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.
You work for a production company that specializes in nostalgic styles. You want to do some research on movies released in the 1990's. You'll delve into Netflix data and perform exploratory data analysis to better understand this awesome movie decade!
You have been supplied with the dataset netflix_data.csv, along with the following table detailing the column names and descriptions. Feel free to experiment further 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
# Read in the Netflix CSV as a DataFrame
netflix_df = pd.read_csv("netflix_data.csv")# Start coding here! Use as many cells as you like
# What was the most frequent movie duration in the 1990s? Save an approximate answer as an integer called duration (use 1990 as the decade's start year).
# ...create a decade column
netflix_df['decade'] = round(netflix_df["release_year"]//10) * 10
# ...subset for movies in the decade 1990
movies_in_1990 = netflix_df[netflix_df["decade"] == 1990]
# ...visualize the data o view the disributions of durations
movies_in_1990['duration'].hist(bins = 100)
plt.show()
# ...Give an approximate answer
duration = 90
# ...Alternatively, group by duration and count the number of values for each duration.
# movies_in_1990.groupby("duration")["decade"].count().sort_values(ascending = False)# A movie is considered short if it is less than 90 minutes. Count the number of short action movies released in the 1990s and save this integer as short_movie_count.
# ...subset for action movies from the 1990s movies dataframe
short_action = movies_in_1990[movies_in_1990["genre"] == "Action"]
# ... then subset the short_action dataframe for short movies: duration less than 90
short_movies = short_action[short_action["duration"] < 90]
# ...count the number of movies in the short_movies dataframe; assign to short_movie_count
short_movie_count = len(short_movies)
short_movie_count
# movies_in_1990.head(20)
# short_movies.head(20)