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 likenetflix_df.head(10) #showing a sample of the dataset for ref - first 10 rowsmovie_filter = netflix_df[(netflix_df['type'] == 'Movie') & (netflix_df['release_year'] >= 1990) & (netflix_df['release_year'] <= 1999)] #filtering for movies with the release year between 1990-1999
movie_filter.head(10)hist = plt.hist(movie_filter['duration'], bins = 10)
plt.xlabel('Run Time (mins)')
plt.ylabel('Count')
plt.show() #displaying the distribution of runtime from the selected movies in the 90s
max(movie_filter['duration']) # length of the longest movie
duration = 100 #assigning the most common runtime as a variable for later use if needed - value taken from histogramgenre_subset = movie_filter[(netflix_df['genre'] == 'Action')]
Action_duration_subset = genre_subset[(movie_filter['duration']<90)] #subsetting the dataframe further to refine the selection - this focuses on shorter action movies
Action_duration_subset.head(10)short_movie_count = Action_duration_subset.shape[0]
print(short_movie_count) #total count of these movies to answer the inital question