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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 ]

Here is an Audio Post Explained About Blockchain

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According to Investopedia - Originally developed as the accounting method for the virtual currency Bitcoin , blockchains – which use what's known as distributed ledger technology (DLT) – are appearing in a variety of commercial applications today. Distributed Ledger 1 - What is the Current Trend All the transactions currently can be edited by the server owners. They have full control to change the transaction details. The current trend is either centralized or decentralized. 2- Distributed Trend No one can edit the transaction details. It is transparent to all stakeholders. Video on Distributed Systems References What is Centralized Server Processing Role of Distributed Server Processing