## Fastest precise way to convert a vector of integers into floats between 0 and 1

Consider a randomly generated __m256i vector. Is there a faster precise way to convert them into __m256 vector of floats between 0 (inclusively) and 1 (exclusively) than division by float(1ull<<32)?

## Fast modulo 12 algorithm for 4 uint16_t's packed in a uint64_t

Consider the following union:Is there a fast algorithm for determining whether each component equals 1 modulo 12 or not?

## For loops with pandas – When should I care?

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?

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.