I am familiar with the concept of "vectorization", and how pandas employs vectorized techniques to speed up computation. Vectorized functions broadcast operations over the entire series or DataFrame to achieve speedups much greater than conventionally iterating over the data.

## Vectorize a 6 for loop cumulative sum in python

The mathematical problem is:The expression within the sums is actually much more complex than the one above, but this is for a minimal working example to not over-complicate things. I have written this in Python using 6 nested for loops and as expected it performs very badly (the true form...

## Speeding up loop when normalizing Pandas data

I have a pandas dataframe:This data is actually "sequential" and I would like to transform it to this structure:

## Best way to flatten dataframe based on values on column

I have to process a whole dataframe with some hundered thousands rows, but I can simplify it as below:

## Why does “vectorizing” this simple R loop give a different result?

Perhaps a very dumb question.I am trying to "vectorize" the following loop:I think it is simply x[sig] but the result does not match.

## Why does “vectorizing” this simple R loop give a wrong result?

## Why does “vectorizing” this simple R loop give the wrong result?

## Why “vectorizing” this simple R loop gives wrong result?

## How can I express this large number of computations without for loops?

I work primarily in MATLAB but I think the answer should not be too hard to carry over from one language to another.

## Does the term “vectorization” mean different things in different contexts?

Based on what I've read before, vectorization is a form of parallelization known as SIMD. It allows processors to execute the same instruction (such as addition) on an array simultaneously.