numpy.random.Generator.choice¶
method
- random.Generator.choice(a, size=None, replace=True, p=None, axis=0, shuffle=True)¶
Generates a random sample from a given array
- Parameters
- a{array_like, int}
If an ndarray, a random sample is generated from its elements. If an int, the random sample is generated from np.arange(a).
- size{int, tuple[int]}, optional
Output shape. If the given shape is, e.g.,
(m, n, k)
, thenm * n * k
samples are drawn from the 1-d a. If a has more than one dimension, thesize
shape will be inserted into the axis dimension, so the outputndim
will bea.ndim - 1 + len(size)
. Default is None, in which case a single value is returned.- replacebool, optional
Whether the sample is with or without replacement. Default is True, meaning that a value of
a
can be selected multiple times.- 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.
- shufflebool, 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
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 ofp
is 1 / len(a).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 uniform random sample from a 2-D array along the first axis (the default), without replacement:
>>> rng.choice([[0, 1, 2], [3, 4, 5], [6, 7, 8]], 2, replace=False) array([[3, 4, 5], # random [0, 1, 2]])
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')