sqlite3
0.30000000000000004
| sqlite3 | 0.30000000000000004 | |
|---|---|---|
| 1 | 245 | |
| 224 | 1,398 | |
| 1.8% | - | |
| 6.4 | 2.0 | |
| 8 days ago | about 1 month ago | |
| TypeScript | CSS | |
| Apache License 2.0 | GNU General Public License v3.0 only |
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sqlite3
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Deno Is Webby
Not one that's up to date and maintained, as far as I can google.
But there's a proper sqlite driver now using the new ffi api support in deno. This didn't exist last time I tried googling db support in deno.
https://github.com/denodrivers/sqlite3
0.30000000000000004
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What even is a JSON number?
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.
- Floating Point Math
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Working with Numpy floats and Forex financial instruments
There's no such thing as precision for floats. Floating-point calculations are always inaccurate: read this: https://0.30000000000000004.com/
- Just learned the difference between decimal and float
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how do i make the decimals not fucked up
Edit: This specific example even has its own website: https://0.30000000000000004.com/
- why doest this loop ever terminate?
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Decoding Why 0.6 + 0.3 = 0.8999999999999999 in JS and How to Solve?
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.
- Lies My Calculator and Computer Told Me [pdf]
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64-Bit Bank Balances ‘Ought to Be Enough for Anybody’?
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.
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I don't get these people
You'll love this https://0.30000000000000004.com/