More simple than I thought, I never realised you could get this on WhatsApp. On your phone, go to the WhatsApp group and

“Leave safety behind. Put your body on the line. Stand before the people you fear and speak your mind — even if your voice shakes. When you least expect it, someone may actually listen to what you have to say. Well-aimed slingshots can topple giants. And do your homework.”

Some notes on what I’ve done here. I like to have two sets of the data, leaving one as the raw data that you can compare to the wrangled dataset, hence the raw_data_csv and data.

This architecture brings orders of magnitude improvements in performance on the queries side of the software and that is a good thing because the software users are generally spending more time with reading data than writing.

With this portentous beginning to many a well-intentioned post, you can almost be certain that what follows will be some distillation of painfully direct life advice intended to remind people of what’s important. Consider this article a really extended version of that, directed primarily at digital designers in senior and leadership roles as they are our industry’s best chance to right the ship. In order to bring you to the forward-thinking, uplifting bit, I’m going to have to first unsettle you with some difficult truths.

I have explained what these do in the comments, but to explain why: Jupyter limits the amount of outputs displayed for each cell. So if you want to output a graph of some data, as well as a supporting table nicely in one cell, then you’ll need to run the first command. The second two I use to control how much is outputted. I don’t like it how it defaults to such a low amount of rows/columns, especially when you can limit the amount displayed using functions like .head() instead anyway, so I’ve just gone unlimited. But do be careful when having a look at your data that you are limiting it when you need.

For the last few years of my career, I’ve felt the weighty realization that the design industry’s accepted and most practiced ways of creating digital products are more deeply flawed than I ever imagined and much more than most will admit — as is the way we educate aspiring designers. This has been nagging at me to such an extent that I realized I could not stay complacently on the sidelines watching it happen any longer. If you follow me on Twitter, then you know I haven’t exactly been silent on this topic, but up until now, I haven’t really pushed for action, and that platform doesn’t really lend itself to fully expressing ideas. This then is my attempt to get a lot of my thoughts out in one place to hopefully catalyze new discussions. And I know many of you share these feelings even if you don’t dare talk about them.

Unfortunately, this data export does not come out perfectly from WhatsApp, and when a message contains multiple lines/line breaks, it is very hard to attribute that to the correct message details. I played around with some ways to do this, but couldn’t find anything too simple for it. For the purpose of this analysis I’ve just removed them, as we can still get good enough insights without them. That is what the dropna on datetime column is doing.

Just a little comment on the setup I went for. I used a Jupyter Notebook as for this kind of analysis, that is done in little parts, I think it works really well to execute each cell. Here are a couple of tips with some special functions that help in Jupyter.

So some context for these next few sections, the WhatsApp group I used is for my (field) hockey team, so mainly talking about training, matches and the nights out we had. But you’ll see this shortly!

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is using time as a demarcation between a seasonal period and time leading up to the season. Search patterns can change between a season and lead up period to the season. Marking the time periods can reveal search patterns, allowing you to ask “Are there any keywords or phrases that are appearing regularly before the season?”

Once we have the current entity, commands can modify it. Modifications will generate a new event that we will store in the Event Store. Therefore, we push the current state of the entity into a Read Database so reading can stay to be extremely fast.

Layers in Software Architecture that Every Sofware Architect should Know “All architectures have the same objective - the separation of concerns. They all achieve it by dividing the software…danielrusnok.medium.com

From Monolith to Microservices in 5 Minutes a microservice architectural style is an approach to developing a single application as a suite of small services —…levelup.gitconnected.com

Layers in Software Architecture that Every Sofware Architect should Know “All architectures have the same objective - the separation of concerns. They all achieve it by dividing the software…danielrusnok.medium.com

In the Event-sourcing approach, we are not storing only the current state of entities, but we are storing every state that happened to the entity as snapshots. Entities are not saved as normalized data, but as their direct modifications with the timestamp of an event.