Broadcasting:
=============
refers to the ability of performing operations on arrays with different shapes

Ex:
	+
	1-d + 2-d

==> while the operation, the lower dimension array can automatically expand according to the higher dimension.

Rules for Broadcasting:
======================
1) If arrays are with different dimensions:
	the shape of smaller dimension is must be padded with '1' on the left side until this match with higher dimension array.

[[1 1 1 1],[1 1 1 1],[1 2 3 4]] + [[1 2 3 4],[5 6 7 8],[9 10 11 12]]

2) The size of each dimension must either be the same or one of the dimension must be same.

3) Broadcasting can be applied from last dimension to first dimension.

# WAP IN NUMPY TO ADD A SCALAR TO AN ARRAY.

"""
[1 2 3] + 10
[1,2,3] + [1,1,10]
"""

import numpy

a = numpy.array([1,2,3])
b = numpy.array([[1,2,3],[4,5,6],[7,8,9]])

result = a + 10
res1 = b + 100

print(result)
print(res1)

==============================

import numpy as np

a = np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12]])
b = np.array([10,20,30,40]) # [[1,1,1,1],[1,1,1,1],[10,20,30,40]]

result = a + b

print("The Sum of two differently shaped arrays = ",result)

==============================

# Broadcasting with multi-dimensional arrays

import numpy

a = numpy.ones((2,3,4))
b = numpy.arange(4)

print("The Given array = ",a)
print("The Given array = ",b)

result = a + b

print(result)

=================================================
Arithmetic Operations:
======================

Array Addition:
===============
import numpy

a = numpy.array([10,20,30,40,50])
b = numpy.array([50,40,30,20,10])

sum = a + b

print("The Sum = ",sum)

===============================
import numpy as np

a = np.array([[1,2,3,4,5],[6,7,8,9,10]])
b = np.array([[10,9,8,7,6],[5,4,3,2,1]])

sum = a+b

print("The Sum = ",sum)
=============================
Array Subtraction:
==================

import numpy as np

a = np.arange(1,25).reshape(2,3,4)
b = np.arange(24).reshape(2,3,4)

print("a = ",a)
print("b = ",b)

difference = a-b

print(difference)
==============================
Array Multiplication:
=====================

import numpy

a = numpy.array([1,2,3,4])
b = numpy.array([5,6,7,8])

product = a * b

print("The Product = ",product)

======================================
Array Division:
===============

import numpy as np

a = np.arange(5)
b = np.arange(1,6)

print("a = ",a)
print("b = ",b)

print(a/b)
print(a//b)
print(a%b)

============================
Array Power Operation:
======================
Syntax:
	a ** b

import numpy

a = numpy.arange(5)
b = numpy.arange(1,6)

print("a = ",a)
print("b = ",b)

print(a ** b)

================================================

# Sorting

import numpy as np

a = np.array([[100,12,301],[99,88,101]])

print("The Array before the sorting is = ",a)

# sort()

s_a1 = np.sort(a)  # forward sorting

print("Original Array = ",a)
print("The Sorted Array = ",s_a1)

print(np.sort(a,axis = 1))