We walk through web scraping in Python using the beautiful soup library. We start with a brief introduction to HTML & CSS and discuss what web scraping is. Next we start getting into the basics of the beautiful soup library. This includes how to load a webpage, the basic commands you need to know such as find & find_all, grabbing strings from an HTML elements, etc. The final section of this tutorial is a series of exercises where you can practice your skills. In this section we scrape a webpage for links, we learn how to scrape a table and load it into a pandas dataframe, and we see how you can scrape & download a web image. Hope you enjoy!
Resources used in this video
Simple webpage: https://keithgalli.github.io/web-scra…
Example webpage: https://keithgalli.github.io/web-scra…
Link to source code: https://github.com/KeithGalli/web-scr…
Beautiful Soup Documentation: https://www.crummy.com/software/Beaut…
CSS Selector Reference: https://www.w3schools.com/cssref/css_…
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The Beautiful Soup module is used for web scraping in Python. Learn how to use the Beautiful Soup and Requests modules in this tutorial. After watching, you will be able to start scraping the web on your own.
📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=87Gx3U0BDlo&list=PLWKjhJtqVAbnqBxcdjVGgT3uVR10bzTEB&index=12
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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!
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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When scraping a website with Python, it’s common to use the
Requestslibraries to send
GETrequests to the server in order to receive its information.
However, you’ll eventually need to send some information to the website yourself before receiving the data you want, maybe because it’s necessary to perform a log-in or to interact somehow with the page.
To execute such interactions, Selenium is a frequently used tool. However, it also comes with some downsides as it’s a bit slow and can also be quite unstable sometimes. The alternative is to send a
POSTrequest containing the information the website needs using the request library.
In fact, when compared to Requests, Selenium becomes a very slow approach since it does the entire work of actually opening your browser to navigate through the websites you’ll collect data from. Of course, depending on the problem, you’ll eventually need to use it, but for some other situations, a
POSTrequest may be your best option, which makes it an important tool for your web scraping toolbox.
In this article, we’ll see a brief introduction to the
POSTmethod and how it can be implemented to improve your web scraping routines.
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The internet is an absolutely massive source of data — data that we can access using web scraping and Python!
In fact, web scraping is often the only way we can access data. There is a lot of information out there that isn’t available in convenient CSV exports or easy-to-connect APIs. And websites themselves are often valuable sources of data — consider, for example, the kinds of analysis you could do if you could download every post on a web forum.
To access those sorts of on-page datasets, we’ll have to use 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.
#data science tutorials #beginner #python #python tutorials #web scraping