Extract text from a webpage using BeautifulSoup and Python February 12, 2019 If you're going to spend time crawling the web, one task you might encounter is stripping out visible text content from HTML. If you're working in Python, we can accomplish this using BeautifulSoup. Web scraping is the technique to extract data from a website. The module BeautifulSoup is designed for web scraping. The BeautifulSoup module can handle HTML and XML. It provides simple method for searching, navigating and modifying the parse tree. Related course: Browser Automation with Python Selenium. Get links from website. How to scrape a website that requires login with Python 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.

In this tutorial, we are going to talk about web scraping using python. Firstly, we have to discuss about what is web scraping technique? Whenever we need the data (it can be text, images, links and videos) from web to our database. Lets discuss where we should need the web scraping in real world. Web scraping is the process of collecting and parsing raw data from the Web, and the Python community has come up with some pretty powerful web scraping tools. The Internet hosts perhaps the greatest source of information—and misinformation—on the planet.

In this segment you are going to learn how make a python command line program to scrape a website for all its links and save those links to a text file for later processing. This program will cover many topics from making HTTP requests, Parsing HTML, using command line arguments and file input and output. First off I’m using Python version 3.6.2 and the BeautifulSoup HTML parsing library and the Requests HTTP library, if you don’t have either then type the following command to have them installed on your environment. So let’s get started.

Now let’s begin writing our script. First let’s import all the modules we will need:

Line 1 is the path to my virtual environment’s python interpreter. On line 2 we are import the sys module so we can access system specific parameters like command line arguments that are passed to the script. Line 3 we import the Requests library for making HTTP requests, and the BeautifulSoup library for parsing HTML. Now let’s move on to code.

Here we will check sys.argv, which is a list that contains the arguments passed to the program. The first element in the argv list(argv[0]) is the name of the program, and anything after is an argument. The program requires a url(argv[1]) and filename(argv[2]). If the arguments are not satisfied then the script will display a usage statement. Now let’s move inside the if block and begin coding the script:

On lines 2-3 we are simply storing the command line arguments in the url and file_name variables for readability. Let’s move on to making the HTTP request.

On line 5, we are printing a message to the user so the user knows the program is working.

On line 6 we using the Requests library to make an HTTP get request using requests.get(url) and storing it in the response variable.

On line 7 we are calling the .raise_for_status() method which will return an HTTPError if the HTTP request returned an unsuccessful status code.

On line 1 we are calling bs4.BeautifulSoup() and storing it in the soup variable. The first argument is the response text which we get using response.text on our response object. The second argument is the html.parser which tells BeautifulSoup we are parsing HTML.

On line 2 we are calling the soup object’s .find_all() method on the soup object to find all the HTML a tags and storing them in the links list.

On line 1 we are opening a file in binary mode for writing(‘wb’) and storing it in the file variable.

On line 2 we are simply providing the user feedback by printing a message.

Python Web Scraping Get Text

On line 3 we iterate through the links list which contains the links we grabbed using soup.findall(‘a’) and storing each link object in the link variable.

On line 4 we are getting the a tag’s href attribute by using .get() method on the link object and storing it in the href variable and appending a newline(n) so each link is on its own line.

On line 5 we are printing the link to the file. Notice that were calling .encode() on the href variable, remember opened the file for writing in binary mode and therefore we must encode the string as a bytes-like object otherwise you will get a TypeError.

On line 6 we are closing the file with the .close() method and printing a message on line 7 to the user letting them know the processing is done. Now let’s look at the completed program and run it.

Now all you
have to do is type this into the command line:

Output:

Now all you have to do is open up the links file in an editor to verify they were indeed written.

And that’s all there is to it. You have now successfully written a web scraper that saves links to a file on your computer. You can take this concept and easily expand it for all sorts of web data processing.

Further reading: Requests, BeautifulSoup, File I/O

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

Python Web Scraping Sample

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

Web Scraping Using Python

Open the login page

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

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.

Python Web Scraping Tutorial

** 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.

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. Canoscan lide 110 manual macyellowplane.

Basic Web Scraping In Python

for example:

Python Web Scraping Get Textbooks

That’s it. Mosfet 47n60f.

Full code sample can be found on Github.