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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:

    The data

    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
    date_addedDate added to Netflix
    release_yearYear of Netflix release
    durationDuration of the show in minutes
    descriptionDescription of the show
    genreShow genre
    # Importing pandas and matplotlib
    import pandas as pd
    import matplotlib.pyplot as plt
    
    # Read the CSV file and load it into a DataFrame
    netflix_df = pd.read_csv('netflix_data.csv')
    print(netflix_df)
    # Filter the data to remove TV shows and store as netflix_subset
    netflix_subset = netflix_df[netflix_df['type'] == 'Movie']
    print(netflix_subset)
    
    # Investigate the dataset and keep only the columns "title", "country", "genre", "release_year", "duration", and save into a new DataFrame called netflix_movies.
    netflix_movies = netflix_subset[['title', 'country', 'genre','release_year', 'duration']]
    print(netflix_movies)
    # Filter netflix_movies to find the movies that are shorter than 60 minutes, saving the resulting DataFrame as short_movies.
    short_movies = netflix_movies[netflix_movies.duration < 60]
    # 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.
    
    # Initialize an empty list to store colors
    colors = []
    
    # Iterate over rows of netflix_movies
    for label, row in netflix_movies.iterrows() :
        if row["genre"] == "Children" :
            colors.append("red")
        elif row["genre"] == "Documentaries" :
            colors.append("blue")
        elif row["genre"] == "Stand-Up":
            colors.append("green")
        else:
            colors.append("black")
            
    # Inspect the first 10 values in your list        
    colors[:10]
    
    # Set the figure style and initalize a new figure
    fig = plt.figure(figsize=(12,8))
    
    # Create a scatter plot of duration versus release_year
    plt.scatter(netflix_movies.release_year, netflix_movies.duration, c=colors)
    
    # Create a title and axis labels
    plt.title("Movie Duration by Year of Release")
    plt.xlabel("Release year")
    plt.ylabel("Duration (min)")
    
    # Show the plot
    plt.show()
    
    # Are we certain that movies are getting shorter?
    answer = "maybe"