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.

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

The following scalar types and features are not supported:

**Arbitrary Python objects****Half-precision and extended-precision**real and complex numbers

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.

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

Arrays support iteration and full indexing (i.e. indexing that yields scalar values). Partial indexing (for example indexing a 2-d array with integers, which would give a 1-d subarray in pure Python) isn’t supported.

The following attributes of Numpy arrays are supported:

The following top-level functions are supported:

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

`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`

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*.

Right now, only a selection of the standard ufuncs work in *nopython mode*.

Also, in its current implementation ufuncs working on arrays will only
compile in *nopython mode* if their output array is passed explicitly.
This limitation does not apply when working with scalars.

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

UFUNC | MODE | |
---|---|---|

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 |

UFUNC | MODE | |
---|---|---|

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 |

UFUNC | MODE | |
---|---|---|

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 |

UFUNC | MODE | |
---|---|---|

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 |

UFUNC | MODE | |
---|---|---|

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