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
df = pd.read_csv("netflix_data.csv")# Start coding here! Use as many cells as you like
df.shape # Total number of rows and columns respectivelydf.head(5) # Preview of the netflix dataprint(f"Are there any duplicates? {df.isna().values.any()}")
print(f"Are there any duplicates? {df.duplicated().values.any()}")Number of Movies filmed in the 1990s
# Extract the Movies released in 1990s
movies_90s = df[(df['release_year'] >= 1990) & (df['release_year'] < 2000) & (df['type'] == 'Movie')]
# Find the most frequent movie duration
duration = movies_90s['duration'].mode()[0]
print(f"The most frequent movie duration is: {duration}")# Extract the Movies released in 1990s
durations = df[(df['release_year'] >= 1989) & (df['release_year'] <= 2000) & (df['type'] == 'Movie')]
durationsplt.hist(durations['release_year'], bins=10, edgecolor='black')
plt.xlabel('Release Year')
plt.ylabel('Frequency')
plt.title('Distribution of Movies Released After 1990')
plt.tight_layout()
plt.show()- Action Movies in the year 1999 was on the all time high 📈
df.genre.value_counts() # To view the different genresFind the total shortest action movie
# Extract the 1990s Action Movies
subset_90s_action_short = df[(df['release_year'] >= 1990) & (df['release_year'] < 2000) &
(df['type'] == 'Movie') & (df['genre'] == 'Action') &
(df['duration'] < 90)]
subset_90s_action_short# Set a counter
short_action_count = 0
for label, row in subset_90s_action_short.iterrows():
if row['duration'] > 90:
short_action_count -= 1
else:
short_action_count += 1
print(f"The Total 90s Short Action Movies: {short_movie_count}")