VLR | GPU Monte Carlo Ray Tracing Renderer using NVIDIA OptiX | GPU library

i have this number coming from my api

"vlr": 1749109.14

and i have to formate to show

174.910.914

I tried using pipe but I couldn't get the formatting I wanted.

is there a way to get this formatting, or would it be better to change the value coming from the api?

I'm trying to get some data from text file, those data are repeated every random line

I'm trying to get this from text file, and there are alot of 2/6 blocks. i need to get them all, using aloop.

Example data I need to get it.

I need to place in a selected by User input quantity, numbers that are only in between 1 and 60, values less than 1 and more than 60 should be invalid... And asked for a new input.

Thing is, I don't know how to repeat the question and replace in the vector's square where the wrong value was inserted, to the correct one.

Ex: It keeps asking for the values, ignoring in regards of it being wrong and showing it later... Such as vector[6], the values in the end shows like: [45, 34, 23, 22, 11, 99] or [45, 55, 34, 99, 99, 99]

My task: Develop an algorithm that records bets. The player can perform bets of 6 to 15 numbers, between 1 and 60. The algorithm must then request that the inform user:

1. the quantity of numbers in your bet;
2. receiving bets (between 1 and 60);
3. show the bet placed in order and;
4. show the amount to be charged for it (following a table I was given).

My attempt:

I have an array that has this structure:

i have a big trouble with this code, i need format an array to use in different select, i have an array structure same of this:

I have two dataframes. I need to generate report by matching columns in two dataframes and updating a column in the first dataframe:

Sample Data

I'm trying to speed up my code to take full advantage of NumPy's vectorization. I've been able to vectorize all of the computations in the majority of my code (clr/vlr functions) which I believe is the optimum for NumPy. I don't believe these can be sped up anymore as np.einsum doesn't really apply. I can't figure out how to vectorize the rho function. In particular, it's this nested for-loop that is giving me trouble: 1 - (ratios[i,j]/ (variances[i] + variances[j])). I've tried using np.meshgrid to try and expand the variances but this didn't work. I've been trying to follow this tutorial on np.einsum but, still, it's a bit confusing and I'm still not sure if it applies to this situation as it involves more operations than dot products and matrix multiplication.

Does anyone know how to vectorize this function?

Extra, are there any suggestions to speed up my code for the previous 2 function?