NumPy - Arithmetic Operations

import numpy as np 
a = np.arange(9, dtype = np.float_).reshape(3,3) 

print  First array:  
print a 
print  
   

print  Second array:  
b = np.array([10,10,10]) 
print b 
print  
   

print  Add the two arrays:  
print np.add(a,b) 
print  
   

print  Subtract the two arrays:  
print np.subtract(a,b) 
print  
   

print  Multiply the two arrays:  
print np.multiply(a,b) 
print  
   

print  Divide the two arrays:  
print np.spanide(a,b)

It will produce the following output −

First array:
[[ 0. 1. 2.]
 [ 3. 4. 5.]
 [ 6. 7. 8.]]

Second array:
[10 10 10]

Add the two arrays:
[[ 10. 11. 12.]
 [ 13. 14. 15.]
 [ 16. 17. 18.]]

Subtract the two arrays:
[[-10. -9. -8.]
 [ -7. -6. -5.]
 [ -4. -3. -2.]]

Multiply the two arrays:
[[ 0. 10. 20.]
 [ 30. 40. 50.]
 [ 60. 70. 80.]]

Divide the two arrays:
[[ 0. 0.1 0.2]
 [ 0.3 0.4 0.5]
 [ 0.6 0.7 0.8]]

Let us now discuss some of the other important arithmetic functions available in NumPy.

numpy.reciprocal()

This function returns the reciprocal of argument, element-wise. For elements with absolute values larger than 1, the result is always 0 because of the way in which Python handles integer spanision. For integer 0, an overflow warning is issued.

Example

import numpy as np 
a = np.array([0.25, 1.33, 1, 0, 100]) 

print  Our array is:  
print a 
print  
   

print  After applying reciprocal function:  
print np.reciprocal(a) 
print  
   

b = np.array([100], dtype = int) 
print  The second array is:  
print b 
print  
   

print  After applying reciprocal function:  
print np.reciprocal(b) 

Our array is:
[   0.25    1.33    1.      0.    100.  ]

After applying reciprocal function:
main.py:9: RuntimeWarning: spanide by zero encountered in reciprocal
  print np.reciprocal(a)
[ 4.         0.7518797  1.               inf  0.01     ]

The second array is:
[100]

After applying reciprocal function:
[0]

import numpy as np 
a = np.array([10,100,1000]) 

print  Our array is:  
print a 
print  
   

print  Applying power function:  
print np.power(a,2) 
print  
   

print  Second array:  
b = np.array([1,2,3]) 
print b 
print  
   

print  Applying power function again:  
print np.power(a,b)

Our array is:
[  10  100 1000]

Applying power function:
[    100   10000 1000000]

Second array:
[1 2 3]

Applying power function again:
[        10      10000 1000000000]

import numpy as np 
a = np.array([10,20,30]) 
b = np.array([3,5,7]) 

print  First array:  
print a 
print  
   

print  Second array:  
print b 
print  
   

print  Applying mod() function:  
print np.mod(a,b) 
print  
   

print  Applying remainder() function:  
print np.remainder(a,b) 

First array:                                                                  
[10 20 30]

Second array:                                                                 
[3 5 7]

Applying mod() function:                                                      
[1 0 2]

Applying remainder() function:                                                
[1 0 2]

import numpy as np 
a = np.array([-5.6j, 0.2j, 11. , 1+1j]) 

print  Our array is:  
print a 
print  
   

print  Applying real() function:  
print np.real(a) 
print  
   

print  Applying imag() function:  
print np.imag(a) 
print  
   

print  Applying conj() function:  
print np.conj(a) 
print  
   

print  Applying angle() function:  
print np.angle(a) 
print  
   

print  Applying angle() function again (result in degrees)  
print np.angle(a, deg = True)

Our array is:
[ 0.-5.6j 0.+0.2j 11.+0.j 1.+1.j ]

Applying real() function:
[ 0. 0. 11. 1.]

Applying imag() function:
[-5.6 0.2 0. 1. ]

Applying conj() function:
[ 0.+5.6j 0.-0.2j 11.-0.j 1.-1.j ]

Applying angle() function:
[-1.57079633 1.57079633 0. 0.78539816]

Applying angle() function again (result in degrees)
[-90. 90. 0. 45.]