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_df.head()#Create subset of movies only, note == represents true, whereas = is used to assign value
netflix_subset = netflix_df[netflix_df['type']=='Movie']
netflix_subset.head()#Create further subset removing columns that are not useful to us
netflix_movies = netflix_subset[['title','country','genre','release_year','duration']]
netflix_movies.head()#Filter further for movies shorter than 60 mins
short_movies = netflix_movies[netflix_movies['duration']<60]
short_movies.head(10)#Create new list called colors, and using for loop, iterate through rows, appending colors based on genre
colors = []
for _, row in netflix_movies.iterrows():
# Check the genre and append the corresponding color
if row['genre'] == 'Children':
colors.append('Green')
elif row['genre'] == 'Documentaries':
colors.append('Yellow')
elif row['genre'] == 'Standup':
colors.append('Red')
else:
colors.append('Grey')
print(colors)# Create plot of duration by release year, colour coded by genre
fig, ax = plt.subplots() # Initialize a figure and axes object
# Create a scatter plot
ax.scatter(netflix_movies['release_year'], netflix_movies['duration'], c=colors, alpha=0.5)
# Set labels and title
ax.set_xlabel('Release year')
ax.set_ylabel('Duration (min)')
ax.set_title('Movie Duration by Year of Release')
plt.show()It is hard to say specifically, but the introduction of shorter form "movie" labelled content is driven by an increase in childrens movies and documentaries, both of which are noteably shorted in form that a traditional movie. It would seem that as time has gone on for these types of film, they are still clustering at the top end of the 0-60 mins window.
#Are we certain that movies are getting shorter?
answer = "no"