My beef with spreadsheets is their use as manually updated tracking tools, whose inconsistent formatting makes automation tricky.

Luckily AI can update junky spreadsheets but I think "don't" is the best position I've heard yet.

I don't really agree, moreover I haven't had the same experience at all.

I find engineers almost never using spreadsheets, ever for clearly tabular data. My coworkers certainly never use nested-ifs. Many times they will put a crappy table into jira instead, which multiple team members will be overwriting each other's work.

Seems like rules for using spreadsheets more strictly would suffice. Call this abstraction TypeSheet maybe?

I generally agree with the logic -- I don't like using spreadsheets for anything complex, or that has the shelf life of more than, say, a month. I can remember my logic for putting together all the complex formulas for a while, but eventually I have to recreate my own logic, reverse engineering everything, and it's frustrating.

But when one of your arguments is "Excel has complicated nested formulas that are difficult to follow" I don't think showing off a Python one-liner as an alternative is the greatest example. That one-liner is doing about five things that could have been separated into steps to show off the relative clarity and flexibility of the approach. It is better...that example just doesn't do a good job showing it.

I don't use spreadsheets much myself. But I'm not against them as a means of interfacing with non technical people used to using them.

And I have used them as an alternative to a UI with very poor UX that developers came up with for a team of material scientists to use. This thing was basically unusable. Sometimes, it's just easier to go where the users are rather than to try to get them to use something new or different. These people were dealing with a lot of specifications in PDF form. Loads of tables basically. So, spreadsheets were a good fit as that was what they were doing anyway. They loved it. Soon after they discovered databases, python and sql. So there is that.

The reason spreadsheets are widely used is because nothing better really came along that did the same job for everyone. There were of course lots of tools for specific use cases that are somewhat widely used now. But most of these tools are fairly niche compared to spreadsheets.

These days getting data in and out of spreadsheets is not that big of a deal with AI coding tools. A lot of white collar workers are starting to use those. So, switching to something better/appropriate is much less of a challenge now than it used to be.

A caveat first, most of the data I work with is under 10k rows. YMMV.

I find Excel slower than Python for analysis but I share a lot of data modelling with non technical people, and we both like being able to verify the results with pivot tables and functions typically (I need my work to be easily “defensible” because often facts are different from someones “feelings”). So workflow has become something like:

plan -> code -> generate outputs -> generate an XLSX -> verify with pivots/functions ->share with stakeholders

Python pipeline does the transformation and modelling, excel is review/verification, sharing layer.

The (sorta) issue is that the workbook becomes an output, not a source of truth.

People can add pivots and review tabs, but I ask that they provide corrections/feedback outside of generated sheets (otherwise those changes will disappear the next time I run the pipeline and recreate the workbook).

I've been tasked to rewrite a spreadsheet as a dedicated app.

Most of what is in TFA is correct but it only begins to describe the problem.

My takeaway is very simple: the world runs on broken spreadsheets that are full of bugs and wrong assumptions. And the PowerPoint presentations are shown to business people taking decisions based on the wrong numbers coming from those spreadsheets. And it's the world we live in.

> It was common for the data to be split over two or more sheets.

And when the sheet are separated, for example, by years, that's even more bugs. Stuff is counted twice. Others not at all. The concept of when an entry should be "closed" when it's opened on year X and closed on year "X + 1" is something that bewilders spreadsheets users.

> and errors as the spreadsheets grew or were adapted to new data

Errors in spreadsheets are the big one: spreadsheets are full of errors.

> Another problem with spreadsheets is that the amount of data they can contain is more limited than when you use other data analysis workflows.

Another gigantic issue is that the notion of time is broken in dynamic spreadsheets: not because a spreadsheet cannot be written to correctly deal with it. But because the spreadsheet users don't know how to properly model how values relates to varying time (typically the spreadsheet shall work, for the cell that fetches the value, for the time value of "now" and that's it).

> I know there are lots of people who love using spreadsheets.

It's because you can cosplay being an actual programmer when you're not. The result, sadly, is exactly what you'd expect: buggy spreadsheets full of broken assumptions.

Which are then sent to those meaning real business to be rewritten as dedicated apps...

P.S: as of now I'm working on finance stuff... Same old story: a spreadsheet that has overstayed its welcome, it became gigantic. It's a pure mess of fetching values (and fetching way too many values, which creates technical issues) and bogus little things left and right. So what are we doing, again? Porting that spreadsheet to a proper dedicated app that can, correctly, deal with a proper amount of data, while fixing all the little glitches and gotchas too complicated to fix in a spreadsheet.