Map() function:
===============
Syntax:
	map(function, collection/iterable)

here:
	function: that defines the operation to apply on each element of the collection.

-> map() function is used to apply a given condition/function with condition to each item of the given iterable object/collection (like: tuple, list, set etc.,) and return a map object as a result.
-> map() function can be very useful for applying the same operation to all elements in a collection without using loop explicitly.

Ex: Let's assume that you are managing a list of prices (in USD) of some products, and you want to convert these prices into different currency.
	($1000, $200, $108, $11, $55) ==> (Rs80000, 16000, 10640, 880,...)

prices_usd = [100,200,300,400,500]  # global variable

def convertToEuros(prices_usd):
    priceInEuros = prices_usd * 0.85
    return priceInEuros

p = tuple(map(convertToEuros,prices_usd))
print("Product prices in USD = ",prices_usd)
print("Product prices in Euros = ",p)

map() with lambda function:
===========================
Syntax:
	map(lambda-function, collection)

prices = [100,200,300,400,500]

dicounted_prices = list(map(lambda x : x * 0.9, prices))

print(dicounted_prices)
==============================================================
why use map()
=============
1) cleaner code:
================
we can avoid writing of explicit loops for transforming each items in a collection.

2) map() can improve the functional approach.
Because of this the readability of the program can be improved.

3) Performance is high. Because this function faster than using a loop especially with larger data sets. Basically it was developed with C.

============================================================
reduce():
=========
Syntax:
	reduce(function, collection)

-> used to reduce the collection into single value by applying the function on each element of the given collection.

Ex: [1,3,5,7,9] ==> 1 + 3 + 5 + 7 + 9 ==> 25

Note: 
=====
-> to use reduce() we must import it.
Syntax:
	from functools import reduce

from functools import reduce

expenses = [1000, 200, 50, 100,1500,6000]

totalExpenses = reduce(lambda x,y:x+y,expenses)

print(totalExpenses)

Advantages:
===========
1) To make develop concise codes which can improve the readability of the program

from functools import reduce

expenses = [1000, 200, 50, 100,1500,6000]

total = 0

# with loop
for expense in expenses:
    total += expense
print(total)

# without loop

total = reduce(lambda x,y:x+y,expenses)
print(total)

2) Avoid the extra variables.
3) reduce() provide the flexibility to implement any complex logic.