Python numpy random rand

The Python numpy random rand function generates the uniform distributed random numbers and creates an array of the given shape. To work with this random rand function, we have to import the numpy module. The syntax of this numpy random rand function is

numpy.random.rand(d0, d1, d2,…., dn)

d0, d1, d2,…., dn values are optional, and they specify the array dimensions. For instance, numpy.random.rand(3) means a one dimensional array of size three with random samples.

Python numpy random rand Examples

As we said earlier, the Python rand function parameter values are optional, so we use this function without any parameter.

import numpy as tg

randArr = tg.random.rand()

print(randArr)
0.7278473630715507

Create a 1D Array using numpy random rand

The numpy random rand function creates an array of a given dimension. Here, we used the numpy random rand function to generate a one-dimensional random array of size eight.

import numpy as tg

randArr = tg.random.rand(8)

print(randArr)
[0.51529486 0.70732475 0.13123041 0.08868923 0.91606594 0.94329656
 0.73875145 0.85248642]

2D rand Array

In this example, the numpy rand function returns the two-dimensional array of three rows and five columns.

import numpy as tg

randArr = tg.random.rand(3, 5)

print(randArr)
[[0.82700324 0.78752106 0.91874764 0.71277277 0.41604479]
 [0.7745265  0.88292852 0.56248594 0.09944867 0.5522274 ]
 [0.70205439 0.90095325 0.20693606 0.74183462 0.38383534]]

3D rand Array

import numpy as tg

randArr = tg.random.rand(3, 5, 6)

print(randArr)
Python numpy random rand 1

Numpy ND or Multi Dimensional random rand Array

import numpy as tg

randArr = tg.random.rand(2, 2, 2, 3, 4)

print(randArr)
[[[[[2.22991745e-01 4.82875578e-01 4.02098798e-01 9.82463072e-01]
    [1.27502476e-02 3.78317995e-01 1.31346574e-01 3.58953956e-01]
    [6.31334854e-01 1.80433343e-02 7.73429470e-01 8.47566829e-01]]

   [[7.46991797e-01 9.49487163e-01 6.74764732e-01 8.73915802e-01]
    [1.66405826e-01 6.96463970e-01 2.18829056e-01 6.75685704e-01]
    [1.46913928e-02 2.66258908e-01 3.49072489e-02 7.51997693e-01]]]


  [[[3.15566931e-01 6.77939362e-01 5.68240002e-02 3.78304965e-02]
    [6.26829456e-01 1.64778430e-01 5.80110973e-01 9.45645604e-01]
    [4.52814994e-01 6.12752904e-02 3.98656682e-01 6.12022902e-01]]

   [[8.48650860e-01 9.58124465e-01 6.36882147e-02 5.82332018e-01]
    [4.78369858e-02 5.63250308e-01 6.18235746e-01 4.06755385e-01]
    [9.24566606e-02 8.76239936e-01 2.94192746e-02 1.79601830e-01]]]]



 [[[[2.31480056e-01 8.54221882e-01 6.02771565e-02 1.31022006e-02]
    [6.22401057e-03 3.08509460e-01 3.83590441e-01 8.70963661e-01]
    [9.10762417e-01 4.53411875e-01 5.39746576e-01 8.12068034e-01]]

   [[4.88355065e-01 9.90180989e-01 1.70643645e-01 9.42585049e-01]
    [5.10431032e-01 6.86724036e-01 8.80602910e-01 9.28994548e-01]
    [6.96880899e-04 8.14746646e-01 9.55883374e-02 2.45433858e-01]]]


  [[[9.05425064e-02 3.76958137e-01 4.04775524e-01 6.72941808e-01]
    [5.44979157e-01 4.18175802e-01 5.35779225e-01 1.90175407e-02]
    [2.70055421e-01 6.20119362e-01 6.02102212e-01 5.89668883e-01]]

   [[5.29601610e-01 8.34906099e-01 1.71963671e-01 7.76704873e-01]
    [4.85148164e-02 3.33561971e-01 9.33291412e-01 2.81507135e-01]
    [9.14183251e-01 1.89159336e-02 1.11227617e-01 2.97916564e-01]]]]]