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")import numpy as np
# Filter out "type" = Movie and "release_year" between 1990 and 1999 inclusive
netflix_df_filter = netflix_df[np.logical_and(netflix_df["type"] == 'Movie',
np.logical_and(netflix_df["release_year"] >= 1990,
netflix_df["release_year"] <= 1999))]
# Find the most frequent movie duration
duration = netflix_df_filter['duration'].mode()[0]
# Further filter the DataFrame for "genre" = 'Action'
netflix_df_filter_action = netflix_df_filter[netflix_df_filter["genre"] == 'Action']
# Display the filtered DataFrame
netflix_df_filter_action
# Count the number of rows in netflix_df_filter_action where 'duration' is below 90
short_movie_count = netflix_df_filter_action[netflix_df_filter_action['duration'] < 90].shape[0]
short_movie_count = int(short_movie_count)
# Display the results in the report
from IPython.display import display, Markdown
display(Markdown(f"**Tasks needed to finish project:**"))
display(Markdown(f"**Determine most frequent duration of movies back in the 90's:** {duration} minutes"))
display(Markdown(f"**How many action movies in the 90's below 90 minutes:** {short_movie_count} movies"))IN ADDITION TO THE TASKS TO COMPLE THE PROJECT, WHAT ADDITIONAL INSIGHTS CAN WE PULL FROM THE DATA?
The subsequent content will shed more light on the dataset we are currently using.