Odd behaviour of groupby-agg with certain boolean columns and multi-index
## Problem Description

I stumbled over some very specific off behaviour when trying to do a multi-index groupby over several boolean columns using `agg` with a dictionary to apply a function to each column that would count the number of True-values by using the boolean series as a boolean index on itself and getting the length of that.

Example code:

	NN = 1
	l0 = NN*['a','a','b','b','b','b','b','b','b']
	l1 = NN*['x','y','x','x','x','y','y','z','z']
	l2 = NN*[True,True,False,True,False,False,False,True,True]
	l3 = NN*[True,False,False,True,False,True,False,True,False]
	l4 = NN*[False,False,True,True,True,True,False,True,False]

	df = pd.DataFrame({"C0":l0,'C1':l1,'C2':l2,'C3':l3,'C4':l4})
	print(df.groupby(["C0","C1"])[["C2","C3","C4"]]\
    .agg({"C2":lambda x: len(x[x]),"C3":lambda x: len(x[x]),"C4":lambda x: len(x[x])}))

The result I'm getting is:

		   C2     C3  C4
	C0 C1               
	a  x    1   True   0
	   y    1  False   0
	b  x    1   True   2
	   y    0   True   1
	   z    2   True   1

As you can see, the second column isn't properly aggregated for some reason.

If I set `NN = 10`, I'm getting a proper result:

		   C2  C3  C4
	C0 C1            
	a  x   10  10   0
	   y   10   0   0
	b  x   10  10  30
	   y    0  10  10
	   z   20  10  10

The problem only happens when using a multi-index, only with the `C3` column, only when `NN=1` and only when using the `agg` function (it works fine with `apply` on the single column), so it can be reduced to this call:

	print(df.groupby(["C0","C1"])["C3"].agg(lambda x: len(x[x])))

It seems like the specific combination of the multi-index and the boolean values causes some odd behaviour.

EDIT: I did not write the `C3` data specifically to cause the error, I just played with random combinations around until the error appeared, so I have no clue, why exactly that combination causes the problem.

In my real application this happens to two of three columns in a dataframe with 570 rows, so it's not limited to very short dataframes like in the example.

#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 79 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
LOCALE: None.None

pandas: 0.23.0
pytest: 3.5.1
pip: 10.0.1
setuptools: 39.0.1
Cython: 0.28.2
numpy: 1.14.3
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.4.0
sphinx: 1.7.4
patsy: 0.5.0
dateutil: 2.7.2
pytz: 2018.4
blosc: None
bottleneck: 1.2.1
tables: 3.4.3
numexpr: 2.6.4
feather: None
matplotlib: 2.2.2
openpyxl: 2.5.3
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.4
lxml: 4.2.1
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.7
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: 0.1.5
pandas_gbq: None
pandas_datareader: None
</details>
