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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")
print(netflix_df['type'])

What was the most frequent movie duration in the 1990s?

# What was the most frequent movie duration in the 1990s?
# 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")

#filter the dataframe for movies from the 1990s
netflix_1990s = netflix_df[
    (netflix_df['release_year'] >= 1990) & 
    (netflix_df['release_year'] <= 1999) & 
    (netflix_df['type'] == 'Movie')]

#duration histogram
plt.hist(netflix_1990s['duration'], bins=100)
plt.show()

#find the most frequent duration value
duration = netflix_1990s['duration'].mode()[0]
print('The most frequent duration: ' + str(duration) + ' mins')

Count the number of short action movies (less than 90 mins) released in the 1990s

#Count the number of short  action movies (less than 90 mins) released in the 1990s
# Importing pandas 
import pandas as pd

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

#filter the dataframe for movies from the 1990s
netflix_1990s = netflix_df[
    (netflix_df['release_year'] >= 1990) & 
    (netflix_df['release_year'] <= 1999) & 
    (netflix_df['genre'] == 'Action') & 
    (netflix_df['type'] == 'Movie')]

#Count the number of short  action movies (less than 90 mins) released in the 1990s
short_movie_count = (netflix_1990s['duration'] < 90).sum()
print('The number of short action movies is: ' + str(short_movie_count))

Using For loops

What was the most frequent movie duration in the 1990s?

# What was the most frequent movie duration in the 1990s?
# Importing pandas 
import pandas as pd

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

#filter the dataframe for movies from the 1990s
netflix_1990s = netflix_df[
    (netflix_df['release_year'] >= 1990) & 
    (netflix_df['release_year'] <= 1999) & 
    (netflix_df['type'] == 'Movie')]

#For loop
#Se crea un diccionario, si el valor ya esta en el diccionario se incrementa en 1, de lo contrario inicia su contador en 1
count = {}
for i in netflix_1990s['duration']:
    if i in count:
        count[i] += 1
    else:
        count[i] = 1
        
#find the most frequent duration value
duration = max(count, key = count.get)
frecuency = count[duration]
print('The most frequent duration: ' + str(duration) + ' mins' + ' with a frecuency of ' + str(frecuency))

Count the number of short action movies (less than 90 mins) released in the 1990s

#Count the number of short  action movies (less than 90 mins) released in the 1990s
# Importing pandas 
import pandas as pd

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

#filter the dataframe for movies from the 1990s
netflix_1990s = netflix_df[
    (netflix_df['release_year'] >= 1990) & 
    (netflix_df['release_year'] <= 1999) & 
    (netflix_df['genre'] == 'Action') & 
    (netflix_df['type'] == 'Movie') & 
    (netflix_df['duration'] < 90)]

# counter
count = {}
for i in netflix_1990s['show_id']:
    if i  in count:
        count[i] += 1
    else:
        count[i] = 1

#Count the number of short  action movies (less than 90 mins) released in the 1990s
short_movie_count = sum(count.values())

print('The number of short action movies is: ' + str(short_movie_count))

Prefiltering the dataframe

#Count the number of short  action movies (less than 90 mins) released in the 1990s
# Importing pandas 
import pandas as pd

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

#filter the dataframe for movies from the 1990s
netflix_1990s = netflix_df[
    (netflix_df['release_year'] >= 1990) & 
    (netflix_df['release_year'] <= 1999) & 
    (netflix_df['genre'] == 'Action') & 
    (netflix_df['type'] == 'Movie') & 
    (netflix_df['duration'] < 90)]

#Count the number of short  action movies (less than 90 mins) released in the 1990s
short_movie_count = netflix_1990s['show_id'].nunique()

print('The number of short action movies is: ' + str(short_movie_count))