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")netflix_df# Creating a new variable for movies in 1990s
movies_1990 = netflix_df[(netflix_df['type'] == 'Movie') &
(netflix_df['release_year'] >= 1990) &
(netflix_df['release_year'] <= 1999)].copy()
if not movies_1990.empty: # printing out every 1990s movie
for index, row in movies_1990.iterrows():
print(f"ID: {row['show_id']}, {row['type']}, Duration: {row['duration']} minutes, {row['genre']}")
else:
print("There are no movies released in 1990s.")#Calculating duration of the most frequent movie duration in the 1990s, using the mode() method here to find out.
if not movies_1990['duration'].mode().empty:
duration = movies_1990['duration'].mode()[0]
print(f"The most frequent duration in 1990s movies is: {duration} minutes.")
else:
pass
print(movies_1990['duration'].value_counts())# Visualizating the bar chart
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()
# Setting the counter to zero
short_movie_count = 0
# Iterate over the labels and rows of the DataFrame
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(short_movie_count)