Python 仅选择多索引 DataFrame 的一个索引
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Select only one index of multiindex DataFrame
提问by Skorpeo
I am trying to create a new DataFrame using only one index from a multi-indexed DataFrame.
我正在尝试仅使用多索引 DataFrame 中的一个索引创建一个新的 DataFrame。
A B C
first second
bar one 0.895717 0.410835 -1.413681
two 0.805244 0.813850 1.607920
baz one -1.206412 0.132003 1.024180
two 2.565646 -0.827317 0.569605
foo one 1.431256 -0.076467 0.875906
two 1.340309 -1.187678 -2.211372
qux one -1.170299 1.130127 0.974466
two -0.226169 -1.436737 -2.006747
Ideally, I would like something like this:
理想情况下，我想要这样的东西：
In: df.ix[level="first"]
and:
和：
Out:
A B C
first
bar 0.895717 0.410835 -1.413681
0.805244 0.813850 1.607920
baz -1.206412 0.132003 1.024180
2.565646 -0.827317 0.569605
foo 1.431256 -0.076467 0.875906
1.340309 -1.187678 -2.211372
qux -1.170299 1.130127 0.974466
-0.226169 -1.436737 -2.006747
`
Essentially I want to drop all the other indexes of the multi-index other than level first. Is there an easy way to do this?
基本上我想删除除 level 之外的多索引的所有其他索引first。是否有捷径可寻？
采纳答案by Alex Riley
One way could be to simply rebind df.indexto the desired level of the MultiIndex. You can do this by specifying the label name you want to keep:
一种方法可能是简单地重新绑定df.index到所需的 MultiIndex 级别。您可以通过指定要保留的标签名称来执行此操作：
df.index = df.index.get_level_values('first')
or use the level's integer value:
或使用级别的整数值：
df.index = df.index.get_level_values(0)
All other levels of the MultiIndex would disappear here.
MultiIndex 的所有其他级别都将在此处消失。
回答by Alexander McFarlane
The solution is fairly new and uses the df.xsfunction as
该解决方案是相当新的，并将该df.xs功能用作
In [88]: df.xs('bar', level='first')
Out[88]:
Second Third
one A -2.315312
B 0.497769
C 0.108523
two A -0.778303
B -1.555389
C -2.625022
dtype: float64
Can also do with multiple indices as
也可以使用多个索引作为
In [89]: df.xs(('bar', 'A'), level=('First', 'Third'))
Out[89]:
Second
one -2.315312
two -0.778303
dtype: float64
The setup for the examples is below
示例的设置如下
import pandas as pd
import numpy as np
arrays = [
np.array(['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux']),
np.array(['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two'])
]
index = pd.MultiIndex.from_tuples(list(zip(*arrays)), names=['first', 'second'])
df = pd.DataFrame(np.random.randn(3, 8), index=['A', 'B', 'C'], columns=index)
df.index.names = pd.core.indexes.frozen.FrozenList(['First', 'Second', 'Third'])
df = df.unstack()
