ff | Find Text or File | Data Manipulation library

I'm experienced with Pandas but stumbled upon a problem that I can't seem to figure out.

I have a large dataset ((40,000, 16)) and I am trying to group it by a specific column ("group_name" for this matter) and then for each group apply the following rules so it'd merge into one row per group:

• x1, x2, x3 are the "important" columns, if one row has less nulls than the others, take it. (see example with row D)
• If there are conflicts in any column, it's arbitrary and we can pick whatever.
• Combine the nulls on the important fields (x1, x2, x3), see example with row A.

Here is an example with 6 rows that should turn into 4 groups (aka 4 rows).

So far I have groups = df.groupby['group_name']

I tried many other solutions such as summing each group, applying a transformation, aggregating by each 'important' column, merging on each 'important' column and more. Each solution brought it's own problems so I'm offering this question here without limiting people to a certain way.

Also, I spent nearly two days combining different solutions from other questions but none has seem to work. Perhaps I've missed something.

• Please note that since this is a large dataset, I'd very much like to avoid using for loop on each group since efficiency is something to consider here.

I hope I explained everything properly, please let me know if something is unclear.

Code to re-create the dataframe (thanks to @Henry Ecker from the first answer):

I have the following data frame:

I have recovered an old 6502 emulator I did years ago to implement some new features. During testing I discovered something wrong, surely due to an error in my implementation.
I have to loop through a 16 bit subtraction until the result is negative: quite simple, no? Here is an example:

Yesterday, I created a branch from a branch, pushed it to origin, and merged it back into master. You can see that here:

I'm trying to insert a new column on a pandas data frame with custom values based on a condition. I have written the code as below but it does not work. Am i missing anything here? I dont want to define a list and then insert it because i may not be processing all the data from the dataset. Any easy way to acheive this?

My Original dataset :

I have a page that filters a list according the checkboxes selected. A click event on each checkbox makes an ajax call to the server with the form data from the checkboxes and returns filtered items. This is a simple page so all settings are lost when user moves to another page.

I want to remember checkbox form data in a cookie and on page load want to read that cookie to query server for new data and set relevant checkboxes to 'checked'.

To do this I have modified the checkbox click event to write the form data to a cookie as well as call server to fetch results. This works fine.

I've also written a jQuery function that is called on page load after document.ready to read that cookie. This works well in FireFox and Edge but in Chrome it is completely unreliable; the list is always filtered (ajax call returns data based on cookie values) but the checkboxes are sometimes checked and sometimes not checked.

Data in the cookie is stored like this:

I installed a Kubernetes cluster of three nodes, the control node looked ok, when I tried to join the other two nodes the status for both of is: Not Ready

On control node:

I am a little bit confused about my network setup at home.

This is the setup:

VLAN1 - 172.16.20.0/24 VLAN2 - 10.11.12.0/24

I am in the VLAN2 net (which is my WiFi), for the moment I allowed all traffic between both subnets.

My setup uses a KVM host for most of the services, my firewall lies on this machine and is virtualized (opnsense).

So the KVM network interfaces looks like this:

Inside my components I can use axios calls like this normally