view():
=======
==> a built-in function
	which can be used to create an array from another array
	by changing the datatype in the new array.

ex:
a = [1,2,3,4,5]
a_view_float = [1.0,2.0,3.0,4.0,5.0]

Syntax:
	source_array_name.view(dtype)

import numpy

a = numpy.array([1,2,3,4,5,6,7,8,9,10],dtype = numpy.int64)

print("The Given Array is = ",a)
print("The Type of the given Array is = ",a.dtype)

# creating the array from above array with same elements

b = a.view(dtype = 'f4')
print("The Copied Array is = ",b)
print("The Type of The Above Array = ",b.dtype)
============================================

reshape():
==========
a = 3 X 4	= 12
b  = 4 X 3	= 12

==> built-in function
	which can be used to create the array from another array by reshaping the original array.

Syntax:
	source_array.reshape((x,y))

import numpy

a = numpy.array([1,3,5,7,9,10,8,6,4,2],dtype = 'i2')
c = numpy.array([1,2,3,4,5,6,7,8,9])
e = numpy.array([[1,2,3,4],[5,6,7,8],[9,10,11,12]])

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

# creating the new array from a by reshape

b = a.reshape((5,2))
d = c.reshape((3,3))
f = e.reshape((2,3,2))

print("The Reshaped Array = ",b)
print("The Type = ",b.dtype)

print("The Reshaped Array = ",d)
print("The Type = ",d.dtype)

print(f)
==========================================
Slicing:
========
==> slicing can be used to access the part of the collection.

==> Array Indexing is possible with both +eve and -eve indexing

import numpy

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

print(a[0],a[1],a[2],a[3],a[4],a[5],a[6],a[7],a[8],a[9]) # Positive Indeixng
print(a[-1],a[-2],a[-3],a[-4],a[-5],a[-6],a[-7],a[-8],a[-9],a[-10])

Slicing:
========

import numpy

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

print(a[::1])
print(a[::-1])
print(a[3:8])
print(a[-10:-4])
======================================
# WAP TO CREATE THE ARRAY WITH RANGE OF OBJECTS.

import numpy

# Generating the range of objects
range_objects = range(1,11)

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

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

print("The Array = ",b)
print("The Type = ",b.dtype)
===========================================
logspace():
===========
==> built-in function
==> can use to generate the array with the values that are evenly spaced on a log scale.

Syntax:
	numpy.logspace(start, stop, num, endpoint = True, base = 10.0, dtype)

range(1,10) ==> 1 to 9
range(1,11) ==> 1 to 10

import numpy

a = numpy.logspace(1,10,10,base = 2,dtype = 'f2')

print("a = ",a)
print("Type = ",a.dtype)
==========================================
Array-Iteration:
=================
1) using for loop:
==================

import numpy

a = numpy.array([1,3,5,7,9])

# iteration on array using for loop (forward looping)

for i in a:
    print(i)
print()

# reverse looping

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

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

import numpy

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

print(a[0])
print(a[1])
print(a[2])

print(a[0][0])
print(a[0][1])
print(a[0][2])

for i in a:
    print(i)

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