# Create the years and durations lists years = [2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020] durations = [103, 101, 99, 100, 100, 95, 95, 96, 93, 90] # Create a dictionary with the two lists movie_dict = {} movie_dict["years"] = years movie_dict["durations"] = durations # Print the dictionary print(movie_dict)

# Import pandas under its usual alias import pandas as pd # Create a DataFrame from the dictionary durations_df = pd.DataFrame(movie_dict) # Print the DataFrame print(durations_df)

# Import matplotlib.pyplot under its usual alias and create a figure import matplotlib.pyplot as plt fig = plt.figure() # Draw a line plot of release_years and durations plt.plot(years, durations) plt.xlabel('release_years') plt.ylabel('durations') # Create a title plt.title('Netflix Movie Durations 2011-2020') # Show the plot plt.show()

# Read in the CSV as a DataFrame netflix_df = pd.read_csv("datasets/netflix_data.csv") # Print the first five rows of the DataFrame print(netflix_df.head(5))

# Subset the DataFrame for type "Movie" netflix_df_movies_only = netflix_df[netflix_df["type"] == "Movie"] # Select only the columns of interest col = ["title", "country", "genre", "release_year", "duration"] netflix_movies_col_subset = netflix_df_movies_only[col] # Print the first five rows of the new DataFrame print(netflix_movies_col_subset.head(5))

# Create a figure and increase the figure size fig = plt.figure(figsize=(12,8)) # Create a scatter plot of duration versus year plt.scatter(netflix_movies_col_subset['release_year'], netflix_movies_col_subset['duration']) # Create a title plt.title("Movie Duration by Year of Release") # Show the plot plt.show()

# Filter for durations shorter than 60 minutes short_movies = netflix_movies_col_subset[netflix_movies_col_subset["duration"] < 60] # Print the first 20 rows of short_movies print(short_movies.head(20))

# Define an empty list colors = [] # Iterate over rows of netflix_movies_col_subset for lab, row in netflix_movies_col_subset.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 print(colors[:11])

# Set the figure style and initalize a new figure plt.style.use('fivethirtyeight') fig = plt.figure(figsize=(12,8)) # Create a scatter plot of duration versus release_year plt.scatter(netflix_movies_col_subset["release_year"], netflix_movies_col_subset["duration"], c = colors) # Create a title and axis labels plt.xlabel("Release year") plt.ylabel("Duration (min)") plt.title("Movie duration by year of release") # Show the plot plt.show()

# Are we certain that movies are getting shorter? are_movies_getting_shorter = "We do not know for sure if movies are getting shorter, to get a more accurate answer it is necessary to perform additional analysis. However, with the information we have we can say that no, there is no tendency for movies to get shorter, most of them are in the range of 160 and 90 minutes, which we could say is the average length of movies nowadays."