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
#Establish the criteria - 1990s movies
movies_1990s = netflix_df[(netflix_df['type'] == 'Movie') & (netflix_df['release_year'] >= 1990) & (netflix_df['release_year'] < 2000)]
# Value counts for duration
duration_counts = movies_1990s['duration'].value_counts()
# Get the most common duration and its count (default sorting is descending)
most_common_duration = duration_counts.index[0]
most_common_count = duration_counts.iloc[0]
# Print the results
print("The most common duration is: " + str(most_common_duration) + " minutes")
print("It occurs " + str(most_common_count) + " times")
#Answer to first question, "What was the most frequent movie duration in the 1990s?"
duration = most_common_duration
print(duration)#Fetch all the 1990s movies less than 90 min long
short_90s_action_movies = netflix_df[(netflix_df['type'] == 'Movie') & (netflix_df['release_year'] >= 1990) & (netflix_df['release_year'] <= 1999) & (netflix_df['duration'] < 90) & (netflix_df['genre'] == 'Action')]
short_movie_count = len(short_90s_action_movies)
print("The number of short 90s movies is " + str(short_movie_count))
print(short_movie_count)
pd.set_option('display.max_columns', None)
print(short_90s_action_movies)
type = Movie release_year = 1990-1999 FIND: duration = (Pull most frequent as integer)
type = Movie genre = Action release_year = 1990-1999 duration < 90 FIND: short_movie_count = (pull as integer)