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")import pandas as pd
# Sample data to simulate the netflix_df DataFrame
data = {
'type': ['Movie', 'TV Show', 'Movie', 'Movie'],
'release_year': [1995, 1997, 1992, 2000],
'duration': ['90 min', '45 min', '120 min', '100 min']
}
# Create the DataFrame
netflix_df = pd.DataFrame(data)
## Filter for only movies
movies_df = netflix_df[netflix_df['type'] == 'Movie']
# Filter for movies released in the 1990s (1990 to 1999 inclusive)
movies_90s = movies_df[(movies_df['release_year'] >= 1990) & (movies_df['release_year'] <= 1999)]
# Drop rows with missing duration values
movies_90s = movies_90s.dropna(subset=['duration'])
# Clean and convert the 'duration' column to integer (remove ' min')
movies_90s['duration_clean'] = movies_90s['duration'].astype(str).str.replace(' min', '', regex=False).astype(int)
# Find the most frequent duration (mode)
duration = int(movies_90s['duration_clean'].mode()[0])
# Display the result
print("Most frequent movie duration in the 1990s:", duration)# Read in the Netflix CSV as a DataFrame
netflix_df = pd.read_csv("netflix_data.csv")
# Filter for only movies
movies_df = netflix_df[netflix_df['type'] == 'Movie']
# Filter for movies released in the 1990s (1990 to 1999 inclusive)
movies_90s = movies_df[(movies_df['release_year'] >= 1990) & (movies_df['release_year'] <= 1999)]
# Drop rows with missing duration values
movies_90s = movies_90s.dropna(subset=['duration'])
# Clean and convert the 'duration' column to integer (remove ' min')
movies_90s['duration_clean'] = movies_90s['duration'].astype(str).str.replace(' min', '', regex=False).astype(int)
# Filter for short movies (less than 90 minutes) and genre containing 'Action'
short_action_movies = movies_90s[
(movies_90s['duration_clean'] < 90) &
(movies_90s['genre'].str.contains('Action', na=False))
]
# Count the number of such movies
short_movie_count = len(short_action_movies)
# Display the result
print("Number of short action movies in the 1990s:", short_movie_count)