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Step-by-Step Guide to Reading Different Files in Python

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 In the world of data science, automation, and general programming, working with files is unavoidable. Whether you’re dealing with CSV reports, JSON APIs, Excel sheets, or text logs, Python provides rich and easy-to-use libraries for reading different file formats. In this guide, we’ll explore how to read different files in Python , with code examples and best practices. 1. Reading Text Files ( .txt ) Text files are the simplest form of files. Python’s built-in open() function handles them effortlessly. Example: # Open and read a text file with open ( "sample.txt" , "r" ) as file: content = file.read() print (content) Explanation: "r" mode means read . with open() automatically closes the file when done. Best Practice: Always use with to handle files to avoid memory leaks. 2. Reading CSV Files ( .csv ) CSV files are widely used for storing tabular data. Python has a built-in csv module and a powerful pandas library. Using cs...
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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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