Github Web Scraper Python

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‘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 one of the common way to get data.

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.

  1. Navigate to the folder called PythonWebScrape that you downloaded to your desktop and double-click on the folder Within the PythonWebScrape folder, double-click on the file with the word “BLANK” in the name (PythonWebScrapeBLANK.ipynb). A pop-up window will ask you to Select.
  2. Web scraping with Python Web scraping is an automated, programmatic process through which data can be constantly 'scraped' off webpages. Also known as screen scraping or web harvesting, web scraping can provide instant data from any publicly accessible webpage. On some websites, web scraping.

GitHub - kadnan/ScrapeGen: A simple python tool that generates a requests/bs4 based web scraper.

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. Watch dying young online hulu.

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

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’.

Scrapy Github

This is description of headless browser in wikipedia.

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.

Use it like below.

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.

Reference

I’ve recently had to perform some web scraping from a site that required login.It wasn’t very straight forward as I expected so I’ve decided to write a tutorial for it.

For this tutorial we will scrape a list of projects from our bitbucket account.

The code from this tutorial can be found on my Github.

We will perform the following steps:

  1. Extract the details that we need for the login
  2. Perform login to the site
  3. Scrape the required data

For this tutorial, I’ve used the following packages (can be found in the requirements.txt):

Open the login page

Web crawler github

Web Scraping With Python 2nd

Go to the following page “bitbucket.org/account/signin” .You will see the following page (perform logout in case you’re already logged in)

Check the details that we need to extract in order to login

In this section we will build a dictionary that will hold our details for performing login:

  1. Right click on the “Username or email” field and select “inspect element”. We will use the value of the “name” attribue for this input which is “username”. “username” will be the key and our user name / email will be the value (on other sites this might be “email”, “user_name”, “login”, etc.).
  2. Right click on the “Password” field and select “inspect element”. In the script we will need to use the value of the “name” attribue for this input which is “password”. “password” will be the key in the dictionary and our password will be the value (on other sites this might be “user_password”, “login_password”, “pwd”, etc.).
  3. In the page source, search for a hidden input tag called “csrfmiddlewaretoken”. “csrfmiddlewaretoken” will be the key and value will be the hidden input value (on other sites this might be a hidden input with the name “csrf_token”, “authentication_token”, etc.). For example “Vy00PE3Ra6aISwKBrPn72SFml00IcUV8”.

We will end up with a dict that will look like this:

Keep in mind that this is the specific case for this site. While this login form is simple, other sites might require us to check the request log of the browser and find the relevant keys and values that we should use for the login step.

For this script we will only need to import the following:

First, we would like to create our session object. This object will allow us to persist the login session across all our requests.

Second, we would like to extract the csrf token from the web page, this token is used during login.For this example we are using lxml and xpath, we could have used regular expression or any other method that will extract this data.

** More about xpath and lxml can be found here.

Next, we would like to perform the login phase.In this phase, we send a POST request to the login url. We use the payload that we created in the previous step as the data.We also use a header for the request and add a referer key to it for the same url.

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Now, that we were able to successfully login, we will perform the actual scraping from bitbucket dashboard page

In order to test this, let’s scrape the list of projects from the bitbucket dashboard page.Again, we will use xpath to find the target elements and print out the results. If everything went OK, the output should be the list of buckets / project that are in your bitbucket account.

You can also validate the requests results by checking the returned status code from each request.It won’t always let you know that the login phase was successful but it can be used as an indicator.

Github Web Scraper Python Download

for example:

Web Scraping Python Code

That’s it.

Github Web Scraper Python

Full code sample can be found on Github.