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Getting key with maximum value in dictionary?
...came with same performance on my machine on python 2.7. Testing: f1 - 18 µs per loop Testing: f2 - 33.7 µs per loop Testing: f3b - 50 µs per loop Testing: f4b - 30.7 µs per loop Testing: f5 - 28 µs per loop Testing: f6 - 23 µs per loop Testing: f7 - 18 µs per loop Testing: f8 - 43.9 ...
Get list from pandas DataFrame column headers
...he difference in performance is obvious:
%timeit df.columns.tolist()
16.7 µs ± 317 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
%timeit df.columns.values.tolist()
1.24 µs ± 12.3 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
For those who hate typing, you can...
Find integer index of rows with NaN in pandas dataframe
... df.loc[pd.isna(df['b']), :].index
And their corresponding timings:
333 µs ± 9.95 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
280 µs ± 220 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)
313 µs ± 128 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)
6.84 ...
Can Python test the membership of multiple values in a list?
...(range(50000))
>>> %timeit bigset >= bigsubset
1.14 ms ± 13.9 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
>>> %timeit all(x in bigset for x in bigsubset)
5.96 ms ± 37 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
Using subset testing is still fas...
Find nearest value in numpy array
...n. I wonder why it is so slow anyways. Plain np.searchsorted takes about 2 µs for my test set, the whole function about 10 µs. Using np.abs it's getting even worse. No clue what python is doing there.
– Michael
Feb 17 '15 at 18:07
...
How do I copy a string to the clipboard on Windows using Python?
...ith Unicode characters too. I have tested characters ±°©©αβγθΔΨΦåäö to work on Win10 64-bit, with Python 3.5 and pyperclip 1.5.27.
– np8
Jul 3 '16 at 15:55
...
Is it possible to use argsort in descending order?
...00)
>>> n = 30
>>> timeit (-avgDists).argsort()[:n]
1.93 µs ± 6.68 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
>>> timeit avgDists.argsort()[::-1][:n]
1.64 µs ± 3.39 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
>>> timeit avg...
Twitter image encoding challenge [closed]
...e is that I don't have a very good error function. I currently use ∑(∆r²+∆g²+∆b²)/3, which works OK. I tried ∑(0.299∆r²+0.587∆g²+0.114∆b²), based (with no physical justification) on YUV's Y component, but it was too tolerant with blue errors. I'll try to find papers about this ...
Getting indices of True values in a boolean list
...;> %timeit list(compress(xrange(len(t)), t))
1000 loops, best of 3: 696 µs per loop
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