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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...

Hadoop 2x vs 3x top differences

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In many interviews, the first question for Hadoop developers is what are the differences between Hadoop 2 and 3. You already know that Hadoop upgraded from version 1. The below list is useful to know the differences. I have given Hadoop details in the form of questions and answers so that beginners can understand. Hadoop 2.x Vs 3.x The major change in hadoop 3 is no storage overhead. So, you may be curious about how Hadoop 3 is managing storage. My plan is for you is first to go through the list of differences and check the references section, to learn more about Hadoop storage management. References Real story of storage management in Hadoop Follow me on twitter Applyanalytics