Random sampling in NumPy | random () function

numpy.random.random() — 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.random (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.random () function

 
# numpy import

import numpy as geek

 

 
# output array

out_arr = geek.random.random (size = 3 < / code> )

print ( "Output 1D Array filled with random floats:" , out_arr) 

Output:

 Output 1D Array filled with random floats: [0.21698734 0.01617363 0.70382199] 

Code # 2:

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

  
# import numpy

import numpy as geek

 

 
# output array

out_arr = geek.random.random (size = ( 2 , 4 ))

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

Output:

 Output 2D Array filled with random floats: [[0.95423066 0.35595927 0.76048569 0.90163066] [0.41903408 0.85596254 0.21666156 0.05734769]] 

Code # 3:

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

 
# numpy import

import numpy as geek

 
# output array

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

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

Output:

 Output 3D Array filled with random floats: [[[0.07861816 0.79132387 ] [0.9112629 0.98162851] [0.0727613 0.03480279]] [[0.11267727 0.07631742] [0.47554553 0.83625053] [0.67781339 0.37856642]]]