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")
#%%
# Filter the DataFrame for movies released in the 1990s
netflix_90s = netflix_df[(netflix_df['release_year'] >= 1990) & (netflix_df['release_year'] < 2000)]
# Count the number of movies released each year in the 1990s
movies_per_year = netflix_90s['release_year'].value_counts().sort_index()
# Plot the data
plt.figure(figsize=(10, 10))
movies_per_year.plot(kind='bar', color='red')
plt.title('Number of Movies Released on Netflix in the 1990s')
plt.xlabel('Year')
plt.ylabel('Number of Movies')
plt.show()
#%%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")
# Count the number of productions per country
country_productions = netflix_df['country'].value_counts()
# Convert the Series to a list of tuples
country_productions_list = list(country_productions.items())
# Display the list of countries with their number of productions
print(country_productions_list)
# Aggregate number of productions per release year
yearly_productions = netflix_df['release_year'].value_counts().sort_index()
plt.figure(figsize=(10, 6))
# Plot the number of productions per year
plt.plot(yearly_productions.index, yearly_productions.values, linestyle='-')
plt.xlabel('Release Year')
plt.ylabel('Number of Productions')
plt.title('Number of Productions per Year')
plt.grid(True)
plt.show()