2.1. Numba Types

2.1.1. Basic Types

“Basic” Numba types can be expressed through simple expressions. The symbols below refer to attributes of the main numba module (so if you read “boolean”, it means that symbol can be accessed as numba.boolean).

2.1.1.1. Numbers

The following table contains the elementary numeric types currently defined by Numba, and their various aliases.

Type name(s) Shorthand Comments
boolean b1 represented as a byte
uint8, byte u1 8-bit unsigned byte
uint16 u2 16-bit unsigned integer
uint32 u4 32-bit unsigned integer
uint64 u8 64-bit unsigned integer
int8, char i1 8-bit signed byte
int16 i2 16-bit signed integer
int32 i4 32-bit signed integer
int64 i8 64-bit signed integer
float32 f4 float32
float64, double f8 float64
complex64 c8 single-precision complex number
complex128 c16 double-precision complex number

2.1.1.2. Arrays

The easy way to declare array types is to subscript an elementary type according to the number of dimensions. For example a 1-dimension single-precision array:

>>> numba.float32[:]
array(float32, 1d, A, nonconst)

or a 3-dimension array of the same underlying type:

>>> numba.float32[:,:,:]
array(float32, 3d, A, nonconst)

However, this is not enough to express all possibilities, such as a particular contiguity or a structured array.

2.1.2. Advanced Types

For more advanced declarations, you have to use constructors provided by Numba.

Warning

The APIs documented here are not guaranteed to be stable. Unless necessary, it is recommended to let Numba infer argument types by using the signature-less variant of @jit.

2.1.2.1. Numpy scalars

Non-trivial scalars such as structured types need to be constructed programmatically.

numba.from_dtype(dtype)

Create a Numba type corresponding to the given Numpy dtype:

>>> struct_dtype = np.dtype([('row', np.float64), ('col', np.float64)])
>>> numba.from_dtype(struct_dtype)
Record([('row', '<f8'), ('col', '<f8')])
class numba.types.NPDatetime(unit)

Create a Numba type for Numpy datetimes of the given unit. unit should be a string amongst the codes recognized by Numpy (e.g. Y, M, D, etc.).

class numba.types.NPTimedelta(unit)

Create a Numba type for Numpy timedeltas of the given unit. unit should be a string amongst the codes recognized by Numpy (e.g. Y, M, D, etc.).

See also

Numpy datetime units.

2.1.2.2. Arrays

class numba.types.Array(dtype, ndim, layout)

Create an array type. dtype should be a Numba type. ndim is the number of dimensions of the array (a positive integer). layout is a string giving the layout of the array: A means any layout, C means C-contiguous and F means Fortran-contiguous.

2.1.2.3. Optional types

class numba.optional(typ)

Create an optional type based on the underlying Numba type typ. The optional type will allow any value of either typ or None.

>>> @jit((optional(intp),))
... def f(x):
...     return x is not None
...
>>> f(0)
True
>>> f(None)
False