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!
# reading the dataset
netflix_df = pd.read_csv("netflix_data.csv")
netflix_df
# subsetting the dataset "type" to contain only movie categories
netflix_subset = netflix_df[netflix_df["type"] == "Movie"]
netflix_subset
#isolating columns of interest in the netflix_movies dataset
netflix_movies = netflix_subset[["title", "country", "genre", "release_year", "duration"]]
netflix_movies
#subsetting only movies that have a duration of less than 60 minutes
short_movies = netflix_movies[netflix_movies["duration"] < 60]
# inspecting the different movie genres to understand some of the factors contributing to low movie duration
short_movies.groupby("genre").count()
#creating an empty colors list over which to iterate over the different genres in the dataset using a for loop and if statements
colors = []
for lab, row in netflix_movies.iterrows():
if row["genre"] == "Children":
colors.append("red")
elif row["genre"] == "Documentaries":
colors.append("blue")
elif row["genre"] == "Stand-Up":
colors.append("black")
else:
colors.append("orange")
#inspecting the first 15 items in the colors list
colors[:15]
#plotting a scatter plot to understand the relationship between the release date and duration of the movies
fig = plt.figure(figsize=(12,8))
plt.scatter(netflix_movies.release_year, netflix_movies.duration ,c = colors)
plt.title("Movie Duration by Year of Release")
plt.xlabel("Release year")
plt.ylabel("Duration (min)")
plt.show()
#can we definitively state that the movie duration has decreased over time??
answer = "no"