# numpy.random.RandomState.choice

method

random.RandomState.choice(a, size=None, replace=True, p=None)

  Generates a random sample from a given 1-D array

  New in version 1.7.0.

  Note

  New code should use the `choice` method of a `default_rng()` instance instead; please see the Quick Start.

  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

  See also

  `randint`, `shuffle`, `permutation`
  `Generator.choice`

    which should be used in new code

  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)
      array([0, 3, 4]) # random
      >>> #This is equivalent to np.random.randint(0,5,3)

  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])
      array([3, 3, 0]) # random

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

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

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

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