大约有 800 项符合查询结果（耗时：0.0074秒） [XML]
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
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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...
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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Measure time in Linux - time vs clock vs getrusage vs clock_gettime vs gettimeofday vs timespec_get?
...r, hopefully not, wall-clock?)
What is the precision of the clock? (s, ms, µs, or faster?)
After how much time does the clock wrap around? Or is there some mechanism to avoid this?
Is the clock monotonic, or will it change with changes in the system time (via NTP, time zone, daylight savings time, ...
How do you get the magnitude of a vector in Numpy?
...
In [4]: %timeit np.sqrt((a*a).sum(axis=1))
100000 loops, best of 3: 15.6 µs per loop
In [5]: %timeit np.sqrt(np.einsum('ij,ij->i',a,a))
100000 loops, best of 3: 8.71 µs per loop
or vectors:
In [5]: a = np.arange(100000)
In [6]: %timeit np.sqrt(a.dot(a))
10000 loops, best of 3: 80.8 µs p...
How to access the ith column of a NumPy multidimensional array?
.... Using the copied version is much faster:
%timeit A_c1_ref.sum() # ~248 µs
%timeit A_c1_copy.sum() # ~12.8 µs
This is due to the different number of strides mentioned before:
A_c1_ref.strides[0] # 40000 bytes
A_c1_copy.strides[0] # 4 bytes
Although it might seem that using column copies...
Rank items in an array using Python/NumPy, without sorting array twice
...)
%%timeit -n10000 -r5
x = (rankdata(l) - 1).astype(int)
>>> 128 µs ± 2.72 µs per loop (mean ± std. dev. of 5 runs, 10000 loops each)
%%timeit -n10000 -r5
a = np.array(l)
r = a.argsort().argsort()
>>> 69.1 µs ± 464 ns per loop (mean ± std. dev. of 5 runs, 10000 loops each)...