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")#What was the most frequent movie duration in the 1990s?
print(f'What was the most frequent movie duration in the 1990s?')
#Import numpy
import numpy as np
#Get the variables which describe the release_year>=1990 and release_year<2000
release_year_over_1990=netflix_df["release_year"]>=1990
release_year_under_2000=netflix_df["release_year"]<2000
#Create new data frame which will show the results of the logic below
formated_df=netflix_df[np.logical_and(release_year_over_1990,release_year_under_2000)]
#Subset the Movie type
netflix_movies_90s = formated_df[formated_df["type"] == "Movie"]
#Show the results by histogram
plt.hist(netflix_movies_90s["duration"])
plt.title('Movies time duration in the 90s')
plt.xlabel('Time duration of movies(min.)')
plt.ylabel('Number of movies(pcs.)')
plt.xticks([0,25,50,75,100,125,150,175,200])
plt.show()
#Conslusion:
duration=100
print(f'The most frequent movie duration in the 90s was {duration}')
#How much action movies where released in 90s have duration under 90 minutes?
print('How much action movies where released in 90s have duration under 90 minutes?')
#Filter movies under 90 minutes
movies_under_90_min=formated_df[formated_df["duration"] < 90]
#Number of action movies under 90 mins
short_movie_count=0
for label, row in movies_under_90_min.iterrows() :
#Filter movies with action genre
if row["genre"] == "Action" :
short_movie_count = short_movie_count + 1
else:
short_movie_count = short_movie_count
#Conlcusion:
print(f'There are {short_movie_count} action movies in 90s under 90 minutes')