Exploring Star Wars Survey Data

Guided Project: Star Wars Survey

For this guided project from dataquest we will be working with the Star Wars survey data. The team at FiveThirtyEight surveyed Star Wars fans using the online tool Survey Monkey. They received 835 responses, which we can download from their Github repository.

1. Overview

The following code will read the data into a pandas dataframe

In [1]:
import pandas as pd
star_wars = pd.read_csv("star_wars.csv", encoding = "ISO-8859-1")

We need to specify an encoding because the data set has some characters that aren't in Python's default utf-8 encoding. For more information about encoding, check out Joel Spolsky's blog.

Let us check out the first few rows of the data

In [2]:
star_wars.head(10)
Out[2]:
RespondentID Have you seen any of the 6 films in the Star Wars franchise? Do you consider yourself to be a fan of the Star Wars film franchise? Which of the following Star Wars films have you seen? Please select all that apply. Unnamed: 4 Unnamed: 5 Unnamed: 6 Unnamed: 7 Unnamed: 8 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. ... Unnamed: 28 Which character shot first? Are you familiar with the Expanded Universe? Do you consider yourself to be a fan of the Expanded Universe?ξ Do you consider yourself to be a fan of the Star Trek franchise? Gender Age Household Income Education Location (Census Region)
0 NaN Response Response Star Wars: Episode I The Phantom Menace Star Wars: Episode II Attack of the Clones Star Wars: Episode III Revenge of the Sith Star Wars: Episode IV A New Hope Star Wars: Episode V The Empire Strikes Back Star Wars: Episode VI Return of the Jedi Star Wars: Episode I The Phantom Menace ... Yoda Response Response Response Response Response Response Response Response Response
1 3.292880e+09 Yes Yes Star Wars: Episode I The Phantom Menace Star Wars: Episode II Attack of the Clones Star Wars: Episode III Revenge of the Sith Star Wars: Episode IV A New Hope Star Wars: Episode V The Empire Strikes Back Star Wars: Episode VI Return of the Jedi 3 ... Very favorably I don't understand this question Yes No No Male 18-29 NaN High school degree South Atlantic
2 3.292880e+09 No NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN Yes Male 18-29 $0 - $24,999 Bachelor degree West South Central
3 3.292765e+09 Yes No Star Wars: Episode I The Phantom Menace Star Wars: Episode II Attack of the Clones Star Wars: Episode III Revenge of the Sith NaN NaN NaN 1 ... Unfamiliar (N/A) I don't understand this question No NaN No Male 18-29 $0 - $24,999 High school degree West North Central
4 3.292763e+09 Yes Yes Star Wars: Episode I The Phantom Menace Star Wars: Episode II Attack of the Clones Star Wars: Episode III Revenge of the Sith Star Wars: Episode IV A New Hope Star Wars: Episode V The Empire Strikes Back Star Wars: Episode VI Return of the Jedi 5 ... Very favorably I don't understand this question No NaN Yes Male 18-29 $100,000 - $149,999 Some college or Associate degree West North Central
5 3.292731e+09 Yes Yes Star Wars: Episode I The Phantom Menace Star Wars: Episode II Attack of the Clones Star Wars: Episode III Revenge of the Sith Star Wars: Episode IV A New Hope Star Wars: Episode V The Empire Strikes Back Star Wars: Episode VI Return of the Jedi 5 ... Somewhat favorably Greedo Yes No No Male 18-29 $100,000 - $149,999 Some college or Associate degree West North Central
6 3.292719e+09 Yes Yes Star Wars: Episode I The Phantom Menace Star Wars: Episode II Attack of the Clones Star Wars: Episode III Revenge of the Sith Star Wars: Episode IV A New Hope Star Wars: Episode V The Empire Strikes Back Star Wars: Episode VI Return of the Jedi 1 ... Very favorably Han Yes No Yes Male 18-29 $25,000 - $49,999 Bachelor degree Middle Atlantic
7 3.292685e+09 Yes Yes Star Wars: Episode I The Phantom Menace Star Wars: Episode II Attack of the Clones Star Wars: Episode III Revenge of the Sith Star Wars: Episode IV A New Hope Star Wars: Episode V The Empire Strikes Back Star Wars: Episode VI Return of the Jedi 6 ... Very favorably Han Yes No No Male 18-29 NaN High school degree East North Central
8 3.292664e+09 Yes Yes Star Wars: Episode I The Phantom Menace Star Wars: Episode II Attack of the Clones Star Wars: Episode III Revenge of the Sith Star Wars: Episode IV A New Hope Star Wars: Episode V The Empire Strikes Back Star Wars: Episode VI Return of the Jedi 4 ... Very favorably Han No NaN Yes Male 18-29 NaN High school degree South Atlantic
9 3.292654e+09 Yes Yes Star Wars: Episode I The Phantom Menace Star Wars: Episode II Attack of the Clones Star Wars: Episode III Revenge of the Sith Star Wars: Episode IV A New Hope Star Wars: Episode V The Empire Strikes Back Star Wars: Episode VI Return of the Jedi 5 ... Somewhat favorably Han No NaN No Male 18-29 $0 - $24,999 Some college or Associate degree South Atlantic

10 rows × 38 columns

The data has several columns, including

  • RespondentID - An anonymized ID for the respondent (person taking the survey)
  • Gender - The respondent's gender
  • Age -The respondent's age
  • Household Income -The respondent's income
  • Education- The respondent's education level
  • Location (Census Region) - The respondent's location
  • Have you seen any of the 6 films in the Star Wars franchise? - Has a Yes or No response.
  • Do you consider yourself to be a fan of the Star Wars film franchise? Has a Yes or No response.

Remove the rows where RespondentID is NaN

In [3]:
star_wars = star_wars[pd.notnull(star_wars['RespondentID'])]

Check the number of rows and columns

In [4]:
star_wars.shape
Out[4]:
(1186, 38)

Let us look at the list of columns

In [5]:
star_wars.columns
Out[5]:
Index(['RespondentID',
       'Have you seen any of the 6 films in the Star Wars franchise?',
       'Do you consider yourself to be a fan of the Star Wars film franchise?',
       'Which of the following Star Wars films have you seen? Please select all that apply.',
       'Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8',
       '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.',
       'Unnamed: 10', 'Unnamed: 11', 'Unnamed: 12', 'Unnamed: 13',
       'Unnamed: 14',
       'Please state whether you view the following characters favorably, unfavorably, or are unfamiliar with him/her.',
       'Unnamed: 16', 'Unnamed: 17', 'Unnamed: 18', 'Unnamed: 19',
       'Unnamed: 20', 'Unnamed: 21', 'Unnamed: 22', 'Unnamed: 23',
       'Unnamed: 24', 'Unnamed: 25', 'Unnamed: 26', 'Unnamed: 27',
       'Unnamed: 28', 'Which character shot first?',
       'Are you familiar with the Expanded Universe?',
       'Do you consider yourself to be a fan of the Expanded Universe?ξ',
       'Do you consider yourself to be a fan of the Star Trek franchise?',
       'Gender', 'Age', 'Household Income', 'Education',
       'Location (Census Region)'],
      dtype='object')

2. Cleaning and Mapping Yes/No Columns

Convert Have you seen any of the 6 films in the Star Wars franchise to Boolean type

In [6]:
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?'].map({'Yes':True,'No':False})
In [7]:
#checking the column
star_wars['Have you seen any of the 6 films in the Star Wars franchise?'].value_counts(dropna = False)
Out[7]:
True     936
False    250
Name: Have you seen any of the 6 films in the Star Wars franchise?, dtype: int64

Convert Do you consider yourself to be a fan of the Star Wars franchise? column to the Boolean type

In [8]:
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?'].map({'Yes':True,'No':False})
In [9]:
star_wars['Do you consider yourself to be a fan of the Star Wars film franchise?'].value_counts(dropna = False)
Out[9]:
True     552
NaN      350
False    284
Name: Do you consider yourself to be a fan of the Star Wars film franchise?, dtype: int64

3. Cleaning and Mapping Checkbox Columns

The next six columns represent a single checkbox question. The respondent checked off a series of boxes in response to the question Which of the following Star Wars films have you seen? Please select all that apply.

The columns for this question are

  • Which of the following Star Wars films have you seen? Please select all that apply. -Whether or not the respondent saw Star Wars: Episode I the Phantom Menace.
  • Unamed: 4 - Whether or not the respondent saw Star Wars: Episode II Attack of the Clones.
  • Unamed: 5 -Whether or not the respondent saw Star Wars: Episode III Revenge of the Sith
  • Unamed: 6 -Whether or not the respondent saw Star Wars: Episode IV A New Hope
  • Unamed: 7 -Whether or not the respondent saw Star Wars: Episode V The Empire Strikes Back
  • Unamed: 8 -Whether or not the respondent saw Star Wars: Episode VI Return of the Jedi

For each of these columns, if the value in a cell is the name of the movie, that means the respondent saw the movie. If the value is NaN, the respondent either didn't answer or didn't see the movie. We'll assume that they didn't see the movie.

We'll need to convert each of these columns to a Boolean, then rename the column to somthing more intuitive.

Convert each column above so that it only contains the values True and False

Be very careful with spacing when constructing your mapping dictionary.

In [10]:
#look at the names of the movies
star_wars.iloc[0,:]
Out[10]:
RespondentID                                                                                                                                                                      3.29288e+09
Have you seen any of the 6 films in the Star Wars franchise?                                                                                                                             True
Do you consider yourself to be a fan of the Star Wars film franchise?                                                                                                                    True
Which of the following Star Wars films have you seen? Please select all that apply.                                                                  Star Wars: Episode I  The Phantom Menace
Unnamed: 4                                                                                                                                        Star Wars: Episode II  Attack of the Clones
Unnamed: 5                                                                                                                                        Star Wars: Episode III  Revenge of the Sith
Unnamed: 6                                                                                                                                                  Star Wars: Episode IV  A New Hope
Unnamed: 7                                                                                                                                       Star Wars: Episode V The Empire Strikes Back
Unnamed: 8                                                                                                                                           Star Wars: Episode VI Return of the Jedi
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.                                               3
Unnamed: 10                                                                                                                                                                                 2
Unnamed: 11                                                                                                                                                                                 1
Unnamed: 12                                                                                                                                                                                 4
Unnamed: 13                                                                                                                                                                                 5
Unnamed: 14                                                                                                                                                                                 6
Please state whether you view the following characters favorably, unfavorably, or are unfamiliar with him/her.                                                                 Very favorably
Unnamed: 16                                                                                                                                                                    Very favorably
Unnamed: 17                                                                                                                                                                    Very favorably
Unnamed: 18                                                                                                                                                                    Very favorably
Unnamed: 19                                                                                                                                                                    Very favorably
Unnamed: 20                                                                                                                                                                    Very favorably
Unnamed: 21                                                                                                                                                                    Very favorably
Unnamed: 22                                                                                                                                                                  Unfamiliar (N/A)
Unnamed: 23                                                                                                                                                                  Unfamiliar (N/A)
Unnamed: 24                                                                                                                                                                    Very favorably
Unnamed: 25                                                                                                                                                                    Very favorably
Unnamed: 26                                                                                                                                                                    Very favorably
Unnamed: 27                                                                                                                                                                    Very favorably
Unnamed: 28                                                                                                                                                                    Very favorably
Which character shot first?                                                                                                                                  I don't understand this question
Are you familiar with the Expanded Universe?                                                                                                                                              Yes
Do you consider yourself to be a fan of the Expanded Universe?ξ                                                                                                                         No
Do you consider yourself to be a fan of the Star Trek franchise?                                                                                                                           No
Gender                                                                                                                                                                                   Male
Age                                                                                                                                                                                     18-29
Household Income                                                                                                                                                                          NaN
Education                                                                                                                                                                  High school degree
Location (Census Region)                                                                                                                                                       South Atlantic
Name: 1, dtype: object
In [11]:
import numpy as np

name_map = {'Star Wars: Episode I  The Phantom Menace':True,
            'Star Wars: Episode II  Attack of the Clones': True,
            'Star Wars: Episode III  Revenge of the Sith': True,
            'Star Wars: Episode IV  A New Hope': True,
            'Star Wars: Episode V The Empire Strikes Back': True,
            'Star Wars: Episode VI Return of the Jedi': True,
             np.NaN: False}

Convert the columns to Boolean

In [12]:
for i in list(range(3,9)):
    star_wars.iloc[:,i] = star_wars.iloc[:,i].map(name_map)
In [13]:
#check the columns
star_wars.iloc[:,3:9].head()
Out[13]:
Which of the following Star Wars films have you seen? Please select all that apply. Unnamed: 4 Unnamed: 5 Unnamed: 6 Unnamed: 7 Unnamed: 8
1 True True True True True True
2 False False False False False False
3 True True True False False False
4 True True True True True True
5 True True True True True True

Rename the columns

In [14]:
star_wars.columns[3:9]
Out[14]:
Index(['Which of the following Star Wars films have you seen? Please select all that apply.',
       'Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8'],
      dtype='object')
In [16]:
star_wars = star_wars.rename(columns = {'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'})
In [17]:
#check the head of the dataframe
star_wars.head()
Out[17]:
RespondentID Have you seen any of the 6 films in the Star Wars franchise? Do you consider yourself to be a fan of the Star Wars film franchise? seen_1 seen_2 seen_3 seen_4 seen_5 seen_6 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. ... Unnamed: 28 Which character shot first? Are you familiar with the Expanded Universe? Do you consider yourself to be a fan of the Expanded Universe?ξ Do you consider yourself to be a fan of the Star Trek franchise? Gender Age Household Income Education Location (Census Region)
1 3.292880e+09 True True True True True True True True 3 ... Very favorably I don't understand this question Yes No No Male 18-29 NaN High school degree South Atlantic
2 3.292880e+09 False NaN False False False False False False NaN ... NaN NaN NaN NaN Yes Male 18-29 $0 - $24,999 Bachelor degree West South Central
3 3.292765e+09 True False True True True False False False 1 ... Unfamiliar (N/A) I don't understand this question No NaN No Male 18-29 $0 - $24,999 High school degree West North Central
4 3.292763e+09 True True True True True True True True 5 ... Very favorably I don't understand this question No NaN Yes Male 18-29 $100,000 - $149,999 Some college or Associate degree West North Central
5 3.292731e+09 True True True True True True True True 5 ... Somewhat favorably Greedo Yes No No Male 18-29 $100,000 - $149,999 Some college or Associate degree West North Central

5 rows × 38 columns

4. Cleaning the Ranking Columns

The next six columns ask the respondents to rank the Star Wars movies in order of least favorite to most favorite. 1 means the film was the most favorite and 6 means it was the least favorite. Each of the following columns can contain the value 1, 2, 3, 4, 5, 6, or NaN:

  • 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. How much the respondent liked Star Wars: Episode I The Phantom Menace
  • Unnamed: 10 - How much the respondent liked Star Wars: Episode II Attack of the Clones
  • Unnamed: 11 - How much the respondent liked Star Wars: Episode III Revenge of the Sith
  • Unnamed: 12 - How much the respondent liked Star Wars: Episode IV A New Hope
  • Unnamed: 13 - How much the respondent liked Star Wars: Episode V The Empire Strikes Back
  • Unnamed: 14 - How much the respondent liked Star Wars: Episode VI Return of the Jedi

Fortunately these columns don't require a lot of cleanup. We'll need to convert each column to a numeric type, though, then rename the columns so that we can tell what they represent more easily.

Convert columns 9 to 15 to float type

In [19]:
star_wars.iloc[:,9:15] = star_wars.iloc[:,9:15].astype(float)

Rename columns 9 to 15

In [20]:
star_wars.columns[9:15]
Out[20]:
Index(['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.',
       'Unnamed: 10', 'Unnamed: 11', 'Unnamed: 12', 'Unnamed: 13',
       'Unnamed: 14'],
      dtype='object')
In [21]:
star_wars = star_wars.rename(columns = {'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.':'rank_1',
                                        'Unnamed: 10':'rank_2',
                                         'Unnamed: 11':'rank_3',
                                         'Unnamed: 12':'rank_4',
                                         'Unnamed: 13': 'rank_5',
                                         'Unnamed: 14':'rank_6'})
In [23]:
star_wars.iloc[:,9:15].head()
Out[23]:
rank_1 rank_2 rank_3 rank_4 rank_5 rank_6
1 3.0 2.0 1.0 4.0 5.0 6.0
2 NaN NaN NaN NaN NaN NaN
3 1.0 2.0 3.0 4.0 5.0 6.0
4 5.0 6.0 1.0 2.0 4.0 3.0
5 5.0 4.0 6.0 2.0 1.0 3.0

5. Finding the Highest-Ranked Movie

In [27]:
ranking = star_wars.mean()[9:15]
import matplotlib.pyplot as plt %matplotlib inline
In [28]:
ranking.plot.bar()
Out[28]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f740a67e630>

We see that Star Wars: Episode V The Empire Strikes Back has the highest rating (remember that lower is better) and Star Wars: Episode III Revenge of the Sith has the lowest rating.

6. Finding the Most Viewed Movie

In [30]:
movie_seen = star_wars.sum()[3:9]
In [31]:
movie_seen.plot.bar()
Out[31]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f7408382080>

We see that Episode V is the most popular and Episode III is the least popular.

7. Exploring the Data by Binary Segments

We know which movies the survey population as a whole has ranked the highest. Now let's examine how certain segments of the survey population responded. We will focus on two columns which segment our data into two groups.

  • Do you consider yourself to be a fan of the Star Wars film franchise? - True or False
  • Gender - Male or Female

Let's first segment the data based on Star Wars fans.

In [32]:
fan = star_wars[star_wars['Do you consider yourself to be a fan of the Star Wars film franchise?']==True]
In [33]:
fan_rank = fan.iloc[:,9:15].mean()
In [34]:
fan_rank.plot.bar()
Out[34]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f74082da080>

For Star Wars fans, Episode V is still the highest ranked, and Episode III is still the lowest ranked.

In [39]:
fan_seen = fan.iloc[:,3:9].sum()
In [40]:
fan_seen.plot.bar()
Out[40]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f74082235f8>
In [36]:
not_fan = star_wars[star_wars['Do you consider yourself to be a fan of the Star Wars film franchise?']==False]
In [37]:
not_fan_rank = not_fan.iloc[:,9:15].mean()
In [38]:
not_fan_rank.plot.bar()
Out[38]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f740824d6a0>
In [41]:
not_fan_seen = not_fan.iloc[:,3:9].sum()
In [42]:
not_fan_seen.plot.bar()
Out[42]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f74081d3da0>

Now let us segment the data based on gender

In [43]:
male = star_wars[star_wars['Gender']=='Male']
In [44]:
male_rank = male.iloc[:,9:15].mean()
In [45]:
male_rank.plot.bar()
Out[45]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f74080c4518>
In [46]:
male_seen = male.iloc[:,3:9].sum()
In [47]:
male_seen.plot.bar()
Out[47]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f7408316eb8>
In [48]:
female = star_wars[star_wars['Gender'] == 'Female']
In [49]:
female_rank = female.iloc[:,9:15].mean()
In [50]:
female_rank.plot.bar()
Out[50]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f7408023a58>
In [51]:
female_seen = female.iloc[:,3:9].sum()
In [53]:
female_seen.plot.bar()
Out[53]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f7407f97b70>

From the above plots we see that overall Episode 5 is the most popular and Episode 3 is the least popular. The results do not depend on Gender.

8. Next Steps

Now let's segment the data based on Education

In [54]:
star_wars['Education'].value_counts()
Out[54]:
Some college or Associate degree    328
Bachelor degree                     321
Graduate degree                     275
High school degree                  105
Less than high school degree          7
Name: Education, dtype: int64
In [55]:
graduate = star_wars[star_wars['Education'] == 'Graduate degree']
In [56]:
graduate.iloc[:,9:15].mean().plot.bar()
Out[56]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f7407f23a58>
In [57]:
graduate.iloc[:,3:9].sum().plot.bar()
Out[57]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f7407e987b8>

Let's look at Location (Census Region)

In [58]:
star_wars['Location (Census Region)'].value_counts()
Out[58]:
East North Central    181
Pacific               175
South Atlantic        170
Middle Atlantic       122
West South Central    110
West North Central     93
Mountain               79
New England            75
East South Central     38
Name: Location (Census Region), dtype: int64

Let us figure out the region that see the most Star Wars movies

In [65]:
star_wars['Total_View'] = star_wars['seen_1']*1+star_wars['seen_2']*1+star_wars['seen_3']*1+star_wars['seen_4']*1+star_wars['seen_5']*1+star_wars['seen_6']*1
In [69]:
star_wars.groupby('Location (Census Region)').sum()['Total_View'].sort_values(ascending = False)
Out[69]:
Location (Census Region)
Pacific               663
East North Central    624
South Atlantic        603
Middle Atlantic       462
West South Central    358
West North Central    340
Mountain              324
New England           294
East South Central    153
Name: Total_View, dtype: int64

So the Pacific region sees the most Star Wars movies and the East South Central sees the least Star Wars movies.

Now let's look at columns 15 to 29

In [72]:
star_wars.iloc[:,15:29].head()
Out[72]:
Please state whether you view the following characters favorably, unfavorably, or are unfamiliar with him/her. Unnamed: 16 Unnamed: 17 Unnamed: 18 Unnamed: 19 Unnamed: 20 Unnamed: 21 Unnamed: 22 Unnamed: 23 Unnamed: 24 Unnamed: 25 Unnamed: 26 Unnamed: 27 Unnamed: 28
1 Very favorably Very favorably Very favorably Very favorably Very favorably Very favorably Very favorably Unfamiliar (N/A) Unfamiliar (N/A) Very favorably Very favorably Very favorably Very favorably Very favorably
2 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
3 Somewhat favorably Somewhat favorably Somewhat favorably Somewhat favorably Somewhat favorably Unfamiliar (N/A) Unfamiliar (N/A) Unfamiliar (N/A) Unfamiliar (N/A) Unfamiliar (N/A) Unfamiliar (N/A) Unfamiliar (N/A) Unfamiliar (N/A) Unfamiliar (N/A)
4 Very favorably Very favorably Very favorably Very favorably Very favorably Somewhat favorably Very favorably Somewhat favorably Somewhat unfavorably Very favorably Very favorably Very favorably Very favorably Very favorably
5 Very favorably Somewhat favorably Somewhat favorably Somewhat unfavorably Very favorably Very unfavorably Somewhat favorably Neither favorably nor unfavorably (neutral) Very favorably Somewhat favorably Somewhat favorably Very unfavorably Somewhat favorably Somewhat favorably

Let's look at the column names

In [73]:
star_wars.columns[15:29]
Out[73]:
Index(['Please state whether you view the following characters favorably, unfavorably, or are unfamiliar with him/her.',
       'Unnamed: 16', 'Unnamed: 17', 'Unnamed: 18', 'Unnamed: 19',
       'Unnamed: 20', 'Unnamed: 21', 'Unnamed: 22', 'Unnamed: 23',
       'Unnamed: 24', 'Unnamed: 25', 'Unnamed: 26', 'Unnamed: 27',
       'Unnamed: 28'],
      dtype='object')

From the dataset we have

  • 'Please state whether you view the following characters favorably, unfavorably, or are unfamiliar with him/her. - Han Solo
  • Unnamed: 16 - Luke Skywalker
  • Unnamed: 17 -Princess Leia Organa
  • Unnamed: 18 -Anakin Skywalker
  • Unnamed: 19 -Obi Wan Kenobi
  • Unnamed: 20 -Emperor Palpatine
  • Unnamed: 21 -Darth Vader
  • Unnamed: 22 -Lando Calrissian
  • Unnamed: 23 -Boba Fett
  • Unnamed: 24 -C-3P0
  • Unnamed: 25 -R2D2
  • Unnamed: 26 -Jar Jar Binks
  • Unnamed: 27 -Padme Amidala
  • Unnamed: 28 - Yoda

Let's rename the columns to character names

In [74]:
star_wars = star_wars.rename(columns = {'Please state whether you view the following characters favorably, unfavorably, or are unfamiliar with him/her.':'Han_Solo',
                                        'Unnamed: 16':'Luke_Skywalker',
                                         'Unnamed: 17': 'Princess_Leia',
                                         'Unnamed: 18':'Anakin_Skywalker',
                                         'Unnamed: 19':'Obi_Wan_Kenobi',
                                         'Unnamed: 20':'Emperor_Palpatine',
                                          'Unnamed: 21':'Darth_Vader',
                                          'Unnamed: 22':'Lando',
                                          'Unnamed: 23':'Boba',
                                          'Unnamed: 24':'C3P0',
                                         'Unnamed: 25':'R2D2',
                                          'Unnamed: 26':'Jar_Jar',
                                          'Unnamed: 27':'Padme',
                                          'Unnamed: 28':'Yoda'})
In [75]:
star_wars.iloc[:,15:29].head()
Out[75]:
Han_Solo Luke_Skywalker Princess_Leia Anakin_Skywalker Obi_Wan_Kenobi Emperor_Palpatine Darth_Vader Lando Boba C3P0 R2D2 Jar_Jar Padme Yoda
1 Very favorably Very favorably Very favorably Very favorably Very favorably Very favorably Very favorably Unfamiliar (N/A) Unfamiliar (N/A) Very favorably Very favorably Very favorably Very favorably Very favorably
2 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
3 Somewhat favorably Somewhat favorably Somewhat favorably Somewhat favorably Somewhat favorably Unfamiliar (N/A) Unfamiliar (N/A) Unfamiliar (N/A) Unfamiliar (N/A) Unfamiliar (N/A) Unfamiliar (N/A) Unfamiliar (N/A) Unfamiliar (N/A) Unfamiliar (N/A)
4 Very favorably Very favorably Very favorably Very favorably Very favorably Somewhat favorably Very favorably Somewhat favorably Somewhat unfavorably Very favorably Very favorably Very favorably Very favorably Very favorably
5 Very favorably Somewhat favorably Somewhat favorably Somewhat unfavorably Very favorably Very unfavorably Somewhat favorably Neither favorably nor unfavorably (neutral) Very favorably Somewhat favorably Somewhat favorably Very unfavorably Somewhat favorably Somewhat favorably
In [76]:
star_wars['Han_Solo'].value_counts()
Out[76]:
Very favorably                                 610
Somewhat favorably                             151
Neither favorably nor unfavorably (neutral)     44
Unfamiliar (N/A)                                15
Somewhat unfavorably                             8
Very unfavorably                                 1
Name: Han_Solo, dtype: int64

Let's convert these ratings to number: 1 being most favorite, and 6 being least favorite

In [77]:
favor_map = {'Very favorably':1,'Somewhat favorably':2,
            'Neither favorably nor unfavorably (neutral)': 3,
            'Unfamiliar (N/A)':4,
             'Somewhat unfavorably': 5,
             'Very unfavorably': 6}
In [80]:
for i in list(range(15,29)):
    star_wars.iloc[:, i] = star_wars.iloc[:,i].map(favor_map)
In [82]:
star_wars.iloc[:,15:29].mean().plot.bar()
Out[82]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f7407e63b00>
In [84]:
star_wars.iloc[:,15:29].mean().sort_values()
Out[84]:
Han_Solo             1.387214
Yoda                 1.421308
Obi_Wan_Kenobi       1.440000
Luke_Skywalker       1.457280
R2D2                 1.480723
Princess_Leia        1.490975
C3P0                 1.675937
Anakin_Skywalker     2.484812
Lando                2.745122
Padme                2.831695
Darth_Vader          2.842615
Boba                 3.036946
Emperor_Palpatine    3.369779
Jar_Jar              3.695493
dtype: float64

We see that the most favored character is Han Solo and the least favored character is Jar_jar.

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