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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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Five top SQL Query Performance Tuning Tips

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SQL query runs faster when you write it in a specific method. You can say it as tuning. There are five tuning tips: List of Performance Tuning Tips use index columns, use group by, avoid duplicate column in SELECT & Where, use Left Joins use a co-related subquery. Five top SQL Query Performance Tuning Tips SQL Performance Tuning Tip: 01 Use  indexes in the where clause of SQL . Let me elaborate more on that. Be sure the columns that you are using in the WHERE clause should be already part of the Index columns of that database Table. An example SQL Query: SELECT *  FROM emp_sal_nonppi WHERE dob <= 2017-08-01; SQL Performance Tuning Tip: 02 Use GROUP BY . Some people use a  DISTINCT clause to eliminate duplicates . You can achieve this by GROUP BY. An example SQL Query: SELECT E.empno, E.lastname FROM emp E,emp_projact EP WHERE E.empno = EP.empno GROUP BY E.empno, E.lastname; SQL Performance Tuning Tip: 03 Avoid using duplicates in the Query. Some people use the same col
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