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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


    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_subset = netflix_df[netflix_df['type'] == 'Movie']
    #remove TV Shows
    netflix_movies = pd.DataFrame(netflix_subset[['title','country', 'genre', 'release_year', 'duration']]) 
    #View only short movies
    short_movies = netflix_movies[netflix_movies['duration'] < 60]
    #View only older movies
    older_movies = netflix_movies[netflix_movies['release_year'] < 2005]
    #Set colors for genres (short movies)
    colora = []
    for lab, row in short_movies.iterrows():
        if row['genre'] == 'Children' :
        elif row['genre'] == 'Documentaries' :
        elif row['genre'] == 'Stand-Up' :
    #set colors for genres (all movies)
    colors = []
    for lab, row in netflix_movies.iterrows():
        if row['genre'] == 'Children' :
        elif row['genre'] == 'Documentaries' :
        elif row['genre'] == 'Stand-Up' :
    #Plot of short movies 
    a = short_movies['release_year']
    b = short_movies['duration']
    fig = plt.figure(figsize = (12,8))
    plt.scatter(x = a, y = b, c = colora)
    plt.xlabel("Release year")
    plt.ylabel("Duration (min)")
    plt.title("Movie Duration by Year of Release", fontweight = 'bold')
    #Plot of all movies 
    x = netflix_movies['release_year']
    y = netflix_movies['duration']
    fig2 = plt.figure(figsize = (12,8))
    plt.scatter(x = x, y = y, c = colors)
    plt.xlabel("Release year")
    plt.ylabel("Duration (min)")
    plt.suptitle("Movie Duration by Year of Release", fontweight = 'bold')
    plt.title("Correlation: " + str(x.corr(y)))
    #There is a small negative correlation between release date and duration, indicating that movies are getting shorter. Since there seems to be a lot more short movies (under 60 min) since 2005 it is worth checking what difference removing them makes:
    older_movies = netflix_movies[netflix_movies['release_year'] < 2005]
    x2 = older_movies['release_year']
    y2 = older_movies['duration']
    print("correlation up to 2005: ")
    #This is a big reduction meaning the correlation is mostly driven by an increase in short movies since 2005.
    #Are we certain that movies are getting shorter?
    answer = "maybe"