Performant Numpy: Glossary

Numpy comprises primarily compiled, explicitly-typed code, which allows optimisations that can’t be done for Python’s interpreted, implicitly typed code

Avoid Python loops and using whole-array functions to get better performance

Using compiled code will frequently improve performance over Numpy. This can be done in a compiled language, or by using Numba on Python code.

Numpy will broadcast operations to all elements, and has many functions to implement commonly-performed tasks across arrays

Conditionals can be recast as array masks, arrays of true/false values

Use numpy.einsum for more complex array multiplications and reductions, but only where the performance boost is worth the reduced readability

Numpy automatically expands smaller arrays to match the shape of larger ones

Axes are read right to left, and must be either the same size or size 1

Where one array has more dimensions, the smaller array is interpreted as having size 1 on the additional axes

Use the @vectorize decorator to turn elemental functions into ufuncs

Use the @guvectorize decorator to turn elemental functions into generalised ufuncs

Both a signature (showing datatypes and dimensionalities) and a layout (showing relationships between indices) are required

Explicitly initialise the output array within your generalised ufunc where possible

Use the @jit decorator to just-in-time compile plain Python functions (operating on Numpy arrays or otherwise)

Use the nopython=True argument to @jit to raise an error if something can’t be compiled, so you know to fix it to get maximum speed