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 pltCreating a DataFrame, netflix_df from the CSV file, available at the path "netflix_data.csv" and printing the first five rows of the DataFrame to inspect the data and ensure it was created successfully.
netflix_df = pd.read_csv("netflix_data.csv")
print(netflix_df.head())
print(netflix_df.columns)Subsetting the netflix_df DataFrame such that only rows where the type is a "Movie" are preserved. Assigning the result to netflix_subset.
netflix_subset = netflix_df[netflix_df["type"] == "Movie"]
print(netflix_subset.shape)Subsetting the netflix_movies DataFrame again to preserve only the columns title, country, genre, release_year, and duration and save the DataFrame as netflix_movies DataFrame.
netflix_movies = netflix_subset.iloc[:,[2, 5, 10, 7, 8]]
print(netflix_movies.shape)
print(netflix_movies.columns)Filtering 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]
print(short_movies.shape)
print(short_movies.columns)Iterating through the rows of netflix_movies and assign colors to four genre groups ("Children", "Documentaries", "Stand-Up", and "Other" for everything else). Save the results in a colors list. Initialize a 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".
# Initialize an empty list to store colors
colors = []
# Assign colors based on genre
for index, row in netflix_movies.iterrows():
if row["genre"] == "Children":
colors.append("green")
elif row["genre"] == "Documentaries":
colors.append("blue")
elif row["genre"] == "Stand-Up":
colors.append("red")
else:
colors.append("orange") # Assign 'Other' category an 'orange' color
# Create a scatter plot
fig, ax = plt.subplots()
ax.scatter(netflix_movies["release_year"], netflix_movies["duration"],
c=colors, alpha=0.5)
# Set labels and title
ax.set_xlabel("Release year")
ax.set_ylabel("Duration (min)")
ax.set_title("Movie Duration by Year of Release")
# Show the legend for the colors
legend_labels = ["Children", "Documentaries", "Stand-Up", "Other"]
ax.legend(handles=scatter.legend_elements()[0], labels=legend_labels)
# Show the plot
plt.show()So after analyzing the chart, are we certain that movies are getting shorter?
# The answer is
answer = "maybe"