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 plt
#Load and inspect the Netflix data
netflix_df = pd.read_csv("netflix_data.csv")
print(netflix_df.iloc[:,5])#Filter the data to remove TV shows
netflix_subset = netflix_df[netflix_df["type"] == "Movie"]#Subset the columns of the new DataFrame
netflix_movies = netflix_subset.loc[:, ["title", "country", "genre", "release_year", "duration"]]#Filter the DataFrame by Movie duration
short_movies = netflix_movies[netflix_movies["duration"] < 60]
print(short_movies.iloc[20,:])#Assign colors to movie genres
colors = []
for index, row in netflix_movies.iterrows() :
genre = row['genre']
if genre == "Children":
colors.append('blue')
elif genre == "Documentaries":
colors.append('green')
elif genre == "Stand-Up":
colors.append('red')
elif genre == "Dramas":
colors.append('purple')
elif genre == "Horror Movies":
colors.append('black')
elif genre == "Action":
colors.append('orange')
elif genre == "Independent Movies":
colors.append('black')
elif genre == "Comedies":
colors.append('yellow')
elif genre == "Sports Movies":
colors.append('lime')
elif genre == "Uncategorized":
colors.append('gray')
elif genre == "International Movies":
colors.append('violet')
elif genre == "Sci-Fi":
colors.append('silver')
elif genre == "Classic Movies":
colors.append('gold')
elif genre == "Thrillers":
colors.append('olive')
elif genre == "Anime Features":
colors.append('indigo')
elif genre == "Music":
colors.append('pink')
elif genre == "Cult Movies":
colors.append('brown')
elif genre == "Romantic Movies":
colors.append('magenta')
elif genre == "LGBTQ Movies":
colors.append('navy')import matplotlib.pyplot as plt
# Initialize a figure object
fig, ax = plt.subplots(figsize=(12, 8))
# Create a scatter plot
scatter_plot = ax.scatter(
x=netflix_movies['release_year'],
y=netflix_movies['duration'],
c=colors,
alpha=0.7, # Adjust transparency if needed
)
# Set labels and title
ax.set_xlabel('Release year')
ax.set_ylabel('Duration (min)')
ax.set_title('Movie Duration by Year of Release')
# Add a colorbar
colorbar = plt.colorbar(scatter_plot, ax=ax, label='Genre')
colorbar.set_ticks([]) # Hide colorbar ticks if you prefer
# Show the plot
plt.show()#Are we certain that movies are getting shorter?
answer = "maybe"