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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
    
    # Start coding!
    netflix_df = pd.read_csv('netflix_data.csv')
    netflix_df.head()
    #Create subset of movies only, note == represents true, whereas = is used to assign value
    netflix_subset = netflix_df[netflix_df['type']=='Movie']
    netflix_subset.head()
    #Create further subset removing columns that are not useful to us
    netflix_movies = netflix_subset[['title','country','genre','release_year','duration']]
    netflix_movies.head()
    #Filter further for movies shorter than 60 mins
    short_movies = netflix_movies[netflix_movies['duration']<60]
    short_movies.head(10)
    #Create new list called colors, and using for loop, iterate through rows, appending colors based on genre
    
    colors = []
    
    for _, row in netflix_movies.iterrows():
        # Check the genre and append the corresponding color
        if row['genre'] == 'Children':
            colors.append('Green')
        elif row['genre'] == 'Documentaries':
            colors.append('Yellow')
        elif row['genre'] == 'Standup':
            colors.append('Red')
        else:
            colors.append('Grey')
            
    print(colors)
    # Create plot of duration by release year, colour coded by genre
    fig, ax = plt.subplots()  # Initialize a figure and axes object
    
    # Create a scatter plot
    ax.scatter(netflix_movies['release_year'], netflix_movies['duration'], c=colors, alpha=0.5)
    
    # Set labels and title
    ax.set_xlabel('Release year')
    ax.set_ylabel('Duration (min)')
    ax.set_title('Movie Duration by Year of Release')
    
    plt.show()

    It is hard to say specifically, but the introduction of shorter form "movie" labelled content is driven by an increase in childrens movies and documentaries, both of which are noteably shorted in form that a traditional movie. It would seem that as time has gone on for these types of film, they are still clustering at the top end of the 0-60 mins window.

     #Are we certain that movies are getting shorter?
    answer = "no"