2.5. Supported NumPy features

One objective of Numba is having a seamless integration with NumPy. NumPy arrays provide an efficient storage method for homogeneous sets of 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 calls to 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 direct memory accesses when possible.
  • Numba is able to generate ufuncs and gufuncs. This means that it is possible to implement ufuncs and gufuncs within Python, getting speeds comparable to that of ufuncs/gufuncs implemented in C extension modules using the NumPy C API.

The following sections focus on the Numpy features supported in nopython mode, unless otherwise stated.

2.5.1. Scalar types

Numba supports the following Numpy scalar types:

  • Integers: all integers of either signedness, and any width up to 64 bits
  • Booleans
  • Real numbers: single-precision (32-bit) and double-precision (64-bit) reals
  • Complex numbers: single-precision (2x32-bit) and double-precision (2x64-bit) complex numbers
  • Datetimes and timestamps: of any unit
  • Character sequences (but no operations are available on them)
  • Structured scalars: structured scalars made of any of the types above and arrays of the types above

The following scalar types and features are not supported:

  • Arbitrary Python objects
  • Half-precision and extended-precision real and complex numbers
  • Nested structured scalars the fields of structured scalars may not contain other structured scalars

The operations supported on scalar Numpy numbers are the same as on the equivalent built-in types such as int or float. You can use a type’s constructor to convert from a different type or width.

Structured scalars support attribute getting and setting.

See also

Numpy scalars reference.

2.5.2. Array types

Arrays of any of the scalar types above are supported, regardless of the shape or layout. Operations

Arrays support iteration and full indexing (i.e. indexing that yields scalar values). Partial indexing by a single integer is supported, but other kinds of partial indexing (for example indexing a 3-d array with a 2-tuple) are not supported. Attributes

The following attributes of Numpy arrays are supported: Methods

The following methods of Numpy arrays are supported in their basic form (without any optional arguments):

The corresponding top-level Numpy functions (such as numpy.sum()) are similarly supported.

2.5.3. Functions

The following top-level functions are supported:

The following constructors are supported, only with a numeric input:

  • numpy.bool_
  • numpy.complex64
  • numpy.complex128
  • numpy.float32
  • numpy.float64
  • numpy.int8
  • numpy.int16
  • numpy.int32
  • numpy.int64
  • numpy.intc
  • numpy.intp
  • numpy.uint8
  • numpy.uint16
  • numpy.uint32
  • numpy.uint64
  • numpy.uintc
  • numpy.uintp

2.5.4. Modules random

Numba supports top-level functions from the numpy.random module, but does not allow you to create individual RandomState instances. The same algorithms are used as for the standard random module (and therefore the same notes apply), but with an independent internal state: seeding or drawing numbers from one generator won’t affect the other.

The following functions are supported, but only with scalar output: you can’t pass a size argument. Initialization Permutations

2.5.5. Standard ufuncs

One objective of 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 nopython mode. Limitations

Right now, only a selection of the standard ufuncs work in nopython mode. Following is a list of the different standard ufuncs that Numba is aware of, sorted in the same way as in the NumPy documentation. Math operations

name object mode nopython mode
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 Yes
mod Yes Yes
fmod Yes Yes
abs Yes Yes
absolute Yes Yes
fabs Yes Yes
rint Yes Yes
sign Yes Yes
conj Yes Yes
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 Yes
reciprocal Yes Yes
conjugate Yes Yes Trigonometric functions

name object mode nopython mode
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 object mode nopython mode
bitwise_and Yes Yes
bitwise_or Yes Yes
bitwise_xor Yes Yes
bitwise_not Yes Yes
invert Yes Yes
left_shift Yes Yes
right_shift Yes Yes Comparison functions

name object mode nopython mode
greater Yes Yes
greater_equal Yes Yes
less Yes Yes
less_equal Yes Yes
not_equal Yes Yes
equal Yes Yes
logical_and Yes Yes
logical_or Yes Yes
logical_xor Yes Yes
logical_not Yes Yes
maximum Yes Yes
minimum Yes Yes
fmax Yes Yes
fmin Yes Yes Floating functions

name object mode nopython mode
isfinite Yes Yes
isinf Yes Yes
isnan Yes Yes
signbit Yes Yes
copysign Yes Yes
nextafter Yes Yes
modf Yes No
ldexp Yes (*) Yes
frexp Yes No
floor Yes Yes
ceil Yes Yes
trunc Yes Yes
spacing Yes Yes

(*) not supported on windows 32 bit