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")# Start coding here! Use as many cells as you like
import pandas as pd
import matplotlib.pyplot as plt
netflix_df = pd.read_csv("netflix_data.csv")
#first problem
#subset the dataframe to get the movies in range 1990-1999
netflix_1990s = netflix_df[(netflix_df["release_year"] > 1989) & (netflix_df["release_year"] < 2000)]
print(netflix_1990s["duration"])
#counting the number of duration to get the most frequent and sorting it
netflix_number_duration = netflix_1990s["duration"].value_counts().reset_index(name="count").rename(columns={"index":"duration"})
print(netflix_number_duration)
duration = netflix_number_duration.iloc[0,0]
count = netflix_number_duration.iloc[0,1]
print(f"The duration that is most frequent in the 1990s is {duration} with {count} movies")
#second problem
#subset the original dataframe to filter movies in the 1990s
netflix_1990s = netflix_df[(netflix_df["release_year"] > 1989) & (netflix_df["release_year"] < 2000)]
#Subset to get under 90 mins duration and action as genre
short_action_movies_1990s = netflix_1990s[(netflix_1990s["duration"] < 90) & (netflix_1990s["genre"] == "Action") & (netflix_1990s["type"] == "Movie")]
print(short_action_movies_1990s)
#Count the number of movies
short_movie_count = short_action_movies_1990s["title"].count()
print(short_movie_count)