Numpy Support in numba

One objective of numba is having a seamless integration with NumPy. NumPy arrays provide an efficient storage method for homogeneous sets if data. NumPy dtypes provide type information useful when compiling, and the regular, structured storage of potentially large amounts of data in memory provides an ideal memory layout for code generation. numba excels at generating code that executes on top of NumPy arrays.

NumPy support in Numba comes in many forms: * numba understands NumPy ufuncs and is able to generate equivalent native code for many of them. * NumPy arrays are directly supported in numba. Access to Numpy arrays is very efficient, as indexing is lowered to memory accessing when possible. * numba is able to generate ufuncs/gufuncs. This means that it is possible to implement ufuncs/gufuncs within Python, getting speeds comparable to that of ufuncs/gufuncs implemented in C extension modules using the NumPy C API.

Supported ufuncs

One objective on numba is having all the standard ufuncs in NumPy understood by numba. When a supported ufunc is found when compiling a function, numba maps the ufunc to equivalent native code. This allows the use of those ufuncs in numba code that gets compiled in no-python mode.


Right now support for ufuncs is quite limited in no-python mode. Meaning that only a selection of the ufuncs work in no-python mode.

Also, in its current implementation ufuncs working on arrays will only compile in no-python mode if the output is explicit (the output array is passed as second argument). Note that this limitation does not apply when applied to scalars

Following is a list of the different NumPy ufuncs that numba is aware of, sorted in the same way as in the NumPy documentation.

Math operations

name python-mode no-python
add Yes Yes
subtract Yes Yes
multiply Yes Yes
divide Yes Yes
logaddexp Yes Yes
logaddexp2 Yes Yes
true_divide Yes Yes
floor_divide Yes Yes
negative Yes Yes
power Yes Yes
remainder Yes No
mod Yes No
fmod Yes No
abs Yes Yes
absolute Yes Yes
fabs Yes Yes
rint Yes Yes
sign Yes Yes
conj Yes No
exp Yes Yes
exp2 Yes Yes
log Yes Yes
log2 Yes Yes
log10 Yes Yes
expm1 Yes Yes
log1p Yes Yes
sqrt Yes Yes
square Yes No
reciprocal Yes No
conjugate Yes No

Trigonometric functions

name python-mode no-python
sin Yes Yes
cos Yes Yes
tan Yes Yes
arcsin Yes Yes
arccos Yes Yes
arctan Yes Yes
arctan2 Yes Yes
hypot Yes Yes
sinh Yes Yes
cosh Yes Yes
tanh Yes Yes
arcsinh Yes Yes
arccosh Yes Yes
arctanh Yes Yes
deg2rad Yes Yes
rad2deg Yes Yes
degrees Yes Yes
radians Yes Yes

Bit-twiddling functions

name python-mode no-python
bitwise_and Yes No
bitwise_or Yes No
bitwise_xor Yes No
bitwise_not Yes No
invert Yes No
left_shift Yes No
right_shift Yes No

Comparison functions

name python-mode no-python
greater Yes No
greater_equal Yes No
less Yes No
less_equal Yes No
not_equal Yes No
equal Yes No
logical_and Yes No
logical_or Yes No
logical_xor Yes No
logical_not Yes No
maximum Yes No
minimum Yes No
fmax Yes No
fmin Yes No

Floating functions

name python-mode no-python
isfinite Yes No
isinf Yes No
isnan Yes No
signbit Yes No
copysign Yes Yes
nextafter Yes No
modf Yes No
ldexp Yes* No
frexp Yes No
floor Yes Yes
ceil Yes Yes
trunc Yes Yes
spacing Yes No

* not supported on windows 32 bit