Nearly ten years ago, I worked for a navigation company. My primary job was to set locations of interest on a map so our users could route to them. We wanted to map the most popular places that our customers were most likely to want to visit. So, of course, we included large tourist attractions. That was not much of a problem. But then we realized that our users probably get hungry while on the road. It would be a good idea to throw in quick, fast food restaurants on the unit too.
Hey, did you know that running a Subway sandwich shop only needs enough room for the bar, a baby oven, a bread oven, and a sink? No grills are needed. No fryers. If you are crafty, you could probably run a Subway out of a closet. Did you also know that because of this, there are more Subway sandwich shops than McDonald’s locations? I do. And when you have a few hundred locations that can fit in a thimble to locate, doing so manually takes months. Luckily, we had a member of the team responsible for scraping.
Finally, did you know that Subway has its restaurant locations listed on the website? A few laborious scraping attempts honestly saved us tens of thousands of dollars in work hours. Not only that, it took a project that would have taken over a year originally and condensed it to a few months. Scraping saved us. This is just one example of how Python web scraping can be incredibly helpful. If you want to figure out more about scraping the data you need, take a look at the table of contents below.
Table of Contents
What is Python Web Scraping?
On the surface, executing a Python scrape means pulling in a large amount of specific data from somewhere else. But before we can dive into that, we should break it apart. What even is Python? You have at least heard about it in passing. And in that case, what is scraping?
What is Python?
Python is one of the most popular programming languages on the internet. 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 if you ask your resident tech guru what programming language you should try and learn, the answer will no doubt be “Python.”
Usually, you can find Python coding behind a wide range of programs and applications. Especially in the data science industry, Python has a dedicated spot. 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.
What is scraping?
Okay, so what is a Python scrape then? Python scraping refers to 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 fit 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.
What is 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. When you use a Python scraper, you configure where it needs to look for data and what data you need.
What is a Python Proxy?
First, we should learn about proxies in general. What is a proxy? Chances are that since you are reading an article about Python web scraping, you already have a solid idea. Just in case, a proxy acts as a facilitator between you and the website or web service you are accessing. Your requests go through the proxy instead of directly to the destination. Then, the site or service’s response gets processed through the proxy instead of coming directly to you. This gives you a sense of security because your destination only sees that the proxy is accessing it. It does not see your IP address (your identifying information for internet interactions).
The idea of a Python proxy is simply a proxy that is configured to work well with your python scraping efforts. Regardless if you built your own Python scraping tool or acquired one online, you can change the settings so the tool uses the proxy for its efforts. There are a few settings that help make a proxy more suitable for scraping efforts.
Do I Need to Use a Python Web Proxy?
Python web scraping absolutely needs a proxy. There is no way around that. Scraping takes a huge amount of data and processes it automatically. This process works exceedingly fast. So websites are able to see easily 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. When you use a proxy for scraping, you use many proxies at once. This way, the scraper will alternate which proxy it is using to pull the information. Spreading the load this way makes each different proxy request information at slower rates.
Since you are using multiple proxies simultaneously, you still automatically pull the data at incredible speeds. Another benefit to this is that if the site you are scraping does get suspicious of a proxy, you can replace it. 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. So now it is not only more difficult for your destination website to notice the scrape, but its efforts to block it are far less effective. So having proxies with incredible speeds, unlimited bandwidth, maximum uptime, and automatic replacement are a must.
Efficiency is on the line, so you cannot trust your scraping efforts to less reputable or lower quality proxies. You need to look for a team that is working hard to keep the best proxies performing as fast as possible with the most uptime available. And an easy-to-use dashboard and 24/7 customer support do not hurt either. If you can get your hands on this quality of great proxies, then you will be in good shape to get scraping.
And it should go without saying to avoid free proxies. These are often traps for viruses and malware. Even if they are not, they are being used by so many people that there is no way you will find the performance you need. And finally, you cannot know what everyone else is using the proxy for. There could easily be a data breach that puts you in a tight spot.
How to do Web Scraping in Python
With the right scraper and proxy, you can scrape data from a website using Python. The details depend on the specific settings of the scraper and proxies you use. There should always be instructions and appropriate user support in regards to getting set up correctly. Regardless, it should be as simple as getting your proxies and specifying these proxies in your scraping tool.
Another thing that helps a ton is having a proxy provider who offers 24/7 customer support. This way, the worst case is that you call the support line to get some guidance. On a basic level, you will have your proxy information, and you just have to put that information in your scraping configuration settings.
So now we know there is a lot of benefit to Python web scraping. When used correctly, it can save you a lot of time and manual research work. Just make sure you have some reliable proxies to keep the operation going. When you have your scraper and proxies configured, you are ready to go after the data you need.
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