Netflix! What started in 1997 as a DVD rental service has since exploded into one of the largest entertainment and media companies.
Given the large number of movies and series available on the platform, it is a perfect opportunity to flex your exploratory data analysis skills and dive into the entertainment industry. Our friend has also been brushing up on their Python skills and has taken a first crack at a CSV file containing Netflix data. They believe that the average duration of movies has been declining. Using your friends initial research, you'll delve into the Netflix data to see if you can determine whether movie lengths are actually getting shorter and explain some of the contributing factors, if any.
You have been supplied with the dataset netflix_data.csv , along with the following table detailing the column names and descriptions:
The data
netflix_data.csv
| 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 |
# Importing pandas and matplotlib
import pandas as pd
import matplotlib.pyplot as plt
# Start coding!
netflix_df = pd.read_csv("netflix_data.csv")
netflix_subset = netflix_df[netflix_df['type'] == 'Movie']
#remove TV Shows
netflix_movies = pd.DataFrame(netflix_subset[['title','country', 'genre', 'release_year', 'duration']])
#View only short movies
short_movies = netflix_movies[netflix_movies['duration'] < 60]
#View only older movies
older_movies = netflix_movies[netflix_movies['release_year'] < 2005]
#Set colors for genres (short movies)
colora = []
for lab, row in short_movies.iterrows():
if row['genre'] == 'Children' :
colora.append('pink')
elif row['genre'] == 'Documentaries' :
colora.append('blue')
elif row['genre'] == 'Stand-Up' :
colora.append('green')
else:
colora.append('yellow')
#set colors for genres (all movies)
colors = []
for lab, row in netflix_movies.iterrows():
if row['genre'] == 'Children' :
colors.append('pink')
elif row['genre'] == 'Documentaries' :
colors.append('blue')
elif row['genre'] == 'Stand-Up' :
colors.append('green')
else:
colors.append('yellow')
#Plot of short movies
a = short_movies['release_year']
b = short_movies['duration']
fig = plt.figure(figsize = (12,8))
plt.scatter(x = a, y = b, c = colora)
plt.xlabel("Release year")
plt.ylabel("Duration (min)")
plt.title("Movie Duration by Year of Release", fontweight = 'bold')
plt.show()
#Plot of all movies
x = netflix_movies['release_year']
y = netflix_movies['duration']
fig2 = plt.figure(figsize = (12,8))
plt.scatter(x = x, y = y, c = colors)
plt.xlabel("Release year")
plt.ylabel("Duration (min)")
plt.suptitle("Movie Duration by Year of Release", fontweight = 'bold')
plt.title("Correlation: " + str(x.corr(y)))
plt.show()
#There is a small negative correlation between release date and duration, indicating that movies are getting shorter. Since there seems to be a lot more short movies (under 60 min) since 2005 it is worth checking what difference removing them makes:
older_movies = netflix_movies[netflix_movies['release_year'] < 2005]
x2 = older_movies['release_year']
y2 = older_movies['duration']
print("correlation up to 2005: ")
print(x2.corr(y2))
#This is a big reduction meaning the correlation is mostly driven by an increase in short movies since 2005.
#Are we certain that movies are getting shorter?
answer = "maybe"
print("answer:")
print(answer)