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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 objects useful command to delete them

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R-Commands
R-Commands
The entities that R creates and manipulates are known as objects. These may be variables, arrays of numbers, character strings, functions, or more general structures built from such components. During an R session, objects are created and stored by name. This post tells you how to delete them.

 The R command
> objects()

 (alternatively, ls()) can be used to display the names of (most of) the objects which are currently stored within R. The collection of objects currently stored is called the workspace. The data visualization in R Language with GGplot a good idea to start.

 To remove objects the function rm is available:
> rm(x, y, z, ink, junk, temp, foo, bar)

 All objects created during an R session can be stored permanently in a file for use in future R sessions.

 At the end of each R session you are given the opportunity to save all the currently available objects. If you indicate that you want to do this, the objects are written to a file called .RData5 in the current directory, and the command lines used in the session are saved to a file called .Rhistory.

 When R is started at later time from the same directory it reloads the workspace from this file. At the same time the associated commands history is reloaded.
  • It is recommended that you should use separate working directories for analyses conducted  ith R. 
  • It is quite common for objects with names x and y to be created during an analysis. Names like this are often meaningful in the context of a single analysis, but it can be quite hard to decide what they might be when the several analyses have been conducted in the same directory.

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