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Alireza Mika
Alireza Mika

Posted on • Updated on

Smart Automatic Web Scraping in Python

In the last few years, web scraping has been one of my day to day and frequently needed tasks. I was wondering if I can make it smart and automatic to save lots of time. So I made AutoScraper!

The project code is available on github:

This project is made for automatic web scraping to make scraping easy.
It gets a url or the html content of a web page and a list of sample data which we want to scrape from that page. This data can be text, url or any html tag value of that page. It learns the scraping rules and returns the similar elements. Then you can use this learned object with new urls to get similar content or the exact same element of those new pages!


Install latest version from git repository using pip:

$ pip install git+

How to use

Getting similar results

Say we want to fetch all related post titles in a stackoverflow page:

from autoscraper import AutoScraper

url = ''

# We can add one or multiple candidates here.
# You can also put urls here to retrieve urls.
wanted_list = ["How to call an external command?"]

scraper = AutoScraper()
result =, wanted_list)

Here's the output:

    'How do I merge two dictionaries in a single expression in Python (taking union of dictionaries)?', 
    'How to call an external command?', 
    'What are metaclasses in Python?', 
    'Does Python have a ternary conditional operator?', 
    'How do you remove duplicates from a list whilst preserving order?', 
    'Convert bytes to a string', 
    'How to get line count of a large file cheaply in Python?', 
    "Does Python have a string 'contains' substring method?", 
    'Why is β€œ1000000000000000 in range(1000000000000001)” so fast in Python 3?'

Now you can use the scraper object to get related topics of any stackoverflow page:


We can also append a topic from linked topics section to the wanted list to get all related & linked topics! Or if we want to get its urls, we can add one of the urls too.

Getting exact result

Say we want to scrape live stock prices from yahoo finance:

from autoscraper import AutoScraper

url = ''

wanted_list = ["124.81"]

scraper = AutoScraper()

# Here we can also pass html content via the html parameter instead of the url (html=html_content)
result =, wanted_list)

You can also pass any custom requests module parameter. for example you may want to use proxies or custom headers:

proxies = {
    "http": '',
    "https": '',

result =, wanted_list, request_args=dict(proxies=proxies))

Now we can get the price of any symbol:


You may want to get other info as well. For example if you want to get market cap too, you can just append it to the wanted list. By using the get_result_exact method, it will retrieve the data as the same exact order in the wanted list.

Saving the model

We can now save the built model to use it later. To save:

# Give it a file path'yahoo-finance')

And to load:


Generating the scraper python code

We can also generate a stand-alone code for the learned scraper to use it anywhere:

code = scraper.generate_python_code()

It will print the generated code. There's a class named GeneratedAutoScraper which has the methods get_result_similar and
get_result_exact which you can use. You can also use get_result method to get both.


I hope this project is useful for you and save your time, too.
I look forward to hear any feedback or suggestion.

Top comments (3)

ilyazub profile image
Illia Zub • Edited

Thanks for the autoscraper!

Can you explain an algorithm that builds selectors? As I understand, it finds nodes with the wanted text and builds selectors for the similar nodes.

alirezamika profile image
Alireza Mika

Yeah, it basically tries to create the chain of nodes from bottom to the top starting with the node containing the wanted text. And after the chain is built, it can traverse it from top to bottom.

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