Viewing profile — brahbrah

If you have a mortgage, it ain’t half bad

> I could not imagine this ever happening when dealing with the IRS. Have you tried? I haven’t tried it with the IRS, but I have tried asking my states permitting and planning depa…

> And is the only source I know to offer 2 weeks of hourly forecasts Enjoy the data directly from the source producing them. American weather agency: https://www.nco.ncep.noaa.gov/…

Would you pay it off early if you have a 2.5% interest rate and could get a guaranteed 5+% on a CD?

> If they weren't, Delta would say so, since it makes them look better. That they won't say means the parts were used in service. More likely they would just never comment whether …

> have to be accessed through python It’s because most of the people doing these computations don’t have the capacity to become experts in multiple fields. They understand the math…

The explanation I’ve always heard for why New Zealand was settled by Polynesians so late is that they preferred to start their voyages against the currents when they were fresh so …

Mining is not needed to keep the grid reliable or smooth imbalances. You can achieve this by dispatching or cutting off the marginal generators needed to serve the load in real tim…

Dask added an actor model after seeing it in Ray. I’ve used dasks in some computationally intensive applications (already had existing dask infrastructure which is why we didn’t go…

This is why you see a lot of 1 year and 1 day sentences. Any federal sentence 1 year or less must be served 100% in full. If it’s 1 year and 1 day it’s eligible for the time reduct…

Vermont also consumes ~3x more energy (all energy, not just electricity) than it produces and has the lowest energy consumption of any state. https://www.eia.gov/state/?sid=VT

Just speaking it off my ass here, but it could be the scale of those companies that make those teams viable. Like let’s say you have some standardized systems that can cover the us…

To be fair pandas was never meant to be a replacement for sql, it was meant to be a replacement for excel in financial models. Which it still excels at (pun intended).

So something like this? def add(df1, df2, meta_cols, val_cols=None): # join on meta cols # add val cols (default to all non meta cols if None) # return df with all meta and val col…

(Taken from an old comment of mine) If you were to say “pandas in long format only” then yes that would be correct, but the power of pandas comes in its ability to work in a long r…

The first issue I have with it is that they've now convinced a large portion of people that read this article that a very good tool is not as good as it actually is. This is a diss…

This is a very valid point that I can’t disagree with. I’ve gone through the pain of learning that subset of the language decently well, but also been lucky enough to work at place…

So in addition to what akasaka said (another thumbs up for line profiler from me, great tool) this isn’t a problem with linalg.norm being slow. It’s plenty fast, but calling it tho…

No, their v1.5 is still calling norm on every polygon. They’re still using it wrong On Google colab import numpy as np import time vals = np.random.randn(1000000, 2) point = np.arr…

On Google colab import numpy as np import time vals = np.random.randn(1000000, 2) point = np.array([.2, .3]) s = time.time() for x in vals: np.linalg.norm(x - point) ~296x faster, …

Yea sorry that was just pseudo code. You want it in this form: centers = np.array([ [3, 3], [4, 4] ]) point = np.array([3.5, 3.5]) vals = centers - point np.linalg.norm(vals, axis=…

Agreed it was well written, but kinda pointless though since they could have “solved” the problem using the existing tools in a couple lines of code without any new deps. All that …