Proxies And Python Web Scraping (Why A Proxy Is Required)
At a time when data has become a crucial asset to individuals and companies alike, web scraping has made the process of harnessing the benefits of such data easier. From allowing faster sneaker copping to helping businesses thrive via competitive pricing, web scraping has a wide array of uses and applications.
Understanding how to go about it can, therefore, prove to be highly beneficial. There are several approaches and, in this article, we cover one of the most popular ones – Python web scraping. Let’s start by going through the basics. If you are already familiar with these concepts, feel free to use the table of contents to jump ahead.
What Is Python Web Scraping?
To fully understand what python web scraping is, we must first pull it apart and break it down to its individual elements – python and web scraping:
What is Python?
Python is one of the most popular programming languages on the internet. You must have heard about it at some point in time, at least in passing. It is impossible to look for jobs in the tech industry without seeing countless requests for individuals with Python experience.
It is versatile and has countless applications across websites, companies, users, and everything in between. Chances are that if you ask your resident tech guru what programming language you should try and learn, the answer will be “Python.”
Usually, you can find Python coding behind a wide range of programs and applications, especially in the data science industry. It follows a familiar structure of declaring variables, adding conditions, executing loops, etc. Typically, nothing about Python should throw you for a loop if you are familiar with any other object-based programming language. Now, onto the next element.
What is web scraping?
Web scraping refers to the process of automatically collecting a large amount of specific data from a web page. As such, Python scraping refers to the process of using Python to automatically pull huge amounts of data quickly. Market research, location scouting, competitor pricing evaluation, and much more rely on sifting through enormous mounds of data. When I say “enormous,” I mean big enough to take hundreds or thousands of work hours to manually look through it all. This intensive and time-consuming task can be completed in a fraction of the time with Python scraping.
You can use Python to code a program that automatically looks at the data you are interested in. You can program it so that it follows your instructions to browse information and match it to the criteria you specify. Based on how you build the program, you can get it to deliver a huge dump of raw Python data that fits what you were looking for. Or, with a bit more work, you can have it organize the data the way you would like and send you a cleaner list. Neither option is technically wrong, and the one that works best depends on what you are looking for.
Why Use a Python Scraper?
There are many tools on the internet that are written to scrape data from the internet. Each one differs in how technical, user-friendly, and customizable they are. These help you avoid having to program your own scraping tool to get the Python data you need.
However, there are several upsides to Python web scraping. The main upside is customization. When you use a Python scraper, you are fully in charge of the design. You can configure where it needs to look for data and what data you need. Also, by using python, you can complete the process on your own. You don’t have to depend on any third party to collect the data that you need.
Despite all the benefits, there are a few challenges that you are bound to face when you try to Python scrape. Scraping takes a huge amount of data and processes it automatically. This process works exceedingly fast. So websites can easily see that it is much faster than a human is capable of. When this happens, websites are usually quick to block the IP address that is making so many quick requests. Thankfully, there is a solution – using a python web proxy.
What Is a Python Proxy and Why Do You Need One?
Let’s start by defining what a proxy is in general. A proxy acts as a facilitator between you and the website or web service you are accessing. Your requests go through the proxy IP address instead of directly through your device’s IP address (i.e. your identifying information for internet interactions). This provides a solution to the problem of getting banned during web scraping.
A Python proxy is simply a proxy that is configured to work well with your python web scraping efforts. There are a few settings that help make a proxy more suitable for scraping with python. When you build your own Python web scraping tool, or if you acquire one online, you can apply such settings to make the tool use proxies for its efforts.
You want to make sure to have your proxies in bulk so that you have plenty to swap in when the original proxies get banned. This way, the scraper will alternate which proxy IP address it is using to pull the information. Spreading the load this way makes each different proxy request information at slower rates. But, since you are using multiple proxies simultaneously, you are still automatically pulling data at incredible speeds.
With proxies, it is not only more difficult for your destination website to notice the scrape, but its efforts to block it are far less effective. Additionally, they can give you a sense of security because your destination only sees the proxy accessing it – it does not see your IP address.
How to Scrape Data From a Website Using Python and Proxies
With the right scraper and proxy, you can scrape data from any website using Python. The details depend on the specific settings of the scraper and the proxies you use. With a reliable provider, you will have access to instructions and appropriate user support in regards to getting set up correctly. Generally, you will be given your proxy information, and you just have to put that information in your scraping configuration settings. But, before that, you will have to choose a particular type of proxy to use. Here are the best proxy types for Python web scraping:
Rotating proxies are a kind of proxy that get a change in IP address at regular intervals. This change happens after a pre-determined amount of time or, in the case of an IP ban, it happens automatically. As such, with these kinds of proxies, you don’t have to worry about the interruptions caused by bans. This makes them great for web scraping.
The counterparts of rotating proxies are called static proxies. As their name implies, these work with a single IP address. This makes them less than ideal for web scraping. Learn more about that here.
Residential IP addresses come from Internet Service Providers (ISPs) who assign them to homeowners when they purchase Wi-Fi sources like modems. As such, for companies to give you access to a residential proxy IP address, they have to get it from an actual user. This makes them relatively expensive. However, because they are associated with the residences of real people, they look legitimate to websites. If you use a residential proxy to Python scrape, it will initially look like a regular user is connected to the site. Bottom line is that the likelihood of being detected and banned is low with this kind of proxy. As such, they are an excellent choice for web scraping.
The counterparts of residential proxies, datacenter proxies, come from data centers. They are not associated with physical addresses or ISPs which makes it quite easy for websites to detect them. They are cheaper than residential proxies and can definitely be used for web scraping. The main issue is that you would need to purchase tons of them to complete your project. Also, some platforms – e.g. Twitter – have completely forbidden the use of datacenter proxies.
Rotating residential proxies
A proxy can be a combination of the two types outlined above and this is the absolute best kind for web scraping. You get decreased odds of being detected and banned plus constant rotation that further decreases the odds. Additionally, even if you do get banned, the proxy automatic rotation will allow the process to go on seamlessly.
Finding the Best Python Web Proxy
It might be tempting to skimp out on cost but it is best to avoid free proxies. These are often traps for viruses and malware. Even when they are not, they are typically used by so many people that there is no way you will find the performance you need. Additionally, you cannot know what everyone else is using the proxy for. There could easily be a data breach that puts you at risk.
If you are looking for maximum efficiency, you cannot trust your scraping efforts to less reputable or lower quality proxies. Having proxies with incredible speeds, unlimited bandwidth, maximum uptime, and automatic replacement are a must if you want your scraping projects to run smoothly. And only reliable providers can give you such quality of great proxies.
You need to look for a team that is working hard to keep their proxies functioning at the highest possible level of performance. They should also have an easy-to-use dashboard that you can navigate and 24/7 customer support. That way, no matter what challenges you face, you can always get guidance.
We offer you all of this and much more at Blazing SEO. Whether you choose to go with a residential proxy to reduce the odds of being detected while you Python scrape, or you decide to go for a bunch of datacenter proxies, with our wide variety of proxies, you can be sure you’ll get what you need.
When the power of proxies is harnessed, Python web scraping can save you a lot of time and manual research work while offering a bunch of other benefits. Making sure you are only using reliable proxies is the key to keeping the operation going at maximum speed, efficiency, and security.
The information contained within this article, including information posted by official staff, guest-submitted material, message board postings, or other third-party material is presented solely for the purposes of education and furtherance of the knowledge of the reader. All trademarks used in this publication are hereby acknowledged as the property of their respective owners.
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