如何使用ix索引到 pandas 多索引

问题描述

我已经设置了一些这样的代码:

I have setup some code like this:

import pandas as pd
import numpy as np
arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
          ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two'],
          ['aaa', 'bbb', 'ccc', 'ccc', 'ddd', 'eee', 'eee', 'eee' ]]
tuples = zip(*arrays)
index = pd.MultiIndex.from_tuples(tuples, names=['A', 'B', 'C'])
df = pd.DataFrame(np.random.randn(8, 4), index=index)

df
Out[161]:
                    0         1         2         3
A   B   C
bar one aaa  0.682220 -0.598889 -0.600635 -0.488069
    two bbb -0.134557  1.614224 -0.191303  0.073813
baz one ccc -1.006877 -0.137264 -0.319274  1.465952
    two ccc  0.107222  0.358468  0.165108 -0.258715
foo one ddd  0.360562  1.759095 -1.385394 -0.646850
    two eee -1.113520  0.221483  2.226704 -0.994636
qux one eee -0.609271 -0.888330  0.824189  1.772536
    two eee -0.008346 -0.688091  0.263303  1.242485

我想根据条件与A，B和C组的组合找到匹配的行.

I want find matching rows based on combinations of criteria with the groups A, B and C.

例如用sql术语:选择*其中A in('foo'，'qux')and C ='eee'

e.g. in sql terms: select * where A in ('foo', 'qux') and C='eee'

我可以用ix实现吗?例如像这样:

Can I acheive this with ix? e.g. something like:

df.ix(['foo', 'qux'],:,'eee')

对于非常大的数据集，实现此目标的理想方式是什么?

What is the idomatic way of achieving this for very large datasets?

(我目前正在使用pandas 0.7，但如果绝对必要可以升级)

(I'm currently using pandas 0.7 but can upgrade if absolutely necessary)

推荐答案

自熊猫0.14起，您可以将选择器的元组传递给df.loc来切片MultiIndex:

As of Pandas 0.14, you can pass a tuple of selectors to df.loc to slice a MultiIndex:

In [782]: df.loc[(['foo','qux'], slice(None), 'eee'), :]
Out[782]:
                    0         1         2         3
A   B   C
foo two eee  1.615853 -1.327626  0.772608 -0.406398
qux one eee  0.472890  0.746417  0.095389 -1.446869
    two eee  0.711915  0.228739  1.763126  0.558106

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