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

👁 Image


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

👁 Image
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. .
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