Python: Operations on Numpy Arrays

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: 

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: 

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. 

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. 

Output:

First array:
[ 5 15 20]

Second array:
[2 5 9]

Applying mod() function:
[1 0 2]

Applying remainder() function:
[1 0 2]