view():
=======
==> built-in function
	can use to create an array from another array
	the array object in view is same as original array but possible to change its type.

a = [1,3,5,7,9]
a_copy = [1,3,5,7,9]
a_view = [1.0,3.0,5.0,7.0,9.0]

Syntax:
	source-array.view(dtype)

import numpy

a = numpy.array([1.2,2.1,1.3,3.1,1.4,4.1])

print("The Original Array = ",a)
print("Type = ",a.dtype)

# b = a.copy(dtype = 'i2')
b = a.copy()

print("b = ",b)
print("type = ",b.dtype)

c = a.view(dtype = 'i1')
print("c = ",c)
print("Type = ",c.dtype)
==========================================
reshape():
==========
array ==> 3 X 4 ==> 12
reshape to ==> 4 X 3 ==> 12
reshape ==> 6 X 2 ==> 12

Note:
====
the reshape parameters product must be equal to total number of elements in the original array,
then only the reshape is possible.

==> reshape() built-in function
	can be use to reshape the original array
	and create the new array.

Syntax:
	original_array.reshape((parameters))

for 2d-array: x, y
for 3d-array: x, y, z

import numpy

a = numpy.array([1,2,3,4,5,6])  # 1d-array
b = numpy.array([[1,2,3,4],[5,6,7,8],[9,10,11,12]])  # 2d-array

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

r1 = a.reshape((3,2))
r2 = a.reshape((2,3))
# r3 = a.reshape((3,3))
r3 = b.reshape((12,))
r4 = b.reshape((1,3,4))

print("r1 = ",r1)
print("r2 = ",r2)
print("r3 = ",r3)
print("r4 = ",r4)
================================================
Array Indexing:
===============
import numpy

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

# Positive Indexing values
print(a[0],a[1],a[2],a[3],a[4])
print(b[0],b[1],b[2])
print(b[0][0],b[0][1],b[0][2])
print(b[1][0],b[1][1],b[1][2])
print(b[2][0],b[2][1],b[2][2])

# Negative Indexing Values
print(a[-1],a[-2],a[-3],a[-4],a[-5])
print(b[-1],b[-2],b[-3])
print(b[-1][0],b[-1][1],b[-1][2])

=============================================
Array Slicing:
==============

import numpy

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

print(a[::1])
print(a[::-1])

print(a[2:6])
print(a[-10:-4])

=================================================
# WAP TO CREATE THE ARRAY WITH RANGE OF OBJECTS.

import numpy

objects = range(1,11)

a = numpy.array(objects)
b = numpy.array(range(10,101,10))

print("Array with range of objects = ",a)
print("Array with range of objects = ",b)
====================================================
logspace():
===========
log10
log2
==> built-in function
	can use to generate an array with log values 
	these objects can be evenly spaced.

Syntax:
	numpy.logspace(start, stop, num, endpoint = True, base = 10/2, dtype)

import numpy

a = numpy.logspace(1,11,6,base = 2)

print("a = ",a)

===================================================
Array-Iteration:
================
1) Using for loop:
==================

import numpy

a = numpy.array([1,3,5,7,9]) # 1d-array
b = numpy.array([[1,2,3],[4,5,6],[7,8,9]])
c = numpy.array([[[1,2,3,4],[5,6,7,8]],[[9,10,11,12],[13,14,15,16]]])

for i in a:
    print(i)

for i in a[::-1]:
    print(i)

for i in b:
    print(i)

for i in b:
    for j in i:
        print(j,end = "\t")
    print()

print("Printing 2d-arrays:")

for i in c:
    print(i)

print("Printing 1D-array:")

for i in c:
    for j in i:
        print(j)

print("3d-Array ELements = ")
for i in c:
    for j in i:
        for k in j:
            print(k,end = "\t")
        print()