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
# Step 1: Filter for movies released in the 1990s
# We will consider years from 1990 to 1999
movies_90s = netflix_df[(netflix_df['release_year'] >= 1990) & (netflix_df['release_year'] < 2000)]
# Step 2: Check the duration column for missing values
# Replace NaN with a default value or drop them (we will drop them for now)
movies_90s = movies_90s[movies_90s['duration'].notna()]
# Ensure duration is treated as a string for manipulation
movies_90s['duration'] = movies_90s['duration'].astype(str)
# Now strip the " min" part and convert to integers
movies_90s['duration'] = movies_90s['duration'].str.replace(' min', '').astype(int)
# Finding the most frequent movie duration
duration = movies_90s['duration'].mode()[0] # mode returns the most frequent value
# Step 3: Count the number of short action movies (less than 90 minutes)
# First, filter for action movies
short_movies_action = movies_90s[(movies_90s['type'] == 'Movie') &
(movies_90s['genre'].str.contains('Action', na=False)) &
(movies_90s['duration'] < 90)]
# Count the number of short action movies
short_movie_count = short_movies_action.shape[0]
# Display the results
print("Most frequent movie duration in the 1990s:", duration)
print("Count of short action movies (less than 90 minutes) in the 1990s:", short_movie_count)