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
# Read the CSV file and load it into a DataFrame
netflix_df = pd.read_csv('netflix_data.csv')
print(netflix_df)# Filter the data to remove TV shows and store as netflix_subset
netflix_subset = netflix_df[netflix_df['type'] == 'Movie']
print(netflix_subset)
# Investigate the dataset and keep only the columns "title", "country", "genre", "release_year", "duration", and save into a new DataFrame called netflix_movies.
netflix_movies = netflix_subset[['title', 'country', 'genre','release_year', 'duration']]
print(netflix_movies)# Filter netflix_movies to find the movies that are shorter than 60 minutes, saving the resulting DataFrame as short_movies.
short_movies = netflix_movies[netflix_movies.duration < 60]# Using a for loop and if/elif statements, iterate through the rows of netflix_movies and assign colors of your choice to four genre groups ("Children", "Documentaries", "Stand-Up", and "Other" for everything else). Save the results in a colors list.
# Initialize an empty list to store colors
colors = []
# Iterate over rows of netflix_movies
for label, row in netflix_movies.iterrows() :
if row["genre"] == "Children" :
colors.append("red")
elif row["genre"] == "Documentaries" :
colors.append("blue")
elif row["genre"] == "Stand-Up":
colors.append("green")
else:
colors.append("black")
# Inspect the first 10 values in your list
colors[:10]
# Set the figure style and initalize a new figure
fig = plt.figure(figsize=(12,8))
# Create a scatter plot of duration versus release_year
plt.scatter(netflix_movies.release_year, netflix_movies.duration, c=colors)
# Create a title and axis labels
plt.title("Movie Duration by Year of Release")
plt.xlabel("Release year")
plt.ylabel("Duration (min)")
# Show the plot
plt.show()
# Are we certain that movies are getting shorter?
answer = "maybe"