numpy.random.Generator.choice

method

Generator.choice()

choice(a, size=None, replace=True, p=None, axis=0):

Generates a random sample from a given 1-D array

Parameters

a1-D array-like or int

If an ndarray, a random sample is generated from its elements. If an int, the random sample is generated as if a were np.arange(a)

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 from the 1-d a. If a has more than one dimension, the size shape will be inserted into the axis dimension, so the output ndim will be a.ndim - 1 + len(size). Default is None, in which case a single value is returned.

replaceboolean, optional

Whether the sample is with or without replacement

p1-D array-like, optional

The probabilities associated with each entry in a. If not given the sample assumes a uniform distribution over all entries in a.

axisint, optional

The axis along which the selection is performed. The default, 0, selects by row.

shuffleboolean, optional

Whether the sample is shuffled when sampling without replacement. Default is True, False provides a speedup.

Returns

samplessingle item or ndarray

The generated random samples

Raises

ValueError

If a is an int and less than zero, if p is not 1-dimensional, if a is array-like with a size 0, if p is not a vector of probabilities, if a and p have different lengths, or if replace=False and the sample size is greater than the population size.

See also

integers, shuffle, permutation

Examples

Generate a uniform random sample from np.arange(5) of size 3:

>>> rng = np.random.default_rng()
>>> rng.choice(5, 3)
array([0, 3, 4]) # random
>>> #This is equivalent to rng.integers(0,5,3)

Generate a non-uniform random sample from np.arange(5) of size 3:

>>> rng.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])
array([3, 3, 0]) # random

Generate a uniform random sample from np.arange(5) of size 3 without replacement:

>>> rng.choice(5, 3, replace=False)
array([3,1,0]) # random
>>> #This is equivalent to rng.permutation(np.arange(5))[:3]

Generate a non-uniform random sample from np.arange(5) of size 3 without replacement:

>>> rng.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])
array([2, 3, 0]) # random

Any of the above can be repeated with an arbitrary array-like instead of just integers. For instance:

>>> aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher']
>>> rng.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])
array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'], # random
      dtype='<U11')