使用复合（分层）索引从Pandas数据框中选择行 [英] Selecting rows from a Pandas dataframe with a compound (hierarchical) index

问题描述

我怀疑这是微不足道的，但我还没发现可以让我根据分层键的值从Pandas dataframe 中选择行的咒语。因此，例如，假设我们有以下数据框：

I'm suspicious that this is trivial, but I yet to discover the incantation that will let me select rows from a Pandas dataframe based on the values of a hierarchical key. So, for example, imagine we have the following dataframe:

import pandas
df = pandas.DataFrame({'group1': ['a','a','a','b','b','b'],
                       'group2': ['c','c','d','d','d','e'],
                       'value1': [1.1,2,3,4,5,6],
                       'value2': [7.1,8,9,10,11,12]
})
df = df.set_index(['group1', 'group2'])

df看起来像我们预期的那样：

df looks as we would expect:

如果df未在group1上编入索引，我可以执行以下操作：

If df were not indexed on group1 I could do the following:

df['group1' == 'a']

但是这个带有索引的数据帧失败了。所以也许我应该把它想象成一个带有等级索引的Pandas系列：

But that fails on this dataframe with an index. So maybe I should think of this like a Pandas series with a hierarchical index:

df['a','c']

不。那也失败了。

那么如何选出以下所有行：

So how do I select out all the rows where:

1. group1 = ='a'


2. group1 =='a'& group2 =='c'


3. group2 =='c'


4. group1 in ['a'，'b'，'c']

1. group1 == 'a'
2. group1 == 'a' & group2 == 'c'
3. group2 == 'c'
4. group1 in ['a','b','c']

推荐答案

尝试使用 xs 非常精确：

In [5]: df.xs('a', level=0)
Out[5]: 
        value1  value2
group2                
c          1.1     7.1
c          2.0     8.0
d          3.0     9.0

In [6]: df.xs('c', level='group2')
Out[6]: 
        value1  value2
group1                
a          1.1     7.1
a          2.0     8.0

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