How to Use Python AutoScraper With Proxies for Automated Web Scraping

Data is everything today. The problem with data is that it gets old fast. If you are like many business owners, developers, or technicians, you know the importance of the most up-to-date data possible, and that is why you are likely considering the use of web scraping technologies. The key is to consider automated web scraping.

What Is Automated Web Scraping?

Typically, the process of web scraping involves a manual process in which the user tells the system to go and fetch the most up to date information from a website. That works well enough for most applications, but it does not always make things easy. The better option is to use automated web scraping, which will do more of the work for you without the actual steps manually required otherwise.

When you use automated data scraping, you are able to set up a process that will regularly scrape at a set time period (and timeframe) to meet your objectives. In this tutorial, we will show you how to automate web scraping to your specific goals and needs. To accomplish this task, we will provide insight into how to use AutoScraper.

AutoScraper is one of several web scraping libraries available on Python. You probably have used it, but if not, you’ll want to get an update on how to use AutoScraper before moving forward with this process. Overall, it’s a reliable solution, and it does not have to be challenging to set up or use.

Why Python?

Before going further, let’s include some details about why you should be using Python for this task. Python is a type of object-oriented, high level programming language that utilizes dynamic semantics. The functionality has many benefits including for Rapid Application Development and for scripting in general.

Python prioritizes readable object-oriented code. That is what makes it so beneficial to the development of websites or apps. Python and web scrapers are a natural fit. They are easy to use and easy to learn, and there is a large library of options (thanks to Python’s huge following of users). That means that there are large numbers of libraries already available that you can implement, reducing the need to write everything from scratch.

Python is great, that’s clear. However, to use it for data scraping, you need to produce a program that interacts with the specific patterns found on the HTML of the website you wish to target. It must be able to follow the directions you require and then print it out as you desire.

What Is AutoScraper?

AutoScraper is a web scraping library that makes web scraping easy and automatically fast. It is also a lightweight solution, which means it does not slow down your processes or network. You can learn how to set it up rather easily because it’s designed for more ready use than other tools.

AutoScraper is written in Python3. It’s quite an intelligent, easy-to-use tool that works well even for beginners (which is why many people start here when it comes to building out tools for web scraping).

One of the benefits of AutoScraper is that it accepts either the HTML of the website or the URL of the site. It will then scrape the data after following the rules you provide to it. It will match the data from the web page you want to scrape and then pulls that information.

What are the benefits of using AutoScraper?

You certainly have a lot of options when it comes to web scraping with Python. Though you can choose just about any other tool, there are some key benefits to using AutoScraper itself that are hard to overlook.

1.     It is easy. Simply, this is one of the best reasons to use AutoScraper. You can enter a few lines of code, and that will set up the web scraper. You do not have to do much more than that once in place. That makes using it easy to do overall.
2.     Efficiency at its best. Another key benefit of AutoScraper is that it is efficient overall. The model will learn the structure of the web pages you are targeted and then adapt to minor changes as necessary. The result is that it is able to reduce the need for numerous adjustments by you (it simply saves you time in this area!)
3.     It is versatile. Another benefit of Autoscraper is that you can use it on just about any website. You can, if needed, integrate it into large data pipelines as well.

For all of these reasons, it’s a simple solution and one you don’t have to think twice about, especially if you are already familiar with Python.

How to Install AutoScraper

To take advantage of any web scraping tools Python offers, you have to install the necessary components. In this case, we need to download the AutoScraper library. There are a few different ways to do this, such as using this library. The simplest way is to use the Python package index (also called PyPI) repository. To do that, you will need to use this command:

Pip install autoscraper

You will need to install the git version based on the operating system you have. Once you install git, you can then install AutoScraper using the following command in the command prompt:

pip install git+https://github.com/alirezamika/autoscraper.git

The next step is to import the required libraries. Keep in mind that you just need AutoScraper since it will handle all aspects of web scraping without the need for other libraries as well. To do that, enter the following into the command line:

From autoscraper import AutoScraper

Once you’ve done that, it is then possible to start scraping. (More on the automation component in just a bit).

Define the Web Scraping Function

The next step in this process is to determine and identify the URL from which you wish to fetch data by utilizing this tool. Let’s say we want to gather information from WordPress on its current pricing. In this situation, we need to tell AutoScraper what we want to get.

Here is an example of what the code may look like:

URLToScrape + https://wordpress.com/pricing/

WantedList = [

“https://wordpress.com/website-builder/”, ,

https://wordpress.com/hosting/

]

Scraper = AutoSraper()

Data = Scraper. Build(URLToScrape, wanted_list+WantedList)

Print(data)

You can update this to meet any of your goals, of course. It is important to note that AutoScraper does not provide support for JavaScript rendering. That means that on websites where this is used, you will not be able to scrape all of the category links out there.

Let’s break down some of the features of the code above so you can understand how and why it works.

UrlToScrape

This is the URL that we want AutoScraper to scrape. That’s pretty self-explanatory.

WantedList

You will then need to tell it what to do. The WantedLIst does this. It assigns sample data that you wish to scrape from the given URL you have supplied.

In order to do this, and get the category page links from the target URL, you need to provide two example urls to the WantedList.

In this situation, one of the links is a data sample of a JavaScript-recede category button. The other link is a data sample of a static category button that will not have any subcategories.

AutoScraper()

The next component is the AutoScraper(). When inputting this, it creates an objective to initiate various functions within the AutoScraper library. This method will scrape data in a similar way as the WantedLIst from the target URL.

Once you complete this and execute the Python script, the data list will include the category page links.

What About Dynamic Content?

As noted, AutoScript does not play well with dynamic content. However, you can still use it. To do so, integrate AutoScraper with a lightweight module solution. A good and common option is the use of Splash. If you want more robust and heavy functionality, the Playwright, Puppeteer, and Selenium libraries can be a good option overall.

Note that with AutoScraper, you can save the model that you created and then load it whenever you want to do so or when it is required to do so. The benefit is that this will save you time and frustration over a period of use. If you save the model, you will have to call AutoScraper’s save function. You will need to supply the name of the file you want to save at this point. Use the following

Scraper.save(‘data’) # saving the model

In this particular statement to the system, it will allow you to save the model. Replace the term “data” with the word that is associated with the file name of what you want to save. Then, run the line. AutoScraper will save the model in the data file as a result. This allows you to save the model.

Once you have it saved, the next step is to load the model when you want to run it. To do that, you will need to load the following model using the name of the file as an argument. For example, it may look like:

Scraper.load(‘data’) # loading the model

How to Use AutoScraper with Proxies

Automated data scraping is a powerful tool and solution, but sometimes when you automate web scraping, things get too good. That is, you may find yourself blocked and unable to access the files you need.

One of the best ways around this is to use a proxy. A proxy is a very important part of the web scraping process. Remember that your goal is to pull data from a website, and to do that, you will need to constantly go back and pull that information. Most website owners have no interest in you doing this to their system (not only is it a matter of using their data, but chances are good you are doing this with your competition, and that means companies will do what it takes to limit your ability to web scrape.

A proxy is a solution.

It allows you to acquire data in the same way you were doing so. However, it involves far fewer risks and less exposure. As you learn how to build a web scraping tool, the use of proxies will be critical in the process overall.

Proxies work to help ensure the web scraping process occurs. They allow you to acquire the data you need without putting your IP address out there (and therefore, not allowing other websites, like the target one you are trying to scrape) from getting your IP address. Most often, the goal is to just avoid blocking your IP address so you can continue to access the information you need.

So, how do you do this with AutoScraper, then?

When it comes to web scraping automation, the process of using a proxy with AutoScraper is not too difficult.

You will use the AutoScraper functions below to do so:

Build

Get_result_similiar

Get_result_exact

These functions within AutoScraper accept the request-related arguments in the request_args parameter.

Let’s outline an example using the example.com website.

from autoscraper import AutoScraper

UrlToScrape = “https://example.com/”

WantedList = [“YOUR_REAL_IP_ADDRESS”]

proxy = {

“http”: “proxy_endpoint”,

“https”: “proxy_endpoint”,

}

InfoScraper = AutoScraper()

InfoScraper.build(UrlToScrape, wanted_list=WantedList)

data = InfoScraper.get_result_similar(UrlToScrape, request_args={“proxies”: proxy})

print(data)

The key is, of course, to copy the displayed IP address and place that within the above data code where it says Your Real IP Address.

You will then need to test it out. When you do, you should see the exact results you need.

Keep in mind that proxies are an essential component of the web scraping process. If you did not use them, there are numerous risks involved with the process, and that includes exposing your IP address. Let’s take a look at some additional details to help you through this process.

Keep These Best Practices in Mind When Using Web Scraping through AutoScraper and the Use of a Proxy

When it comes to the power to automate web scraping, AutoScrapper is a solid tool to use. However, like everything else out there, there are a few key factors that can trip you up and make the process less accurate or less beneficial to you.

The best practice, then, when using the Python proxy or AutoScraper tools, is to remember the following.

#1: Make sure to follow the rules

Every website has a terms of service or a set of rules that anyone using the site must follow. These are the rules for using and accessing the site. Before you engage in web scraping, make sure that you have read through these rules. If the site does not allow web scraping for any reason, you should second guess following through with this process.

#2: Make sure you use rotating proxies

It does not take much for the AutoScraper Python process to get underway. Once you get started, though, you need to keep ensuring access. To do that, you need to use rotating proxies. This will help you to avoid being detected. It also helps you to get around the other common problem: rate limits!

Instead, use rotating proxies that will change up your IP address on a routine basis. Though not all are the same, most will do this enough to minimize the risk that your data will be exposed.

We encourage you to check out our Rayobyte tools to learn more about how we can help you to navigate this process. We offer both residential and mobile proxies for example, that are ideal for this process. They keep your information safe over the long term.

#3: Throttle

One of the ways to help ensure web scraping activities do not get stopped (or at least delayed too long) is to use throttling. It allows you to implement a delay in the processes. That is important because, when a human is visiting a website, there will be natural delays between the requests made to the website.

By throttling the function, you are better at copying human behavior. The result is that you are less likely to actually be banned from the site.

#4: Keep Your System Up to Date

Another key feature is to ensure that you stay up to date. That is, all of the scraping scripts you develop and use are only going to be effective as long as they are the most accurate representation of the data you need.

It is a good idea to keep a proxy list updated, too. You are likely to need to adapt to changes that occur naturally over the lifespan of a website. Structural changes, for example, can create limitations for your web scraping process. You also want to consider proxy IP rotations to ensure that does not get stagnant.

#5: Monitor Processes

Finally, make sure you monitor the processes over time. Specifically, make sure that your proxies are operating the way you expect and that there has not been a delay or functional change. You also want to ensure that your scraper is performing as expected. There could be times when you need to update this over time. The sooner you do it, the more efficient the process will be for you.

Do You Really Need to Use a Proxy with Python for Data Scraping?

Automated web scraping is a very valuable tool for any industry today. However, it is important to consider what you are doing when you are scraping the web. You are assessing the data and information from another website – one that you are not likely to own and, therefore, may be limited in what you can access and use.

The bottom line is that proxies and Python web scraping go hand-in-hand. It is nearly a requirement.

For those who have been web scraping for some time, you know the value of the process, how to use that data, and how accurate data is critical to your overall goals. Python makes this process easy to do and can be very fast and efficient overall (and AutoScraper makes it even easier to achieve that goal).

Requests library

You can also use the Requests library. It has a simple tool that allows you to add proxies to your web scraper. You can do that by utilizing this process:

import requests

proxies = {

‘http’: ‘http://10.10.1.10:3128’,

‘https’: ‘http://10.10.1.10:1080’,

}

session = requests.Session()

session.proxies.update(proxies)

session.get(‘http://WebsiteName.com’)

When you use this process, it adds a single Python proxy to the HTTP and HTTPS requests you have. This lasts the entire session.

How to Overcome Common Challenges

There are a lot of exceptional tools out there, and AutoScraper Python is one of them. However, web scraping automation isn’t always simplistic and there are times when you may find some problems limiting your success.

• Authentication concerns: Some websites will have an authentication process. This requires that a username and password be entered, or some other data must be entered to access the page you need. To identify these sites, you’ll need to actually log in. Look at the header. Determine what information is being sent to the server.
• CAPTCHAs: This is another common concern that typically leads to the page timing out. You may be having this problem if you see frequent time-outs during the process. Check the site to see if a CAPTCHA is present. If so, you will need to avoid them by using rotating residential proxies. These help to keep that process at bay.
• Autohack PY: Autohack PY is a type of script that will allow you to hack into nearby networks. Minimize use of these tools when not appropriate.

The power of web scraping automation is too big to avoid. If you are one of the many organizations looking for the most effective way to do so (and you want to ensure that the process does not create inaccurate data, you need the right proxies in place. Rayobyte can help you with that process. Reach out to us for more information and guidance.

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