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How to Scrape E-Commerce Data With Node.js and Puppeteer

Web scraping is nothing new. However, the technologies that are used to build websites are constantly developing. Hence, the techniques that have to be used to scrape a website have to adapt.

Why Node.js?

A lot of websites use front-end frameworks like React, Vue.js, Angular, etc., which load the content (or parts of the content) after the initial DOM is loaded. This especially applies to performance-optimized e-commerce websites, where price and production information are loaded asynchronously.

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How to Extract Data From Tables in Multiple Google Docs and Combine Into One Table in Coda

If your organization creates multiple Google Docs every day and you want a better way to sort, filter, and organize all these Google Docs, this solution may be useful for you. This tutorial walks you through how to extract the important metadata from a table in a Google Doc and sync that data into a table in Coda.

I often see templates for project briefs, meeting notes, or candidate interview as Google Docs at other organizations which are duplicated over and over again. These Google Docs are then stored in Google Drive which can get very unwieldy to manage unless someone is consistently organizing the folders in Google Drive. Another common trait in these Google Docs: a table near the top of the Google Doc summarizing what the doc is about.

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Intro to Yelp Web Scraping Using Python

Originally published June 17, 2020

Like many programmers who hold degrees that are not even relevant to computer programming, I was struggling to learn coding by myself since 2019 in the hope to succeed in the job. As a self-taught developer, I’m more practical and goal-oriented about things that I’ve learned. This is why I like web scraping particularly, not only it has a wide variety of use cases such as product monitoring, social media monitoring, content aggregation, etc, but also it’s easy to pick up.

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How to Build Pivot Tables

Did you know that a pivot table allows you to quickly summarize your data based on a group, pivot, and aggregation columns? This summary might include sums, averages, or other statistics, which the pivot table splits the statistics is a meaningful way for different subgroups and draws attention to useful information.

Fig. 1: A pivot table showing the average sunshine hours for each city in each month. This table was constructed by applying the pivoting function to a dataset that contains at least one column for month (group column), one column for city (pivot), and one column for sunshine hours (aggregation column).