The internet is an absolutely massive source of data. Unfortunately, the vast majority if it isn’t available in conveniently organized CSV files for download and analysis. If you want to capture data from many websites, you’ll need to try web scraping.

Don’t worry if you’re still a total beginner — in this tutorial we’re going to cover how to do web scraping with Python from scratch, starting with some answers to frequently-asked questions about web scraping.

If you’re already familiar with the concept, feel free to scroll past these and jump right into the tutorial!

What is Web Scraping in Python?

Some websites offer data sets that are downloadable in CSV format, or accessible via an Application Programming Interface (API). But many websites with useful data don’t offer these convenient options.

Consider, for example, the National Weather Service’s website. It contains up-to-date weather forecasts for every location in the US, but that weather data isn’t accessible as a CSV or via API. It has to be viewed on the NWS site.

If we wanted to analyze this data, or download it for use in some other app, we wouldn’t want to painstakingly copy-paste everything. Web scraping is a technique that lets us use programming to do the heavy lifting. We’ll write some code that looks at the NWS site, grabs just the data we want to work with, and outputs it in the format we need.

In this tutorial, we’ll show you how to perform web scraping using Python 3 and the Beautiful Soup library. We’ll be scraping weather forecasts from the National Weather Service, and then analyzing them using the Pandas library.

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