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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...

5 Key Ideas on SAS Banking Analytics

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SAS is providing solutions for banking. Getting away with financial crime just got harder. The latest SAS Financial Crimes Suite arms institutions to detect potential suspicious activity more efficiently than ever. A new customer due diligence solution within the suite more accurately detects changes in a customer’s risk profile. Enhanced anti-money laundering and case management capabilities also make it easier to have a complete view of threats across an institution’s financial crimes investigation unit. “A comprehensive view of potential threats will help in efforts to thwart criminals from successful attempts of hiding illicit funds,” says James Wester, global payments research director at IDC Financial Insights.  “A technology infrastructure with customer risk rating and high-performance analytics will help speed detection and investigation in all channels.”. SAS Analytics Suite for Banking Crimes Today’s rigorous regulatory environment requires banks to move qui...