Project: Investigating Netflix Movies
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 |
# Kyesswa Steven
# Date: 11/4/2023
# linktree : linktr.ee/kyesswasteven
# Website URL : "https://kyesswasteven.me"
# Importing pandas and matplotlib
import pandas as pd
import matplotlib.pyplot as plt
# Load the CSV file and store as netflix_df
netflix_df = pd.read_csv("netflix_data.csv")
# Filter the data to remove TV shows and store as netflix_subset
netflix_subset = netflix_df[netflix_df['type'] == 'Movie']
# Investigate the Netflix movie data and select relevant columns
netflix_movies = netflix_subset[["title", "country", "genre", "release_year", "duration"]]
# Filter netflix_movies to find the movies that are shorter than 60 minutes
short_movies = netflix_movies[netflix_movies['duration'] < 60]
# Create a dictionary to map genres to colors
genre_colors = {
'Children': 'blue',
'Documentaries': 'green',
'Stand-Up': 'red'
}
# Iterate through the rows of netflix_movies and assign colors to genre groups
colors = []
for index, row in netflix_movies.iterrows():
genre = row['genre']
if genre in genre_colors:
colors.append(genre_colors[genre])
else:
colors.append('purple') # Assign 'purple' for "Other" genre
# Initialize a figure object called fig and create a scatter plot
fig, ax = plt.subplots(figsize=(10, 6))
ax.scatter(netflix_movies['release_year'], netflix_movies['duration'], c=colors, alpha=0.5)
ax.set_xlabel('Release year')
ax.set_ylabel('Duration (min)')
ax.set_title('Movie Duration by Year of Release')
# Show the plot
plt.show()
# Now, inspect the plot and answer the question
answer = "maybe"