11–20 of 34 posts
There are some nice exercises here, good work. For question 48 it might be simpler to just write np.sort(a)[-5:] instead of using argsort() and then using fancy indexing. Better yet, use np.partition(a, kth=-5)[-5:] which scales linearly with the size of the array. Also, the one-hot encoding puzzle (51) would be more efficiently solved using (arr[:, None] == np.unique(arr)).view(np.int8) In general, `for` loops over…
Does anyone know of any similar resources for Pandas? I've found the following to be quite helpful but would love to know if anyone knows of other resources in a similar vein: https://pandas.pydata.org/pandas-docs/stable/cookbook.html
I started writing a '100-pandas-puzzles' set of exercises here: https://github.com/ajcr/100-pandas-puzzles There's also pandas_exercises by Guilherme Samora ( https://github.com/guipsamora/pandas_exercises ) which is very good - it's split across multiple notebooks and is more extensive than my repo.
This is very similar in spirit to https://github.com/rougier/numpy-100/blob/master/100%20Numpy... . In fact, now that I look at it a bit more, it seems like all of this post's examples are reworded versions of Nicolas Rougier's "numpy 100"...
For #3, you can make a boolean array with np.ones/np.zeros with the same dtype arg, saves a little bit of space.
ie np.ones((3,3), dtype=bool)
For #14, you can make use of the same compound boolean statements as you can in pandas to make it a bit simpler.
ie a[(a > 5) & (a For #15, this is a built in numpy function.
np.maximum(a,b).
That's as far as I've made it, but I'm really enjoying them.
One thing that is holding me back in numpy is not knowing the runtime complexity of operations—of course I can profile code, but I should have better awareness when writing code in the first place. Without an algorithms background, I don't have strong intuitions on the runtime complexity of the primitives (np.unique). Any suggestions?
One thing that is holding me back in numpy is not knowing the runtime complexity of operations—of course I can profile code, but I should have better awareness when writing code in the first place. Without an algorithms background, I don't have strong intuitions on the runtime complexity of the primitives (np.unique). Any suggestions?
One thing that is holding me back in numpy is not knowing the runtime complexity of operations—of course I can profile code, but I should have better awareness when writing code in the first place. Without an algorithms background, I don't have strong intuitions on the runtime complexity of the primitives (np.unique). Any suggestions?
Switch to Julia! Hit @edit unique([1,2,3,2]) in the REPL and you see the implementation.
This is very similar in spirit to https://github.com/rougier/numpy-100/blob/master/100%20Numpy... . In fact, now that I look at it a bit more, it seems like all of this post's examples are reworded versions of Nicolas Rougier's "numpy 100"...
There's also Rosalind for bioinformatics problems to be solved in Python. http://rosalind.info/problems/locations/