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Mastering flat_map in Python with List Comprehension

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Introduction In Python, when working with nested lists or iterables, one common challenge is flattening them into a single list while applying transformations. Many programming languages provide a built-in flatMap function, but Python does not have an explicit flat_map method. However, Python’s powerful list comprehensions offer an elegant way to achieve the same functionality. This article examines implementation behavior using Python’s list comprehensions and other methods. What is flat_map ? Functional programming  flatMap is a combination of map and flatten . It transforms the collection's element and flattens the resulting nested structure into a single sequence. For example, given a list of lists, flat_map applies a function to each sublist and returns a single flattened list. Example in a Functional Programming Language: List(List(1, 2), List(3, 4)).flatMap(x => x.map(_ * 2)) // Output: List(2, 4, 6, 8) Implementing flat_map in Python Using List Comprehension Python’...
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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 us...
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