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. Our friend has also been brushing up on their Python skills and has taken a first crack at a CSV file containing Netflix data. They believe that the average duration of movies has been declining. Using your friends initial research, you'll delve into the Netflix data to see if you can determine whether movie lengths are actually getting shorter and explain some of the contributing factors, if any.
You have been supplied with the dataset netflix_data.csv , along with the following table detailing the column names and descriptions:
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
# Start coding!
#Load the CSV file and store as netflix_df
netflix_df = pd.read_csv('netflix_data.csv')
display(netflix_df.head())
# Filter the data to remove TV shows and store as netflix_subset
netflix_subset = netflix_df[netflix_df['type']!='TV Show']
# keeping only the columns "title", "country", "genre", "release_year", "duration", and saving this into a new DataFrame called netflix_movies
netflix_movies = netflix_subset[["title", "country", "genre", "release_year", "duration"]]
# Filter netflix_movies to find the movies that are shorter than 60 minutes, saving the resulting DataFrame as short_movies; inspect the result to find possible contributing factors.
short_movies = netflix_movies[netflix_movies['duration'] < 60]
display(short_movies.head(20))
# Using a for loop and if/elif statements, iterate through the rows of netflix_movies and assign colors of your choice to four genre groups ("Children", "Documentaries", "Stand-Up", and "Other" for everything else). Save the results in a colors list.
color = ['red', 'blue', 'green', 'black']
colors = []
for k, v in netflix_movies['genre'].items():
if v =='Children':
colors.append(color[0])
elif v == 'Documentaries':
colors.append(color[1])
elif v == 'Stand-Up':
colors.append(color[2])
else:
colors.append(color[3])
# Initialize a figure object called fig and create a scatter plot for movie duration by release year using the colors list to color the points and using the labels "Release year" for the x-axis, "Duration (min)" for the y-axis, and the title "Movie Duration by Year of Release".
fig = plt.figure(figsize=(12, 8))
plt.scatter(x= netflix_movies['release_year'], y = netflix_movies['duration'], color = colors)
plt.xlabel('Release year')
plt.ylabel('Duration (min)')
plt.title('Movie Duration by Year of Release')
plt.show()
answer = 'maybe'Here is what I learned from this project.
- Subset the dataframe with
conditional filtering - Subset the dataframe with
columns (and re-order the columns) - Assign values into a list with
for-loop, and use the list to determine the color in the visualization. Visualizethe scatter plot of release_year and duration with matplotlib, and make someadjustmentsto the plot