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. This data does contain null values and some outliers, but handling these is out of scope for the project. Feel free to experiment after submitting!
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
import seaborn as snsnetflix_df = pd.read_csv("netflix_data.csv") # Loading the CSV file and store as netflix_dfnetflix_subset = netflix_df[netflix_df['type'] != "TV Show"] # Filtering the data to remove TV shows and store as netflix_subsetnetflix_movies = netflix_subset.loc[:, ['title', 'country', 'genre', 'release_year', 'duration']] # Keeping only the columns "title", "country", "genre", "release_year", "duration", and saving this into a new DataFrame called netflix_movies
short_movies = netflix_movies[netflix_movies['duration'] < 60] # Filtering netflix_movies to find the movies that are strictly shorter than 60 minutes, saving the resulting DataFrame as short_movies
# Using a for loop and if/elif statements, iterating 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)
colors = []
for lab, rows in netflix_movies.iterrows():
if rows['genre'] == 'Children':
colors.append('green')
elif rows['genre'] == 'Documentaries':
colors.append('blue')
elif rows['genre'] == 'Stand-Up':
colors.append('red')
else:
colors.append('purple')# Initializing a matplotlib figure object called fig and create a scatter plot for movie duration by release year using the colors list to color the points and using the labels "Release year" for the x-axis, "Duration (min)" for the y-axis, and the title "Movie Duration by Year of Release"
sns.set_style('darkgrid')
fig = plt.figure()
plt.scatter(netflix_movies['release_year'], netflix_movies['duration'],color = colors)
plt.title('Movie Duration by Year of Release')
plt.xlabel('Release year')
plt.ylabel('Duration (min)')
plt.grid(True)
plt.show()# Are we certain that movies are getting shorter?
answer = 'No'