u/sauron3579

I have a column of datetime data I need to convert to a numeric format, but to_numeric appears to be doing the conversion incorrectly. The returned column is accurate in us, rather than the default unit. I can't just work with the data in us because, since to_numeric isn't supposed to do that, I'm unsure if it will be consistent when changing the environment.

Example output of running print(df) print(df.dtypes)

Original from csv read:

Date

0 2026-05-12

...

Date str

to_datetime

Date

0 2026-05-12

Date datetime64\[us\]

to_numeric (note the 9 zeroes)

Date

0 1778544000000000

Date int64

to_datetime

Date

0 1970-01-21 14:02:24

Date datetime64\[ns\]

Is there a way to force this to be consistent? To_numeric doesn't seem to have any parameters I can use to change the unit.

I recently picked Arena back up. I'm a fan of more controlling decks, so I was playing with both the all-in Monument and control versions of Lessons (the control one being the variant with [[Roaring Furnace]] and multiple [[Ral, Crackling Conduit]] main deck and no monument). Got to plat real fast, started getting my feet under me with matchups and grinding out an honest 60% win rate in best of 3. Slow but steady climbing. Really enjoyable deck, definitely not a turn 3 meta when you're mainboarding a dozen removal spells. No particularly polarizing matchups other than losing as soon as an [[Unholy Annex]] or [[Professor Delian Fel]] resolved.

Then I decided to look at some stats. Hmm, this landfall nonsense seems to have a way higher winrate than anything else at high ranks. and it won and double finaled the PT. Guess I'll import the top list from untapped and give it whirl.

I proceeded to go on a 25-6 blitz. 80% winrate. In best of 3. Just landed in Mythic at #792.

I am not good with this deck. I have been playing Magic for a decade. I have played other card games, including at a very high level when I had more free time. Not quite top level, but good enough that I was competitive and could take sets off top level players. I know what it takes to actually be good. I have put in none of that work. I have not been grinding matchups, I have not been testing, I have not been reading the meta and teching to stay a step ahead. My sideboarding is wildly inconsistent and just guessing most of the time, there's no set plan. There are matchups where I'm not even sure if I should be boarding out proactive or reactive pieces and I flip flop round to round. It's so disappointing that a deck I enjoy less and have less of an idea of what I'm doing on is performing so much stronger for me.

This deck is straight up not okay. And the Cub is not the problem. My best hands are not the ones with cub. To be frank, it's just okay in this deck. The best hands are when I can go Llanowar Elves, Payoff, Icetill + fetch. Llanowar Elves has committed far more war crimes with introducing PEDs than the cub has, and Icetill is by far the strongest card in the deck. Cub might be the right ban to knock the deck down but still keep it in the meta without being dominant, but do not mistake it for the culprit.

About
Name Mono-Green Landfall

Deck
14 Forest
3 Ba Sing Se
4 Llanowar Elves
4 Fabled Passage
1 Promising Vein
4 Escape Tunnel
4 Keen-Eyed Curator
2 Esper Origins
4 Sazh's Chocobo
4 Icetill Explorer
4 Meltstrider's Resolve
4 Mightform Harmonizer
4 Badgermole Cub
4 Earthbender Ascension

Sideboard
2 Pawpatch Formation
3 Mossborn Hydra
2 Heritage Reclamation
2 Surrak, Elusive Hunter
2 Origin of Metalbending
4 Sapling Nursery

How do we play this matchup now that phase rush is gone? Used to be one of my absolute favorite matchups to play with tons of skill expression around proper layering and proccing phase to short trade, but now that options doesn't seem to be there. Stormraider's seems like it'll take way too long to proc to use the same approach. What are people doing now?

How do I achieve something similar to the pivot functionality in SQL to turn certain values that make rows unique into columns?

I'm looking at the page for pandas.dataframe.pivot() and it looks like it might do what I want, but I'm really not sure.

I have a dataframe of the following structure:

Model   Output   Input 1    Input 2    Input 3

xg           1000      a              b               c

gb           1200      a             b                c

xg           1300      d              e               f

gb           1400      d              e               f

...

I'd like to turn it into:

Input 1    Input 2    Input 3    gb        xg

a              b                c              1200   1000

d              e                f               1400   1300

In SQL I would do something like

Table

pivot

(

sum(output)

for model in ('gb', 'xg')

)

Not sure how to replicate that with pandas.

There's been multiple variants on Izzet Lessons that have been popping up online recently. Namely, there seems to be the classic version with double talent + [[monument to endurance]], an all-in monument version that drops the [[stormchaser's talent]], a version with [[Eddymurk Crab]] and 4 stormchaser's in main while dropping [[artist's talent]] + monument, and a control version that utilizes [[roaring furnace//steaming sauna]], [[Legend of Kuruk]], and [[Ral, Crackling Wit]] that just wants to draw the game out and win long.

Looking at the PT lists, I haven't looked at every Lessons player, but it seems like most are on the all-in monument version. I found this a bit surprising, as that's the version I've been struggling with the most to use. It doesn't have the option to pivot into a pseudo-prowess gameplan like all the other version (even control) by going in on stormchaser's post-board, and you also don't really win late. If you get the engine together it can close things out quick, but tapping out for a 3 mana artifact without the tempo pieces to back it up is so difficult. What has everyone else been running and finding success with?

I have a table of data about 200k rows long, and I'm trying to calculate a percentile of a column based on a subset of the data based on keys in other columns. What I am currently using is the following:

=percentile.inc(iferror(small(if((table\[column1\]>0)\*(table\[column2\]=A5)\*..., table\[column3\]), row(table\[column1)-1), ""), A12)

This is currently taking way too long to run fir how many times I need to repeat this. Any suggestions to optimize the conditional percentile?