Tex live utility listing failed 20191/8/2024 Even the slightest amount of sweat on your fingers (natural after a long day at the keyboard) just makes it feel worse. It's a little disconcerting and slightly disgusting, as if the keyboard needed to be cleaned. The keycaps themselves, however, are thin, and the top surface somehow manages to be slippery, like really really old keycaps that have thousands of hours of gaming and untold quantities of pizza grease. The only other keyboard I've used that feels this sturdy is my IBM Wheelwriter! It's also a bit heavy for a plastic keyboard, so perhaps its physical weight increases its desk-grip and stability. The keyboard overall feels extremely solid, and grips the desk very firmly. There is hardly any key wobble (even less than my rubber-dome office board) and very little board flex. I am not personally a fan but I will keep using it for a week or so before I decide to sell it. If you are a CAD/Photoshop/Logic user with a billion AutoHotKey macros you will probably love this thing in your shop/studio. Great buy if you want a highly programmable board with a split but otherwise-traditional layout. Do not choose tactile switches for this one like I did. TL/DR: It's really sturdy but the typing feel kind of reminds me of rubber domes. I also make a lot of comparisons to the ErgoDox EZ, because that's what was on my desk all day today. In case anyone is interested, here are some thoughts on my new Kinesis Edge Freestyle keyboard with Cherry MX brown switches I actually started writing this as a typing test, but then I realized I wanted to start actually writing about the thing. But I am definitely interested in finding the balance between generality and concision. Maybe by this time next year I'll just be using Julia where we don't have to perform such gyrations to avoid basic row-wise iteration. Just one piece of special syntax and a bunch of utility aliases are the only thing standing between Python and a whole new world of functional programming style. Imagine if you could write lambda x: tuple(map(op.add(3), x)) as something like (x) :: tmap(op.add(3), x) - it's now about as concise than a list comprehension like lambda x: tuple(y+3 for y in x) and (in my opinion) just as readable. See also: everything-is-lazy-by-default itertools. With Col at least, it's easy to explain how it If it's even a tiny bit too verbose, lambda remains the lesser of two evils pretty much by default, because you don't need to learn it or pip install it. I'm having a hard time envisioning a concise API for such a general case that also isn't offensively magical (and internally fragile). In that sense it would become a truly general system for constructing anaphoric functions.īut since metaprogramming in Python is already so limited, too much generality might wipe away any of the ergonomic benefits. So instead of Col it could be called G (for graph), and you could use it to construct arbitrary callable "pipelines". ![]() In theory this Col business could be abstracted to a kind of lazy computation graph. Would you mind sharing an example of a typical Pandas workflow you might use? Out of curiosity, what are you mostly doing with Pandas if not columnwise operations? Joining? Groupby aggregations or pivots? A lot of that stuff is already pretty pipelineable. I tend to do a lot of column assignments in the data cleaning stage of my workflow.įor my personal use, it is not something I do very much and certainly not enough to bother with a new package. Honestly, not that I that can think of, in its current state. ![]() In terms of how useful this is, a quick look indicates it is mainly useful for modifying or creating columns based on some criteria. The regular Pandas way: b_above_4 = data > 4ĭata.loc = data.locĭata.loc = -data.loc To save you a click through to Github, here's an example comparing plain Pandas with Anaphora: Tips and feedback on documentation (which is very much a "first draft" right now) would be appreciated as well. ![]() It would be great to get API/UX feedback from others who use Pandas regularly, and code/tests feedback from experienced Python devs. It is intended to make the transition between "logic" and "code" as seamless and fluent as possible, so you can spend more time analyzing your data. The idea is not only save keystrokes but also to make Pandas code more concise and readable. So wrote I the pandas-anaphora package to try and make my life easier. ![]() Hi r/python! I'm a data scientist who's fed up with the verbosity of Pandas code, when compared to R+dplyr and the Spark DataFrame API.
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