BUG: allow describe() for on boolean-only columns

[lia-simeone]

I know I can obtain the expected output by using include=['bool'], but that feels bad to me as a user. I want describe() to know that I'm only asking for boolean columns and not freak out.

Thank you!

Code Sample, a copy-pastable example if possible

>>> test_df = pd.DataFrame({'test_ind_1': [False, False, True, True, False, True , True, False],  'test_ind_2': [False, True, True, False, False, True, True,True]})
>>> test_df.describe()

Expected Output

│       test_ind_1 test_ind_2
│count           8          8
│unique          2          2
│top          True       True
│freq            4          5

Actual output

ValueError: No objects to concatenate

output of pd.show_versions()

INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-327.18.2.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8

pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.22.1
numpy: 1.11.1
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 5.0.0
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.4.1
pytz: 2016.6.1
blosc: None
bottleneck: 1.0.0
tables: 3.2.0
numexpr: 2.4.3
matplotlib: 1.5.1
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 1.0.0
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.5
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
pandas_datareader: None

[jreback]

must be something in the exlusion logic, as an all object dtype works. PR's are welcome.

[agraboso]

The problem is in generic.py#L5141-L5143:

if len(self._get_numeric_data()._info_axis) > 0:
    # when some numerics are found, keep only numerics
    data = self.select_dtypes(include=[np.number])

_get_numeric_data() keeps boolean columns (BoolBlock inherits from NumericBlock), but select_dtypes(include=[np.number]) does not.

>>> test_df._get_numeric_data()
  test_ind_1 test_ind_2
0      False      False
1      False       True
2       True       True
3       True      False
4      False      False
5       True       True
6       True       True
7      False       True
>>> test_df.select_dtypes(include=[np.number])
Empty DataFrame
Columns: []
Index: [0, 1, 2, 3, 4, 5, 6, 7]

I'm working on a fix, but I'm worried I may be getting too deep into the internals of pandas...