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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...

How to Understand Pickling and Unpickling in Python

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Here are the Python pickling and unpickling best examples and the differences between these two. These you can use to serialize and deserialize the python data structures. The concept of writing the total state of an object to the file is called  pickling,  and to read a Total Object from the file is called  unpickling. Pickle and Unpickle The process of writing the state of an object to the file (converting a class object into a byte stream) and storing it in the file is called pickling. It is also called object serialization . The process of reading the state of an object from the file ( converting a byte stream back into a class object) is called unpickling.  It is an inverse operation of pickling. It is also called object deserialization .  The pickling and unpickling can implement by using a pickling module since binary files support byte streams. Pickling and unpickling should be possible using binary files. Data types you can pickle Integers Boolea...