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# numpy.random.choice

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

  Generates a random sample from a given 1-D array

  > New in version 1.7.0.

  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 a was np.arange(n) 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 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 : 1-D ndarray, shape (size,) 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`

  Examples

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

      >>> np.random.choice(5, 3)
      array([0, 3, 4])
      >>> #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])

  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])
      >>> #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])

  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'],
            dtype='|S11')