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(arr1)

print('\nSecond array:') 
arr2 = np.array([12, 12]) 
print(arr2)

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))

# 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(arr1)

​

print('\nSecond array:') 

arr2 = np.array([12, 12]) 

print(arr2)

​

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))

Output:

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      ]]

numpy.reciprocal() 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. Example: 

# 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(arr)

print('\nAfter applying reciprocal function:') 
print(np.reciprocal(arr))

arr2 = np.array([25], dtype = int)
print('\nThe second array is:')
print(arr2)

print('\nAfter applying reciprocal function:') 
print(np.reciprocal(arr2))

# 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(arr)

​

print('\nAfter applying reciprocal function:') 

print(np.reciprocal(arr))

​

arr2 = np.array([25], dtype = int)

print('\nThe second array is:')

print(arr2)

​

print('\nAfter applying reciprocal function:') 

print(np.reciprocal(arr2))

Output 

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:
[25]

After applying reciprocal function:
[0]

numpy.power() 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(arr)

print('\nApplying power function:') 
print(np.power(arr, 2))

print('\nSecond array is:') 
arr1 = np.array([1, 2, 3]) 
print(arr1)

print('\nApplying power function again:') 
print(np.power(arr, arr1))

# Python code to perform power operation

# on NumPy array

​

​

import numpy as np 

​

​

arr = np.array([5, 10, 15]) 

​

print('First array is:') 

print(arr)

​

print('\nApplying power function:') 

print(np.power(arr, 2))

​

print('\nSecond array is:') 

arr1 = np.array([1, 2, 3]) 

print(arr1)

​

print('\nApplying power function again:') 

print(np.power(arr, arr1))

Output:

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]

numpy.mod() 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(arr) 

print('\nSecond array:') 
print(arr1)

print('\nApplying mod() function:') 
print(np.mod(arr, arr1))

print('\nApplying remainder() function:') 
print(np.remainder(arr, arr1))

# 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(arr) 

​

print('\nSecond array:') 

print(arr1)

​

print('\nApplying mod() function:') 

print(np.mod(arr, arr1))

​

print('\nApplying remainder() function:') 

print(np.remainder(arr, arr1))

Output:

First array:
[ 5 15 20]

Second array:
[2 5 9]

Applying mod() function:
[1 0 2]

Applying remainder() function:
[1 0 2]