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

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 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 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]

Important :
In code 3, plot 1 clearly shows Gaussian Distribution as it is being created from the values generated through random.normal() method thus following Gaussian Distribution.
plot 2 doesn’t follow any distribution as it is being created from random values generated by random.rand() method.

Note :
Code 3 won’t run on online-ID. Please run them on your systems to explore the working.
.