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bundles / numpy 2.4.3 / numpy / random / RandomState / choice

cython_function_or_method

numpy.random:RandomState.choice

Signature

def   choice ( a ,  size = None ,  replace = True ,  p = None )

Summary

Generates a random sample from a given 1-D array

Extended Summary

Parameters

a : 1-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 it were np.arange(a)

size : int 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.

replace : boolean, optional

Whether the sample is with or without replacement. Default is True, meaning that a value of a can be selected multiple times.

p : 1-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.

Returns

samples : single item or ndarray

The generated random samples

Raises

: ValueError

If a is an int and less than zero, if a or p are not 1-dimensional, if a is an array-like of 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

Notes

Setting user-specified probabilities through p uses a more general but less efficient sampler than the default. The general sampler produces a different sample than the optimized sampler even if each element of p is 1 / len(a).

Sampling random rows from a 2-D array is not possible with this function, but is possible with Generator.choice through its axis keyword.

Examples

Generate a uniform random sample from np.arange(5) of size 3: 
np.random.choice(5, 3)
✗
Generate a non-uniform random sample from np.arange(5) of size 3: 
np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])
✗
Generate a uniform random sample from np.arange(5) of size 3 without replacement: 
np.random.choice(5, 3, replace=False)
✗
Generate a non-uniform random sample from np.arange(5) of size 3 without replacement: 
np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])
✗
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']
✓
np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])
✗

See also

permutation
randint
random.Generator.choice

which should be used in new code

shuffle

Aliases

  • numpy.random.choice
  • numpy.random.RandomState.choice