pandas 在熊猫中，我如何为 datetime 列分组 weekday() ？
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in pandas how can I groupby weekday() for a datetime column?
提问by monkut
I'd like to filter out weekend data and only look at data for weekdays (mon(0)-fri(4)). I'm new to pandas, what's the best way to accomplish this in pandas?
我想过滤掉周末数据，只查看工作日（mon(0)-fri(4)）的数据。我是Pandas的新手，在Pandas中完成此任务的最佳方法是什么？
import datetime
from pandas import *
data = read_csv("data.csv")
data.my_dt
Out[52]:
0 2012-10-01 02:00:39
1 2012-10-01 02:00:38
2 2012-10-01 02:01:05
3 2012-10-01 02:01:07
4 2012-10-01 02:02:03
5 2012-10-01 02:02:09
6 2012-10-01 02:02:03
7 2012-10-01 02:02:35
8 2012-10-01 02:02:33
9 2012-10-01 02:03:01
10 2012-10-01 02:08:53
11 2012-10-01 02:09:04
12 2012-10-01 02:09:09
13 2012-10-01 02:10:20
14 2012-10-01 02:10:45
...
I'd like to do something like:
我想做类似的事情：
weekdays_only = data[data.my_dt.weekday() < 5]
AttributeError: 'numpy.int64' object has no attribute 'weekday'
AttributeError: 'numpy.int64' 对象没有属性 'weekday'
but this doesn't work, I haven't quite grasped how column datetime objects are accessed.
但这不起作用，我还没有完全掌握如何访问列日期时间对象。
The eventual goal being to arrange hierarchically to weekday hour-range, something like:
最终目标是按层次排列到工作日的小时范围，例如：
monday, 0-6, 7-12, 13-18, 19-23
tuesday, 0-6, 7-12, 13-18, 19-23
回答by Maximilian
your call to the function "weekday" does not work as it operates on the index of data.my_dt, which is an int64 array (this is where the error message comes from)
您对函数“weekday”的调用不起作用，因为它对 data.my_dt 的索引进行操作，这是一个 int64 数组（这是错误消息的来源）
you could create a new column in data containing the weekdays using something like:
您可以使用以下内容在包含工作日的数据中创建一个新列：
data['weekday'] = data['my_dt'].apply(lambda x: x.weekday())
then you can filter for weekdays with:
然后您可以使用以下方法过滤工作日：
weekdays_only = data[data['weekday'] < 5 ]
I hope this helps
我希望这有帮助
回答by Kartik
Faster way would be to use DatetimeIndex.weekday, like so:
更快的方法是使用DatetimeIndex.weekday，像这样：
temp = pd.DatetimeIndex(data['my_dt'])
data['weekday'] = temp.weekday
Much much faster, especially for a large number of rows. For further info, check thisanswer.
快得多，尤其是对于大量行。有关更多信息，请查看此答案。
