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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...

Understand Data power why quality everyone wants

Information and data quality is new service work for data intense companies. I have seen not only in Analytics projects but in Mainframe projects, there is the Data Quality team. How incorrect data impact on us Information quality problems and their impact are all around us: A customer does not receive an order because of incorrect shipping information. Products are sold below cost because of wrong discount rates. A manufacturing line is stopped because parts were not ordered—the result of inaccurate inventory information. A well-known U.S. senator is stopped at an airport (twice) because his name is on a government "Do not fly" list. Many communities cannot run an election with results that people trust. Financial reform has created new legislation such as Sarbanes—Oxley.  Incorrect data leads to many problems. The role of Data Science is to use quality data for effective decisions. What is information Information is not simply data, strings of numbers, lis...