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Learn How to Use Python Proxies in Web Scraping Projects

People with good experience in web scraping will easily get information from online websites. Python is the object-oriented language that experts use to collect public data from web pages automatically.

Compared to other coding languages, classes and objects provided by Python are the simplest to use. Moreover, several libraries that come with Python can work to create the best tool for web scraping purposes.

Important Python Libraries For Web Scraping

Three important libraries used to simplify the web scraping process include BeautifulSoup, Selenium, and Pandas. You must properly install these libraries before learning how to scrap a web page using Python proxies. In case you get an error message like “NameError: name * is not defined,” then most probably one of these libraries was not installed properly.

Finding A Place For Python Web Scraper

You need a good coding environment before getting started with the programming part. Those who have installed Visual Studio Code can create files like .py. You will be able to get a fully-featured IDE (Integrated Development Environment). However, we suggest you install PyCharm with intuitive UI and is popularly used for web scraping purposes.

We assume you are using PyCharm for this tutorial. Run PyCharm. On the project area, you have to right-click to create a new Python File. Give the file a short name that can define the project.

Building Lists And Defining Objects

When one uses Python, it allows them to design objects without needing to assign a proper type. You can quickly create an object by giving a title and assigning a value. In Python, sets and dictionaries are part of the collection. However, you will find lists easy to use because they permit delicate members and are mutable and ordered.

Before we create objects, it is time to rerun the application. Make sure there isn’t any error that can cause problems during the programming process. Troubleshoot any error before starting the data extracting process.

Data Extracting With Python Web Scraper

Now we have arrived at the most difficult and fun part – data extracting from HTML files. In any case, we want to take small sections from several web pages and store them into a list. Hence, there is a need to process every small section that should be added to a list.

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You can use attributes to narrow down the search. For instance, you can set up a statement like “if the attribute is equal to Y is false then…” We shall use classes because they are easy to find. Before you continue with the data extraction process, visit the target URL in Google or Yahoo.

Press CTRL+U in Google Chrome to open the source page. Alternatively, right-click and choose “view page source.” Once you are on the source page of the website, find the nearest class where the information is stored if you want to save time, press F12 which will open DevTools. You can then choose Element Picker.

If your target is complex, then more time and effort will be required to extract the data. In the loop, make the first statement to search every element that matches tags, where “title” is included in the “class” attribute. Then, execute another search for all the <a> tags in the file. Finally, the “name” variable will be assigned an object.

Exporting The Data

When using Python to scrap a web page, he has to double-check the code regularly. Even when they don’t find any runtime or syntax error while running a program, they can still notice a semantic error. In case of semantic error, you should ensure the data is not assigned to the wrong object.

Conclusion

We hope now you understand how to use Python proxies in web scraping projects. You should install the important libraries that come with Python to scrape the web pages easily. Moreover, you must use a good code editor to reduce the chances of getting errors while running the program.

Featured Image by Gerd Altmann from Pixabay