Specifying default dtype for np.array(1.)

Question

Is there a way to specify default dtype that's used with constructs like np.array(1.)?

In particular I want np.array(1.) to be np.float32 and np.array(1) to be np.int32. Instead I'm getting np.float64 and np.int64

Answer 1

The default depends on your system. On a 64-bit system, default types will be 64-bit. On a 32-bit system, default types will be 32-bit. There is no way to change the default short of re-compiling numpy with a different system C header.

You can of course specify dtypes explicitly, e.g.

>>> x = np.array(1, dtype='int32')

Edit: as kazemakase mentions below, the above is only true for int32/int64. In recent numpy versions, the default for floating-point is float64 regardless of the system.

Answer 2

You could use np.float32 or np.int32 as np.ndarray constructor:

>>> np.float32([1.])
array([ 1.], dtype=float32)
>>> np.int32([1])
array([1], dtype=int32)

but that returns a numpy-scalar if given a scalar input (not a rank 0 array):

>>> np.float32(1)
1.
>>> np.asarray(np.float32(1))  # Use np.asarray to convert it to an array
array(1.0, dtype=float32)

Redefining the default dtype seems to be not so easy, see also:

• How to set the default datatype as 'float32' for numpy & pandas?
• Can i set float128 as the standard float-array in numpy
• Python: Making numpy default to float32

If you don't care additional overhead you can always use a dictionary as "switch" to get correct dtype for those you find inappropriate:

defaults = {np.dtype('int64'): np.int32,
            np.dtype('float64'): np.float32}

before = 1.
np.array(before, dtype=defaults.get(np.result_type(before), None))

This will however fail with complicated types like characters (strings) or objects.