基于索引的Pandas Dataframe Mask [英] Pandas Dataframe Mask based on index

问题描述

我有以下数据框:

import pandas as pd
index = pd.date_range('2013-1-1',periods=10,freq='15Min')
data = pd.DataFrame(data=[1,2,3,4,5,6,7,8,9,0], columns=['value'], index=index)

如何根据索引值生成掩码?我知道我可以做类似的事情:

How can I generate a mask based on the index value? I know I can do something like:

data['value'] > 3
Out[40]: 
2013-01-01 00:00:00    False
2013-01-01 00:15:00    False
2013-01-01 00:30:00    False
2013-01-01 00:45:00     True
2013-01-01 01:00:00     True
2013-01-01 01:15:00     True
2013-01-01 01:30:00     True
2013-01-01 01:45:00     True
2013-01-01 02:00:00     True
2013-01-01 02:15:00    False
Freq: 15T, Name: value, dtype: bool

我想生成一个掩码，以仅考虑索引在特定范围内的某些行.我正在考虑做类似data['index'].time() > datetime.time(1,15)的操作来生成遮罩.当然，除data['index']之外，其他操作都会失败，因为索引不是列的名称.如何引用掩码中某行的索引值?

I want to generate a mask to only consider some rows where the index is in a certain range. I was thinking of doing something like data['index'].time() > datetime.time(1,15) to generate a mask. Except of course data['index'] fails because index is not the name of a column. How can you reference the index value for a row in a mask?

推荐答案

您可以使用indexer_between_time屏蔽:

In [11]: data.index.indexer_between_time(start='01:15', end='02:00')
Out[11]: array([5, 6, 7, 8])

In [12]: data.iloc[data.index.indexer_between_time(start='1:15', end='02:00')]
Out[12]:
                     value
2013-01-01 01:15:00      6
2013-01-01 01:30:00      7
2013-01-01 01:45:00      8
2013-01-01 02:00:00      9

如您所见，您可以通过属性.index访问索引.

As you can see, you access the index by the attribute .index.

注意:indexer_between_time默认情况下include_start和include_end均为True，它还提供了tz参数以将时间与其他时区进行比较.

Note: indexer_between_time by default both include_start and include_end are True, it also offers a tz argument to compare the time to a different timezone.

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