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1. Friend's data into a dictionary

Someone's feet on table facing a television

Netflix! What started in 1997 as a DVD rental service has since exploded into the largest entertainment/media company by market capitalization, boasting over 200 million subscribers as of January 2021.

Given the large number of movies and series available on the platform, it is a perfect opportunity to flex our 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. For their first order of business, they have been performing some analyses, and they believe that the average duration of movies has been declining.

As evidence of this, they have provided us with the following information. For the years from 2011 to 2020, the average movie durations are 103, 101, 99, 100, 100, 95, 95, 96, 93, and 90, respectively.

# 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 = {'years': years, 'durations': durations}

# Print the dictionary
movie_dict

2. Creating a DataFrame from a dictionary

# 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
durations_df

3. A visual inspection of our data

# 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)

# Create a title
plt.title("Netflix Movie Durations 2011-2020")

# Show the plot
plt.show()

4. Loading the rest of the data from a CSV

# Read in the CSV as a DataFrame
netflix_df = pd.read_csv("datasets/netflix_data.csv")

# Print the first five rows of the DataFrame
netflix_df.head()

5. Filtering for movies!

# Subset the DataFrame for type "Movie"
netflix_df_movies_only = netflix_df[netflix_df['type'] == 'Movie']

# Select only the columns of interest
netflix_movies_col_subset = netflix_df_movies_only[['title', 'country', 'genre', 'release_year', 'duration']]

# Print the first five rows of the new DataFrame
netflix_movies_col_subset[0:5]

6. Creating a scatter plot

# 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()

7. Digging deeper

# 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
short_movies[0:20]

8. Marking non-feature films

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