Python social-media pinterest web-scraper web-scraping request instagram-scraper twitter-scraper facebook-scraper scrapping-python selenium-python reddit-scraper quora-scraper tiktok-scraper medium-scraper pinterest-scrapper. Scrapy is a Python framework for web scraping that provides a complete package for developers without worrying about maintaining code. Beautiful Soup is also widely used for web scraping. It is a Python package for parsing HTML and XML documents and extract data from them. It is available for Python 2.6+ and Python 3. Web scraping a web page involves fetching it and extracting from it. Fetching is the downloading of a page (which a browser does when a user views a page). Therefore, web crawling is a main component of web scraping, to fetch pages for later processing. Once fetched, then extraction can take place. Step 1:Install Python 2. Since we will be using Python scripts to extract data from the Facebook page then we need to install Python interpreter to execute them.Installation instructions will vary depending on whether you are using Mac OS X,Linux/UNIX or Windows.I will cover the installation in brief.But it is very easy and there is a lot of detailed instructions online incase you can’t.
Learning Outcomes
- To understand the benefits of using async + await compared to simply web scraping with the requests library.
- Learn how to create an asynchronous web scraper from scratch in pure python using asyncio and aiohttp.
- Practice downloading multiple webpages using Aiohttp + Asyncio and parsing HTML content per URL with BeautifulSoup.
The following python installations are for a Jupyter Notebook, however if you are using a command line then simply exclude the ! symbol
Note: The only reason why we use nest_asyncio is because this tutorial is written in a jupyter notebook, however if you wanted to write the same web scraper code in a python file, then you would’nt need to install or run the following code block:
Why Use Asychronous Web Scraping?
Writing synchronous web scrapers are easier and the code is less complex, however they’re incredibly slow.
This is because all of the requests must wait for the current request to finish one by one. There can only be one request running at a given time.
In contrast, asynchronous web requests are able to execute without depending on previous requests within a queue or for loop. Asychronous requests happen simultaneously.
How Is Asychronous Web Scraping Different To Using Python Requests?
Instead of thinking about creating a for loop with Xn requests, you need to think about creating an event loop. For example the environment for NodeJS, by design executes in a single threaded event loop.
Web Scraper Google Chrome
However for Python, we will manually create an event loop with asyncio.
Inside of your event loop, you can set a number of tasks to be completed and every task will be created and executed asychronously.
How To Web Scrape A Single Web Page Using Aiohttp
Firstly we define a client session with aiohttp:
Then with our session, we execute a get response on a single URL:
Thirdly, notice how we use the await keyword in front of response.text() like this:
Also, note that every asynchronous function starts with:
Finally we run asyncio.run(main()), this creates an event loop and executes all tasks within it.
After all of the tasks have been completed then the event loop is automatically destroyed.
How To Web Scrape Multiple Pages Using Aiohttp
When scraping multiple pages with asyncio and aiohttp, we’ll use the following pattern to create multiple tasks that will be simulataneously executed within an asyncio event loop:
To start with we create an empty list and then for every URL, we will attach an uncalled/uninvoked function, an AioHTTP session and the URL to the list.
The asyncio.gather(*tasks), basically tells asyncio to keep running the event loop until all of these functions within the python have been completed. It will return a list that is the same length as the number of functions (unless one of the functions within the list returned zero results).
Now that we know how to create and execute multiple tasks, let’s see this in action:
Create Web Scraper Python Tutorial
Adding HTML Parsing Logic To The Aiohttp Web Scraper
As well as collecting the HTML response from multiple webpages, parsing the web page can be useful for SEO and HTML Content Analysis.
Therefore let’s create second function which will parse the HTML page and will extract the title tag.
Conclusion
Asynchronous web scraping is more suitable when you have a larger number of URLs that need to be processed quickly.
Also, notice how easy it is to add on a HTML parsing function with BeautifulSoup, allowing you to easily extract specific elements on a per URL basis.