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 |
Perform exploratory data analysis on the netflix_data.csv data to understand more about movies from the 1990s decade.
- What was the most frequent movie duration in the 1990s? Save an approximate answer as an integer called duration (use 1990 as the decade's start year).
- A movie is considered short if it is less than 90 minutes. Count the number of short action movies released in the 1990s and save this integer as short_movie_count.
# Importing pandas and matplotlib
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Read in the Netflix CSV as a DataFrame
netflix_df = pd.read_csv("netflix_data.csv")
display(netflix_df)# Inspecting the dataset.
netflix_df.info()# Subsetting only movie data
netflix_movies = netflix_df[netflix_df['type']=='Movie']# Extracting only 1990s decades movies
nineties_movies = netflix_movies[(netflix_movies["release_year"]>=1990) & (netflix_movies["release_year"]<2000)]
nineties_movies# Visualizing the distribution of movie duration column
# Setting color palette
sns.set_palette('Reds')
# Mode value in duration column
mode = nineties_movies['duration'].mode()[0]
# nineties_movies['duration'].hist(bins=30)
sns.displot(nineties_movies['duration'], kde=True)
plt.title("1990s decades movie duration distribution")
plt.xticks(ticks=range(0, int(nineties_movies['duration'].max())+10, 10), rotation=90)
plt.axvline(mode, color='r', linestyle='--', label=f'Mode: {mode}')
plt.xlabel('Duration (minutes)')
plt.ylabel('Number of Movies')
plt.show()duration = 94The most frequent movie duration in the 1990s is approximately 94 mins.
# Subsetting the dataset to find action movies
action_movies = nineties_movies[nineties_movies['genre']=='Action']
# Again subsetting the dataset to find short action movies
sort_action = action_movies[action_movies['duration'] < 90]
short_movie_count = sort_action['duration'].count()# another way to count short action movies
count = 0
for index, row in action_movies.iterrows():
if row['duration'] < 90:
count += 1
else:
count = count
print(count)The number of short action movies released in the 1990s is 7.