django-money VS 0.30000000000000004

I know this is not point of the article but:

> The PR was bigger than what I felt I could sensibly review and, in honesty, my desire to go through the hours of work I could tell this would take for a project I no longer used was not stellar.

The PR: https://github.com/django-money/django-money/pull/2/files?di...

Do others share this sentiment?

This doesn't look like a particularly big PR to me, judging solely by the amount of code changed and the nature of the changes at first glance.

Are most of your PRs at work tiny, couple lines of code at most? Am I sloppy for not even consider reviewing this for "hours"? Are all code bases I have worked on sloppy because features often require changing more code than this?

Currently I'm storing prices as django-money field but now I'm questioning the benefits of it and considering just storing prices in USD cents by default since the payment providers are handling currency exchanges anyway, what's your take on it?

Da du ja Django benutzt: Benutz auf deinem Model DecimalField oder Django Money wenn du es etwas high-leveliger haben willst.

oh, thank you, I have bookmarked this, page, will come in handy later, thank you!

https://0.30000000000000004.com/

Although it would be good to move in the direction of using a BigDecimal equivalent by default when ingesting unknown data.

There's no such thing as precision for floats. Floating-point calculations are always inaccurate: read this: https://0.30000000000000004.com/

Edit: This specific example even has its own website: https://0.30000000000000004.com/

In everyday math, we know adding 0.6 + 0.3 equals 0.9, right? But when we turn to computers it results in 0.8999999999999999. Surprisingly, this doesn’t just happen only in JavaScript; it’s the same in many programming languages like Python, Java, C too. Also, it’s not just about this specific calculation. There are many more decimal calculations showing similar not-quite-right answers.

Surprisingly common values like 0.1 don't have a precise representation in binary for most formats, including standard floating point number formats. See https://0.30000000000000004.com/ for more detail than you can shake a stick at.

Also if the local tax code states using 5 decimal places for intermediate values when you will introduce “errors” using formats that give greater precision as well as those that give less precision. Having work on mortgage and pension calculations I can state that the (very) small errors seen at individual steps because of this can balloon significantly through repeated calculations.

Furthmore, the name floating point gives away the other issue. Floating point numbers are accurate to a given number of significant figures not decimal places. For large numbers any decimal places you have in the result are at best an estimate, and as above any rounding errors at each stage can compound into a much larger error by the end of a calculation.

You'll love this https://0.30000000000000004.com/