Complete Beginner’s Guide to Web Scraping in Python

A guide to using Beautiful Soup and Selenium to collect data from websites .Complete Beginner’s Guide to Web Scraping in Python

Introduction

There is a large amount of data publicly available on the internet that can contribute to useful, important and interesting insights through data science projects. A lot of this information is able to be systematically extracted using Python libraries.

The goal for this article is to walk you through the different tools available for web scraping in Python and when these tools should be used. We will cover how to use BeatifulSoup and Selenium to build an automated web scraper. This tutorial will go from start to finish in the scraping process from seeing a website with interesting data to automating the information collection process.

Before we get started, I would like to note 2 things:

1. Before scraping websites with Python, you should check the website’s policies around accessing information automatically as some websites discourage accessing information with automated tools.
2. As you will see in the tutorial, scraping a site is heavily impacted by the structure of the website. This means you will have to change your approach as you scrape different sites. It also means that if the structure of a site changes, you will have to adjust your approach for the scraper to work on the same site.

For this introduction to web scraping, we will demonstrate how to scrape box score data from nba.com using Beautiful Soup and Selenium. While the specific details of each step are specific to nba.com data, this process can be followed to scrape most websites on the internet.

Beautiful Soup will be used to scrape links to box score pages on nba.com and Selenium will be used to scrape the player data on each of these pages.

web-scraping python data-science selenium

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