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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...

Machine Learning Quick Tutorial - Part:1

The following are the list of languages useful for Machine learning. There's no such thing as one language being "better" than another. It's a case of picking the right tool for the job. Your Resume has value if you put any one of these languages. Python The Python language has increased in usage because it's easy to learn and easy to read. Python has good libraries such as scikit-learn, PyML, Jython and pybrain. R R is an open-source statistical programming language. The syntax is not the easiest to learn, but I do encourage you to have a look at it. It also has a large number of machine learning packages and visualization tools.  The R-Java project allows Java programmers to access R functions from Java code. Matlab The Matlab language is used widely within academia for technical computing and algorithm creation. Like R, it also has a facility for plotting visualizations and graphs. Scala A new breed of languages is emerging that takes advantag...