Github Web Scraper Python

‘Web scraping’ is a logic to get web page data as HTML format. With this information, not only could get text/image data inside of target page, we could also find out which tag has been used and which link is been included in. When you need lots of data for research in your system, this is. GitHub - kadnan/ScrapeGen: A simple python tool that generates a requests/bs4 based web scraper. AutoScraper: A Smart, Automatic, Fast and Lightweight Web Scraper for Python. 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. Web Scraping Using Python What is Data Extraction? Data extraction is a process that involves retrieval of data from different website sources. Firms extract data in order to analyze it, migrate the data to a data repository (data warehouse) or use it in their businesses. The most popular web scraping extension. Start scraping in minutes. Automate your tasks with our Cloud Scraper. No software to download, no coding needed.

Use parser

But not like CSV or Excel sheet, raw HTML is pretty rough and disordered data.

This is the case of getting raw HTML data from Bleacher Report. It will return data like this.

It is inconvenient for workers to find target data from here. You need a process of organizing before going further. Maybe you could make a parser yourself, but that’s not an effective way. Python have some great modules for this, and I will use one of this named BeautifulSoap.

Before going on, install it via python package manager with pip install beautifulsoup4.

With this code, raw HTML data has been converted to beautifulsoap object. Now you can get data with text or tag info.

Why it needs to use browser

There are a problem on process above, not in parser, but in HTML request process. If we just request data by http request method, it cannot get dynamically rendered part because they are not in HTML file before loading process. Here is some example for this case.

This is the main page of Samsung SDS official site.

This is the ordinary form of main page. Now let’s check how this will be look like after disabling javascript.

Python Scrapy Github

Menu in top has been disappeared, because they are being rendered dynamically from javascript controller. And also, menu texts will not being scraped when you get this HTML page with http request process. So, to get all of these data, you need to get fully rendered result data. For getting it, it has to be rendered in some kind of ‘fake browser’, and that’s why we will use ‘headless browser’.

This is description of headless browser in wikipedia.

Github Web Scraper Python Tutorial

Scraping via headless browser

To make scraper via headless browser, we need headless browser, and module to make this run in virtual. I will use PhantomJS here for headless browser, and will make it run with Selenium. You need to install these first.

PhantomJS can be installed by downloading from main page, but can be installed with brew or npm. You can use one of 2 command below.

Try make a class for scraper.

HBScraper class is the class for getting ‘loaded’ page data. It initiates browser setting like max loading time, window size, etc. on __init__ method. You can do scraping and get scraped result from browser with scrape_page.

Autoscraper

Use it like below.

Web Scraping Python Code

Because it needs time for loading, scraping with headless browser needs more time to get result than just getting data with HTTP request. But to get exact data of target page, you will need to consider of using it.

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