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1. Loading your 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.

If we're going to be working with this data, we know a good place to start would be to probably start working with pandas. But first we'll need to create a DataFrame from scratch. Let's start by creating a Python object covered in Intermediate Python: a dictionary!

# 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
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)

# 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
netflix_df[:5]
# 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]
# 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
short_movies[0:20]
# 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      
colors[0:10]
# 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.title("Movie duration by year of release")
plt.xlabel("Release year")
plt.ylabel("Release year")

# Show the plot
plt.show()
# Are we certain that movies are getting shorter?
are_movies_getting_shorter = "maybe"
Hidden output