2.1. Types and signatures¶
2.1.1. Rationale¶
As an optimizing compiler, Numba needs to decide on the type of each variable to generate efficient machine code. Python’s standard types are not precise enough for that, so we had to develop our own finegrained type system.
You will encounter Numba types mainly when trying to inspect the results of Numba’s type inference, for debugging or educational purposes. However, you need to use types explicitly if compiling code aheadoftime.
2.1.2. Signatures¶
A signature specifies the type of a function. Exactly which kind of signature is allowed depends on the context (AOT or JIT compilation), but signatures always involve some representation of Numba types to specifiy the concrete types for the function’s arguments and, if required, the function’s return type.
An example function signature would be the string "f8(i4, i4)"
(or the equivalent "float64(int32, int32)"
) which specifies a
function taking two 32bit integers and returning a doubleprecision float.
2.1.3. Basic types¶
The most basic 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
).
Many types are available both as a canonical name and a shorthand alias,
following Numpy’s conventions.
2.1.3.1. Numbers¶
The following table contains the elementary numeric types currently defined by Numba and their aliases.
Type name(s)  Shorthand  Comments 

boolean  b1  represented as a byte 
uint8, byte  u1  8bit unsigned byte 
uint16  u2  16bit unsigned integer 
uint32  u4  32bit unsigned integer 
uint64  u8  64bit unsigned integer 
int8, char  i1  8bit signed byte 
int16  i2  16bit signed integer 
int32  i4  32bit signed integer 
int64  i8  64bit signed integer 
intc  –  C intsized integer 
uintc  –  C intsized unsigned integer 
intp  –  pointersized integer 
uintp  –  pointersized unsigned integer 
float32  f4  singleprecision floatingpoint number 
float64, double  f8  doubleprecision floatingpoint number 
complex64  c8  singleprecision complex number 
complex128  c16  doubleprecision complex number 
2.1.3.2. Arrays¶
The easy way to declare array types is to subscript an elementary type according to the number of dimensions. For example a 1dimension singleprecision array:
>>> numba.float32[:]
array(float32, 1d, A)
or a 3dimension array of the same underlying type:
>>> numba.float32[:, :, :]
array(float32, 3d, A)
This syntax defines array types with no particular layout (producing code
that accepts both noncontiguous and contiguous arrays), but you can
specify a particular contiguity by using the ::1
index either at
the beginning or the end of the index specification:
>>> numba.float32[::1]
array(float32, 1d, C)
>>> numba.float32[:, :, ::1]
array(float32, 3d, C)
>>> numba.float32[::1, :, :]
array(float32, 3d, F)
2.1.4. Advanced types¶
For more advanced declarations, you have to explicitly call helper functions or classes 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 signatureless variant of @jit.
2.1.4.1. Inference¶

numba.
typeof
(value)¶ Create a Numba type accurately describing the given Python value.
None
is returned if the value isn’t supported in nopython mode.>>> numba.typeof(np.empty(3)) array(float64, 1d, C) >>> numba.typeof((1, 2.0)) (int64, float64) >>> numba.typeof([0]) reflected list(int64)
2.1.4.2. Numpy scalars¶
Instead of using typeof()
, nontrivial scalars such as
structured types can also 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)]) >>> ty = numba.from_dtype(struct_dtype) >>> ty Record([('row', '<f8'), ('col', '<f8')]) >>> ty[:, :] unaligned array(Record([('row', '<f8'), ('col', '<f8')]), 2d, A)

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.
2.1.4.3. 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 Ccontiguous andF
means Fortrancontiguous.