Do you mean the new default datetime resolution of microseconds instead of the previous nanosecond resolution? Obviously this will require adjustments to any code that requires ns resolution, but I'd bet that's a tiny minority of all pandas code ever written. Do you have a particular use case in mind for the problems this will cause?
I would describe it as the huge majority, reflecting on my pandas use over the years. Pretty much all of the data worth exploring in pandas over excel, some data gui, or polars involves timestamps.
This could have been due to refactoring a text written by the stated, human author. Not only is Anthrophic a deeply moral company — emdash — it blah blah.
Also, you just when you say the word "genuine" was in there `43` times. In actuality, I counted only 46 instances, far lower than the number you gave.
Would you like to provide actual proof that your favorite toy benefits people's health before daring others to challenge you? The imagined data you’ve yet to provide can't possibly justify the harm it's causing by pushing people on the edge to suicide.
From my limited experience trying exactly this, it gets you 80% of the way there, then devolves into an infuriating and time-wasting exercise in endless iteration and prompting to sweep clustering parameters and labeling details to nail the remaining 20% needed for acceptance by downstream "customers" (i.e., nontechnical business people).
If your end goal is to show an audience of nontechnical stakeholders an overview of your dataset in a static medium (like a slide), I would suggest you do the cluster labeling yourself, with the help of interactive tooling to make the semantic cluster structure explorable. One option is to throw the dataset into Apple's recently published and open-sourced Embedding Atlas (https://github.com/apple/embedding-atlas), take a screenshot of the cluster viz, poke around in the semantic space, and manually annotate the top 5-10 most interesting clusters right in Google Slides or PowerPoint. If you need more control over the embedding and projection steps (and you have a bit more time), write your own embedding and projection, then use something like Plotly to build a quick interactive viz just for yourself; drop a screenshot into a slide and annotate it. Feels super dumb, but is guaranteed to produce human-friendly output you can actually present confidently as part of your data story and get on with your life.
This is so nostalgic. I remember feeling like I was so good at Jezzball. In later levels I'd start a wall near one corner of the screen, closer to one edge than the opposite edge, to ensure the shorter wall would connect, and sacrificing the longer wall. The surviving wall would create a "corridor" in which to trap balls with tiny horizontal walls, often such that they ended up completely stationary.
> It just seems like such a narrow set of facts where a child is big and smart enough to open the door but dumb enough to jump out and get seriously hurt.
I had to guess, I'd guess you aren't a parent or spend much time interacting with children :)
Also, auto-lock reduces theft and carjacking risk, which is nice.
I'm talking about child locks, not auto-lock. Locking the door from the inside. A commenter above suggests that it's to stop the little idiots from popping out into the middle of the street .7845 seconds after I put it in park. That actually makes some sense.
Ah got it. The conversation upthread had focused on auto-lock, and someone had mentioned child locks in passing, and my read of your comment was on the auto-lock on shift to drive (or on starting to move). And my bad for falsely guessing you weren't speaking from personal experience!
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