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 likeFirst of all I am going to answer project Q. :)
Get all the movies for 1990s
# Filter the DataFrame for movies released in the 1990s
netflix_90s_movies_df = netflix_90s_df[(netflix_90s_df['release_year'] >= 1990) & (netflix_90s_df['release_year'] < 2000) & (netflix_90s_df['type'] == 'Movie')]
# Display the first few rows of the DataFrame
netflix_90s_movies_df.head()Now I want to know most frequent duration of these movies
# Calculate the most frequent duration of movies in the 1990s
most_frequent_duration = netflix_90s_movies_df['duration'].mode()[0]
# Display the most frequent duration
print(most_frequent_duration)
duration=most_frequent_durationExcellent, now count the number of movies in netflix_90s_movies_df less than 90 minutes
# Count the number of action movies with a duration less than 90 minutes
action_movies_less_than_90_min = netflix_90s_movies_df[(netflix_90s_movies_df['duration'] < 90) & (netflix_90s_movies_df['genre'] == 'Action')].shape[0]
# Display the count
print(action_movies_less_than_90_min)
short_movie_count = action_movies_less_than_90_minNow I will explore creating visuals.
I want to create a graph with the quantity of movies of every genre, please create 2 visuals.
Let's create two visuals to explore the quantity of movies for each genre in the netflix_90s_movies_df dataset. We will use Plotly to create these visuals.
import plotly.express as px
import pandas as pd
# Group the data by genre and count the number of movies in each genre
genre_count_df = netflix_90s_movies_df.groupby('genre').size().reset_index(name='count')
# First visual: Bar chart of the number of movies per genre
fig1 = px.bar(genre_count_df, x='genre', y='count', title='Number of Movies per Genre (Bar Chart)',
labels={'count': 'Number of Movies', 'genre': 'Genre'})
fig1.show()