♻️ change nan default handling behavior to SkipNa

[jvdd]

Change default behavior for nan-handling (i.e., argminmax) to ignore (or skip) nans instead of returning (the first) nan index

Reasons why argminmax default will ignore nans:
✔️ no performance regressions for default implementation
✔️ backwards compatible
🤝 same as default pandas & polars behavior
❗ ↔️ numpy default (which returns nan index)

Other contributions of this PR:

• updated benchmarks (use original names & make these default to ignore-nan variant) => should result in 0 regressions
  • remove unused benchmark functions for float benchmarks
• renamed the float_simd.rs to more concrete names w.r.t. their nan handling behavior

[codspeed-hq]

CodSpeed Performance Report

Merging #28 nans_change_default (439b58b) will not alter performances.

Summary

🔥 0 improvements
❌ 0 regressions
✅ 32 untouched benchmarks

🆕 20 new benchmarks
⁉️ 12 dropped benchmarks

Benchmarks breakdown

	Benchmark	nans_v3	nans_change_default	Change
🆕	scalar_random_long_f16	N/A	3.3 ms	N/A
🆕	sse_random_long_f16	N/A	476.5 µs	N/A
🆕	avx2_random_long_f16	N/A	236.4 µs	N/A
🆕	impl_random_long_f16	N/A	236.6 µs	N/A
🆕	scalar_nanargminmax_f32	N/A	2.2 ms	N/A
🆕	sse_nanargminmax_f32	N/A	968 µs	N/A
🆕	avx2_nanargminmax_f32	N/A	466.9 µs	N/A
🆕	impl_nanargminmax_f32	N/A	467.1 µs	N/A
🆕	scalar_random_long_f32	N/A	1.7 ms	N/A
🆕	sse_random_long_f32	N/A	712.1 µs	N/A
🆕	avx_random_long_f32	N/A	403 µs	N/A
🆕	impl_random_long_f32	N/A	403.2 µs	N/A
🆕	scalar_nanargminmax_f64	N/A	2.4 ms	N/A
🆕	sse_nanargminmax_f64	N/A	2.3 ms	N/A
🆕	avx2_nanargminmax_f64	N/A	1.1 ms	N/A
🆕	impl_nanargminmax_f64	N/A	1.1 ms	N/A
🆕	scalar_random_long_f64	N/A	1.9 ms	N/A
🆕	sse_random_long_f64	N/A	1.4 ms	N/A
🆕	avx_random_long_f64	N/A	804.7 µs	N/A
🆕	impl_random_long_f64	N/A	804.9 µs	N/A
⁉️	scalar_random_long_f32	1.7 ms	N/A	N/A
⁉️	sse_random_long_f32	712 µs	N/A	N/A
⁉️	avx_random_long_f32	402.9 µs	N/A	N/A
⁉️	impl_random_long_f32	403.1 µs	N/A	N/A
⁉️	scalar_random_long_f64	1.9 ms	N/A	N/A
⁉️	sse_random_long_f64	1.4 ms	N/A	N/A
⁉️	avx_random_long_f64	804.5 µs	N/A	N/A
⁉️	impl_random_long_f64	804.7 µs	N/A	N/A
⁉️	scalar_random_long_f16	2.3 ms	N/A	N/A
⁉️	sse_random_long_f16	475.6 µs	N/A	N/A
⁉️	avx2_random_long_f16	235.6 µs	N/A	N/A
⁉️	impl_random_long_f16	235.9 µs	N/A	N/A

[jvdd]

⬆️ only regression is for scalar f16 implementation (highlighted in red) - but this regression was already present in the nans_v3 branch (due to the check for nan to return when first nan encountered)