                                    830 non-null    object \n",
      " 26  Unnamed: 26                                                                                                                                    821 non-null    object \n",
      " 27  Unnamed: 27                                                                                                                                    814 non-null    object \n",
      " 28  Unnamed: 28                                                                                                                                    826 non-null    object \n",
      " 29  Which character shot first?                                                                                                                    828 non-null    object \n",
      " 30  Are you familiar with the Expanded Universe?                                                                                                   828 non-null    object \n",
      " 31  Do you consider yourself to be a fan of the Expanded Universe?ÂÃ¦                                                                             213 non-null    object \n",
      " 32  Do you consider yourself to be a fan of the Star Trek franchise?                                                                               1068 non-null   object \n",
      " 33  Gender                                                                                                                                         1046 non-null   object \n",
      " 34  Age                                                                                                                                            1046 non-null   object \n",
      " 35  Household Income                                                                                                                               858 non-null    object \n",
      " 36  Education                                                                                                                                      1036 non-null   object \n",
      " 37  Location (Census Region)                                                                                                                       1043 non-null   object \n",
      "dtypes: bool(7), float64(1), object(30)\n",
      "memory usage: 295.5+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(star_wars.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning the Ranking Columns-Conversion to dtype `float`\n",
    "- We will rename the ranking columns to simplified form using the `pandas.DataFrame.rename()` [method](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rename.html). and convert the values to float using pandas.DataFrame.astype() [method](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.astype.html#pandas.DataFrame.astype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['RespondentID',\n",
      "       'Have you seen any of the 6 films in the Star Wars franchise?',\n",
      "       'Do you consider yourself to be a fan of the Star Wars film franchise?',\n",
      "       'seen_1', 'seen_2', 'seen_3', 'seen_4', 'seen_5', 'seen_6', 'ranking_1',\n",
      "       'ranking_2', 'ranking_3', 'ranking_4', 'ranking_5', 'ranking_6',\n",
      "       'Please state whether you view the following characters favorably, unfavorably, or are unfamiliar with him/her.',\n",
      "       'Unnamed: 16', 'Unnamed: 17', 'Unnamed: 18', 'Unnamed: 19',\n",
      "       'Unnamed: 20', 'Unnamed: 21', 'Unnamed: 22', 'Unnamed: 23',\n",
      "       'Unnamed: 24', 'Unnamed: 25', 'Unnamed: 26', 'Unnamed: 27',\n",
      "       'Unnamed: 28', 'Which character shot first?',\n",
      "       'Are you familiar with the Expanded Universe?',\n",
      "       'Do you consider yourself to be a fan of the Expanded Universe?ÂÃ¦',\n",
      "       'Do you consider yourself to be a fan of the Star Trek franchise?',\n",
      "       'Gender', 'Age', 'Household Income', 'Education',\n",
      "       'Location (Census Region)'],\n",
      "      dtype='object')\n",
      "ranking_1    object\n",
      "ranking_2    object\n",
      "ranking_3    object\n",
      "ranking_4    object\n",
      "ranking_5    object\n",
      "ranking_6    object\n",
      "dtype: object\n",
      "ranking_1    float64\n",
      "ranking_2    float64\n",
      "ranking_3    float64\n",
      "ranking_4    float64\n",
      "ranking_5    float64\n",
      "ranking_6    float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "cols={'Please rank the Star Wars films in order of preference with 1 being your favorite film in the franchise and 6 being your least favorite film.':'ranking_1','Unnamed: 10':'ranking_2','Unnamed: 11':'ranking_3','Unnamed: 12':'ranking_4','Unnamed: 13':'ranking_5','Unnamed: 14':'ranking_6'}\n",
    "star_wars=star_wars.rename(columns=cols)\n",
    "print(star_wars.columns)\n",
    "print(star_wars[star_wars.columns[9:15]].dtypes) #checking dtypes \n",
    "star_wars[star_wars.columns[9:15]]=star_wars[star_wars.columns[9:15]].astype(float)\n",
    "print(star_wars[star_wars.columns[9:15]].dtypes) #rechecking dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Determining the Highest Ranking Movie\n",
    "- We can find the highest-ranked movie by taking the mean of each of the ranking columns using the `pandas.DataFrame.mean()` [method](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.mean.html).\n",
    "- We will also plot a bar chart of mean values for quick visualization using `matplotlib.pyplot.bar()` [method](https://matplotlib.org/3.2.1/api/_as_gen/matplotlib.pyplot.bar.html)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ranking_1    3.732934\n",
      "ranking_2    4.087321\n",
      "ranking_3    4.341317\n",
      "ranking_4    3.272727\n",
      "ranking_5    2.513158\n",
      "ranking_6    3.047847\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8c45f08>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean=star_wars[star_wars.columns[9:15]].mean()\n",
    "print(mean)\n",
    "plt.bar(range(6),mean) #using matplotlib directly\n",
    "plt.show()\n",
    "mean.plot(kind='bar') #calling matplotlib method from pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Results\n",
    "From above it is clear that the original trilogy (rannking_1 to rannking_3) is more popular than the later episodes. The third episode `Star Wars: Episode III  Revenge of the Sith` had the highest ratings. The fifth episode `Star Wars: Episode V The Empire Strikes Back` had the lowest ratings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding the most Viewed Movie\n",
    "- We sill use the `df.sum()` [method]( to compute the sum of each of the `seen` columns and then make a [bar chart](https://matplotlib.org/examples/api/barchart_demo.html) of each ranking to find and display the most viewed movie. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seen_1    673\n",
      "seen_2    571\n",
      "seen_3    550\n",
      "seen_4    607\n",
      "seen_5    758\n",
      "seen_6    738\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8d4bdc8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAESCAYAAAAG+ZUXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAATOElEQVR4nO3df5Dcd33f8ecrlg2Blsg/zsaV5IgYhZhMY2NUjyjTDrFJaps08jCYMROwcJVRpnV+MGQSFKadtNNOC/2jEHc6TtQ4tZySENcJsYZ4Ao7A6bQTAzI4BuJQHcZYFwnrCEZ20CTg+N0/9qPx5XzyrXS7t97PPR8zN/v9fr6f3X2/kXnd57773d1UFZKkvnzXpAuQJI2e4S5JHTLcJalDhrskdchwl6QOGe6S1KF1ky4A4LzzzqvNmzdPugxJmioPPPDA16tqZqljL4hw37x5MwcOHJh0GZI0VZJ89WTHPC0jSR0y3CWpQ4a7JHXIcJekDhnuktQhw12SOmS4S1KHDHdJ6tAL4k1MkqbL5t1/sKrP9+j73rSqz9cDV+6S1CHDXZI6ZLhLUocMd0nqkOEuSR3yahlJWqSHq4FcuUtShwx3SeqQ4S5JHTLcJalDhrskdWjZcE/yqiQPLvh5Msm7kpyT5N4kB9vt2W1+ktySZDbJQ0kuH38bkqSFlg33qvpSVV1WVZcBrwWOAx8BdgP7q2oLsL/tA1wDbGk/u4Bbx1G4JOnkTvW0zFXAl6vqq8B2YG8b3wtc17a3A3fUwP3A+iQXjqRaSdJQTjXcbwB+u21fUFVHANrt+W18A3BowX3m2pgkaZUMHe5JzgJ+HPhfy01dYqyWeLxdSQ4kOTA/Pz9sGZKkIZzKyv0a4LNV9Xjbf/zE6ZZ2e7SNzwGbFtxvI3B48YNV1Z6q2lpVW2dmZk69cknSSZ1KuL+NZ0/JAOwDdrTtHcDdC8ZvbFfNbAOOnTh9I0laHUN9cFiSlwA/AvzUguH3AXcm2Qk8Blzfxu8BrgVmGVxZc9PIqpUkDWWocK+q48C5i8b+ksHVM4vnFnDzSKqTJJ0W36EqSR0y3CWpQ4a7JHVoqr+JqYdvS5GkcXDlLkkdMtwlqUOGuyR1yHCXpA4Z7pLUIcNdkjpkuEtShwx3SeqQ4S5JHTLcJalDhrskdchwl6QOGe6S1CHDXZI6ZLhLUoeGCvck65PcleTPkzyc5HVJzklyb5KD7fbsNjdJbkkym+ShJJePtwVJ0mLDrtx/BfjDqvoB4FLgYWA3sL+qtgD72z7ANcCW9rMLuHWkFUuSlrXsNzEleRnwT4F3AlTVt4FvJ9kOvKFN2wvcB7wH2A7cUVUF3N9W/RdW1ZGRVy+9QPktYZq0YVbu3wfMA/8jyeeS/HqSlwIXnAjsdnt+m78BOLTg/nNtTJK0SoYJ93XA5cCtVfUa4Fs8ewpmKVlirJ4zKdmV5ECSA/Pz80MVK0kazjDhPgfMVdWn2v5dDML+8SQXArTbowvmb1pw/43A4cUPWlV7qmprVW2dmZk53folSUtYNtyr6mvAoSSvakNXAX8G7AN2tLEdwN1tex9wY7tqZhtwzPPtkrS6ln1BtfkZ4ENJzgIeAW5i8IvhziQ7gceA69vce4BrgVngeJsrSVpFQ4V7VT0IbF3i0FVLzC3g5hXWJUlaAd+hKkkdMtwlqUOGuyR1aNgXVDUBvstR0uly5S5JHTLcJalDhrskdchwl6QO+YKqJsYXjKXxceUuSR0y3CWpQ4a7JHXIcJekDhnuktQhw12SOmS4S1KHDHdJ6pDhLkkdGirckzya5PNJHkxyoI2dk+TeJAfb7dltPEluSTKb5KEkl4+zAUnSc53Kyv2Hq+qyqjrxXaq7gf1VtQXY3/YBrgG2tJ9dwK2jKlaSNJyVnJbZDuxt23uB6xaM31ED9wPrk1y4gueRJJ2iYcO9gI8neSDJrjZ2QVUdAWi357fxDcChBfeda2OSpFUy7KdCvr6qDic5H7g3yZ8/z9wsMVbPmTT4JbEL4KKLLhqyDEnSMIZauVfV4XZ7FPgIcAXw+InTLe32aJs+B2xacPeNwOElHnNPVW2tqq0zMzOn34Ek6TmWDfckL03y909sAz8KfAHYB+xo03YAd7ftfcCN7aqZbcCxE6dvJEmrY5jTMhcAH0lyYv5vVdUfJvkMcGeSncBjwPVt/j3AtcAscBy4aeRVS5Ke17LhXlWPAJcuMf6XwFVLjBdw80iqkySdFt+hKkkdMtwlqUOGuyR1yHCXpA4Z7pLUIcNdkjpkuEtShwx3SeqQ4S5JHTLcJalDhrskdchwl6QOGe6S1CHDXZI6ZLhLUocMd0nqkOEuSR0y3CWpQ0OHe5IzknwuyUfb/iuSfCrJwSS/k+SsNv6itj/bjm8eT+mSpJM5lZX7zwEPL9h/P/CBqtoCPAHsbOM7gSeq6pXAB9o8SdIqGirck2wE3gT8etsPcCVwV5uyF7iubW9v+7TjV7X5kqRVMuzK/YPALwLPtP1zgW9W1dNtfw7Y0LY3AIcA2vFjbb4kaZUsG+5Jfgw4WlUPLBxeYmoNcWzh4+5KciDJgfn5+aGKlSQNZ5iV++uBH0/yKPBhBqdjPgisT7KuzdkIHG7bc8AmgHb8e4BvLH7QqtpTVVurauvMzMyKmpAk/V3LhntV/VJVbayqzcANwCeq6ieATwJvadN2AHe37X1tn3b8E1X1nJW7JGl8VnKd+3uAdyeZZXBO/bY2fhtwbht/N7B7ZSVKkk7VuuWnPKuq7gPua9uPAFcsMeevgetHUJsk6TT5DlVJ6pDhLkkdMtwlqUOGuyR1yHCXpA4Z7pLUIcNdkjpkuEtShwx3SeqQ4S5JHTLcJalDhrskdchwl6QOGe6S1CHDXZI6ZLhLUocMd0nqkOEuSR0y3CWpQ8uGe5IXJ/l0kj9N8sUk/66NvyLJp5IcTPI7Sc5q4y9q+7Pt+ObxtiBJWmyYlfvfAFdW1aXAZcDVSbYB7wc+UFVbgCeAnW3+TuCJqnol8IE2T5K0ipYN9xr4q7Z7Zvsp4Ergrja+F7iubW9v+7TjVyXJyCqWJC1rqHPuSc5I8iBwFLgX+DLwzap6uk2ZAza07Q3AIYB2/Bhw7iiLliQ9v6HCvar+tqouAzYCVwCXLDWt3S61Sq/FA0l2JTmQ5MD8/Pyw9UqShnBKV8tU1TeB+4BtwPok69qhjcDhtj0HbAJox78H+MYSj7WnqrZW1daZmZnTq16StKRhrpaZSbK+bX838EbgYeCTwFvatB3A3W17X9unHf9EVT1n5S5JGp91y0/hQmBvkjMY/DK4s6o+muTPgA8n+Q/A54Db2vzbgN9MMstgxX7DGOqWJD2PZcO9qh4CXrPE+CMMzr8vHv9r4PqRVCdJOi2+Q1WSOmS4S1KHDHdJ6pDhLkkdMtwlqUOGuyR1yHCXpA4Z7pLUIcNdkjpkuEtShwx3SeqQ4S5JHTLcJalDhrskdchwl6QOGe6S1CHDXZI6ZLhLUoeG+YLsTUk+meThJF9M8nNt/Jwk9yY52G7PbuNJckuS2SQPJbl83E1Ikv6uYVbuTwM/X1WXANuAm5O8GtgN7K+qLcD+tg9wDbCl/ewCbh151ZKk57VsuFfVkar6bNt+CngY2ABsB/a2aXuB69r2duCOGrgfWJ/kwpFXLkk6qVM6555kM/Aa4FPABVV1BAa/AIDz27QNwKEFd5trY5KkVTJ0uCf5e8DvAu+qqiefb+oSY7XE4+1KciDJgfn5+WHLkCQNYahwT3Img2D/UFX9Xht+/MTplnZ7tI3PAZsW3H0jcHjxY1bVnqraWlVbZ2ZmTrd+SdIShrlaJsBtwMNV9V8WHNoH7GjbO4C7F4zf2K6a2QYcO3H6RpK0OtYNMef1wDuAzyd5sI29F3gfcGeSncBjwPXt2D3AtcAscBy4aaQVS5KWtWy4V9X/Yenz6ABXLTG/gJtXWJckaQV8h6okdchwl6QOGe6S1CHDXZI6ZLhLUocMd0nqkOEuSR0y3CWpQ4a7JHXIcJekDhnuktQhw12SOmS4S1KHDHdJ6pDhLkkdMtwlqUOGuyR1yHCXpA4N8wXZv5HkaJIvLBg7J8m9SQ6227PbeJLckmQ2yUNJLh9n8ZKkpQ2zcr8duHrR2G5gf1VtAfa3fYBrgC3tZxdw62jKlCSdimXDvar+N/CNRcPbgb1tey9w3YLxO2rgfmB9kgtHVawkaTine879gqo6AtBuz2/jG4BDC+bNtTFJ0ioa9QuqWWKslpyY7EpyIMmB+fn5EZchSWvb6Yb74ydOt7Tbo218Dti0YN5G4PBSD1BVe6pqa1VtnZmZOc0yJElLOd1w3wfsaNs7gLsXjN/YrprZBhw7cfpGkrR61i03IclvA28AzksyB/wy8D7gziQ7gceA69v0e4BrgVngOHDTGGqWJC1j2XCvqred5NBVS8wt4OaVFiVJWhnfoSpJHTLcJalDhrskdchwl6QOGe6S1CHDXZI6ZLhLUocMd0nqkOEuSR0y3CWpQ4a7JHXIcJekDhnuktQhw12SOmS4S1KHDHdJ6pDhLkkdMtwlqUOGuyR1aCzhnuTqJF9KMptk9zieQ5J0ciMP9yRnAP8NuAZ4NfC2JK8e9fNIkk5uHCv3K4DZqnqkqr4NfBjYPobnkSSdxDjCfQNwaMH+XBuTJK2SVNVoHzC5HvhnVfWTbf8dwBVV9TOL5u0CdrXdVwFfGmkhz+884Our+Hyrzf6mV8+9gf2N2vdW1cxSB9aN4cnmgE0L9jcChxdPqqo9wJ4xPP+ykhyoqq2TeO7VYH/Tq+fewP5W0zhOy3wG2JLkFUnOAm4A9o3heSRJJzHylXtVPZ3kp4GPAWcAv1FVXxz180iSTm4cp2WoqnuAe8bx2CMykdNBq8j+plfPvYH9rZqRv6AqSZo8P35AkjpkuEtShwx3SerQmg73JD8y6RpGIcnLkly8xPgPTaKeUUvy8iQvb9szSd6c5AcnXdc4JPmPk65hXNrl0W9O8gOTrmUUklyU5MVtO0luSvJfk/zLJGO5WOWU6lvLL6gmeayqLpp0HSuR5K3AB4GjwJnAO6vqM+3YZ6vq8knWt1JJfgrYDQR4P/BO4IvA64H/XFW3Ta66lUlyy+Ih4B3AHQBV9bOrXtQIJfn9qrqubW9n8N/pfcA/Bv5TVd0+uepWLskXGLz7/niS9wMXA78PXAlQVf9ikvVN/LfLuCU52RuoApy7mrWMyXuB11bVkSRXAL+Z5L1V9XsMepx2Pw38IPDdwFeBV1bV15KcDXwSmNpwB97MIOw+zrP/VjcAD0yqoBH73gXb7wGurKqvJDkP2A/cPpGqRue7qup4234j8I+q6hngfyb50wnWBayBcAf+CfB24K8WjYfBJ1hOuzOq6ghAVX06yQ8DH02yEejhz7LvtP8DHU/y5ar6GkBVPZFk2vu7BPj3wNXAL1TVXyT55araO+G6RmXhv8+6qvoKQFV9PckzE6pplA4lubKqPgE8yuBjV76a5AWxaFwL4X4/cLyq/njxgSSr+WFl4/JUkour6ssAbQX/BgZ/HvZwXvqZJGdW1XeAN50YbOc6p/o1o6p6CnhXktcyWO39AVPe0yKXJnmSwULqRUle3v7qOovBu9en3U8CdyT5t8Ax4MEknwPOBt49ycJgjZ9z70GSS4FvVdXsovEzgbdW1YcmU9loJLkIOFxVTy8a3wBcUlV/NJnKRitJgH8FvK6q3j7pesYpyXoG/3Z/MulaRiHJJcD3M1gszwGfaadnJspwb5L8SVW9btJ1jIv9Ta+eewP7G5ee/gRcqRdPuoAxs7/p1XNvYH9jYbg/q/c/YexvevXcG9jfWBjuktQhw/1ZPVwT/nzsb3r13BvY31gY7s96x6QLGDP7m1499wb2NxZrJtzbZ1ocTHIsyZNJnmrX4AJQVV+YZH0rZX/T21/PvYH9Taq/NXMpZJJZ4J9X1cOTrmUc7G969dwb2N+krJmVO/D4C+1//BGzv+nVc29gfxOxllbuvwK8nMHb8v/mxHj7gK2pZ3/Tq+fewP4mZS18tswJLwOOAz+6YKyALv4Dw/6mWc+9gf1NxJpZuUvSWrJmzrkn+f4k+9sH7JPkh5L860nXNSr2N7167g3sb1LWTLgD/x34JeA7AFX1EIMvRuiF/U2vnnsD+5uItRTuL6mqTy8ae3rJmdPJ/qZXz72B/U3EWgr3r2fwJdIFkOQtwJHJljRS9je9eu4N7G8i1swLqkm+D9jD4Mt5nwC+Ary9qh6dZF2jYn/Tq+fewP4mVtdaCfcTkryUwRfbPjXpWsbB/qZXz72B/a22NXNaJskFSW4D7qqqp5K8OsnOSdc1KvY3vXruDexvUtZMuAO3Ax8D/kHb/3/AuyZWzejdjv1Nq9vptzewv4lYS+F+XlXdCTwD0L5w+W8nW9JI2d/06rk3sL+JWEvh/q0k5/LsK9rbgGOTLWmk7G969dwb2N9ErKXPlnk3sA+4OMn/BWaAt0y2pJGyv+nVc29gfxOxllbuFwPXMLhc6WPAQfr65WZ/06vn3sD+JmIthfu/qaongbOBNzK4LvXWyZY0UvY3vXruDexvItZSuJ94geNNwK9W1d3AWROsZ9Tsb3r13BvY30SspXD/iyS/BrwVuCfJi+irf/ubXj33BvY3EWvmHapJXgJcDXy+qg4muRD4h1X18QmXNhL2N7167g3sb2J1rZVwl6S1ZOJ/OkiSRs9wl6QOGe6S1CHDXZI6ZLhLUof+P2to+R6SLAScAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum=star_wars[star_wars.columns[3:9]].sum()\n",
    "print(sum)\n",
    "plt.bar(range(6),sum) #using matplotlib directly\n",
    "plt.show()\n",
    "sum.plot(kind='bar') #calling matplotlib method from pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Later episodes were viewed more than the original trilogy. The fifth episode `Star Wars: Episode V The Empire Strikes Back` was the most viewed movie though it had the lowest ratings. The third episode `Star Wars: Episode III  Revenge of the Sith` was the least viewed movie though it had the highest ratings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring the Data by Binary Segments\n",
    "- We will try to establish trends by examining demographic factors such as gender by splitting the columns into two groups and determining highest ranked and most viewed movies separately. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ranking_1    4.037825\n",
      "ranking_2    4.224586\n",
      "ranking_3    4.274882\n",
      "ranking_4    2.997636\n",
      "ranking_5    2.458629\n",
      "ranking_6    3.002364\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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1HAH+KXA7QFVduOVFdSjJl6rq7OHyrzD4t/pp4OeBz1bVB2dZ30YkeRB41fB07j3AswzvXzXc/gszra/FQE9y31q7gJdV1UlbWU/XktwPvKGqnhneqvgW4GNV9ZtJvlJVr55pgRs07O8s4CTgMWBHVT2d5PnA3VX1ypkWuEFJ7gEeAv4Lg1tkBPg4w+s3qup/z666jVv9bzDJl4Hzq2olyd8D7qqqfzzbCo9dkgNV9Y+Gy39n8pRkf1WdNbvqpruXSx/9BPDPgCdHtgf4060vp3PbquoZgKp6OMm5wC1JzmD8fXX65nBVfQ94NsmfV9XTAFX17STfn3FtXVgCLgf+A/Bvq2p/km/3PchX+ZEkP87gkG6qagWgqv4myeHZlrZhDyS5tKr+K3BvkqWqWk7yMuBvZ11cq4H+OeAFVbV/dEeSO7a+nM49luSsI/0NZ+pvAW4Aejv7WeW7SX6sqp4FXntk4/AYZe8Dvaq+D3wkyX8f/vevaOv/xZOBfQwmF5XkJVX12PD3PX2fcLwL+M0k/5HBHRb/b5JHgEeG+2aqyUMu00ry41U1Ooufe0l2MJjFPjZm3zlV9X+Gy33t76Sqem7M9u3AqVV1/3C9l/2NSnIBcE5VvW9kexP9HZHkx4CfqKq/HK73tr8kLwR+isE34kNV9Vcj+2fS2/Ee6L3/BeLR2F+/2V9/zaq34+60xRF9//FvEvvrN/vrr5n0drwHeus/nthfv9lff82kt+M90CWpGcd7oLf8Ix/YX9/ZX3/NpLemfyma5O+P2fytqvrbI/urqrePyrM/+5tnLfc3r721HugPM3jW6ZMMvmOeAjwKPA78SlXtm111G2d/9jfPWu5vXntr/ZDL/2Rw2fH2qnoxg/uDfBL418DvzLSybthfv9lff81lb63P0Jeramnctnm478JG2Z/9zbOW+5vX3lq63HicJ5L8e+ATw/V/ATyZZBsNXEKO/fWd/fXXXPbW+gx9O3A18LMMjnPdCXwAeArYWVUHZ1jehtmf/c2zlvub196aDnRJOp40fchleEvLfwMssqrXqjpvVjV1yf76zf76a157a3qGnuRe4DoGt/L83pHtfT5dajX76zf766957a31QN9XVa+dPLKf7K/f7K+/5rW31gP9/QxO9P808IP7a/f16rRR9tdv9tdf89pb64H+l2M2V1X91JYXswnsr9/sr7/mtbemA12SjidNnuWS5Lyquj3JL4zbX1Wf2uqaumR/9jfPWu5v3ntrMtCBfwLcDvzzMfsK6O0/qCH76zf766+57q3pQy7jHjbc51t2jrK/frO//prX3lq/2+Knkvzgp5AkLwFum2E9XbO/frO//prL3loP9M8AtyTZlmQR+F/AlTOtqFv212/2119z2VvTh1wAkrwb2MXgEt1/VVV/OtuKumV//WZ//TWPvTUZ6Eneu3oVeBtwP/AVgKr68Czq6or92d88a7m/ee+t1bNcXjiy/uk1tveV/fWb/fXXXPfW5Axdko5Hrc7Qgfm9xWVX7K/f7K+/5rW3pmfo83qLy67YX7/ZX3/Na2+tB/pc3uKyK/bXb/bXX/PaW+uB/n7m8BaXXbG/frO//prX3loP9Lm8xWVX7K/f7K+/5rW3pgNdko4nTZ/lApDkFcDLgecd2VZVN82uom7ZX7/ZX3/NY29Nz9CTXA2cy+AP/VbgzcCdVXXxLOvqiv31m/3117z21vrNuS4G3gg8VlWXAq8CTpptSZ2yv36zv/6ay95aD/TvVNX3gcNJXsTgt9K9/4XMKvbXb/bXX3PZW7PH0JMEuC/JKcBHGVwA8AzwpZkW1hH76zf766957q31Y+g/OPl/eM/iF1XVfTMtqkP212/211/z2lvrh1zuSvIzAFX18Dz8gXfM/vrN/vprLntrfYb+EPAy4GvA3zC4f3FV1StnWlhH7K/f7K+/5rW31gP9jHHbq+prW13LZrC/frO//prX3poOdEk6nrR+DF2SjhsGuiQ1wkCXpEYY6JLUCANdkhrx/wFuZXceWSUB3wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ranking_1    3.429293\n",
      "ranking_2    3.954660\n",
      "ranking_3    4.418136\n",
      "ranking_4    3.544081\n",
      "ranking_5    2.569270\n",
      "ranking_6    3.078086\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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2H+QC9Jyv52wS+VpXLl/LK+p7JJ0m6T5J39Tk+rFJ8rpRBxtIz/l6ziaRr3UV87Vc1KfO2p7kvmXPsgg95+s5m0S+1lXM12xRA8Bu0fIxagDYFShqACiOogaA4ihqACiOogaA4v4P1CE9DTDIcKAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Highest Ranking Movie for males and females\n",
    "male=star_wars[star_wars['Gender']=='Male']\n",
    "female=star_wars[star_wars['Gender']=='Female']\n",
    "mean_males=male[male.columns[9:15]].mean()\n",
    "print(mean_males)\n",
    "mean_males.plot(kind='bar') #calling matplotlib method from pandas\n",
    "plt.show()\n",
    "mean_females=female[female.columns[9:15]].mean()\n",
    "print(mean_females)\n",
    "mean_females.plot(kind='bar') #calling matplotlib method from pandas\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like the consolidated results above,  it is clear that the original trilogy (rannking_1 to rannking_3) is more popular than the later episodes irrespective of the genders. The third episode `Star Wars: Episode III  Revenge of the Sith` had the highest ratings. The fifth episode `Star Wars: Episode V The Empire Strikes Back` had the lowest ratings uniformly across genders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seen_1    361\n",
      "seen_2    323\n",
      "seen_3    317\n",
      "seen_4    342\n",
      "seen_5    392\n",
      "seen_6    387\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seen_1    298\n",
      "seen_2    237\n",
      "seen_3    222\n",
      "seen_4    255\n",
      "seen_5    353\n",
      "seen_6    338\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Most Viewed Movie for males and females\n",
    "sum_males=male[male.columns[3:9]].sum()\n",
    "print(sum_males)\n",
    "sum_males.plot(kind='bar') #calling matplotlib method from pandas\n",
    "plt.show()\n",
    "sum_females=female[female.columns[3:9]].sum()\n",
    "print(sum_females)\n",
    "sum_females.plot(kind='bar') #calling matplotlib method from pandas\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Later episodes were on the whole viewed more than the original trilogy irrespective of the gender. The fifth episode `Star Wars: Episode V The Empire Strikes Back` was the most viewed movie though it had the lowest ratings both among males and females. The third episode `Star Wars: Episode III  Revenge of the Sith` was the least viewed movie though it had the highest ratings both among males and females."
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}