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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...

Data mining Real life Examples

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Data mining is a process to understand about unused data and to get insights from the data. You need a quick tutorial and examples to perfect with this process. The best example is the Backup data business use case to mine the data for useful information. The backup data is simply wasted unless a restore is required. It should be leveraged for other, more important things. This method is called Data Mining Technique . --- For example, can you tell me how many instances of any single file is being stored across your organization? Probably not.  But if it’s being backed up to a single-instance repository, the repository stores a single copy of that file object, and the index in the repository has the links and metadata about where the file came from and how many redundant copies exist. By simply providing a search function into the repository, you would instantly be able to find out how many duplicate copies exist for every file you are backing up, and where they are coming from. ...