Random sampling in NumPy | sample () function



numpy.random.sample() — this is one of the functions for random sampling in numpy. It returns an array of the given shape and fills it with random floating point numbers in the half-open interval [0.0, 1.0).

Syntax: numpy. random.sample (size = None)

Parameters:
size: [int or tuple of ints, optional] Output shape. If the given shape is, eg, (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned.

Return: Array of random floats in the interval [0.0, 1.0). or a single such random float if size not provided.

Code # 1:

# Python program explaining
# numpy.random.sample () function

 
# numpy import

import numpy as geek

 
# output random value

out_val = geek.random.sample ()

print ( "Output random value:" , out_val) 

Output:

 Output random value: 0.9261509680895836 

Code # 2:

# Python program explaining
# numpy.random.sample () function

 
# numpy imports

import numpy as geek

  

 
# output array

out_arr = geek.random.sample (size = ( 3 , 3 ))

print ( "Output 2D Array filled with random floats:" , out_arr) 

Output:

 Output 2D Array filled with random floats: [[0.75908777 0.88295677 0.60979136] [0.68157065 0.75100312 0.08321613] [0.8360331 0.64808891 0.14731635]] 

Code # 3:

# Python program explaining
# numpy.random.sample () function

 
# numpy import

import numpy as geek

 
# output array

out_arr = geek.random.sample (( 2 , 2 , 3 ))

print ( "Output 3D Array filled with random floats: " , out_arr) 

Output:

 Output 3D Array filled with random floats: [[[0.3073475 0.75709465 0.86934712] [0.21953745 0.48138292 0.30686482]] [[0.48925625 0.60222083 0.14403257] [0.87030919 0.87298872] ]