问题
Say I have the following multicolumn Pandas DataFrame:
arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', ],
['one', 'two', 'one', 'two', 'one', 'two', ]]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
df = pd.DataFrame(np.random.randn(8, 6), columns=arrays)
bar baz foo
one two one two one two
0 1.018709 0.295048 -0.735014 1.478292 -0.410116 -0.744684
1 1.388296 0.019284 -1.298793 1.597739 0.044640 -0.040337
2 -0.151763 -0.424984 -1.322985 -0.350483 0.590343 -2.189122
3 -0.221250 -0.449578 -1.512640 0.077380 -0.485380 -0.687565
4 -0.334315 1.790056 0.245414 -0.236784 -0.788226 0.483709
5 -0.943732 1.437968 -0.114556 -1.098798 0.482486 -1.527283
6 -1.213711 1.573547 0.425109 0.513945 0.731550 1.216149
7 0.709976 1.741406 -0.379932 -1.326460 -1.506532 -0.795053
What is the syntax to select a combination of multiple slices, like selecting ('bar',:) and ('baz':'foo','two')? I know I can do something like:
df.loc[:, [('bar', 'one'), ('baz', 'two')]]
bar baz
one two
0 1.018709 1.478292
1 1.388296 1.597739
2 -0.151763 -0.350483
3 -0.221250 0.077380
4 -0.334315 -0.236784
5 -0.943732 -1.098798
6 -1.213711 0.513945
7 0.709976 -1.326460
And something like:
print(df.loc[:, ('bar', slice(None))])
bar
one two
0 1.018709 0.295048
1 1.388296 0.019284
2 -0.151763 -0.424984
3 -0.221250 -0.449578
4 -0.334315 1.790056
5 -0.943732 1.437968
6 -1.213711 1.573547
7 0.709976 1.741406
But something like:
print(df.loc[:, [('bar', slice(None)), ('baz', 'two')]])
Raises a TypeError exception, while
print(df.loc[:, ['bar', ('baz', 'two')]])
raises a ValueError exception.
So what I am after is a simple syntax to create the following with two slices like:
[('bar', slice(None)), ('baz', 'two')]:
bar baz
one two two
0 -1.438018 1.511736 0.186499
1 -0.432313 -0.478824 -0.055930
2 0.995103 -0.181832 -0.257952
3 0.972293 2.580807 1.536281
4 -0.496261 1.038807 0.209853
5 0.788222 -1.325234 -1.328570
回答1:
I'd like to extend this great answer from @bunji with the pd.IndexSlice[...] method:
In [75]: df.loc[:, pd.IndexSlice[['bar','baz'], 'two']]
Out[75]:
bar baz
two two
0 -0.037198 0.814649
1 1.272708 1.258576
2 0.405093 -0.243942
3 0.126001 1.751699
4 -0.135793 0.753241
5 -0.433305 -0.192642
6 0.939398 1.356368
7 -0.121508 3.719689
another less performative solution - using chained filter method:
In [78]: df.filter(like='two').filter(regex='(bar|baz)')
Out[78]:
bar baz
two two
0 -0.037198 0.814649
1 1.272708 1.258576
2 0.405093 -0.243942
3 0.126001 1.751699
4 -0.135793 0.753241
5 -0.433305 -0.192642
6 0.939398 1.356368
7 -0.121508 3.719689
回答2:
The type error is because you're asking it to look up a list of indices instead of a tuple of indices. Tuples are hashable whereas lists are not so you get an error because it's trying to hash [('bar', slice(None)), ('baz', 'two')]].
Try:
print(df.loc[:, (('bar', slice(None)), ('baz', 'two'))])
回答3:
You can combine the multiple slices and build the indices yourself without too much trouble.
Code:
def combine_slices(frame, *slices):
return list(sorted(sum([
list(frame.columns.get_locs(s)) for s in slices], [])))
df[combine_slices(df, ('bar', slice(None)), ('baz', 'two'))]
Test Code:
import pandas as pd
import numpy as np
arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', ],
['one', 'two', 'one', 'two', 'one', 'two', ]]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
df = pd.DataFrame(np.random.randn(6, 6), columns=arrays)
print(df[combine_slices(df,
('bar', slice(None)),
('baz', 'two'),
)])
Results:
bar baz
one two two
0 -1.438018 1.511736 0.186499
1 -0.432313 -0.478824 -0.055930
2 0.995103 -0.181832 -0.257952
3 0.972293 2.580807 1.536281
4 -0.496261 1.038807 0.209853
5 0.788222 -1.325234 -1.328570
回答4:
You could use the query syntax on pd.MultiIndex
Only issue is that query only works on the index so we'll have to transpose to and from.
df.T.query('ilevel_0 in ["bar", "baz"] or ilevel_1 == "two"').T
bar baz foo
one two one two two
0 0.684387 0.688040 -1.868616 -0.618797 -0.187312
1 -0.111344 -0.633866 -0.245142 -2.673403 0.281421
2 -0.122203 -1.275920 -0.722925 -0.812835 -0.639630
3 -0.512743 -0.273289 -0.733837 -0.091343 1.050064
4 0.867375 -0.442477 -0.342420 1.785535 -0.348037
5 1.148774 0.669942 -0.845356 -1.322135 0.258731
6 -0.707214 1.668921 -0.291904 1.874307 0.152995
7 0.436886 0.102186 -0.720527 0.825798 0.328133
来源：https://stackoverflow.com/questions/42891466/select-a-list-slices-of-a-pandas-multiindex-multicolumn-dataframe