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 likeTask One - What was the most frequent movie duration in the 1990s?
netflix_df.describe()df_1990s_movies = netflix_df[(netflix_df['release_year'] >= 1990) & (netflix_df['release_year'] < 2000)]
df_1990s_movies# Initialize an empty dictionary to store the counts
duration_counts = {}
# Iterate over each duration in the 'duration' column
for x in df_1990s_movies['duration']:
if x in duration_counts:
duration_counts[x] += 1
else:
duration_counts[x] = 1
# Convert the dictionary to a DataFrame for better readability
duration_counts_df = pd.DataFrame(list(duration_counts.items()), columns=['Duration', 'Count'])
# Display the duration counts DataFrame
duration_counts_df# Sort the DataFrame by 'Count' in descending order
sorted_duration_counts_df = duration_counts_df.sort_values(by='Count', ascending=False)
# Get the first value in the 'Duration' column
duration = sorted_duration_counts_df.iloc[0]['Duration']
print('approx. ' + str(duration) + 'mins was the most frequent duration of films in the 1990s')Task 2 - Count the number of short action movies released in the 1990s.
short_action_1990s_movies = df_1990s_movies[(df_1990s_movies['duration'] < 90) & (df_1990s_movies['genre'] == 'Action')]
short_action_1990s_moviesshort_movie_count = len(short_action_1990s_movies)
print('approx. ' + str(short_movie_count) + ' movies in the 1990s were short action films')