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 pltimport pandas as pd
# Read in the CSV as a DataFrame
netflix_df = pd.read_csv("netflix_data.csv")
# Print the first five rows of the DataFrame
netflix_df.head()netflix_df['type'].value_counts()We have 2410 "TV Shows" and "Movies" in the dataset. For this case, I am interesting in movies.
netflix_subset = netflix_df[netflix_df['type'] == "Movie"]
netflix_subset.head()netflix_subset.describe()# Select only the columns of interest
netflix_movies = netflix_subset[['title', 'country', 'genre', 'release_year', 'duration']]
# Print the first five rows of the new DataFrame
netflix_movies.head()# if I would like to know the durations over time
netflix_year = netflix_movies.groupby(netflix_movies['release_year'])['duration'].mean().reset_index()
netflix_year.head()# if I would like to know the duration from 2011 to 2020
netflix_year2 = netflix_year[netflix_year['release_year']>= 2011]
netflix_year2import seaborn as sns
fig = plt.figure()
# Draw a line plot of release_years and durations
plt.plot(netflix_year2['release_year'],netflix_year2['duration'])
plt.ylim(75,115)
# Create a title
plt.title("Netflix Movies Durations 2011 - 2021")
# Show the plot
plt.show()As can be seen in the graph, the data may show that the average number of films has decreased over time. However, this may be a case of misinformation. We will explore the data further.
netflix_df = netflix_df.sort_values('duration', ascending = False)
netflix_df# Create a figure and increase the figure size
fig = plt.figure(figsize=(12,8))
# Create a scatter plot of duration versus year
plt.scatter(netflix_movies['release_year'],netflix_movies['duration'])
# Create a title
plt.title('Movie Duration by Year of Release')
plt.xlabel("Release year")
plt.ylabel("Duration(min)")
# Show the plot
plt.show()