regularity | A friendly regular expression builder for Ruby | Regex library

I would like to first combine three arrays of equal shape element-wise and then join duplicated entries of a smaller array to this. I am able to do this, but my method has many steps and I would like to know if there is a more direct (and faster?) method. Now seeing the regularity in the target, it seems clear some list comprehensions could sort it out. I would however like to keep readability so another person could follow it, but efficient enough to scale to a couple million rows, hence NumPy operations we my first thought.

Take as given three 3D arrays of the same shapes a x b x c, as well as a shallower array 'm' of the same length 'a'. We need to combine a, b and c, remove the 2nd and 3rd columns of m, then broadcast m across the others once they are combined and return the result as a 2D array.

Here is my method. The below generic arrays will be these given inputs -- the numbers are just for labelling, so that the target result will be clearer.

I am currently working on a program that attempts contacting numerous routers which run the Cisco IOS to get their current configs. I am trying to achieve this using the Paramiko module's SSHClient object:

I'm actually trying to transform records of column of payment receipt into pandas dataframe. I read the records row by row and determine which data should be in which column. So I created empty dataframe like this:

I'm constructing large directional graphs (using igraph, from R) and have discovered a strange issue in which vertices are apparently duplicated for certain vertex names. This issue doesn't happen in small graphs, and the issue only appears to arise when the vertex names reach 1e+05. There is a clear regularity to the vertices that get duplicated. To jump ahead, the vertex duplication looks like this (generated in section 2 of the below code):

I'm trying to calculate the entropy over a pandas series. Specifically, I group the strings in Direction as a sequence. Specifically, using this function:

At the company I work, all our APIs send and expect requests/responses that follow the JSON:API standard, making the structure of the request/response content very regular.

Because of this regularity and the fact that we can have hundreds or thousands of records in one request, I think it would be fairly doable and worthwhile to start supporting compressed requests (every record would be something like < 50% of the size of its JSON:API counterpart).

To make a well informed judgement about the viability of this actually being worthwhile, I would have to know more about the relationship between request size and duration, but I cannot find any good resources on this. Anybody care to share their expertise/resources?

Bonus 1: If you were to have request performance issues, would you look at compression as a solution first, second, last?

Bonus 2: How does transmission overhead scale with size? (If I cut the size by 50%, by what percentage will the transmission overhead be cut?)

For a command such as

I find myself enabling and disabling the "Common Language Runtime Exceptions" checkbox in Exception Settings with considerable regularity. I'm tired of having to open the window every time. Is there a keyboard shortcut?

EDIT: as the answer as of June 2020 appears to be "no", I've requested a feature here: https://developercommunity.visualstudio.com/idea/1073035/keyboard-shortcut-to-enabledisable-the-common-runt.html

I've tried to find the appropriate answer but all present much simpler cases than what I have. I need to create a 4-level (nov, end_feb, end_apr, other) factor based on the date information in a data frame i have and then add it as a column. Moreover, i need the code to go fast since the real df I have is over 800 thousand rows

Here is what I have so far with lubridate and %within%. It does work but is terribly slow due to inefficincy, since I have to resort to creating a new column with sapply(df, sub_period_gen(date)). Optimally, I need a way to ensure that the solution is vectorized since I have some other factor generators that work on the same data frame and are also slow