AutoScraper Tutorial – A Python Tool For Automating Web Scraping

Autoscraper is a smart, automatic. Fast and lightweight web scraper for python. It makes web scraping an easy task. It gets a URL or the HTML content of a web page and a list of sample data that we want to scrape from that page.
Autoscraper Banner


Web Scraping is a technique used for scraping from the internet and storing it locally on your system. It is used to scrape data from different websites using Hypertext transfer protocol. Web Scraping is used by a large number of companies that work on Data Harvesting. It is used to create Search Engine bots. 

Autoscraper is a smart, automatic. Fast and lightweight web scraper for python. It makes web scraping an easy task. It gets a URL or the HTML content of a web page and a list of sample data that we want to scrape from that page. It is easy as we only need to write a few lines of code, it’s blazingly fast because it is lightweight and It learns the scraping rules and returns the similar elements.


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In this article, we will explore Autoscraper and see how we can use it to scrape data from the web.


Autoscraper can be installed using the git repository where it is hosted. Before Installing autoscraper you need to download and install the git version according to your operating system. After git is installed we can install autoscraper by running the below-given command in the command prompt.

pip install git+

  1. Importing Required Libraries

We will only import autoscraper as it is sufficient for web scraping alone.

from autoscraper import AutoScraper

  1. Defining Web Scraping function 

Let us start by defining a URL from which will be used to fetch the data and the required data sample which is to be fetched. Here I will fetch titles for different articles on NLP published in Analytics India Magazine.

url = '' 

category = ["8 Open-Source Tools To Start Your NLP Journey"]

  1. Initiate AutoScraper

The next step is calling the AutoScraper function so that we can use it to build the scraper model and perform a web scraping operation. 

scraper = AutoScraper()

  1. Building The object

This is the final step where we create the object and display the result of the web scraping.

scrape = AutoScraper()

final =, category)


Here we saw that it returns the title of the topic based on NLP, similarly, we can also retrieve URLs of the Article by just passing the sample URL in the category we defined above.

category = [""]

scrape = AutoScraper()

final =, category)


Desired URL's
  1. Function for Similar Result

Autoscraper allows you to use the model you build for fetching similar data from a different URL. We need to use the ‘get_result_similar’ function to fetch similar data. In this step, we will retrieve the URLs of different articles on Image Processing.


Similar url's from other link
  1. Function for Exact Result

Instead of getting the similar results sometimes, we want the exact result of the query, autoscraper has the functionality of getting the exact result which means that if we are using the sample URL/Data on the first link then the exact result will also fetch the exact first link of the mentioned URL.


Exact result
  1. Saving the Model

Autoscraper allows us to save the model created and load it whenever required.‘AIM’)   #saving the model

scrape.load(‘AIM’)  #loading the model

Other than all these functionalities autoscraper also allows you to define proxy IP Addresses so that you can use it to fetch data. We just need to define the proxies and pass it as an argument to the build function like the example given below.

proxy = {

    "http": '',

    "https": '',


final =, category, request_args=dict(proxies=proxy))


In this article, we saw how we can use Autoscraper for web scraping by creating a simple and easy to use model. We saw different formats in which data can be retrieved using Autoscraper. We can also save and load the model for using it later which saves time and effort. Autoplotter is powerful, easy to use and time-saving.

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Himanshu Sharma
An aspiring Data Scientist currently Pursuing MBA in Applied Data Science, with an Interest in the financial markets. I have experience in Data Analytics, Data Visualization, Machine Learning, Creating Dashboards and Writing articles related to Data Science.

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