Uncovering the Best Star Wars Movie

In this project, we'll be analyzing Star Wars survey results to learn which Star Wars movie is the best of the bunch.

Approach

To come to a decision, we'll find:

The best-ranked movie from the survey
The most-viewed movie from the survey

Results

From our analysis, we've learned that the best-ranked and most-viewed movies were Star Wars: Episode V The Empire Strikes Back and Star Wars: Episode VI Return of the Jedi respectively.

Data Overview

We'll be using a dataset of 1,187 Star Wars survey responses collected by FiveThirtyEight.

The dataset has 38 columns, with columns such as:

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

# import libs import pandas as pd import numpy as np import matplotlib.pyplot as plt # read in dataset star_wars_df = pd.read_csv("StarWars.csv", encoding="ISO-8859-1") # drop null values from RespondentID star_wars_df = star_wars_df.dropna(subset=["RespondentID"]) # print summary info star_wars_df.describe(include="all")

We can make the following observations:

Responses related to one column are also spread across multiple columns. For example, the Which of the following Star Wars films have you seen? Please select all that apply. column's related answers are in the following columns:
- Unnamed: 4
- Unnamed: 5
- Unnamed: 6
- Unnamed: 7
- Unnamed: 8
There are a number of columns which don't help us find the best movie.

Data Cleaning

Drop Columns

We'll drop the survey response columns which don't give us movie information:

Please state whether you view the following characters favorably, unfavorably, or are unfamiliar with him/her. and related answer columns Unnamed: 16 to 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?æ
Do you consider yourself to be a fan of the Star Trek franchise?

# drop unneeded columns star_wars_df = star_wars_df.drop(star_wars_df.columns[15:33] ,axis=1)

Convert Yes/No to Booleans

Let's map values in the following columns to be True/False instead of Yes/No:

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?

# convert the columns to True/False booleans for col in ['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?']: star_wars_df[col] = star_wars_df[col].map({ "Yes": True, "No": False })

Cleaning Checkbox Columns

Movie viewing

The next six columns represent a single checkbox question of the movies the respondent has seen:

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.
Unnamed: 4: Whether or not the respondent saw Star Wars: Episode II Attack of the Clones.
Unnamed: 5: Whether or not the respondent saw Star Wars: Episode III Revenge of the Sith.
Unnamed: 6: Whether or not the respondent saw Star Wars: Episode IV A New Hope.
Unnamed: 7: Whether or not the respondent saw Star Wars: Episode V The Empire Strikes Back.
Unnamed: 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, it means the respondent saw that movie. If the value is NaN, we'll assume they didn't see the movie. We'll rename the columns and map them to boolean values.

# iterate over the marked columns for count, col in enumerate(star_wars_df.columns[3:9], start=1): # if the value is a string holding the movie name, return True, otherwise return False star_wars_df[col] = star_wars_df[col].apply(lambda x: True if isinstance(x, str) else False) # rename the column star_wars_df = star_wars_df.rename(columns={col: f"seen_movie_{count}"})

Movie ranking

The next six columns ask the respondent to rank the movies in order of most favourite (1) to least favourite (6). Similarly the responses have been spread across 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.: 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.

Here, we'll just need to convert each column to a numeric type and then rename them.

# iterate over marked column for count, col in enumerate(star_wars_df.columns[9:15], start=1): # convert column to float type star_wars_df[col] = star_wars_df[col].astype(float) # rename the column star_wars_df = star_wars_df.rename(columns={col: f"ranking_movie_{count}"})

Data Analysis

Before analysing, we should segment our data to be those who consider themselves a fan of the franchise:

fans_df = star_wars_df[star_wars_df["Do you consider yourself to be a fan of the Star Wars film franchise?"] == True]

Best-Ranked Movie

# create bar chart with ranked movies ax = fans_df.loc[:, "ranking_movie_1":"ranking_movie_6"].mean().plot(kind="bar", figsize=(10, 5)) # set labels and title ax.set_xlabel("Movie") ax.set_ylabel("Ranking") plt.title("Best Ranking Movies Among Fans") # show plot plt.show()

Recall, a lower average ranking towards 1 is better than a higher average ranking towards 6. This is the order of the movies by best ranking:

Star Wars: Episode V The Empire Strikes Back.
Star Wars: Episode VI Return of the Jedi.
Star Wars: Episode IV A New Hope.
Star Wars: Episode I The Phantom Menace.
Star Wars: Episode II Attack of the Clones.
Star Wars: Episode III Revenge of the Sith.

Most-Viewed Movie

# create bar chart with most seen movies ax = fans_df.loc[:, "seen_movie_1":"seen_movie_6"].sum().plot(kind="bar", figsize=(10, 5)) # set labels and title ax.set_xlabel("Movie") ax.set_ylabel("Count") plt.title("Most Viewed Movies") # show plot plt.show()

This is the order of the movies by viewership:

Star Wars: Episode V The Empire Strikes Back.
Star Wars: Episode VI Return of the Jedi.
Star Wars: Episode I The Phantom Menace.
Star Wars: Episode IV A New Hope.
Star Wars: Episode II Attack of the Clones.
Star Wars: Episode III Revenge of the Sith.

The best ranked movies also tend to be the most frequently watched, with only movie 1 and 4 trading places.

Results

From our analysis, we've learned that the best-ranked and most-viewed movies were Star Wars: Episode V The Empire Strikes Back and Star Wars: Episode VI Return of the Jedi respectively.

Uncovering the Best Star Wars Movie

Approach

Results

Data Overview

Data Cleaning

Drop Columns

Convert Yes/No to Booleans

Cleaning Checkbox Columns

Movie viewing

Movie ranking

Data Analysis

Best-Ranked Movie

Most-Viewed Movie

Results

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