pandas 在熊猫中按时间分组的更快方法
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/17288636/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
Faster way to groupby time of day in pandas
提问by joeb1415
I have a time series of several days of 1-minute data, and would like to average it across all days by time of day.
我有几天的 1 分钟数据的时间序列，并希望按一天中的时间平均所有天。
This is very slow:
这是非常缓慢的：
from datetime import datetime
from pandas import date_range, Series
time_ind = date_range(datetime(2013, 1, 1), datetime(2013, 1, 10), freq='1min')
all_data = Series(randn(len(time_ind)), time_ind)
time_mean = all_data.groupby(lambda x: x.time()).mean()
Takes almost a minute to run!
运行需要将近一分钟！
While something like:
虽然类似：
time_mean = all_data.groupby(lambda x: x.minute).mean()
takes only a fraction of a second.
只需要几分之一秒。
Is there a faster way to group by time of day?
有没有更快的方法来按一天中的时间分组？
Any idea why this is so slow?
知道为什么这这么慢吗？
采纳答案by bmu
Both your "lambda-version" and the time property introduced in version 0.11seems to be slow in version 0.11.0:
您的“lambda-version”和0.11 版中引入的 time 属性在 0.11.0版中似乎都很慢：
In [4]: %timeit all_data.groupby(all_data.index.time).mean()
1 loops, best of 3: 11.8 s per loop
In [5]: %timeit all_data.groupby(lambda x: x.time()).mean()
Exception RuntimeError: 'maximum recursion depth exceeded while calling a Python object' in <type 'exceptions.RuntimeError'> ignored
Exception RuntimeError: 'maximum recursion depth exceeded while calling a Python object' in <type 'exceptions.RuntimeError'> ignored
Exception RuntimeError: 'maximum recursion depth exceeded while calling a Python object' in <type 'exceptions.RuntimeError'> ignored
1 loops, best of 3: 11.8 s per loop
With the current master both methods are considerably faster:
使用当前的 master，这两种方法都快得多：
In [1]: pd.version.version
Out[1]: '0.11.1.dev-06cd915'
In [5]: %timeit all_data.groupby(lambda x: x.time()).mean()
1 loops, best of 3: 215 ms per loop
In [6]: %timeit all_data.groupby(all_data.index.time).mean()
10 loops, best of 3: 113 ms per loop
'0.11.1.dev-06cd915'
So you can either update to a master or wait for 0.11.1 which should be released this month.
所以你可以更新到 master 或者等待本月应该发布的 0.11.1。
回答by Andy Hayden
It's faster to groupby the hour/minute/.. attributes rather than .time. Here's Jeff's baseline:
按小时/分钟/.. 属性而不是 .. 属性分组更快.time。这是杰夫的基线：
In [11]: %timeit all_data.groupby(all_data.index.time).mean()
1 loops, best of 3: 202 ms per loop
and without time it's much faster (the fewer attributes the faster it is):
没有时间它会快得多（属性越少，速度越快）：
In [12]: %timeit all_data.groupby(all_data.index.hour).mean()
100 loops, best of 3: 5.53 ms per loop
In [13]: %timeit all_data.groupby([all_data.index.hour, all_data.index.minute, all_data.index.second, all_data.index.microsecond]).mean()
10 loops, best of 3: 20.8 ms per loop
Note: time objects don't accept a nanosecond (but that's DatetimeIndex's resolution).
注意：时间对象不接受纳秒（但这是 DatetimeIndex 的分辨率）。
We should probably convert the index to have time objects to make this comparison fair:
我们可能应该将索引转换为具有时间对象以使这种比较公平：
In [21]: res = all_data.groupby([all_data.index.hour, all_data.index.minute, all_data.index.second, all_data.index.microsecond]).mean()
In [22]: %timeit res.index.map(lambda t: datetime.time(*t))
1000 loops, best of 3: 1.39 ms per loop
In [23]: res.index = res.index.map(lambda t: datetime.time(*t))
So it's around 10 times faster for maximum resolution, and you can easily make it coarser (and faster) e.g. groupby just the hour and minute..
因此，对于最大分辨率而言，它大约快 10 倍，并且您可以轻松地使其更粗糙（更快），例如按小时和分钟分组。
