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# Project: Investigating Netflix Movies

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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:

#### netflix_data.csv

ColumnDescription
show_idThe ID of the show
typeType of show
titleTitle of the show
directorDirector of the show
castCast of the show
countryCountry of origin
release_yearYear of Netflix release
durationDuration of the show in minutes
descriptionDescription of the show
genreShow genre
# Title: Analyzing Netflix Movies: Are They Getting Shorter?

# Summary: This Python code uses the pandas and matplotlib libraries to analyze Netflix movie data. It begins by reading the data and creating a subset that includes only movies. The code extracts relevant columns, identifies short movies, assigns colors to genres, and creates a scatter plot of movie duration by the year of release. Finally, it provides an answer to the question of whether movies are getting shorter, which is left open-ended as "maybe." This code showcases data analysis and visualization techniques for exploring trends in movie duration on the Netflix platform.

# Importing pandas and matplotlib
import pandas as pd
import matplotlib.pyplot as plt

# Read the Netflix data into a DataFrame

# Display the first 10 rows of the DataFrame
print(netflix_df[0:10])

# Create a subset of the DataFrame containing only movies
netflix_subset = netflix_df[netflix_df['type'] == 'Movie']

# Define the columns to include in the analysis
subset_columns = ['title', 'country', 'genre', 'release_year', 'duration']

# Extract the relevant columns for further analysis
netflix_movies = netflix_subset[subset_columns]

# Create a subset of short movies with a duration less than 60 minutes
short_movies = netflix_movies[netflix_movies['duration'] < 60]

# Initialize an empty list called colors to store color values
colors = []

# Iterate through the rows of netflix_movies and assign colors based on the genre
for label, row in netflix_movies.iterrows():
genre = row['genre']

if genre == "Children":
colors.append("red")
elif genre == "Documentaries":
colors.append("blue")
elif genre == "Stand-Up":
colors.append("green")
else:
colors.append("black")

# Display the first 10 colors
print(colors[:10])

# Create a scatter plot of movie duration by year of release, coloring the points with the assigned colors
fig = plt.figure(figsize=(12, 8))
_ = plt.scatter(data=netflix_movies, x='release_year', y='duration', c=colors)
_ = plt.xlabel('Release year')
_ = plt.ylabel('Duration (min)')
_ = plt.title('Movie Duration by Year of Release')
_ = plt.show()

# Provide your answer to the question of whether movies are getting shorter