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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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How to Use Chaid Useful for Data Science Developers

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The Chaid is one of the most asked skills for Data Science engineers. The CHAID Analysis (Chi-Square Automatic Interaction Detection) is a form of analysis that determines how variables best combine to explain the outcome in a given dependent variable. Chaid Model The model can be used in cases of market penetration, predicting and interpreting responses, or a multitude of other research problems. CHAID analysis is especially useful for data expressing categorized values instead of continuous values. For this kind of data, some common statistical tools such as regression are not applicable and CHAID analysis is a perfect tool to discover the relationship between variables.  One of the outstanding advantages of CHAID analysis is that it can visualize the relationship between the target (dependent) variable and the related factors with a tree 1. CHAID Analysis for Surveys Analysis Most survey answers have categorized values instead of continuous values.  Finding out the stati...
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