![]() ![]() We'll cover how to extract data in this case by downloading and parsing the HTML with the help of Cheerio. Case 2 – Server-side Rendered HTMLīesides getting data asynchronously via an API, another common technique used by web servers is to render the data directly into the HTML before serving the page up. Let's move on to covering scraping HTML that's rendered by the web server in Case 2. I hope this prevents me from being blacklisted for spamming requests to take their data for my own entirely benevolent purposes.Īnd with that, we've successfully scraped a JSON API using Node.js. As a courtesy to our beloved data hosts, I like to put in a delay after each fetch to make sure I am not bombarding their servers with too many requests at once. I mostly just extracted the request into its own async function called fetchPlayerYearOverYear and then looped over an array of IDs to fetch them all. This will copy something like this to your clipboard: Right click the "playerdashboardyearoveryear" row and select Copy → Copy as cURL. Ok, so back to the Network tab in the browser's developer tools. Run brew install jq on the command line to get it.Ĭombining these tools with a bash script is probably sufficient for a bunch of scraping needs, but in this article we'll migrate over to using node.js after figuring out the exact request we want to make. Note you may need to install jq if you do not already have it. We'll be using the terminal ( Applications/Utilities/Terminal on a Mac) now to quickly iterate with the tools curl and jq. Now that we know how to manually find the data we care about, let's work on automating it with a script. We have now confirmed this is the API request we're interested in scraping. With some careful inspection, we can see that the second item in the resultSets entry in this response matches the data for our table. Recalling the HTML we inspected from earlier, we were looking for a dataset named "Base" and the second set ( sets from before) in it to find our table data. The JSON response from the API request we found, truncated for readability Let's head on over to and find the page with the stats we care about, in this case LeBron's player page: Step 1: Check if the data is loaded dynamically ![]() ![]() Our goal will be to write a script that will save LeBron James' year-over-year career stats. Okay with some preliminary understanding of data formats under our belt, it's time to take a stab at scraping some real data. We'll use as our case study to learn these techniques. In this case, we'll go over a method of intercepting these API requests and work with their JSON payloads directly via a script written in Node.js. Case 1 – Using APIs DirectlyĪ very common flow that web applications use to load their data is to have JavaScript make asynchronous requests ( AJAX) to an API server (typically REST or GraphQL) and receive their data back in JSON format, which then gets rendered to the screen. Learning to read and understand this format will go a long way to helping you work with data on the web. Note these instructions were written with Chrome 78 and will likely vary slightly with different browsers. So without further adieu, let's begin with a quick primer on CSV vs JSON. We'll even try out curl and jq on the command line for a bit. I'll go through the way I investigate what is rendered on the page to figure out what to scrape, how to search through network requests to find relevant API calls, and how to automate the scraping process through scripts written in Node.js. There are several different ways to scrape, each with their own advantages and disadvantages and I'm going to cover three of them in this article:įor each of these three cases, I'll use real websites as examples (, , and respectively) to help ground the process. Whether you're a student, researcher, journalist, or just plain interested in some data you've found on the internet, it can be really handy to know how to automatically save this data for later analysis, a process commonly known as "scraping". ![]()
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