使用MultiIndex的Pandas数据框：检查字符串是否包含在索引级别中

Question

Let's say I have a multi-indexed pandas dataframe that looks like the following one, taken from the documentation .

import numpy as np
import pandas as pd

arrays = [np.array(['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux']),
          np.array(['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two'])]

df = pd.DataFrame(np.random.randn(8, 4), index=arrays)

Which looks like this:

                0         1         2         3
bar one -0.096648 -0.080298  0.859359 -0.030288
    two  0.043107 -0.431791  1.923893 -1.544845
baz one  0.639951 -0.008833 -0.227000  0.042315
    two  0.705281  0.446257 -1.108522  0.471676
foo one -0.579483 -2.261138 -0.826789  1.543524
    two -0.358526  1.416211  1.589617  0.284130
qux one  0.498149 -0.296404  0.127512 -0.224526
    two -0.286687 -0.040473  1.443701  1.025008

Now I only want the rows where "ne" is contained in second level of the MultiIndex.

Is there any way to slice the MultiIndex for (partly) contained strings?

Answer 1

You can apply a mask like:

df = df.iloc[df.index.get_level_values(1).str.contains('ne')]

which returns:

bar one -0.143200  0.523617  0.376458 -2.091154
baz one -0.198220  1.234587 -0.232862 -0.510039
foo one -0.426127  0.594426  0.457331 -0.459682
qux one -0.875160 -0.157073 -0.540459 -1.792235

EDIT: It is possible also applying a logical mask on multiple levels, eg:

df = df.iloc[(df.index.get_level_values(0).str.contains('ba')) | (df.index.get_level_values(1).str.contains('ne'))]

returns:

bar one  0.620279  1.525277  0.379649 -0.032608
    two  0.465240 -0.190038  0.795730  1.720368
baz one  0.986828 -0.080394 -0.303319  0.747483
    two  0.487534  1.597006  0.114551  0.299502
foo one -0.085700  0.112433  0.704043  0.264280
qux one -0.291758 -1.071669  0.794354 -1.805530