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Python map() and lambda() Use Cases and Examples

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 In Python, map() and lambda functions are often used together for functional programming. Here are some examples to illustrate how they work. Python map and lambda top use cases 1. Using map() with lambda The map() function applies a given function to all items in an iterable (like a list) and returns a map object (which can be converted to a list). Example: Doubling Numbers numbers = [ 1 , 2 , 3 , 4 , 5 ] doubled = list ( map ( lambda x: x * 2 , numbers)) print (doubled) # Output: [2, 4, 6, 8, 10] 2. Using map() to Convert Data Types Example: Converting Strings to Integers string_numbers = [ "1" , "2" , "3" , "4" , "5" ] integers = list ( map ( lambda x: int (x), string_numbers)) print (integers) # Output: [1, 2, 3, 4, 5] 3. Using map() with Multiple Iterables You can also use map() with more than one iterable. The lambda function can take multiple arguments. Example: Adding Two Lists Element-wise list1 = [ 1 , 2 , 3 ]
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Bank ATM Analytics - SAP Hana

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Problem:

ATM(Automated Teller Machine) has become an integral part of our society. Recent estimates developed by ATMIA place the number of ATMs in use currently at over 2.2 million, or 1 ATM per around over 3000 people in the world. If we think of very specific banks than a recent information in wikipedia states that SBI Group (State Bank Of India) has around 45000+ ATM. So we see the numbers are huge. But there is a very big problem and i would like to use a typical example from India to illustrate this. Here, many times the ATM runs out of currency notes of specific denomination as lets say Rs 100. The end consumer is left with the option of either accepting currency of higher denomination or seek a different ATM. Here we also have a charge applied by the bank if a consumer withdraws money from a ATM of a bank different from the one in which he holds his account.
Now this is a major inconvenience for the end consumer and also a loss for the bank as their reputation is effected.

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