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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...
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7 Features of Tableau Self-service Engine

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Tableau introduced the Self-Service tool. This Tool helps user queries while importing data from multiple sources. This project is called Project Maestro. This is an additional feature for the data analysis engine.




Self-Service Engine in Tableau


  • The visual ways of inspecting, joining and editing data. Results could then be piped into Tableau for analysis.
  • Speedier data import and analysis. Tableau's new data engine works based on Hyper technology. You can see now faster to import and analyze large data sets with Tableau.
  • Hundreds of thousands of records being imported per second, as well as being visualized in real-time as the import process continued. This engine developed based on feedback from the user community.
  • It supports natural language queries.
  • Tableau is aiming for true natural speech, not merely being able to type in questions that require using exact field names and functions.
  • The example is you can ask questions like Tell me the cheapest houses in near California.
  • Tableau would suggest visualizations and other analyses based on your data set. Pre-built recommended dashboards from cloud services, including combining data from multiple services.


Also read

Tableau 9 for data science-Real life data science exercises

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