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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 ]

A Quick guide to Amazon RDS

Amazon Aurora is a MySQL-compatible relational database management system (RDBMS) that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. It provides up to 5X the performance of MySQL at one tenth the cost of a commercial database. Amazon Aurora allows you to encrypt data at rest as well as in transit for your mission-critical workloads. Key points on Amazon Aurora Amazon Aurora is a relational database engine that combines the speed and reliability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. It delivers up to five times the throughput of standard MySQL running on the same hardware. Amazon Aurora is designed to be compatible with MySQL 5.6, so that existing MySQL applications and tools can run without requiring modification.  Amazon Aurora joins MySQL, Oracle, Microsoft SQL Server, and PostgreSQL as the fifth database engine avail