AutoScraper Introduction: Fast and Light Automatic Web Scraper for 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!

Installation

Install latest version from git repository using pip:

$ pip install git+https://github.com/alirezamika/autoscraper.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 = 'https://stackoverflow.com/questions/2081586/web-scraping-with-python'

# 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 = scraper.build(url, wanted_list)
print(result)

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 “1000000000000000 in range(1000000000000001)” so fast in Python 3?'
]

Now you can use the

scraper

object to get related topics of any stackoverflow page:

scraper.get_result_similar('https://stackoverflow.com/questions/606191/convert-bytes-to-a-string')

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 = 'https://finance.yahoo.com/quote/AAPL/'

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 = scraper.build(url, wanted_list)
print(result)

You can also pass any custom

requests

module parameter. for example you may want to use proxies or custom headers:

proxies = {
    "http": 'http://127.0.0.1:8001',
    "https": 'https://127.0.0.1:8001',
}

result = scraper.build(url, wanted_list, request_args=dict(proxies=proxies))

Now we can get the price of any symbol:

scraper.get_result_exact('https://finance.yahoo.com/quote/MSFT/')

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
scraper.save('yahoo-finance')

And to load:

scraper.load('yahoo-finance')

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()
print(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.

Thanks

I hope this project is useful for you and save your time, too.

I look forward to hear any feedback or suggestion.