Start Your Project with Us

Whatever your project size is, we will handle it well with all the standards fulfilled! We are here to give 100% satisfaction.

  • Any feature, you ask, we develop
  • 24x7 support worldwide
  • Real-time performance dashboard
  • Complete transparency
  • Dedicated account manager
  • Customized solutions to fulfill data scraping goals

For job seekers, please visit our Career Page or send your resume to


Netflix is an OTT platform where it’s easy to watch unlimited movies and Shows. You can extract Netflix data to collect all episode names, ratings, cast, plan pricing, similar shows, etc. With this data, it’s easy to analyze what the users watch these days, and it will help in sentiment analysis.

We will use Python here to scrape Netflix data. We assume that you have installed Python on your PC. Let’s start with data scraping now!

Scrape Netflix Data

To start here, we will make a folder to install the different libraries we need during this tutorial.

Here, we will install a couple of libraries

1. Requests will assist us in making an HTTP connection using Netflix.

2. BeautifulSoup will assist us in making an HTML tree to get smooth data scraping.


We will extract Netflix page data. Within this folder, it’s easy to make a Python file where we would write the code. Our interest would be:

1.jpg 2.jpg 3.jpg
  • Name of show
  • Total seasons
  • Subject
  • Episode Names
  • Genre
  • Episode Overview
  • Category
  • Cast
  • Social media links

We understand this is a longer data list; however, in the end, you will get a readymade code to scrape Netflix data for any page.

Let’s find the locations of all these elements


The title gets stored under the h1 tag of a class title-title.


Total seasons get stored under the span tag in a duration class.


The about segment gets stored under the div tag in a class hook-text.


The episode’s title gets stored under the p tag having class episode-synopsis.


Genre gets stored under the span tag having class item-genres.


The show category data gets stored under the span tag having a class item-mood-tag.


Social Media links could be available under the tag having a class name called social-link.


The cast gets stored under the span tag having class item-cast.

Let’s begin with making the regular GET requests to the targeted webpage and observe what happens.


If you find 200, then you have successfully extracted our targeted page. Now, let’s scrape details from this data with BeautifulSoup.


Let us initially scrape all data properties in sequence. As discussed here, we would be using similar HTML locations.


Now, let’s scrape the episode data.


The whole data is within ol tag. Therefore, we initially get the ol tag and all li tags within it. After that, we utilized a loop to scrape title & description data.

Now, let’s scrape the genre data.


The genre could be available under the class item-genre. Here, we have utilized a loop to scrape all genres.

Let’s scrape the rest of the data properties having similar techniques.

Lets-scrape-the-rest-of-the-data-properties-having-similar-techniques.jpg We-have-managed-to-scrape-all-the-data-from-Netflix.jpg

We have succeeded in extracting all data from Netflix.

Complete Code


Using this code, we have extracted Name, Seasons name, Subject, Genre, Mood, Cast, Social links, etc. By making some changes in this code, you can scrape data from Netflix.


You can utilize Web Scraping API for scraping data from Netflix without being blocked. This is a fast way to scrape complete Netflix pages. By changing a show title ID you can extract nearly all shows from Netflix. You need to get IDs of these shows. Instead of BS4, you can use Xpath for creating HTML tree for web scraping services.

We hope you have liked this small tutorial about scraping Netflix data. Let us know if you want any help with your web extraction and Mobile App Scraping Services demands.


View More

Top Amazon Scraping APIs 2024 – For Better Data Extraction and Analysis

Discover the top Amazon scraping APIs of 2024 for enhanced data extraction and analysis, boosting your ecommerce insights.

Location Intelligence Web Scraping in 2024 – Get Better Data Insights

Leverage location intelligence web scraping in 2024 to gain valuable geographic insights, optimize operations, and enhance decision-making for business success.


View More

Review Analysis of McDonald’s in Orlando - A Comparative Study with Burger King

Analyzing McDonald’s reviews in Orlando alongside Burger King to uncover customer preferences and satisfaction trends.

Actowiz Solutions Growth Report

Actowiz Solutions: Empowering Growth Through Innovative Solutions. Discover our latest achievements and milestones in our growth report.

Case Studies

View More

Case Study - Revolutionizing Medical Price Comparison with Actowiz Solutions

Revolutionizing healthcare with Actowiz Solutions' advanced medical data scraping and price comparison, ensuring transparency and cost savings for patients.

Case Study - Empowering Price Integrity with Actowiz Solutions' MAP Monitoring Tools

This case study shows how Actowiz Solutions' tools facilitated proactive MAP violation prevention, safeguarding ABC Electronics' brand reputation and value.


View More

Maximize Growth with Price Sensitivity and Price Matching in 2024

Maximize growth in 2024 with insights on price sensitivity, price matching, price scraping, and effective pricing data collection techniques.

Unleash the power of e-commerce data scraping

Leverage the power of e-commerce data scraping to access valuable insights for informed decisions and strategic growth. Maximize your competitive advantage by unlocking crucial information and staying ahead in the dynamic world of online commerce.