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Python: Operations on Numpy Arrays
  • Last Updated : 30 Jan, 2020

NumPy is a Python package which means ‘Numerical Python’. It is the library for logical computing, which contains a powerful n-dimensional array object, gives tools to integrate C, C++ and so on. It is likewise helpful in linear based math, arbitrary number capacity and so on. NumPy exhibits can likewise be utilized as an effective multi-dimensional compartment for generic data.

NumPy Array: Numpy array is a powerful N-dimensional array object which is in the form of rows and columns. We can initialize NumPy arrays from nested Python lists and access it elements. A Numpy array on a structural level is made up of a combination of:

  • The Data pointer indicates the memory address of the first byte in the array.
  • The Data type or dtype pointer describes the kind of elements that are contained within the array.
  • The shape indicates the shape of the array.
  • The strides are the number of bytes that should be skipped in memory to go to the next element.

Operations on Numpy Array

Arithmetic Operations:

# Python code to perform arithmetic
# operations on NumPy array
import numpy as np 
# Initializing the array
arr1 = np.arange(4, dtype = np.float_).reshape(2, 2
print('First array:'
print('\nSecond array:'
arr2 = np.array([12, 12]) 
print('\nAdding the two arrays:'
print(np.add(arr1, arr2))
print('\nSubtracting the two arrays:'
print(np.subtract(arr1, arr2))
print('\nMultiplying the two arrays:')
print(np.multiply(arr1, arr2))
print('\nDividing the two arrays:')
print(np.divide(arr1, arr2))


First array:
[[ 0.  1.]
 [ 2.  3.]]

Second array:
[12 12]

Adding the two arrays:
[[ 12.  13.]
 [ 14.  15.]]

Subtracting the two arrays:
[[-12. -11.]
 [-10.  -9.]]

Multiplying the two arrays:
[[  0.  12.]
 [ 24.  36.]]

Dividing the two arrays:
[[ 0.          0.08333333]
 [ 0.16666667  0.25      ]]


This function returns the reciprocal of argument, element-wise. For elements with absolute values larger than 1, the result is always 0 and for integer 0, overflow warning is issued.


# Python code to perform reciprocal operation
# on NumPy array
import numpy as np 
arr = np.array([25, 1.33, 1, 1, 100]) 
print('Our array is:')
print('\nAfter applying reciprocal function:'
arr2 = np.array([25], dtype = int)
print('\nThe second array is:')
print('\nAfter applying reciprocal function:'


Our array is:
[  25.      1.33    1.      1.    100.  ]

After applying reciprocal function:
[ 0.04       0.7518797  1.         1.         0.01     ]

The second array is:

After applying reciprocal function:


This function treats elements in the first input array as the base and returns it raised to the power of the corresponding element in the second input array.

# Python code to perform power operation
# on NumPy array
import numpy as np 
arr = np.array([5, 10, 15]) 
print('First array is:'
print('\nApplying power function:'
print(np.power(arr, 2))
print('\nSecond array is:'
arr1 = np.array([1, 2, 3]) 
print('\nApplying power function again:'
print(np.power(arr, arr1))


First array is:
[ 5 10 15]

Applying power function:
[ 25 100 225]

Second array is:
[1 2 3]

Applying power function again:
[   5  100 3375]


This function returns the remainder of division of the corresponding elements in the input array. The function numpy.remainder() also produces the same result.

# Python code to perform mod function
# on NumPy array
import numpy as np 
arr = np.array([5, 15, 20]) 
arr1 = np.array([2, 5, 9]) 
print('First array:'
print('\nSecond array:'
print('\nApplying mod() function:'
print(np.mod(arr, arr1))
print('\nApplying remainder() function:'
print(np.remainder(arr, arr1))


First array:
[ 5 15 20]

Second array:
[2 5 9]

Applying mod() function:
[1 0 2]

Applying remainder() function:
[1 0 2]

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