Why NumPy?
==========
1) Python List ==> reserve with non-contiguous memory.
NumPy-ndarray ==> reserve with contiguous memory.
2) Python-List will consume more time and performance get disturbed.
NumPy-ndarray will consume less time and performance is good.
3) Python: 10+ datatypes ==> Heterogeneous datatypes
NumPy: 1-datatype ==> ndarray

import sys
import numpy

a = 123
b = 1.23
c = 1-2j
d = True
e = "python"
f = [1,2,3,4,5]
g = (2,3,4,5,6)

arr = numpy.array(f)
arr1 = numpy.array([1.2,2.3,3.4,4.5,5.6])

print("The Size of all the above data definitions are = ")
print(sys.getsizeof(a))
print(sys.getsizeof(b))
print(sys.getsizeof(c))
print(sys.getsizeof(d))
print(sys.getsizeof(e))
print(sys.getsizeof(f))
print(sys.getsizeof(g))
print(sys.getsizeof(arr))
print(sys.getsizeof(arr1))

===============================================================
ndarray:
========
built-in datatype in NumPy
	a pre-defined class: ndarray

ndarray
======
	==> most important object in NumPy.
	==> N-dimensional Array Type
	==> describe a collection of items of the same type
	==> which can be accessed using a zero-based index.

dtype
=====
	==> Each item of ndarray takes the same size of a memory block.
	==> datatype object

array Scalar type
=================
	==> any item can be extracted from an ndarray by using slicing is represented by a python object of an array called as "array scalar type".

=============================
How to create an ndarray?
=========================
[1,2,3] ==> 1d-array ==> row
[1
2
3] ==
; 1-d array ==> column
array():
========
Syntax:
	import numpy
	numpy.array(object, dtype = None, copy = True, order = None, subok = False, ndmin = 0)

order:
======
	C ==> row major
	F ==> Column major
	A ==> Any

1-D array:
=========
Syntax:
	numpy.array([list/tuple])

2-D array:
==========
Syntax:
	numpy.array([[1d],[1d],[1d],...[1d]])

3-D array:
=========
Syntax:
	numpy.array([[2d],[2d],[2d],...[2d]])

import numpy as rk
# creating an array with array() by consider the only object parameter

a = rk.array((1,2,3,4))# 1-d array

print(a,type(a))

b = rk.array(10)

print(b,type(b))

# 2D-array
# Collection of more than one 1-d array ==> 2d-array
c = rk.array([[1,2,3],[4,5,6]])

print(c,type(c))

d = rk.array([[1,2,3],[4,5,6],[7,8,9],[10,11,12]])

print(d,type(d))

# 3-d array
# collection of 2-d arrays

e = rk.array([[[1,2,3,4],[5,6,7,8]],[[9,10,11,12],[13,14,15,16]]])

print(e,type(e))