With the current rising demand for data, web scraping is becoming a useful technique for extracting content from online resources. Companies and individuals are increasingly obtaining web data for performing market research, tracking industry trends, attracting new customers, and more. However, most people are unaware of the best web scraping tips, which often makes them to get undesired results. More so, websites are increasingly implementing measures to foil web scraping attempts.

Therefore, to get the best results, you need to understand the ground rules of harvesting online data successfully.

In this article, we’ll talk about the challenges of performing advanced Python web scraping, along with their workarounds.

Let’s start by describing some of the most common web scraping complexities.

Advanced Python Web Scraping Challenges

1. Dynamic websites and client-side rendering

Nowadays, most websites are adopting better client-side programming practices for gaining increased interactivity and user-friendliness. While it’s every developer’s dream to have a modern-looking website, this is the nightmare of every Python web scraper.

Dynamic web pages are not friendly to most web scrapers. Websites with elements loaded via AJAX (Asynchronous JavaScript and XML) calls, preloaders such as percentage bars, lazy image loading capabilities, or infinite scrolling features make extracting their content complicated.

For example, here is the code for a web page that uses the jQuery ajax() method to allow users to load content from an external resource:

<!DOCTYPE html>
  <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.4.1/jquery.min.js"></script>
    $(document).ready(function () {
      $("button").click(function () {
          url: "external_doc.txt", success: function (query_result) {
  <div id="example">
    <h1>Example of site using Ajax()</h1>
  <button>Click to Fetch External Content</button>

Here’s how the web page looks on a browser:

Since AJAX is used, a user can click on the “Click to Fetch External Content” button and insert additional text, without reloading the entire web page.

Because the page’s entire content is not loaded initially, unless a user completes an action, this obliges a web scraper to follow further steps before harvesting that juicy data.

Therefore, a website heavily depending on client-side JavaScript for loading dynamic elements complicates the process of extracting its content.

2. User authentication

Some websites employ authentication systems that verify the identity of users accessing them. To extract data from such websites, a web scraper needs to create an account and post the login details to be authenticated.

For uncomplicated sites, authentication may be as simple as making an HTTP POST-Request with user credentials.

However, some websites require extra verification measures before authenticating users. For example, standard user credentials (like username & password), a site may require additional data, such as headers settings, to be incorporated in the POST payload.

Furthermore, even if a scraper manages to log in successfully, their activities can raise some red flags. A normal user may log in to a web application, click on one link, move to another page, then another one—in some logical order.

However, if a scraping bot written in Python accesses a site and navigates 100 pages a minute, it can point to some unusual activities, leading to getting logged out or suspension of the account.

Examples of some web scraping tips for circumventing this involve avoiding making too many requests and crawling the site normally like a human user.

3. Blacklisting (Server-side)

Server-side blacklisting is also a common problem that web scrapers encounter. Each time a user makes a request, the server responds with the required web page. If the requests are too many, it can bring the server to its knees.

Therefore, some web applications have deployed anti-scraping technologies that measure incoming traffic and users’ browsing behaviour, and block automated scripts from accessing their site/application.

Blacklisting normally occurs when a server gets an unusually high number of requests from a single IP address or parallel requests from the same IP address. Other red flags include repetitive behaviors, such as making ‘x’ number of requests after every ‘y’ seconds, and defined browsing patterns, such as making ‘x’ number of clicks after every ‘y’ seconds.

Servers can analyze such metrics and prevent further access after a certain threshold is reached. This blacklisting can be temporary or permanent, depending on the stipulated criteria.

However, if you learn some web scraping tips, you can avoid the blacklisting and scrape data like a boss!

4. Honeypot traps

A honeypot is a security technique used to identify unwanted visitors on web applications. There are several ways of creating honeypots.

For example, some webmasters design honeypot traps by setting up HTML links that are not visible to a typical human user on a browser. They can achieve this by applying the CSS style property of “display:none” or by using subtle colors that camouflage the links with the page’s background color.

Here is a simple example of how a web page can set up a honeypot trap:

<div class="webpage">
    <h1 class="webpage-title">Subscription Page</h1>
    <p class="webpage-description">Thanks for visiting our site</p>
    <a class="webpage-btn" href="https://www.example.com/subscribe">Click here to subscribe</a>

  <!-- fake content hidden to human users -->

  <div class="honeypot" style="display: none">
    <h1 class="honeypot-title">Visit example.com to subscribe</h1>
    <p class="honeypot-description">You should visit mywebsite.com to subscribe</p>
    <a class="honeypot-btn" href="https://www.example.com/honeypot">Click here now!!</a>

Since a web scraper only examines the page’s source code, if any of these links is visited, the server can detect that it’s an automated program, and block further access.

While honeypots are useful methods for ensuring the security of web applications, they can hamper efforts to extract data from websites. Nonetheless, with sufficient web scraping tips, you can know how to overcome them.

5. Captchas and Redirects

Another challenge that complicates web scraping in Python is presence of redirects and captchas on web applications. Although the mechanisms are important in enhancing performance, they can result in great accessibility challenges to web scrapers.

For example, when a website redirects its older URLs to newer pages, such as forwarding HTTP URLs to more secure HTTPS links, it can return a 3xx status response code that obliges the client to take additional action before the request is completed.

Furthermore, to ward off malicious clients, a request may be redirected to a web page having freakish captchas, which a data extractor needs to resolve before continuing with its activities.

6. Layout structure difficulties

Since most web scrapers depend on predictable HTML markup structures to detect sections of a website, including unexpected elements or using other unconventional methods can throw them into disarray.

For example, some sites can have different page layouts, which makes automatic navigation difficult—such as page one to five of a directory listing may appear different from page six to ten of the same directory.

Frequently updating the structure of a website can also frustrate the work of web crawlers. If a bot is created while paying attention to the layout of the web page, any sudden structural changes can dwindle the initial scraping logic.

Furthermore, some websites obfuscate their data, which makes them less accessible to web scrapers—such as through serving texts as images.

Web Scraping Tips for Resolving Challenges

Ethically harvesting online data seems like a cat-and-mouse game between the site’s developer and the scraper trying to sidestep the obstructions. Therefore, if you do not consider the best tips for web scraping, you can end up being unsuccessful, especially in large-scale advanced Python scraping situations.

After illustrating the complexities that may impair the data extraction process, let’s now address some ways of getting around them and harvesting the sweet web data without a fuss.

1. Picking the right tool

We can’t emphasize enough the importance of picking the right tool for your Python web scraping tasks. With a properly built tool, extracting and exporting the collected data will be fast and smooth.

Importantly, you need a powerful tool that has the necessary technical stack and capabilities to handle data extraction from modern websites that heavily depend on JavaScript and other dynamic elements to enhance the user experience. A scalable, secure, and fault-tolerant tool can assist you in overcoming various difficulties when performing advanced web scraping in Python.

For example, Zenscrape Scraper API is a versatile tool with excellent features for extracting data in a fast, simple, yet extensible way. With the tool, you can harvest large amounts of data from modern websites without worrying of getting blocked.

2. Getting around complex client-side rendering

As discussed earlier, complex client-side rendering, such as asynchronous loading, can get in the way of successfully scraping web data. Foremost, to tackle such issues, you need to inspect the page’s source code by right clicking anywhere on the page and choosing the “View Page Source” option.

If the target content is not available in the page’s source code, then it is possibly being rendered asynchronously using JavaScript and not through the raw HTML responses from the server. You can also use the browser’s developer tools to check if the website is making any XHR requests or AJAX calls. This way, you can identify the requests that are fetching the data you need to scrape.

Consequently, you can incorporate some JavaScript logic in your scraping program to handle the client-side rendering dynamics.

Furthermore, you can render the web page in a headless browser, like the headless Chrome browser, and scrape the JavaScript-based dynamic content. A headless Chrome lacks the UI and allows you to run the browser in a server environment, resulting in a more resource-saving and lightweight scraping of the required data.

Similarly, for pages with different layouts, you can add a condition in your code that tells apart the different layouts and allow you to harvest data smoothly.

3. Getting around authentication

Next, let’s talk about some web scraping tips for tackling user authentication issues.

If a web application requires permissions to access its content, you can include the login steps, such as inputting a username and password, into your scraper’s code.

You can also create a session cookie that keeps track of your scraping preferences and persists your login. If you encounter any hidden field, you can try logging in manually and use the browser’s developer tools to check for any hidden data being sent to the server.

Additionally, you can use the browser’s tools to scrutinize if authentication relies on headers settings and reproduce that behavior in the scraper’s design, as well.

4. Getting around blacklisting

As discussed earlier, some websites deploy anti-scraping technologies that detect anomalies and blacklist users. However, there are several web scraping tips you can use to stay under the radar and scrape data successfully.

First, since sending several requests to a server using the same IP address can point to abnormal activities, you can use proxy servers and IP address rotation to avoid being throttled. With such techniques, it’ll look like several users are accessing the website at the same time, ensuring you minimize the danger of being blocked.

More so, slamming the server with quick requests that follow a predefined pattern is a recipe to getting blacklisted. To get around this, you can reduce the crawling rate by including some delay time between actions—just like a normal human user would do it.

Also, instead of following a repetitive browsing behavior, you can change your browsing pattern frequently and make it difficult to differentiate between a normal user and a bot.

Another technique to avoid being blacklisted is to rotate a browser’s user-agent. Essentially, a user-agent refers to a unique string that recognizes the specific browser being used, its version, as well as the operating system it runs on.

Each time a request is made, the browser sends the user-agent to the web server. Therefore, if you rotate the user-agent for each request, you can fool the server and avoid being blocked.

5. Getting around honeypot traps and captchas

To avoid falling for honeypot traps, you should ensure that your scraper only follows links with sufficient visibility and credibility. Your scraper should be programmed such that it can bypass hidden links or other faked content that can complicate the data extraction process.

Furthermore, other web scraping tips for tackling redirects and captchas include incorporating a middleware program that solves the hindrances and using a state-of-the-art image recognition technology that cracks conventional captchas automatically.

6. Respecting the website’s policies

It’s also important to respect the website’s policies; otherwise, you can get banned from accessing it. For example, if a website has a robot exclusion standard that is specified in the robots.txt file, which prohibits web crawlers and robots from scanning its content, it’s appropriate to obey such instructions.

In such a situation, you can opt for other methods of harvesting the data, such as contacting the site owners directly and explaining your intentions.

Even if the website allows for scraping, performing it intensively can inundate the server, leading to performance issues and hurting users’ experience.

Therefore, it’s recommended to scrape during non-peak periods and avoid causing a resource crunch to the web server. Besides, avoiding the peak hours can significantly increase the scraping speed and generate better results.

Wrapping up

Those are our best web scraping tips for advanced Python data harvesting!

While extracting data from websites can be complex, there are several ways of circumventing the bottlenecks and ensuring the process is quick, trouble-free, and fruitful.

Check out our web scraper API and our web scraping tool. Both make web scraping incredibly easy and scalable.