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Project: Investigating Netflix Movies

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 data manipulation 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 have been performing some analyses, and they believe that the average duration of movies has been declining. Using your friends initial research, you'll delve into the Netflix data to if you can explain some of the factors that may be contributing to the shortening movie lengths.

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
descriptionDescription of the show
genreShow genre
# Importing pandas and matplotlib
import pandas as pd
import matplotlib.pyplot as plt

# Start coding!
netflix_movies = pd.read_csv('netflix_data.csv')
netflix_movies.head()
# Filtering the data by movie type
movie_type = netflix_movies[netflix_movies['type'] == 'Movie']

# Subsetting the columns
movie_data = movie_type[['type', 'title', 'director', 'cast', 'country', 'date_added','release_year',  'duration',  'description', 'genre']]
movie_data.head()
movie_duration = movie_type[movie_type['duration'] < 60]
movie_duration.head(20)
colors = []

# Iterate through each row of the DataFrame
for index, movie in movie_duration.iterrows():
    # Check if the genre is "Children", "Documentaries", or "Stand-Up"
    if movie['genre'] == 'Children':
        colors.append('blue')
    elif movie['genre'] == 'Documentaries':
        colors.append('green')
    elif movie['genre'] == 'Stand-Up':
        colors.append('yellow')
    # If it's none of the above genres, add a default color
    else:
        colors.append('red')

print(colors)
# Grouping movies by year and calculating the mean duration
movie_duration_by_year = movie_duration.groupby('release_year')['duration'].mean()

# Creating a bar plot with the genre colors
plt.bar(movie_duration_by_year.index, movie_duration_by_year.values, color=colors)

# Adding labels and title
plt.xlabel('Year')
plt.ylabel('Average Duration (minutes)')
plt.title('Average Movie Duration by Year')

# Displaying the plot
plt.show()
# Creating a scatter plot
plt.scatter(movie_duration['release_year'], movie_duration['duration'])

# Adding labels and title
plt.xlabel('Year')
plt.ylabel('Duration (minutes)')
plt.title('Movie Duration by Year')

# Displaying the plot
plt.show()
# Creating a bar plot with the genre colors
plt.bar(movie_duration_by_year.index, movie_duration_by_year.values, color=colors)

# Adding labels and title
plt.xlabel('Year')
plt.ylabel('Average Duration (minutes)')
plt.title('Average Movie Duration by Year')

# Displaying the plot
plt.show()
# Creating a scatter plot
plt.scatter(movie_duration['release_year'], movie_duration['duration'])

# Adding labels and title
plt.xlabel('Year')
plt.ylabel('Duration (minutes)')
plt.title('Movie Duration by Year')

# Displaying the plot
plt.show()
# Creating a scatter plot with titles and colors
plt.scatter(movie_duration['release_year'], movie_duration['duration'], color='purple')

# Adding labels and title
plt.xlabel('Year')
plt.ylabel('Duration (minutes)')
plt.title('Movie Duration by Year')

# Displaying the plot
plt.show()

Yes the movies are getting shorter