大约有 900 项符合查询结果（耗时：0.0137秒） [XML]
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...
Is it possible to set a number to NaN or infinity?
...──────────────┤
│ cmath │ cmath.nan ¹ │ cmath.nanj │ cmath.inf ¹ │ cmath.infj │
╘══════════╧════════════════╧═════════════════╧══...
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...
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 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...
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 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 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 ...