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.

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

Column	Description
`show_id`	The ID of the show
`type`	Type of show
`title`	Title of the show
`director`	Director of the show
`cast`	Cast of the show
`country`	Country of origin
`date_added`	Date added to Netflix
`release_year`	Year of Netflix release
`duration`	Duration of the show in minutes
`description`	Description of the show
`genre`	Show genre

First of all we need to import libraries and then we need to read the file to work with it

# 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")

# Get information about the dataset
print(netflix_df.info(), '\n')

netflix_df.describe()

With the info method we can see that this dataset don't have True missing values like `None` tyoe or `NaN`.

netflix_df.sample(15)

Lets get a visualization of our duration data

netflix_df.hist(bins = 15)
plt.show

For the release year data we have a left bias distribution (negative distribution)

For the duration data we could apreciate that we have something similar to a simetric distribution but the first bin.

Part 1

#Extrac a Series with all the 1990 movies durations
movie_duration_1990 = netflix_df[(netflix_df['release_year'] >= 1990) & (netflix_df['release_year'] <= 1999)]['duration'] 
movie_duration_90s = movie_duration_1990.reset_index(drop=True)
print(movie_duration_90s)

#Visualizate the distribution
movie_duration_90s.hist(bins = 10)

#The mos frequent movie duration refers to the mode concept 

#Calculate the mode
duration = movie_duration_90s.mode()
duration = int(duration)
print(duration)

Now we knoe the distributon and we also know that the mode is close to the mean

Part 2

‌
‌
‌