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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...

Quick Guide: Machine Learning Examples and Uses

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I want to share with you the best real-time examples on machine learning.  Because of new computing technologies, machine learning today is not like machine learning of the past.  While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Machine learning use cases The heavily hyped, self-driving Google car? The essence of machine learning.  Online recommendation offers like those from Amazon and Netflix? Machine learning applications for everyday life.  Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation.  Fraud detection? One of the more obvious, important uses in our world today. Best example : "pattern recognition" is best example for Machine Learning Related: Take Quiz on Machine Learning Where can you apply mach...