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 were np.arange(a) size:int or tuple of ints, optional Output shape. If the given shape is, e.g.,(m,n,k), thenm*n*k samples are drawn. Default is None, in which case asingle 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 allentries 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 ofprobabilities, if a and p have different lengths, or ifreplace=False and the sample size is greater than the populationsize

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 withoutreplacement:

>>>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 size3 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-likeinstead 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')