![]() ![]() As we’ve seen in this tutorial, performing advanced scraping operations is actually quite easy using Scrapy’s framework. In a similar regard, you may want to extract the text from one news article at a time, rather than downloading all 10,000 articles at once. As your dataset grows it becomes more and more costly to manipulate it in terms of memory or processing power. You’ll want to make sure you’re operating at least moderately efficiently before attempting to process 10,000 websites from your laptop one night. Performance considerations can be crucial Pre-processing text, normalizing text, and standardizing text before performing an action or storing the value is best practice before most NLP or ML software processes for best results. Think of all of the different spellings and capitalizations you may encounter in just usernames. ![]() Slight variations of user-inputted text can really add up. ![]() Getting consistent results across thousands of pages is tricky It’s important that our Scrapy crawlers are resilient, but keep in mind that changes will occur over time. Nowadays, modern front-end frameworks are oftentimes pre-compiled for the browser which can mangle class names and ID strings, sometimes a designer or developer will change an HTML class name during a redesign. While these errors can sometimes simply be flickers, others will require a complete re-architecture of your web scrapers. Sometimes Amazon will decide to raise a Captcha, or Twitter will return an error. On occasion, AliExpress for example, will return a login page rather than search listings.
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