大约有 900 项符合查询结果（耗时：0.0222秒） [XML]
Is it possible to set a number to NaN or infinity?
...──────────────┤
│ cmath │ cmath.nan ¹ │ cmath.nanj │ cmath.inf ¹ │ cmath.infj │
╘══════════╧════════════════╧═════════════════╧══...
What is the most efficient string concatenation method in python?
...ng/path/'
The contenders are
f'http://{domain}/{lang}/{path}' - 0.151 µs
'http://%s/%s/%s' % (domain, lang, path) - 0.321 µs
'http://' + domain + '/' + lang + '/' + path - 0.356 µs
''.join(('http://', domain, '/', lang, '/', path)) - 0.249 µs (notice that building a constant-length tuple i...
Drop rows with all zeros in pandas data frame
...,0,1]})
In [91]: %timeit df[(df.T != 0).any()]
1000 loops, best of 3: 686 µs per loop
In [92]: df[(df.sum(axis=1) != 0)]
Out[92]:
a b
1 0 1
2 1 0
3 1 1
In [95]: %timeit df[(df.sum(axis=1) != 0)]
1000 loops, best of 3: 495 µs per loop
In [96]: %timeit df[df.values.sum(axis=1) != 0]
1...
How to add footnotes to GitHub-flavoured Markdown?
...b Flavored Markdown doesn't support footnotes, but you can manually fake it¹ with Unicode characters or superscript tags, e.g. <sup>1</sup>.
¹Of course this isn't ideal, as you are now responsible for maintaining the numbering of your footnotes. It works reasonably well if you only ha...
Numpy argsort - what is it doing?
...*2)
In [81]: %timeit using_argsort_twice(x)
100000 loops, best of 3: 3.45 µs per loop
In [79]: %timeit using_indexed_assignment(x)
100000 loops, best of 3: 4.78 µs per loop
In [80]: %timeit using_rankdata(x)
100000 loops, best of 3: 19 µs per loop
In [82]: %timeit using_digitize(x)
10000 loop...
How to truncate milliseconds off of a .NET DateTime
...nce is between 50% and about 100% depending on the runtime; net 4.7.2: 0.35µs vs 0.62 µs and core 3.1: 0.18 µs vs 0.12 µs that's micro-seconds (10^-6 seconds)
– juwens
Feb 3 at 15:55
...
How to find the statistical mode?
...ered Dec 14 '12 at 8:00
Rasmus BååthRasmus Bååth
3,62222 gold badges2121 silver badges2525 bronze badges
...
How to extract the n-th elements from a list of tuples?
...ray(elements)[:,1]
and the timings:
list comprehension: 4.73 ms ± 206 µs per loop
list(map): 5.3 ms ± 167 µs per loop
dict: 2.25 ms ± 103 µs per loop
list(zip) 5.2 ms ± 252 µs per loop
numpy array: 28.7 ms ± 1.88 ms per loop
Note that map() a...
Find the most frequent number in a numpy vector
...t collections.Counter(a).most_common()[0][0]
100000 loops, best of 3: 11.3 µs per loop
>>>
>>> import numpy
>>> numpy.bincount(a).argmax()
3
>>> %timeit numpy.bincount(a).argmax()
100 loops, best of 3: 2.84 ms per loop
>>>
>>> import scipy.sta...
Replace all elements of Python NumPy Array that are greater than some value
...r case:
In [292]: timeit np.minimum(a, 255)
100000 loops, best of 3: 19.6 µs per loop
In [293]: %%timeit
.....: c = np.copy(a)
.....: c[a>255] = 255
.....:
10000 loops, best of 3: 86.6 µs per loop
If you want to do it in-place (i.e., modify arr instead of creating result) you can ...