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Python Logic to Find All Unique Pairs in an Array

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 Here's the Python logic for finding all unique pairs in an array that sum up to a target value. Python Unique Pair Problem Write a Python function that finds all unique pairs in an array whose sum equals a target value. Avoid duplicates in the result. For example: Input: arr = [2, 4, 3, 5, 7, 8, 9] , target = 9 Output: [(2, 7), (4, 5)] Hints Use a set for tracking seen numbers. Check for complements efficiently. Example def find_unique_pairs(arr, target):     """     Finds all unique pairs in the array that sum up to the target value.     Parameters:     arr (list): The input array of integers.     target (int): The target sum value.     Returns:     list: A list of unique pairs that sum to the target value.     """     seen = set()     pairs = set()     for num in arr:         complement = target - num         if complement in seen:...
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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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