大约有 800 项符合查询结果（耗时：0.0119秒） [XML]
Pandas - Get first row value of a given column
...ter than using .iloc:
In [1]: %timeit -n 1000 df['Btime'].values[20]
5.82 µs ± 142 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [2]: %timeit -n 1000 df['Btime'].iloc[20]
29.2 µs ± 1.28 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
...
Pandas conditional creation of a series/dataframe column
...map( lambda x: 'red' if x == 'Z' else 'green')
1000 loops, best of 3: 239 µs per loop
1000 loops, best of 3: 523 µs per loop
1000 loops, best of 3: 263 µs per loop
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Getting a map() to return a list in Python 3.x
...imeit -r5 ordinals = list(range(45))
... list(map(chr, ordinals))
...
3.91 µs ± 60.2 ns per loop (mean ± std. dev. of 5 runs, 100000 loops each)
>>> %%timeit -r5 ordinals = list(range(45))
... [*map(chr, ordinals)]
...
3.84 µs ± 219 ns per loop (mean ± std. dev. of 5 runs, 100000 loo...
LaTeX source code listing like in professional books
...r 12 '09 at 17:13
Tormod FjeldskårTormod Fjeldskår
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How to implement the Android ActionBar back button?
... answered Apr 17 '15 at 7:30
Sågär ŚåxëńáSågär Śåxëńá
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How do I create a list of random numbers without duplicates?
...cted from 0 to 99, without duplicates. Benchmarking in IPython, yields 103 µs ± 513 ns for %timeit random.sample(range(1000), 100) , and 17 µs ± 1.24 µs for %timeit np.random.permutation(1000)[:100] .
– Ant Plante
Sep 4 at 10:26
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Is there a NumPy function to return the first index of something in an array?
...or idx, val in np.ndenumerate(a) if val==0))
100000 loops, best of 3: 17.6 µs per loop
In [287]: %timeit np.argmax(a==0)
1000 loops, best of 3: 254 µs per loop
In [288]: %timeit np.where(a==0)[0][0]
1000 loops, best of 3: 314 µs per loop
This is an open NumPy GitHub issue.
See also: Numpy:...
Remove accents/diacritics in a string in JavaScript
..., ü -> ue, Ä -> Ae, Ö -> Oe, Ü -> Ue, å -> aa, Å -> Aa, ß -> ss, ẞ -> SS,
– Marius
Jul 13 '17 at 18:28
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\d is less efficient than [0-9]
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Pandas percentage of total with groupby
...les'].sum().rename("count")
c / c.groupby(level=0).sum()
3.42 ms ± 16.7 µs per loop
(mean ± std. dev. of 7 runs, 100 loops each)
2nd Paul H
state_office = df.groupby(['state', 'office_id']).agg({'sales': 'sum'})
state = df.groupby(['state']).agg({'sales': 'sum'})
state_office.div(state, lev...