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, numpy and matplotlib
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
# Read in the Netflix CSV as a DataFrame
netflix_df = pd.read_csv("netflix_data.csv")# Examining the data
netflix_df.head()# More information on the dataset
netflix_df.info()# Section 1
# Retrieve the duration specific data from the dataset (duration, release_year and genre)
duration_and_release_data = netflix_df.loc[:, ["duration", "release_year", "genre"]]
filtered_duration_and_release_data = duration_and_release_data[
np.logical_and(duration_and_release_data["release_year"] >= 1990,
duration_and_release_data["release_year"] < 2000)]
filtered_duration_and_release_data.head()# Visualize the distribution of movie durations
plt.hist(filtered_duration_and_release_data["duration"])
plt.title("Duration Distribution")
plt.ylabel("Count of movies")
plt.xlabel("Durations(mins)")
plt.grid(True)
plt.show()# Finding the mean (something additional)
average_count = filtered_duration_and_release_data["duration"].mean()
print("Average : ", average_count)
# Finding the most frequent duration using value_counts()
frequent_durations = filtered_duration_and_release_data["duration"].value_counts()
# Saving the most frequent as duration (answer)
duration = frequent_durations.index[0]
print("Most frequent duration: ", duration)# Section 2
# Filtering the short action movies
short_action_movies = filtered_duration_and_release_data[np.logical_and(
filtered_duration_and_release_data["duration"] < 90,
filtered_duration_and_release_data["genre"] == "Action")]
short_action_movies.head()# Getting the count of short action movies
short_movie_count = short_action_movies.count()[0]
print("Short Action Movie Count: " , short_movie_count)