Context
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 |
Data Analysis with python (Pandas and Matplotlib)
# Import pandas and matplotlib
import pandas as pd
import matplotlib.pyplot as plt
# Load the CSV file as a DataFrame
netflix_df = pd.read_csv('netflix_data.csv')
# Subset the df for "type" -> "Movie"
netflix_subset = netflix_df[netflix_df["type"] == "Movie"]
# Keep only the columns: title, country, genre, release_year and duration
netflix_movies = netflix_subset[["title", "country", "genre", "release_year", "duration"]]
# Filter for "duration" shorter than 60 minutes
short_movies = netflix_movies[netflix_movies["duration"] < 60]
print('\n Result')
# Assign colors to "genre" groups
colors = []
# Iterative over rows of netflix_movies
for label, row in netflix_movies.iterrows():
if row ["genre"] == "Children" :
colors.append("blue")
elif row ["genre"] == "Documentaries" :
colors.append("green")
elif row["genre"] == "Stand-Up" :
colors.append("red")
else:
colors.append("black")
# Set the figure style and initialize a new figure
fig = plt.figure(figsize=(12,8))
#Create a scatter plot of "duration" vs "release_year"
plt.scatter(netflix_movies.release_year, netflix_movies.duration, c=colors)
#Create a title and axis labels
plt.xlabel("Release year")
plt.ylabel("Duration (min)")
plt.title("Movie Duration by Year of Release")
# Creating legend handles
from matplotlib.lines import Line2D # Import necessary for custom legend icons
legend_elements = [Line2D([0], [0], marker='o', color='w', label='Children',
markerfacecolor='blue', markersize=10),
Line2D([0], [0], marker='o', color='w', label='Documentaries',
markerfacecolor='green', markersize=10),
Line2D([0], [0], marker='o', color='w', label='Stand-Up',
markerfacecolor='red', markersize=10),
Line2D([0], [0], marker='o', color='w', label='Other',
markerfacecolor='black', markersize=10)]
# Add the legend to the plot
plt.legend(handles=legend_elements, loc='upper left')
plt.show()
print('\n Based on the scatter plot, we are not certain that movies are getting shorter as time goes by since there is much variance in each genre movie.')