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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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R Language: Data types and structures

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To make the best of the R language, you'll need a strong understanding of the basic data types and data structures and how to operate on those. Very Important to understand because these are the things you will manipulate on a day-to-day basis in R. Everything in R is an object.

R language
The basic data types 
  • logical (e.g., TRUE, FALSE)
  • integer (e.g,, 2L, as.integer(3))
  • numeric (real or decimal) (e.g, 2, 2.0, pi)
  • complex (e.g, 1 + 0i, 1 + 4i)
  • character (e.g, "a", "swc")
The basic data structures in R
  • vector
  • list
  • matrix
  • data frame
  • factors
  • tables
Vector in R
A vector is the most common and basic data structure in R and is pretty much the workhorse of R. 
Vectors can be of two types:
  • atomic vectors
  • lists

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