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That is : "Apply function of two arguments cumulatively to the items of sequence, from left to right, so as to reduce the sequence to a single value. "
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Python's reduce() is a function that implements a mathematical technique called folding or reduction. reduce() is useful when you need to apply a function to an iterable and reduce it to a single cumulative value.
Source: R/reduce.R. reduce.Rd. reduce() is an operation that combines the elements of a vector into a single value. The combination is driven by . f , a binary function that takes two values and returns a single value: reducing f over 1:3 computes the value f(f(1, 2), 3) .
Reduce function is an inbuilt function in python whose major task is to give aggregate value as an output. Syntactically it is written as; reduce(func,iter_obj) here reduce operation will be done on “func” function and “iter_obj” is implying the iterable data values used to return result of output.
The R base package provides a function Reduce() , which can come in handy here. Of course it is inspired by functional programming, and actually does something similar to the Reduce step in MapReduce , although it is not inteded for big data applications.
So, if you’re dealing with the any-true problem in Python, then consider using any () instead of reduce (). A Python function called accumulate () lives in itertools and behaves similarly to reduce (). accumulate (iterable [, func]) accepts one required argument, iterable, which can be any Python iterable.
In Python, there are scalars, arrays and lists, and dictionaries (and many others). In R, there are no scalars, just vectors of length one. There is a Perl hash-like implementation in R.
This means that the function returns as soon as it finds a false value without processing the rest of the items in iterable. To solve this problem using Python’s reduce (), you’ll need to write a function that takes two arguments and returns True if both arguments are true. If one or both arguments are false, then the function will return False.
The third argument to Python’s reduce (), called initializer, is optional. If you supply a value to initializer, then reduce () will feed it to the first call of function as its first argument. This means that the first call to function will use the value of initializer and the first item of iterable to perform its first partial computation.
Yes, it's called Reduce.
An example:
Reduce(paste, LETTERS[1:5]) [1] "A B C D E" Reduce(sum, 1:5) [1] 15 #List arguments work the same Reduce(sum, list(1, 2, 3, 4, 5)) [1] 15 For more information about functional programming in R see the help file for ?funprog, an alias for ?Reduce
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Yes. See http://stat.ethz.ch/R-manual/R-patched/library/base/html/funprog.html
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