Content Analytics: Netflix Data Science Investigation

Project Overview

Objectives:

To apply data science methodologies to analyze Netflix content evolution, using statistical analysis and temporal trend investigation to understand content strategy patterns and their implications for the entertainment industry.

Methods:

• Applied Python-based data science workflows for dataset manipulation, preprocessing, and analytical transformation.
• Implemented statistical analysis techniques to identify patterns, distributions, and temporal relationships in content duration data.
• Developed interactive data visualizations to communicate complex trends and support data-driven content strategy insights.

Results:

The data science investigation revealed temporal patterns in Netflix content duration, with statistical analysis identifying correlations between content length, genre categories, and production timelines that inform strategic content decisions.

Conclusion:

This analytical framework demonstrates the application of data science methodologies to entertainment industry challenges, providing actionable insights for content strategy optimization and stakeholder decision-making in the streaming media landscape.

The Dataset:

netflix_data

Below is a snapshot of the dataset used for this analysis:

Column	Description
show_id	The ID of the show
type	Type of show
title	Title of the show
director	Director of the show
cast	Cast of the show
country	Country of origin
date_added	Date added to Netflix
release_year	Year of Netflix release
duration	Duration of the show in minutes
description	Description of the show
genre	Show genre

Python Code and Outputs:

1. Load the CSV file and store as netflix_df.

import pandas as pd

# Load the CSV file and store as netflix_df
netflix_df = pd.read_csv("netflix_data.csv")

# Display the first few rows of the dataframe
print(netflix_df.head())
                    

Output:

show_id	type	title	director	cast	country	date_added	release_year	duration	description	genre
s1	TV Show	3%	null	João Miguel, Bianca Comparato, Michel Gomes, Rodolfo Valente, Vaneza Oliveira, Rafael Lozano, Viviane Porto, Mel Fronckowiak, Sergio Mamberti, Zezé Motta, Celso Frateschi	Brazil	August 14, 2020	2020	4	In a future where the elite inhabit an island paradise far from the crowded slums, you get one chance to join the 3% saved from squalor.	International TV
s2	Movie	7:19	Jorge Michel Grau	Demián Bichir, Héctor Bonilla, Oscar Serrano, Azalia Ortiz, Octavio Michel, Carmen Beato	Mexico	December 23, 2016	2016	93	After a devastating earthquake hits Mexico City, trapped survivors from all walks of life wait to be rescued while trying desperately to stay alive.	Dramas
s3	Movie	23:59	Gilbert Chan	Tedd Chan, Stella Chung, Henley Hii, Lawrence Koh, Tommy Kuan, Josh Lai, Mark Lee, Susan Leong, Benjamin Lim	Singapore	December 20, 2018	2011	78	When an army recruit is found dead, his fellow soldiers are forced to confront a terrifying secret that's haunting their jungle island training camp.	Horror Movies
s4	Movie	9	Shane Acker	Elijah Wood, John C. Reilly, Jennifer Connelly, Christopher Plummer, Crispin Glover, Martin Landau, Fred Tatasciore, Alan Oppenheimer, Tom Kane	United States	November 16, 2017	2009	80	In a postapocalyptic world, rag-doll robots hide in fear from dangerous machines out to exterminate them, until a brave newcomer joins the group.	Action
s5	Movie	21	Robert Luketic	Jim Sturgess, Kevin Spacey, Kate Bosworth, Aaron Yoo, Liza Lapira, Jacob Pitts, Laurence Fishburne, Jack McGee, Josh Gad, Sam Golzari, Helen Carey, Jack Gilpin	United States	January 1, 2020	2008	123	A brilliant group of students become card-counting experts with the intent of swindling millions out of Las Vegas casinos by playing blackjack.	Dramas

2. Filter the data to remove TV shows and store as netflix_subset.

# Filter the data to remove TV shows and store as netflix_subset
netflix_subset = netflix_df[netflix_df["type"] != 'TV Show']

# Display the first few rows of the subsetted dataframe
print(netflix_subset.head())
                    

Output:

show_id	type	title	director	cast	country	date_added	release_year	duration	description	genre
s2	Movie	7:19	Jorge Michel Grau	Demián Bichir, Héctor Bonilla, Oscar Serrano, Azalia Ortiz, Octavio Michel, Carmen Beato	Mexico	December 23, 2016	2016	93	After a devastating earthquake hits Mexico City, trapped survivors from all walks of life wait to be rescued while trying desperately to stay alive.	Dramas
s3	Movie	23:59	Gilbert Chan	Tedd Chan, Stella Chung, Henley Hii, Lawrence Koh, Tommy Kuan, Josh Lai, Mark Lee, Susan Leong, Benjamin Lim	Singapore	December 20, 2018	2011	78	When an army recruit is found dead, his fellow soldiers are forced to confront a terrifying secret that's haunting their jungle island training camp.	Horror Movies
s4	Movie	9	Shane Acker	Elijah Wood, John C. Reilly, Jennifer Connelly, Christopher Plummer, Crispin Glover, Martin Landau, Fred Tatasciore, Alan Oppenheimer, Tom Kane	United States	November 16, 2017	2009	80	In a postapocalyptic world, rag-doll robots hide in fear from dangerous machines out to exterminate them, until a brave newcomer joins the group.	Action
s5	Movie	21	Robert Luketic	Jim Sturgess, Kevin Spacey, Kate Bosworth, Aaron Yoo, Liza Lapira, Jacob Pitts, Laurence Fishburne, Jack McGee, Josh Gad, Sam Golzari, Helen Carey, Jack Gilpin	United States	January 1, 2020	2008	123	A brilliant group of students become card-counting experts with the intent of swindling millions out of Las Vegas casinos by playing blackjack.	Dramas
s7	Movie	122	Yasir Al Yasiri	Amina Khalil, Ahmed Dawood, Tarek Lotfy, Ahmed El Fishawy, Mahmoud Hijazi, Jihane Khalil, Asmaa Galal, Tara Emad	Egypt	June 1, 2020	2019	95	After an awful accident, a couple admitted to a grisly hospital are separated and must find each other to escape — before death finds them.	Horror Movies

3. Subset the Netflix movie data, keeping only the columns "title", "country", "genre", "release_year", "duration" into a new DataFrame netflix_movies.

# Subset the Netflix movie data, keeping only the columns "title", "country", "genre", "release_year", "duration"
netflix_movies = netflix_subset[['title', 'country', 'genre', 'release_year', 'duration']]

# Display the first few rows of the new dataframe
print(netflix_movies.head())
                    

Output:

title	country	genre	release_year	duration
7:19	Mexico	Dramas	2016	93
23:59	Singapore	Horror Movies	2011	78
9	United States	Action	2009	80
21	United States	Dramas	2008	123
122	Egypt	Horror Movies	2019	95

4. Filter movies that are shorter than 1 hour and save as short_movies.

# Filter movies that are shorter than 1 hour and save as short_movies
short_movies = netflix_movies[netflix_movies['duration']<60]

# Display the first few rows of the filtered dataframe
print(short_movies)
                    

Output:

title	country	genre	release_year	duration
#Rucker50	United States	Documentaries	2016	56
100 Things to do Before High School	United States	Uncategorized	2014	44
13TH: A Conversation with Oprah Winfrey & Ava DuVernay	null	Uncategorized	2017	37
3 Seconds Divorce	Canada	Documentaries	2018	53
A 3 Minute Hug	Mexico	Documentaries	2019	28
A Christmas Special: Miraculous: Tales of Ladybug & Cat Noir	France	Uncategorized	2016	22
A Family Reunion Christmas	United States	Uncategorized	2019	29
A Go! Go! Cory Carson Christmas	United States	Children	2020	22
A Go! Go! Cory Carson Halloween	null	Children	2020	22
A Go! Go! Cory Carson Summer Camp	null	Children	2020	21

5. Create a scatter plot for movie duration by release year using genre-based colors.

import matplotlib.pyplot as plt

# Create colors list based on genre
colors = []
for lab, row in netflix_movies.iterrows():
    if row['genre'] == "Children":
        colors.append('yellow')
    elif row['genre'] == "Documentaries":
        colors.append('brown')
    elif row['genre'] == "Stand-Up":
        colors.append('blue')
    else:
        colors.append('grey')
        
# Initialize a matplotlib figure object called fig and create a scatter plot
fig = plt.figure()
plt.scatter(netflix_movies['release_year'], netflix_movies['duration'], color = colors)
plt.xlabel('Release year'); plt.ylabel('Duration (min)'); plt.title('Movie Duration by Year of Release')
plt.show()
                    

Output:

6. Answer the question "Are we certain that movies are getting shorter?"

Based on the scatter plot, it is not definitively clear that movies are consistently getting shorter over time. There are fluctuations in movie durations across different release years, suggesting that other factors, such as genre and production constraints, may play a significant role in determining movie lengths.

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