Scraping data from a JavaScript webpage with Python 19 Jan 2019 This post will walk through how to use the requestshtml package to scrape options data from a JavaScript-rendered webpage. Requestshtml serves as an alternative to Selenium and PhantomJS, and provides a clear syntax similar to the awesome requests package. Web Scraping¶ Web sites are written using HTML, which means that each web page is a structured document. Sometimes it would be great to obtain some data from them and preserve the structure while we’re at it. Web sites don’t always provide their data in comfortable formats such as CSV or JSON.

It is a well-known fact that Python is one of the most popular programming languages for data mining and Web Scraping. There are tons of libraries and niche scrapers around the community, but we’d like to share the 5 most popular of them.

Most of these libraries' advantages can be received by using our API and some of these libraries can be used in stack with it.

The Top 5 Python Web Scraping Libraries in 2020#

1. Requests#

Well known library for most of the Python developers as a fundamental tool to get raw HTML data from web resources.

To install the library just execute the following PyPI command in your command prompt or Terminal:

After this you can check installation using REPL:

• Official docs URL: https://requests.readthedocs.io/en/latest/
• GitHub repository: https://github.com/psf/requests

2. LXML#

When we’re talking about the speed and parsing of the HTML we should keep in mind this great library called LXML. This is a real champion in HTML and XML parsing while Web Scraping, so the software based on LXML can be used for scraping of frequently-changing pages like gambling sites that provide odds for live events.

To install the library just execute the following PyPI command in your command prompt or Terminal:

The LXML Toolkit is a really powerful instrument and the whole functionality can’t be described in just a few words, so the following links might be very useful:

• Official docs URL: https://lxml.de/index.html#documentation
• GitHub repository: https://github.com/lxml/lxml/

3. BeautifulSoup#

Probably 80% of all the Python Web Scraping tutorials on the Internet uses the BeautifulSoup4 library as a simple tool for dealing with retrieved HTML in the most human-preferable way. Selectors, attributes, DOM-tree, and much more. The perfect choice for porting code to or from Javascript's Cheerio or jQuery.

To install this library just execute the following PyPI command in your command prompt or Terminal:

As it was mentioned before, there are a bunch of tutorials around the Internet about BeautifulSoup4 usage, so do not hesitate to Google it!

• Official docs URL: https://www.crummy.com/software/BeautifulSoup/bs4/doc/
• Launchpad repository: https://code.launchpad.net/~leonardr/beautifulsoup/bs4

4. Selenium#

Selenium is the most popular Web Driver that has a lot of wrappers suitable for most programming languages. Quality Assurance engineers, automation specialists, developers, data scientists - all of them at least once used this perfect tool. For the Web Scraping it’s like a Swiss Army knife - there are no additional libraries needed because any action can be performed with a browser like a real user: page opening, button click, form filling, Captcha resolving, and much more.

To install this library just execute the following PyPI command in your command prompt or Terminal:

The code below describes how easy Web Crawling can be started with using Selenium:

As this example only illustrates 1% of the Selenium power, we’d like to offer of following useful links:

• Official docs URL: https://selenium-python.readthedocs.io/
• GitHub repository: https://github.com/SeleniumHQ/selenium

5. Scrapy#

Scrapy is the greatest Web Scraping framework, and it was developed by a team with a lot of enterprise scraping experience. The software created on top of this library can be a crawler, scraper, and data extractor or even all this together.

To install this library just execute the following PyPI command in your command prompt or Terminal:

We definitely suggest you start with a tutorial to know more about this piece of gold: https://docs.scrapy.org/en/latest/intro/tutorial.html

As usual, the useful links are below:

• Official docs URL: https://docs.scrapy.org/en/latest/index.html
• GitHub repository: https://github.com/scrapy/scrapy

What web scraping library to use?#

So, it’s all up to you and up to the task you’re trying to resolve, but always remember to read the Privacy Policy and Terms of the site you’re scraping 😉.

The internet has an amazingly wide variety of information for human consumption. But this data is often difficult to access programmatically if it doesn't come in the form of a dedicated REST API. With Python tools like Beautiful Soup, you can scrape and parse this data directly from web pages to use for your projects and applications.

Let's use the example of scraping MIDI data from the internet to train a neural network with Magenta that can generate classic Nintendo-sounding music. In order to do this, we'll need a set of MIDI music from old Nintendo games. Using Beautiful Soup we can get this data from the Video Game Music Archive.

Getting started and setting up dependencies

Before moving on, you will need to make sure you have an up to date version of Python 3 and pip installed. Make sure you create and activate a virtual environment before installing any dependencies.

You'll need to install the Requests library for making HTTP requests to get data from the web page, and Beautiful Soup for parsing through the HTML.

With your virtual environment activated, run the following command in your terminal:

We're using Beautiful Soup 4 because it's the latest version and Beautiful Soup 3 is no longer being developed or supported.

Using Requests to scrape data for Beautiful Soup to parse

First let's write some code to grab the HTML from the web page, and look at how we can start parsing through it. The following code will send a GET request to the web page we want, and create a BeautifulSoup object with the HTML from that page:

With this soup object, you can navigate and search through the HTML for data that you want. For example, if you run soup.title after the previous code in a Python shell you'll get the title of the web page. If you run print(soup.get_text()), you will see all of the text on the page.

Getting familiar with Beautiful Soup

The find() and find_all() methods are among the most powerful weapons in your arsenal. soup.find() is great for cases where you know there is only one element you're looking for, such as the body tag. On this page, soup.find(id='banner_ad').text will get you the text from the HTML element for the banner advertisement.

soup.find_all() is the most common method you will be using in your web scraping adventures. Using this you can iterate through all of the hyperlinks on the page and print their URLs:

You can also provide different arguments to find_all, such as regular expressions or tag attributes to filter your search as specifically as you want. You can find lots of cool features in the documentation.

Parsing and navigating HTML with BeautifulSoup

Before writing more code to parse the content that we want, let’s first take a look at the HTML that’s rendered by the browser. Every web page is different, and sometimes getting the right data out of them requires a bit of creativity, pattern recognition, and experimentation.

Our goal is to download a bunch of MIDI files, but there are a lot of duplicate tracks on this webpage as well as remixes of songs. We only want one of each song, and because we ultimately want to use this data to train a neural network to generate accurate Nintendo music, we won't want to train it on user-created remixes.

When you're writing code to parse through a web page, it's usually helpful to use the developer tools available to you in most modern browsers. If you right-click on the element you're interested in, you can inspect the HTML behind that element to figure out how you can programmatically access the data you want.

Let's use the find_all method to go through all of the links on the page, but use regular expressions to filter through them so we are only getting links that contain MIDI files whose text has no parentheses, which will allow us to exclude all of the duplicates and remixes.

Create a file called nes_midi_scraper.py and add the following code to it:

This will filter through all of the MIDI files that we want on the page, print out the link tag corresponding to them, and then print how many files we filtered.

Run the code in your terminal with the command python nes_midi_scraper.py.

Downloading the MIDI files we want from the webpage

Now that we have working code to iterate through every MIDI file that we want, we have to write code to download all of them.

Web Scraping Html Table Using Python

In nes_midi_scraper.py, add a function to your code called download_track, and call that function for each track in the loop iterating through them:

In this download_track function, we're passing the Beautiful Soup object representing the HTML element of the link to the MIDI file, along with a unique number to use in the filename to avoid possible naming collisions.

Run this code from a directory where you want to save all of the MIDI files, and watch your terminal screen display all 2230 MIDIs that you downloaded (at the time of writing this). This is just one specific practical example of what you can do with Beautiful Soup.

The vast expanse of the World Wide Web

Html Web Scraping Python Code

Now that you can programmatically grab things from web pages, you have access to a huge source of data for whatever your projects need. One thing to keep in mind is that changes to a web page’s HTML might break your code, so make sure to keep everything up to date if you're building applications on top of this.

If you're looking for something to do with the data you just grabbed from the Video Game Music Archive, you can try using Python libraries like Mido to work with MIDI data to clean it up, or use Magenta to train a neural network with it or have fun building a phone number people can call to hear Nintendo music.

Html Web Scraping Python Program

I’m looking forward to seeing what you build. Feel free to reach out and share your experiences or ask any questions.

Web Scraping Using Python

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