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 department about the legality of various…
> 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/pmb/products/gfs/ European weather…
Would you pay it off early if you have a 2.5% interest rate and could get a guaranteed 5+% on a CD?
> 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 and analytics very well, and they…
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 that they wouldn’t have to fight the…
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 time (the marginal generators serving the…
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 with rays)
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 reduction credit and you can serve less than…
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 cols selected In theory I think that's…
(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 relational or wide ndarray style. Pandas…
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 disservice to the great engineering that…
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 places that compensate very well for that…
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 thousands of separate times in a Python…
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.array([.2, .3]) s = time.time() for x in…
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) < 3 a = time.time() - s s = time.time()…
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=1)
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 content annd profiling and they missed…
They would have gotten the same performance in python with numpy if they did it like this instead of calling norm for every polygon centers = np.array([p.center for p in ps]) norm(centers - point, axis=1) They were just…
This was a silly and unnecessary optimization. He’s just using numpy wrong. Instead of: for p in ps: norm(p.center - point) You should do: centers = np.array([p.center for p in ps]) norm(centers - point, axis=1) You’ll…
Does it support multiindex columns?
fn_graph: https://github.com/BusinessOptics/fn_graph (My favorite one ^) pyungo: https://github.com/cedricleroy/pyungo Loman: https://github.com/janushendersonassetallocation/loman Tributary:…
Not to take away from anything you’ve done here, you guys have put a lot of great effort into this, but this paradigm is not “new”. It’s a common modeling paradigm in banks and hedge funds at least. I’ve built/worked on…
I don’t think it’s so black and white. Depends on what you’re doing. If you’re doing lots of cross dataset operations like econometric and physical system modeling polars can get a bit too verbose. Eg how would you do…
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 department about the legality of various…
> 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/pmb/products/gfs/ European weather…
Would you pay it off early if you have a 2.5% interest rate and could get a guaranteed 5+% on a CD?
> 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 and analytics very well, and they…
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 that they wouldn’t have to fight the…
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 time (the marginal generators serving the…
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 with rays)
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 reduction credit and you can serve less than…
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 cols selected In theory I think that's…
(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 relational or wide ndarray style. Pandas…
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 disservice to the great engineering that…
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 places that compensate very well for that…
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 thousands of separate times in a Python…
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.array([.2, .3]) s = time.time() for x in…
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) < 3 a = time.time() - s s = time.time()…
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=1)
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 content annd profiling and they missed…
They would have gotten the same performance in python with numpy if they did it like this instead of calling norm for every polygon centers = np.array([p.center for p in ps]) norm(centers - point, axis=1) They were just…
This was a silly and unnecessary optimization. He’s just using numpy wrong. Instead of: for p in ps: norm(p.center - point) You should do: centers = np.array([p.center for p in ps]) norm(centers - point, axis=1) You’ll…
Does it support multiindex columns?
fn_graph: https://github.com/BusinessOptics/fn_graph (My favorite one ^) pyungo: https://github.com/cedricleroy/pyungo Loman: https://github.com/janushendersonassetallocation/loman Tributary:…
Not to take away from anything you’ve done here, you guys have put a lot of great effort into this, but this paradigm is not “new”. It’s a common modeling paradigm in banks and hedge funds at least. I’ve built/worked on…
I don’t think it’s so black and white. Depends on what you’re doing. If you’re doing lots of cross dataset operations like econometric and physical system modeling polars can get a bit too verbose. Eg how would you do…