Numpy Array Math | (Data Science ss : 7.2)

Numpy Array Math

In this session we are going to learn some basic math properties & we will write code & solve the problems.

(1) .sum  :

code :

import numpy as np

a=np.array([5,10])

print(np.sum(a))

output :

>> 15

As you all can relate what this .sum will sum the numbers which are present in the array.

(2) .subtract :

code :

a=[10,20]

b=[6,8]

import numpy as np

print(np.subtract(a,b))

output : >> [4   12]

.subtraction help's us to subtract the number which are present in 2 list/array with each other i.e 10 - 6 = 4  & 20 - 8 = 12.

(3) axis :

code :

a=[10,20]                  #   [x1 , x2]

b=[6,8]                      #   [y1 , y2]

import numpy as np

print(np.sum([a,b],axis=0))     #[x1+y1 , x2+y2]

print(np.sum([a,b],axis=1))     #[x1+x2 , y1+y2]

output :

>> [16   28]

      [ 30  14]

In this axis=1 is row & axis=0 is column.The concept of axis will only work for .sum function.


(4) .divide : 

code :

a=[10,7]

b=[5,2]

import numpy as np

print(np.divide(a,b))

output :

>> [2.   3.5]

Remember that division will always give you float value as an output.


(5) .multiply :

code:

a=[10,7]

b=[5,2]

import numpy as np

print(np.multiply(a,b))

output :

>> [50   14]

This will give you output as an multiplication number.

(6) .equal :

code :

a=[1,2,3]

b=[4,2,1]

import numpy as np

print(np.equal(a,b))

output :

>> [False  True  False]

.equal will compare both array's & if the value will match then it will give you True & if the value will not match then it will give you False.

(7) .array_equal :

code :

a=[1,2,3]

b=[4,2,1]



import numpy as np

print(np.array_equal(a,b))

output :

>> False

.array_equal will compare both the array & if all the elements of 1st array matches with the all the element of 2nd array then it will give you True as a Boolean value else it will give you False.


(8) Basic math functions :


a=[1,2,3]
print(np.sqrt(a))           # square root of the elements in the list
print(np.abs(a))            # calculate the absolute value element-wise 
print(np.sum(a))           # sum of the elements in the list
print(np.min(a))           #  minimum element in the list 
print(np.max(a))          #  maximum element in the list
print(np.mean(a))        #  maximum element in the list 
print(np.median(a))     #  median of  element in the list
print(np.std(a))            #  standard deviation element in the list; its formula is SD=sqrt(mean(abs(x-x.mean())^2)
print(np.corrcoef(a))   # calculate the coefficient of the list


output :

>> [1. 1.41421356 1.73205081]
[1 2 3]

     6
     1
     3 
     2.0
     2.0
     0.816496580927726
     1.0