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
import numpy as geek
array = geek.random.rand( 5 )
print ( "1D Array filled with random values : \n" , array)
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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
import numpy as geek
array = geek.random.normal( 0.0 , 1.0 , 5 )
print ( "1D Array filled with random values "
"as per gaussian distribution : \n" , 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)
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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
import numpy as geek
import matplotlib.pyplot as plot
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);
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()
random_array = geek.random.rand( 5 )
print ( "1D Array filled with random values : \n" , random_array)
plot.plot(random_array)
plot.show()
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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.
.
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