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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...

Python Dictionary Vs List With Examples

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Dictionary and List we use interchangeably in Python to store values. For beginners, both look the same. In reality, they both differ. Here are the differences. Dictionary Vs Lists Values in lists are accessed by means of integers called indices, which indicate where in the list a given value is found. Dictionaries access values by means of integers, strings, or other Python objects called keys , which indicate where in the dictionary a given value is found.  In other words, both lists and dictionaries provide indexed access to arbitrary values, but the set of items that can be used as dictionary indices is much larger than, and contains, the set of items that can be used as list indices.   Also, the mechanism that dictionaries use to provide indexed access is quite different from that used by lists. Both lists and dictionaries can store objects of any type. Values stored in a list are implicitly ordered by their positions in the list because the indices that access these val...