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")Filter the data for movies released in the 1990s
# Display the first 10 rows of the dataframe
netflix_df.head(10)#Create and view a dataframe of movies
movies_df = netflix_df[netflix_df['type']=='Movie']
movies_df.head()#Create and view a dataframe of movies released between 1990 and 1999
movies_90s_df = movies_df[(movies_df['release_year']>=1990) & (movies_df['release_year']<=1999)]
movies_90s_df.head()Find the most frequent movie duration
# Visualise the most frequent movie duration in the 1990s
plt.hist(movies_90s_df['duration'], bins=20, edgecolor='black')
plt.xlabel('Duration (minutes)')
plt.ylabel('Frequency')
plt.title('Distribution of Movie Durations in the 1990s')
plt.show()#Save the value as 'duration'
duration=100Count the number of short action movies from the 1990s
#Filter action movies
action_movies_df = movies_90s_df[movies_df['genre']=='Action']
action_movies_df.head()# Filter short action movies (less than 90 minutes)
# Initialize short_movie_count
short_movie_count = 0
# Count how many movies are less than 90 minutes and save the number as 'short_movie_count'
for label, row in action_movies_df.iterrows():
if row['duration'] < 90:
short_movie_count += 1
print(short_movie_count)# Visualise the most frequent movie duration in the 1990s
plt.hist(action_movies_df['duration'], edgecolor='black')
plt.xlabel('Duration (minutes)')
plt.ylabel('Frequency')
plt.title('Action Movie Durations in the 1990s')
plt.show()