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")# Load the dataset
df = pd.read_csv('netflix_data.csv')
# Filter the dataset for movies released in the 1990s
movies_1990s = df[(df['release_year'] >= 1990) & (df['release_year'] < 2000) & (df['type'] == 'Movie')]
# Convert duration to integer
movies_1990s['duration'] = movies_1990s['duration'].astype(int)
# Find the most frequent movie duration in the 1990s
most_frequent_duration = movies_1990s['duration'].mode()[0]
duration = int(most_frequent_duration)
# Visualize the duration column of your filtered data to see the distribution of movie durations
plt.hist(movies_1990s["duration"])
plt.title('Distribution of Movie Durations in the 1990s')
plt.xlabel('Duration (minutes)')
plt.ylabel('Number of Movies')
plt.show()
duration = 100
# Filter the data again to keep only the Action movies
action_movies_1990s = movies_1990s[(movies_1990s['genre'] == "Action")]
# Start the counter
short_movie_count = 0
# Iterate over the labels and rows of the DataFrame and check if the duration is less than 90, if it is, add 1 to the counter, if it isn't, the counter should remain the same
for label, row in action_movies_1990s.iterrows() :
if row["duration"] < 90 :
short_movie_count = short_movie_count + 1
else:
short_movie_count = short_movie_count
print("Most frequent movie duration in the 1990s:", duration)
print("Number of short action movies released in the 1990s:", short_movie_count)