Ishwer Kumar Yadav/

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 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_df[netflix_df["type"] == "movie"]
netflix_df[netflix_df['duration'] > 120]
for index, row in netflix_df.iterrows():
    if row['genre'] == "Children":
    elif row['genre'] == "Documentaries":
    elif row['genre'] == "Stand up":
import matplotlib.pyplot as plt

fig = plt.figure(figsize=(12, 8))
plt.scatter(netflix_df['release_year'], netflix_df['duration'])
plt.xlabel('Release year')
plt.ylabel('Duration (min)')
plt.title('Movie Duration by Year of Release')
netflix_df = pd.read_csv('netflix_data.csv')
# Importing pandas and matplotlib
import pandas as pd
import matplotlib.pyplot as plt

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

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

# Select only the columns of interest
netflix_movies = netflix_subset[["title", "country", "genre", "release_year", "duration"]]

# Filter for durations shorter than 60 minutes
short_movies = netflix_movies[netflix_movies.duration < 60]

# Define an empty list
colors = []

# Iterate over rows of netflix_movies
for label, row in netflix_movies.iterrows() :
    if row["genre"] == "Children" :
    elif row["genre"] == "Documentaries" :
    elif row["genre"] == "Stand-Up":
# Inspect the first 10 values in your list        

# Set the figure style and initalize a new figure
fig = plt.figure(figsize=(12,8))

# Create a scatter plot of duration versus release_year
plt.scatter(netflix_movies.release_year, netflix_movies.duration, c=colors)

# Create a title and axis labels
plt.title("Movie Duration by Year of Release")
plt.xlabel("Release year")
plt.ylabel("Duration (min)")

# Show the plot

# Are we certain that movies are getting shorter?
answer = "maybe"
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