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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.

You work for a production company that specializes in nostalgic styles. You want to do some research on movies released in the 1990's. You'll delve into Netflix data and perform exploratory data analysis to better understand this awesome movie decade!

You have been supplied with the dataset netflix_data.csv, along with the following table detailing the column names and descriptions. Feel free to experiment further after submitting!

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 in minutes
descriptionDescription of the show
genreShow genre
# 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")
netflix_df.head(5)
# Subset the DataFrame for type "Movie"
netflix_df_movies_only =netflix_df.loc[netflix_df['type']=='Movie',['type','title','country','genre','release_year','duration']]

# Select only the columns of interest
netflix_movies_col_subset = netflix_df_movies_only

# Print the first five rows of the new DataFrame
netflix_movies_col_subset
# 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 the dataset to get data from 1990 to 1999
filtered_data = netflix_df_movies_only[(netflix_df_movies_only['release_year'] >= 1990) & (netflix_df_movies_only['release_year'] <= 1999)]

# Display the filtered dataset
print(filtered_data)
# Find the most frequent movie duration in the 1990s
duration = filtered_data['duration'].mode()[0]  # [0] extracts the first mode value
print(f"The most frequent movie duration is: {duration} minutes")
# Filter for Action movies with duration less than 90 minutes from the 1990s
action_short_movies = filtered_data[(filtered_data['genre'] == 'Action') & (filtered_data['duration'] < 90)]

# Count the number of these short action movies
short_movie_count = action_short_movies.shape[0]

print(f"The number of short action movies released in the 1990s is: {short_movie_count}")