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# load csv file and store as netflix_df
netflix_df = pd.read_csv("netflix_data.csv")
# check if correct dataframe is loaded in
netflix_df.head(5)# Filter netflix_df to remove tv shows and store as netflix_subset
netflix_subset = netflix_df[netflix_df['type'] != 'TV Show']
# Inspect netflix_subset
netflix_subset.head(3)# Create new dataframe called netflix movies, with columns "title", "country", "genre", "release_year", and "duration"
netflix_movies = netflix_subset.drop(columns = ["show_id", "type", "director", "cast", "date_added", "description"])
# inspect netflix_movies dataframe
netflix_movies.head(3)# Filter netflix_movies that are strictly shorter than 60minutes, and save as short movies.
# Inspect possible contributing factors
short_movies = netflix_movies[netflix_movies['duration'] < 60]
# Inspect short_movies dataframe
short_movies.head(3)# Create visualization based on release_year
plt.hist("release_year", data = short_movies)
plt.xticks(rotation=45, ha='right')
plt.show()
plt.clfObservation:
- Of all the short movies, most of the short movies were made in 2020
# Create visualization based on release_year
plt.hist("genre", data = short_movies)
plt.xticks(rotation=45, ha='right')
plt.show()
plt.clf()Observation
- Most of the short movies were documentaries, followed by uncategorized, children and then stand-up.
# Using 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.
# Create pandas dataframe to access to iterate over rows
netflix_pddf = pd.DataFrame(netflix_movies, columns = ["show_id", "type", "title", "director", "cast", "country", "date_added", "release_year", "duration", "description", "genre"])
# Create an empty list to store the assigned colors
colors = []
# create loop with if/elif statements
for i, row in netflix_pddf.iterrows():
if row['genre'] == "Children":
colors.append("pink")
elif row['genre'] == "Documentaries":
colors.append("blue")
elif row['genre'] == "Stand-Up":
colors.append("yellow")
else:
colors.append("grey")
# Add a new column 'color' to the DataFrame with the assigned colors
netflix_pddf['color'] = colors
print(netflix_pddf)# Initialize 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".
fig = plt.figure()
plt.scatter(x = "release_year", y ="duration", data = netflix_pddf, c = colors, alpha = 0.5)
plt.xlabel("Release year")
plt.ylabel("Duration (min)")
plt.title("Movie Duration by Year of Release")# After inspecting the plot, answer the question "Are we certain that movies are getting shorter?" by assigning either "yes" or "no" to the variable answer
answer = "no"