Ambiguous behaviour when index is bool type?

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Code Sample, a copy-pastable example

# Your code here

pandas.Series([True, True, False]).value_counts()[[False]]

Problem description

[this should explain why the current behaviour is a problem and why the expected output is a better solution]

pandas/core/indexers.py in check_array_indexer(array, indexer)
    468         # GH26658
    469         if len(indexer) != len(array):
--> 470             raise IndexError(
    471                 f"Boolean index has wrong length: "
    472                 f"{len(indexer)} instead of {len(array)}"

IndexError: Boolean index has wrong length: 1 instead of 2

It seems that Series.__getitem__ and check_bool_indexer do not work well for bool-typed index

Expected Output

<pandas.Series>
False    1
dtype: int64

Output of pd.show_versions()

[paste the output of pd.show_versions() here leaving a blank line after the details tag]

INSTALLED VERSIONS

commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3 python : 3.8.5.final.0 python-bits : 64 OS : Darwin OS-release : 20.6.0 Version : Darwin Kernel Version 20.6.0: Wed Jun 23 00:26:31 PDT 2021; root:xnu-7195.141.2~5/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : None LOCALE : None.UTF-8

pandas : 1.1.4 numpy : 1.19.1 pytz : 2020.1 dateutil : 2.8.1 pip : 20.2.3 setuptools : 49.6.0.post20200814 Cython : None pytest : None hypothesis : None sphinx : 3.2.1 blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 2.11.2 IPython : 7.18.1 pandas_datareader: None bs4 : None bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : 3.3.2 numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pytables : None pyxlsb : None s3fs : None scipy : 1.5.3 sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None

[kwhkim]

I am aware of this edge case. As far as I figured out, a list consisted only of True and Falses works as boolean values(not index).

In this case, I would do

x = pandas.Series([True, True, False]).value_counts()
x[ x.index == False]

or you can slip in some value other than True or False

pandas.Series([True, True, False]).value_counts().append(pandas.Series([0], index = ['None']), verify_integrity=False)[[True, 'None']]

I think there should be something like .ibool for boolean indexing for completeness and consistency even though there is little chance that things like this happen.

I am from R. And from the designing persepective, python including pandas has so many exceptions. For example, even importing a module has some egde case like that import time never imports time.py. I think it’s poor design problem

[simonjayhawkins]

There are far more practical uses for an Indexer with Boolean values than trying to index True/False indexed DataFrame.

hmm. this does make the result inconsistent with say pandas.Series([1, 2, 3])[[0]]

A workflow could potentially have the index generated from unknown data. e.g as in the OP, from value_counts

I think we should mark as a bug, pending furher investigation on how feasible it is is distinguish between a fancy indexer and a boolean indexer.