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Showing content from https://docs.scipy.org/doc/numpy/reference/random/generated/numpy.random.Generator.random.html below:

numpy.random.Generator.random — NumPy v2.3 Manual

method

random.Generator.random(size=None, dtype=np.float64, out=None)#

Return random floats in the half-open interval [0.0, 1.0).

Results are from the “continuous uniform” distribution over the stated interval. To sample \(Unif[a, b), b > a\) use uniform or multiply the output of random by (b - a) and add a:

Parameters:

sizeint or tuple of ints, optional

Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned.

dtypedtype, optional

Desired dtype of the result, only float64 and float32 are supported. Byteorder must be native. The default value is np.float64.

outndarray, optional

Alternative output array in which to place the result. If size is not None, it must have the same shape as the provided size and must match the type of the output values.

Returns:

outfloat or ndarray of floats

Array of random floats of shape size (unless size=None, in which case a single float is returned).

See also

uniform

Draw samples from the parameterized uniform distribution.

Examples

>>> rng = np.random.default_rng()
>>> rng.random()
0.47108547995356098 # random
>>> type(rng.random())
<class 'float'>
>>> rng.random((5,))
array([ 0.30220482,  0.86820401,  0.1654503 ,  0.11659149,  0.54323428]) # random

Three-by-two array of random numbers from [-5, 0):

>>> 5 * rng.random((3, 2)) - 5
array([[-3.99149989, -0.52338984], # random
       [-2.99091858, -0.79479508],
       [-1.23204345, -1.75224494]])

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