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
# Start coding!
netflix_df = pd.read_csv('netflix_data.csv')
netflix_dfnetflix_subset = netflix_df[netflix_df['type'] == 'Movie']
netflix_subsetnetflix_movies = netflix_subset[['title', 'country', 'genre', 'release_year', 'duration']]
netflix_moviesshort_movies = netflix_movies[netflix_movies['duration'] < 60]
short_moviescolors = []
for index, row in netflix_movies.iterrows():
genre = row['genre']
if 'Children' in genre:
colors.append('blue')
elif 'Documentaries' in genre:
colors.append('green')
elif 'Stand-Up' in genre:
colors.append('orange')
else:
colors.append('grey')
fig = plt.figure(figsize=(10, 6))
plt.scatter(netflix_movies['release_year'], netflix_movies['duration'], c=colors)
plt.xlabel("Release year")
plt.ylabel("Duration (min)")
plt.title("Movie Duration by Year of Release")
plt.show()
answer = "no"
print("Are we certain that movies are getting shorter? Answer:", answer)# Import necessary libraries
import pandas as pd
import matplotlib.pyplot as plt
# Load the dataset
netflix_df = pd.read_csv('netflix_data.csv')
# Filter only movies
netflix_movies = netflix_df[netflix_df['type'] == 'Movie']
# Convert 'duration' column to numeric (assuming it's in the format "minutes")
netflix_movies['duration'] = netflix_movies['duration'].astype(str).str.replace(' min', '').astype(int)
# Group by release year and calculate the average duration
avg_duration_by_year = netflix_movies.groupby('release_year')['duration'].mean().reset_index()
# Plotting the trend of average movie duration over the years
plt.figure(figsize=(10, 6))
plt.plot(avg_duration_by_year['release_year'], avg_duration_by_year['duration'], marker='o', linestyle='-')
plt.xlabel('Release Year')
plt.ylabel('Average Duration (minutes)')
plt.title('Trend of Average Movie Duration Over Years')
plt.grid(True)
plt.show()
# Determine if there's a declining trend in movie durations
average_duration_change = avg_duration_by_year['duration'].diff().mean()
# Assessing the trend
if average_duration_change < 0:
answer = "yes, movies are getting shorter"
else:
answer = "no, movies are not getting shorter"
print("Are movies getting shorter over the years? Answer:", answer)