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
import numpy as np
# Read in the Netflix CSV as a DataFrame
netflix_df = pd.read_csv("netflix_data.csv")
netflix_df# Start coding here! Use as many cells as you like
# First filter DataFrame, using boolean NumPy series
# Get only 90s as release year:
is_90s = np.logical_and(netflix_df["release_year"] >= 1990, netflix_df["release_year"] <= 1999)
# Now combine that with type = Movie
is_90s_movie = np.logical_and(is_90s, netflix_df["type"] == "Movie")
# is_90s_movie will now have numpy series of required dataset!
req_netflix_df = netflix_df[is_90s_movie].copy()
# Now visualize the most frequest movie duration using Histogram
plt.hist(req_netflix_df["duration"],30, edgecolor = "black")
plt.xlabel("Duration")
plt.ylabel("Frequency")
plt.show()
# After manually observing the histogram, identify the most frequent movie duration
duration = 105
# Steps to determine 'short_movie_count'
# Get only Action and duration < 90: is_90s_movie_action_short
is_90s_movie_action_short = np.logical_and(req_netflix_df["genre"] == "Action" , req_netflix_df["duration"] < 90)
#type(is_90s_movie_action_short)
final_req_df = req_netflix_df[is_90s_movie_action_short].copy()
temp_count = len(final_req_df)
# type(final_req_df)
# final_req_df.describe()Here I am using the len function on DataFrame as all the required filters are applied, so no need of a 'for' loop
short_movie_count = temp_count
print(f"Count of short action movies from 1990s: {short_movie_count} ")