pandas 熊猫中基于多索引的索引
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MultiIndex-based indexing in pandas
提问by kjo
If I define a hierarchically-indexed dataframe like this:
如果我像这样定义一个分层索引的数据框：
import itertools
import pandas as pd
import numpy as np
a = ('A', 'B')
i = (0, 1, 2)
b = (True, False)
idx = pd.MultiIndex.from_tuples(list(itertools.product(a, i, b)),
names=('Alpha', 'Int', 'Bool'))
df = pd.DataFrame(np.random.randn(len(idx), 7), index=idx,
columns=('I', 'II', 'III', 'IV', 'V', 'VI', 'VII'))
the contents look like this:
内容如下所示：
In [19]: df
Out[19]:
I II III IV V VI VII
Alpha Int Bool
A 0 True -0.462924 1.210442 0.306737 0.325116 -1.320084 -0.831699 0.892865
False -0.850570 -0.949779 0.022074 -0.205575 -0.684794 -0.214307 -1.133833
1 True 0.603602 1.387020 -0.830780 -1.242000 -0.321938 0.484271 0.171738
False -1.591730 1.282136 0.095159 -1.239882 0.760880 -0.606444 -0.485957
2 True -1.346883 1.650247 -1.476443 2.092067 1.344689 0.177083 0.100844
False 0.001407 -1.127299 -0.417828 0.143595 -0.277838 -0.478262 -0.350906
B 0 True 0.722781 -1.093182 0.237536 0.457614 -2.500885 0.338257 0.009128
False 0.321022 0.419357 1.161140 -1.371035 1.093696 0.250517 -1.125612
1 True 0.237441 1.739933 0.029653 0.327823 -0.384647 1.523628 -0.009053
False -0.459148 -0.598577 -0.593486 -0.607447 1.478399 0.504028 -0.329555
2 True -0.583052 -0.986493 -0.057788 -0.639798 1.400311 0.076471 -0.212513
False 0.896755 2.583520 1.520151 2.367336 -1.084994 -1.233548 -2.414215
I know how to extract the data corresponding to a given column. E.g. for column 'VII':
我知道如何提取与给定列对应的数据。例如对于列'VII'：
In [20]: df['VII']
Out[20]:
Alpha Int Bool
A 0 True 0.892865
False -1.133833
1 True 0.171738
False -0.485957
2 True 0.100844
False -0.350906
B 0 True 0.009128
False -1.125612
1 True -0.009053
False -0.329555
2 True -0.212513
False -2.414215
Name: VII
How do I extract the data matching the following sets of criteria:
如何提取符合以下条件集的数据：
Alpha=='B'Alpha=='B',Bool==FalseAlpha=='B',Bool==False, column'I'Alpha=='B',Bool==False, columns'I'and'III'Alpha=='B',Bool==False, columns'I','III', and all columns from'V'onwardsIntis even
Alpha=='B'Alpha=='B',Bool==FalseAlpha=='B',Bool==False, 列'I'Alpha=='B',Bool==False, 列'I'和'III'Alpha=='B'，Bool==False，柱'I'，'III'以及所有从列'V'起Int甚至
(BTW, I did rtfm, more than once even, but I really find it incomprehensible.)
（顺便说一句，我做了 rtfm，甚至不止一次，但我真的觉得它无法理解。）
回答by beardc
xsmay be what you want. Here are a few examples:
xs可能是你想要的。这里有一些例子：
In [63]: df.xs(('B',), level='Alpha')
Out[63]:
I II III IV V VI VII
Int Bool
0 True -0.430563 0.139969 -0.356883 -0.574463 -0.107693 -1.030063 0.271250
False 0.334960 -0.640764 -0.515756 -0.327806 -0.006574 0.183520 1.397951
1 True -0.450375 1.237018 0.398290 0.246182 -0.237919 1.372239 -0.805403
False -0.064493 0.967132 -0.674451 0.666691 -0.350378 1.721682 -0.791897
2 True 0.143154 -0.061543 -1.157361 0.864847 -0.379616 -0.762626 0.645582
False -3.253589 0.729562 -0.839622 -1.088309 0.039522 0.980831 -0.113494
In [64]: df.xs(('B', False), level=('Alpha', 'Bool'))
Out[64]:
I II III IV V VI VII
Int
0 0.334960 -0.640764 -0.515756 -0.327806 -0.006574 0.183520 1.397951
1 -0.064493 0.967132 -0.674451 0.666691 -0.350378 1.721682 -0.791897
2 -3.253589 0.729562 -0.839622 -1.088309 0.039522 0.980831 -0.113494
Edit:
编辑：
For the last requirement you can chain get_level_valuesand isin:
对于最后一个要求，您可以链接get_level_values和isin：
Get the even values in the index (other ways to do this too)
获取索引中的偶数值（其他方法也可以）
In [87]: ix_vals = set(i for _, i, _ in df.index if i % 2 == 0)
ix_vals
Out[87]: set([0L, 2L])
Use these with isin
使用这些 isin
In [89]: ix = df.index.get_level_values('Int').isin(ix_vals)
In [90]: df[ix]
Out[90]: I II III IV V VI VII
Alpha Int Bool
A 0 True -1.315409 1.203800 0.330372 -0.295718 -0.679039 1.402114 0.778572
False 0.008189 -0.104372 0.419110 0.302978 -0.880262 -1.037645 -0.264265
2 True -2.414290 0.896990 0.986167 -0.527074 0.550753 -0.302920 0.228165
False 1.275831 0.448089 -0.635874 -0.733855 -0.747774 -1.108976 0.151474
B 0 True -0.430563 0.139969 -0.356883 -0.574463 -0.107693 -1.030063 0.271250
False 0.334960 -0.640764 -0.515756 -0.327806 -0.006574 0.183520 1.397951
2 True 0.143154 -0.061543 -1.157361 0.864847 -0.379616 -0.762626 0.645582
False -3.253589 0.729562 -0.839622 -1.088309 0.039522 0.980831 -0.113494
回答by TNT
You can use pd.IndexSlice for an intuitive way (Inspired from this answer). Some examples (using pandas 0.18.0):
您可以以直观的方式使用 pd.IndexSlice（受此答案启发）。一些示例（使用Pandas 0.18.0）：
df.sort_index(inplace=True)
idx = pd.IndexSlice
evens = np.arange(2,max(df.index.levels[1])+1,2)
df.loc[idx[['A','B'],evens,True],['III','V']]
Out[]:
III V
Alpha Int Bool
A 2 True -1.041243 -0.561155
B 2 True 0.381918 -0.148990
df.loc[idx[:,evens,:],:]
Out[]:
I II III IV V VI \
Alpha Int Bool
A 2 False 0.791142 0.333383 0.089767 -0.584465 0.295676 -1.323792
True -1.023160 -0.442004 -1.041243 1.613184 -0.561155 0.397923
B 2 False 0.383229 -0.052715 -0.214347 -2.041429 -1.101059 -0.374035
True -0.183386 -0.855367 0.381918 -0.261106 -0.148990 0.621537
VII
Alpha Int Bool
A 2 False 0.717301
True -0.133701
B 2 False 0.166314
True 0.517513
