Simple Web Scraper Python

Posted on 01-05-2021 by admin

TL;DR: This post details how to get a web scraper running on AWS Lambda using Selenium and a headless Chrome browser, while using Docker to test locally. It’s based on this guide, but it didn’t work for me because the versions of Selenium, headless Chrome and chromedriver were incompatible.

Once you’ve put together enough web scrapers, you start to feel like you can do it in your sleep. I’ve probably built hundreds of scrapers over the years for my own projects, as well as for clients and students in my web scraping course.

Occasionally though, I find myself referencing documentation or re-reading old code looking for snippets I can reuse. One of the students in my course suggested I put together a “cheat sheet” of commonly used code snippets and patterns for easy reference.

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The incredible amount of data on the Internet is a rich resource for any field of research or personal interest. To effectively harvest that data, you’ll need to become skilled at web scraping.The Python libraries requests and Beautiful Soup are powerful tools for the job. If you like to learn with hands-on examples and you have a basic understanding of Python and HTML, then this tutorial is.
Our setup is pretty simple. Just create a folder and install Beautiful Soup, pandas, and requests. To create a folder and install the libraries, enter the commands given below. I am assuming that you have already installed Python 3.x. Mkdir scraper pip install beautifulsoup4 pip install requests pip install pandas.

I decided to publish it publicly as well – as an organized set of easy-to-reference notes – in case they’re helpful to others.

While it’s written primarily for people who are new to programming, I also hope that it’ll be helpful to those who already have a background in software or python, but who are looking to learn some web scraping fundamentals and concepts.

Useful Libraries

For the most part, a scraping program deals with making HTTP requests and parsing HTML responses.

I always make sure I have requests and BeautifulSoup installed before I begin a new scraping project. From the command line:

Then, at the top of your .py file, make sure you’ve imported these libraries correctly.

Making Simple Requests

Make a simple GET request (just fetching a page)

Make a POST requests (usually used when sending information to the server like submitting a form)

Pass query arguments aka URL parameters (usually used when making a search query or paging through results)

Inspecting the Response

See what response code the server sent back (useful for detecting 4XX or 5XX errors)

Access the full response as text (get the HTML of the page in a big string)

Look for a specific substring of text within the response

Check the response’s Content Type (see if you got back HTML, JSON, XML, etc)

Extracting Content from HTML

Now that you’ve made your HTTP request and gotten some HTML content, it’s time to parse it so that you can extract the values you’re looking for.

Using Regular Expressions

Using Regular Expressions to look for HTML patterns is famously NOT recommended at all.

However, regular expressions are still useful for finding specific string patterns like prices, email addresses or phone numbers.

Run a regular expression on the response text to look for specific string patterns:

Using BeautifulSoup

BeautifulSoup is widely used due to its simple API and its powerful extraction capabilities. It has many different parser options that allow it to understand even the most poorly written HTML pages – and the default one works great.

Compared to libraries that offer similar functionality, it’s a pleasure to use. To get started, you’ll have to turn the HTML text that you got in the response into a nested, DOM-like structure that you can traverse and search

Look for all anchor tags on the page (useful if you’re building a crawler and need to find the next pages to visit)

Look for all tags with a specific class attribute (eg <li>..</li>)

Look for the tag with a specific ID attribute (eg: <div>..</div>)

Look for nested patterns of tags (useful for finding generic elements, but only within a specific section of the page)

Look for all tags matching CSS selectors (similar query to the last one, but might be easier to write for someone who knows CSS)

Get a list of strings representing the inner contents of a tag (this includes both the text nodes as well as the text representation of any other nested HTML tags within)

Return only the text contents within this tag, but ignore the text representation of other HTML tags (useful for stripping our pesky <span>, <strong>, <i>, or other inline tags that might show up sometimes)

Convert the text that are extracting from unicode to ascii if you’re having issues printing it to the console or writing it to files

Get the attribute of a tag (useful for grabbing the src attribute of an <img> tag or the href attribute of an <a> tag)

Putting several of these concepts together, here’s a common idiom: iterating over a bunch of container tags and pull out content from each of them

Using XPath Selectors

BeautifulSoup doesn’t currently support XPath selectors, and I’ve found them to be really terse and more of a pain than they’re worth. I haven’t found a pattern I couldn’t parse using the above methods.

If you’re really dedicated to using them for some reason, you can use the lxml library instead of BeautifulSoup, as described here.

Storing Your Data

Now that you’ve extracted your data from the page, it’s time to save it somewhere.

Note: The implication in these examples is that the scraper went out and collected all of the items, and then waited until the very end to iterate over all of them and write them to a spreadsheet or database.

I did this to simplify the code examples. In practice, you’d want to store the values you extract from each page as you go, so that you don’t lose all of your progress if you hit an exception towards the end of your scrape and have to go back and re-scrape every page.

Writing to a CSV

Probably the most basic thing you can do is write your extracted items to a CSV file. By default, each row that is passed to the csv.writer object to be written has to be a python list.

Easy Web Scraper Python

In order for the spreadsheet to make sense and have consistent columns, you need to make sure all of the items that you’ve extracted have their properties in the same order. This isn’t usually a problem if the lists are created consistently.

If you’re extracting lots of properties about each item, sometimes it’s more useful to store the item as a python dict instead of having to remember the order of columns within a row. The csv module has a handy DictWriter that keeps track of which column is for writing which dict key.

Writing to a SQLite Database

You can also use a simple SQL insert if you’d prefer to store your data in a database for later querying and retrieval.

More Advanced Topics

These aren’t really things you’ll need if you’re building a simple, small scale scraper for 90% of websites. But they’re useful tricks to keep up your sleeve.

Javascript Heavy Websites

Contrary to popular belief, you do not need any special tools to scrape websites that load their content via Javascript. In order for the information to get from their server and show up on a page in your browser, that information had to have been returned in an HTTP response somewhere.

It usually means that you won’t be making an HTTP request to the page’s URL that you see at the top of your browser window, but instead you’ll need to find the URL of the AJAX request that’s going on in the background to fetch the data from the server and load it into the page.

There’s not really an easy code snippet I can show here, but if you open the Chrome or Firefox Developer Tools, you can load the page, go to the “Network” tab and then look through the all of the requests that are being sent in the background to find the one that’s returning the data you’re looking for. Start by filtering the requests to only XHR or JS to make this easier.

Once you find the AJAX request that returns the data you’re hoping to scrape, then you can make your scraper send requests to this URL, instead of to the parent page’s URL. If you’re lucky, the response will be encoded with JSON which is even easier to parse than HTML.

Content Inside Iframes

This is another topic that causes a lot of hand wringing for no reason. Sometimes the page you’re trying to scrape doesn’t actually contain the data in its HTML, but instead it loads the data inside an iframe.

Again, it’s just a matter of making the request to the right URL to get the data back that you want. Make a request to the outer page, find the iframe, and then make another HTTP request to the iframe’s src attribute.

Sessions and Cookies

While HTTP is stateless, sometimes you want to use cookies to identify yourself consistently across requests to the site you’re scraping.

The most common example of this is needing to login to a site in order to access protected pages. Without the correct cookies sent, a request to the URL will likely be redirected to a login form or presented with an error response.

However, once you successfully login, a session cookie is set that identifies who you are to the website. As long as future requests send this cookie along, the site knows who you are and what you have access to.

Delays and Backing Off

If you want to be polite and not overwhelm the target site you’re scraping, you can introduce an intentional delay or lag in your scraper to slow it down

Some also recommend adding a backoff that’s proportional to how long the site took to respond to your request. That way if the site gets overwhelmed and starts to slow down, your code will automatically back off.

Spoofing the User Agent

By default, the requests library sets the User-Agent header on each request to something like “python-requests/2.12.4”. You might want to change it to identify your web scraper, perhaps providing a contact email address so that an admin from the target website can reach out if they see you in their logs.

More commonly, this is used to make it appear that the request is coming from a normal web browser, and not a web scraping program.

Using Proxy Servers

Even if you spoof your User Agent, the site you are scraping can still see your IP address, since they have to know where to send the response.

If you’d like to obfuscate where the request is coming from, you can use a proxy server in between you and the target site. The scraped site will see the request coming from that server instead of your actual scraping machine.

If you’d like to make your requests appear to be spread out across many IP addresses, then you’ll need access to many different proxy servers. You can keep track of them in a list and then have your scraping program simply go down the list, picking off the next one for each new request, so that the proxy servers get even rotation.

Setting Timeouts

If you’re experiencing slow connections and would prefer that your scraper moved on to something else, you can specify a timeout on your requests.

Handling Network Errors

Just as you should never trust user input in web applications, you shouldn’t trust the network to behave well on large web scraping projects. Eventually you’ll hit closed connections, SSL errors or other intermittent failures.

Learn More

If you’d like to learn more about web scraping, I currently have an ebook and online course that I offer, as well as a free sandbox website that’s designed to be easy for beginners to scrape.

You can also subscribe to my blog to get emailed when I release new articles.

4 Simple Python Ideas to Automate Your Workflow

research@theseattledataguy.comAugust 9, 2019Data Based Consultingprogramming0

Automation. It’s supposed to remove work. So, why not help it along?

Truth be told, automating even simple tasks robustly takes time and a lot of dependency management which can become very complex.

Nevertheless, it can be very rewarding to automate some simple tasks. Even if it was more for fun.

For those that are Python fans already, you know how friendly Python is for interacting with just about anything. From sending HTTP requests, interacting with APIs, to loading and managing databases.

Python is a great choice for automation.

Here are five simple things you can automate that can help your various workflows and projects.

1. Moving the Mouse Automatically So Your Skype/Lynk Shows You As Active at Work

Imagine you have been scrolling through the newsfeed of your favorite social media app for the last ten minutes. Suddenly, your Lynk active symbol turns yellow. You’re inactive and not in a meeting.

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Your nosey boss comes over and taps you on the shoulder.

Surprised, you look up to see him glaring down at you. He has a bad habit of micro-managing every second of your time.

“Hey, uh, I want you to relax and all but how is that module coming along,” he asks. You grit your teeth and provide some sort of polite answer.

“Cool, well, I just want to make sure you are focusing. Janice, our VP, is really expecting us to meet our deadline.”

End of interaction.

If only Lynk didn’t call you out like that.

Wait, you can get around this. After a few moments of Googling, you find a script to move your mouse and start coding up a job to run every few minutes that will make sure your mouse is moving.

These movements of the mouse and the keyboard can help make it look like you are active at work, in the case of Skype or Lynk.

The automation movement uses Python’s PyAutoGUI module. To install the PyAutoGUI module, run the following command:

pip install pyautogui

Python controls and tracks the mouse using your screen’s coordinate system. For instance, if you use a 1920×1080 screen resolution, the coordinate system for your screen would look like this:

As you will notice, you need to get the elements you want to interact with. This is arguably one of the harder parts of creating a web-scraper or automated script that logs in to sites.

There are several ways to detect the elements of a web application to find the login fields.

Common ways of finding elements include ID (like the example above), CSS selectors, name, and XPaths.

A different approach to locating elements for the login process can be found in the official documentation of Selenium. Some websites engage more dynamic content (like several JavaScript!).

Overall, watching Selenium automatically click buttons and log in to your favorite sites is mesmerizing. Things just start moving and working without your interaction.

Web Scraper With Python

Almost like magic!

3. Automatic File Backup

Creating automatic backup files can be very useful if you perform regular backups.

Microsoft edge 12+. You may be conversant with ZIP files (files with the .zip extension). ZIP files can contain several files with their compressed contents. And, as ZIP files can also hold many folders and subfolders, it becomes a handy way to backup files by packaging them into one.

A single ZIP file, called an archive file, can be created automatically using Python functions in the zipfile module.

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You can also open (or extract) ZIP files using Python. The script is shared below.

This script was taken from the book Automate the Boring Stuff with Python(in case you want even more ideas).

4. Automatically Post YouTube Videos to Reddit Threads

Another one-off idea that we recently saw and thought was an interestingway of automating tasks you might do quite often, is using a script to post multiple videos to Reddit.

Simple Web Scraping Using Python

Posting YouTube videos in Reddit threads can also be automated. The use of PRAW, a Python wrapper that enables you to scrape data, can offer much more to your Reddit experience.

To get started, install PRAW using pip.

The script below automatically posts YouTube videos to Reddit threads.

Conclusion

Automating tasks can be a lot of fun. Watching your program automatically log in to your favorite site, or load hundreds of files into a database, can make you feel as if you have re-designed the wheel.

Open Source Web Scraper

And, there are a lot of great libraries out there that can help you automate tasks easily. Excel reports, emails, and other tasks can be simplified in a few lines of code.