choice(a, size=None, replace=True, p=None, axis=0):
Generates a random sample from a given 1-D array
- 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 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 * ksamples 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
a.ndim - 1 + len(size). 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.
- axis : int, optional
The axis along which the selection is performed. The default, 0, selects by row.
- shuffle : boolean, optional
Whether the sample is shuffled when sampling without replacement. Default is True, False provides a speedup.
- samples : single item or ndarray
The generated random samples
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.
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')