rand vs normal in Numpy.random in Python

Last Updated : 17 Nov, 2020

In this article, we will look into the principal difference between the Numpy.random.rand() method and the Numpy.random.normal() method in detail.

About random: For random we are taking .rand() numpy.random.rand(d0, d1, ..., dn) : creates an array of specified shape and fills it with random values. Parameters :

d0, d1, ..., dn : [int, optional]
Dimension of the returned array we require, 

If no argument is given a single Python float 
is returned.

Return :

Array of defined shape, filled with random values.

About normal: For random we are taking .normal() numpy.random.normal(loc = 0.0, scale = 1.0, size = None) : creates an array of specified shape and fills it with random values which is actually a part of Normal(Gaussian)Distribution. This is Distribution is also known as Bell Curve because of its characteristics shape. Parameters :

loc   : [float or array_like]Mean of 
the distribution. 
scale : [float or array_like]Standard 
Derivation of the distribution. 
size  : [int or int tuples]. 
Output shape given as (m, n, k) then
m*n*k samples are drawn. If size is 
None(by default), then a single value
is returned.

Return :

Array of defined shape, filled with 
random values following normal 
distribution.

Code 1 : Randomly constructing 1D array

Python

# Python Program illustrating
# numpy.random.rand() method
 
import numpy as geek
 
# 1D Array
array = geek.random.rand(5)
print("1D Array filled with random values : \n", array)

Output :

1D Array filled with random values : 
 [ 0.84503968  0.61570994  0.7619945   0.34994803  0.40113761]

Code 2 : Randomly constructing 1D array following Gaussian Distribution

Python

# Python Program illustrating
# numpy.random.normal() method
 
import numpy as geek
 
# 1D Array
array = geek.random.normal(0.0, 1.0, 5)
print("1D Array filled with random values "
      "as per gaussian distribution : \n", array)
# 3D array
array = geek.random.normal(0.0, 1.0, (2, 1, 2))
print("\n\n3D Array filled with random values "
      "as per gaussian distribution : \n", array)

Output :

1D Array filled with random values as per gaussian distribution : 
 [-0.99013172 -1.52521808  0.37955684  0.57859283  1.34336863]

3D Array filled with random values as per gaussian distribution : 
 [[[-0.0320374   2.14977849]]

 [[ 0.3789585   0.17692125]]]

Code3 : Python Program illustrating graphical representation of random vs normal in NumPy

Python

# Python Program illustrating
# graphical representation of 
# numpy.random.normal() method
# numpy.random.rand() method
 
import numpy as geek
import matplotlib.pyplot as plot
 
# 1D Array as per Gaussian Distribution
mean = 0 
std = 0.1
array = geek.random.normal(0, 0.1, 1000)
print("1D Array filled with random values "
      "as per gaussian distribution : \n", array);

# Source Code : 
# https://docs.scipy.org/doc/numpy-1.13.0/reference/
# generated/numpy-random-normal-1.py
count, bins, ignored = plot.hist(array, 30, normed=True)
plot.plot(bins, 1/(std * geek.sqrt(2 * geek.pi)) *
          geek.exp( - (bins - mean)**2 / (2 * std**2) ),
          linewidth=2, color='r')
plot.show()


# 1D Array constructed Randomly
random_array = geek.random.rand(5)
print("1D Array filled with random values : \n", random_array)

plot.plot(random_array)
plot.show()

Output :

1D Array filled with random values as per gaussian distribution : 
 [ 0.12413355  0.01868444  0.08841698 ..., -0.01523021 -0.14621625
 -0.09157214]



1D Array filled with random values : 
 [ 0.72654409  0.26955422  0.19500427  0.37178803  0.10196284]