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 numpy, pandas and matplotlib
import numpy as np
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")# Checking the top 5 rows of the netflix dataset
netflix_df.head()# Filtering the dataset for only movies released in 1990s
filter_1990s = np.logical_and(netflix_df["release_year"] >= 1990, netflix_df["release_year"] < 2000)
filter_movies = netflix_df["type"] == "Movie"
filter_1990s_movies = np.logical_and(filter_1990s, filter_movies)
netflix_1990_df = netflix_df[filter_1990s_movies]# Check the top 5 records in filtered netflix dataset
netflix_1990_df.head()# Let's also plot an histogram on the movie duration
plt.hist(netflix_1990_df["duration"], bins = 15)
plt.title("Distribution of movie durations in the 1990s")
plt.xlabel("Movie durations")
plt.ylabel("Number of Movies")
plt.show()# Most frequent duration seeing the above histogram seems to be 100
duration = 100# Total number of short action movies released in the 1990s
# filter_action = netflix_df["genre"] == "Action"
# filter_1990s_action_movies = np.logical_and(filter_action, filter_1990s_movies)
# short_movie_count = len(netflix_df[filter_1990s_action_movies])
filter_1990_short_action_movies = (netflix_1990_df["genre"] == "Action") & (netflix_1990_df["duration"] < 90)
netflix_1990_short_action_movies_df = netflix_1990_df[filter_1990_short_action_movies]
short_movie_count = len(netflix_1990_short_action_movies_df)
print(f"Total number of short action movies released in the 1990s is: {short_movie_count}")# Distinct genres
netflix_df["genre"].unique()# Distinct types
netflix_df["type"].unique()