Can I specify a numpy dtype when generating random values?

Q: is it possible to specify a dtype for random numbers when I create them.

A: No it isn't. randn accepts the shape only as randn(d0, d1, ..., dn)

Simply try this:

x = np.random.randn(10, 10).astype('f')

Or define a new function like

np.random.randn2 = lambda *args, dtype=np.float64: np.random.randn(*args).astype(dtype)x = np.random.randn2(10, 10, dtype='f')

If you have to use your code on the post, try this code instead

x = np.zeros((10, 10), dtype='f')x[:] = np.random.randn(*x.shape)

This assigns the results of randn to the memory allocated by np.zeros

Let me begin by saying that numpy now supports dtypes for random integers. This enhancement can be tracked through Issue #6790 on numpy's github. But as of today, this facility is not available for the gaussian RNG. I needed this same facility so I wrote this patch for numpy, https://gist.github.com/se4u/e44f631b249e0be03c21c6c898059176

The patch only adds support for generating float values and it does not handle other data types, but it might still be helpful to someone.

UPDATE 27 Sep 2020

The numpy.random.Generator provides RNGs that support the dtype keyword for all random variables. E.g. numpy.random.default_rng().standard_normal(size=1, dtype='float32') gives 1 standard gaussian of type float32.

np.random.randn function randomly initializes the array object of a given shape to a "np.float64"You can find this out yourself by doing as follows:

a = np.random.rand(2,3)b = a[1,2]print (type(b))print (type(a))

output as follows:

<class 'numpy.float64'><class 'numpy.ndarray'>