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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...

The Story behind Mainframe to Cloud Real Journey

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Mainframe to CLOUD: Mainframe computing took off in the 1950s and gained much prominence throughout the 1960s. Corporations such as IBM (International Business Machines), Univac, DEC (Digital Equipment Corporation), and Control Data Corporation started developing powerful mainframe systems. Gettyimage.in These mainframe systems mainly carried out number-crunching for scientists and engineers. The main programming language used was Fortran. Then in the 1960s, the notion of database systems was conceived and corporations developed database systems based on the network and hierarchical data models. The database applications at that time were written mainly in COBOL. Cloud Vs Mainframe In the 1970s, corporations such as DEC created the notion of mini-computers. An example is DEC's VAX machine. These machines were much smaller than the mainframe systems. Around that time, terminals were developed. This way, programmers did not have to go to computing centers and use punch cards...