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
# Start coding!
netflix_df = pd.read_csv("netflix_data.csv", index_col=0)
netflix_df
# Filtering and subsetting the TV Shows in the data
netflix_df["type"]
netflix_df["type"] == "TV Show"
netflix_subset = netflix_df["type"] == "TV Show"
netflix_df[netflix_subset]
# Investigating the Netflix movie data and keeping specific columns to store as a new DF
# Columns: title, country, genre, release_year, duration
# DF = netflix_movies
netflix_df
netflix_df["type"] == "Movie"
netflix_movies = netflix_df[netflix_df["type"] == "Movie"]
netflix_movies
netflix_movies = netflix_movies[["title", "country", "genre", "release_year", "duration"]]
netflix_movies
# Filtered netflix_movies to contain movies shorter than 60 mins to create
# a new DataFrame: short_movies
netflix_movies["duration"] < 60
short_movies = netflix_movies[netflix_movies["duration"] < 60]
short_movies
# Iterated through rows to assign colors to genre groups using conditionals and a loop
colors = []
for lab, row in netflix_movies.iterrows() :
if row["genre"] == "Children":
colors.append("yellow")
elif row["genre"] == "Documentaries" :
colors.append("green")
elif row["genre"] == "Stand-Up" :
colors.append("blue")
else:
colors.append("red")
colors
# Initializing a fig
fig = plt.figure(figsize=(12, 8))
plt.scatter(netflix_movies["release_year"], netflix_movies["duration"], c=colors)
plt.xlabel("Release year")
plt.ylabel("Duration (min)")
plt.title("Movie Duration by Year of Release")
plt.show()
#Ans for Question: Are we certian movies are getting shorter?
answer = "maybe"
print(answer)