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It’s been a while since I last posted on this blog (almost 4 years!), as life has been demanding a lot of attention. I may be able to start writing with a bit more frequency, or at least that’s what I hope. So, what have I been up to? Well, a bunch of things. I started a consulting business! I finally decided to take the plunge and start my o...

A couple of days ago I had a conversation with a friend who is learning to program. We were talking about the difficulty of remembering what each concept means and what every keyword does. The conversation eventually led to this question: Ok, but when will I stop needing the docs? I (probably most people) had the same feeling when I was learn...

This article is about the second group of concepts I wanted to talk about after re-reading Clean Architecture. I want to try something different this time: Instead of elaborating each idea in long, continuous prose, I’ll just list them as separate chunks. So, here it goes: We already know that tight coupling is a bad thing to have. It bind...

I recently started re-reading Bob Martin’s Clean Architecture and found two other ideas I wanted to share. One of them (the topic of this article) is the dual nature of the way software developers provide value through code. When you implement (or modify) a feature in your system you are creating value by altering or expanding its behavior. Mos...

This article is a summary of what I consider to be the most important concepts of the book Domain-Driven Design, by Eric Evans. I tried to condense the most important ideas in a single article for anyone interested in the topic. I attempted to pack in as much information as possible, but it was not an easy task: The book is a very condensed work...

We have already covered most of the fundamentals of working with data using the Pandas library. There is one more topic I’d like to discuss before concluding the series: The Apply function. In the previous article, we learned how to create subgroups of data using the groupby function. This is quite useful when you want to gain a better understa...

Sometimes you need to perform operations on subsets of data. Your rows might have attributes in common or somehow form logical groups based on other properties. Common operations like finding the average, maximum, count, or standard deviation of values from groups of data is a really common task, and Pandas makes this really easy to accomplish. ...

Merge/join operations in Pandas let you gather information from many tables into a single dataframe for further processing or analysis. This is another important skill that you will probably use a lot when working with data. If you have some experience with relational databases you can recognize the analogous behavior with table joins. In this ...