Advanced Functions:
===================

Function Aliasing
=================
for the data, we can define two name

a = 10
b = 10

id(a) is id(b) ==> True

a = 20
b = 10

a = [1,2,3,4]
b = [1,2,3,4]

id(a) is id(b) ==> True

a[0] = 11

a = [11,2,3,4]
b = [11,2,3,4]

function aliasing ==> giving/providing the alternative name to the function is called as "function aliasing"
Syntax:
	new-name = function-name


# Function Aliasing

def sumDigits(num):
    n = num
    s_dig = 0

    while n != 0:
        ind_dig = n % 10    # 1 2
        s_dig = s_dig + ind_dig # 1 3
        n = n // 10 # 98765432  9876543
    return s_dig

print(sumDigits(987654321))

# function aliasing

sIndDig = sumDigits

print(sIndDig(97799779))
print(sumDigits(102030405060708090))

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

ANONYMOUS FUNCTION
==================
NAMELESS FUNCTION
KEYWORD: lambda
Syntax:
	identifier = lambda parameter-list : expression

==> lambda function can return a value by default (without return statement)
==> Two reasons for anonymous functions:
	1) For Instant use
	2) to send a function as an argument to the function, lambda function/anonymous function is preferred.

# LAMBDA FUNCTION

# sum of three numbers without the lambda function

# def sumNumbers(a,b,c):
#     s_nums = a + b + c
#     print("The SUm of three numbers = ",s_nums)
#
# sumNumbers(11,21,31)

result = lambda a,b,c : a+b+c

print("The sum of three numbers = ",result(11,21,31))

===============\

# message = lambda name : ("Hello",name,"Good Night")

message = lambda name : "Hello %s,Good Night."%(name)

print(message("Rahul"))

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

to send a function as an argument to the function, lambda function/anonymous function is preferred.
===================================================================================================
there are three function tools are required.
function tools ==> functools
==> in functools, there are three inbuilt methods:
	filter()
	map()
	reduce()

filter()
========
1) filter() can always accept a sequence/collection
2) filter() can perform filtering operation on the given collection/sequence based on the condition
Syntax:
	identifier = filter(condition, sequence/collection)

==> like lambda function, filer() can also return an iterable object
to access the returned object, we can perform the type casting to the filter()

here:
	condition ==> can be passed with other function

# filter()

# WAP TO ACCEPT A LIST AND PRINT THE LIST WITH ONLY EVEN ELEMENTS.

# WITHOUT LAMBDA FUNCTION

def filterEvens(x):
    if x % 2 == 0:
        return True
    else:
        return False

ld = eval(input("Enter a list:"))

res_list = list(filter(filterEvens,ld))

print(res_list)

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

# with lambda function

ld = eval(input("Enter a list:"))

res_list = list(filter(lambda x : x % 2 == 0,ld))

print(res_list)

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

map():
======
1) map() can accept a sequence/collection
2) return an object
need to type cast the map() to get the traceable object.
3) map() will perform the specified operation on each element of the collection/sequence based on the condition.

[1,2,3,4]
[2, 4, 6, 8]

Syntax:
	identifier = map(condition, collection/sequence)

# WAP TO PRINT THE DOUBLE THE ELEMENTS OF THE LIST.

ld = eval(input("Enter the list data:"))

result = list(map(lambda x : 2 * x,ld))

print(result)

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

reduce():
=========
1) to use the reduce(), we should import this from "functools"
Syntax:
	from functools import *
2) reduce() can accept a collection/sequence.
3) based on the condition, reduce() will generate a single value from the sequence/collection. 

[1,3,5,7,9] ==> 945

Syntax:
	identifier = reduce(condition, sequence/collection)

# reduce()

from functools import *

td = eval(input("Enter a tuple data:"))


result = reduce(lambda x,y : x * y,td)

print(result)