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Documentation Index

Fetch the complete documentation index at: https://numpyts.dev/llms.txt

Use this file to discover all available pages before exploring further.

How does numpy-ts performance scale with array size compared to NumPy? This page shows the full picture.
All benchmarks measure computation time from JS and Python, respectively. To learn more, check out benchmark methodology.

Size Scaling Summary

Array SizeAvg SpeedupBest CaseWorst CaseBenchmarks
Small (100)1.12x40.38x0.09x2390
Medium (1K)1.02x77.99x0.12x2390
Large (10K)1.20x2422.82x0.10x2379

Small (100) — by Category

CategoryAvg SpeedupCount
creation1.35x213
arithmetic0.72x295
math0.61x125
trig0.59x216
gradient3.70x22
linalg1.45x269
reductions1.72x413
manipulation1.06x231
io3.04x66
indexing0.89x115
bitwise0.55x10
sorting0.67x75
logic0.94x142
statistics3.03x26
sets2.78x33
random1.03x46
polynomials2.13x27
fft1.16x66

Medium (1K) — by Category

CategoryAvg SpeedupCount
creation1.22x213
arithmetic0.99x295
math0.58x125
trig0.57x216
gradient3.42x22
linalg1.69x269
reductions0.91x413
manipulation1.07x231
io2.32x66
indexing0.62x115
bitwise0.49x10
sorting0.89x75
logic1.39x142
statistics1.46x26
sets2.46x33
random0.89x46
polynomials2.14x27
fft0.70x66

Large (10K) — by Category

CategoryAvg SpeedupCount
creation3.29x213
arithmetic1.61x295
math0.66x125
trig0.66x216
gradient7.11x22
linalg1.37x269
reductions0.57x413
manipulation1.37x231
io2.84x55
indexing0.52x115
bitwise1.01x10
sorting0.93x75
logic3.67x142
statistics0.82x26
sets5.39x33
random0.81x46
polynomials1.93x27
fft0.94x66