to_msgpack fails for Dataframes with numpy bools

[PhyNerd]

Code Sample, a copy-pastable example if possible

import pandas as pd
import numpy as np
pd.DataFrame(pd.Series((np.bool_(1), 2, 3, 4, 5))).to_msgpack('test.msg')

Problem description

When trying to export Dataframes containing numpy bools with the DataFrame.to_msgpack() function one gets the exception:

  File "pandas/io/msgpack/_packer.pyx", line 230, in pandas.io.msgpack._packer.Packer.pack
  File "pandas/io/msgpack/_packer.pyx", line 232, in pandas.io.msgpack._packer.Packer.pack
  File "pandas/io/msgpack/_packer.pyx", line 191, in pandas.io.msgpack._packer.Packer._pack
  File "pandas/io/msgpack/_packer.pyx", line 220, in pandas.io.msgpack._packer.Packer._pack
  File "pandas/io/msgpack/_packer.pyx", line 191, in pandas.io.msgpack._packer.Packer._pack
  File "pandas/io/msgpack/_packer.pyx", line 220, in pandas.io.msgpack._packer.Packer._pack
  File "pandas/io/msgpack/_packer.pyx", line 227, in pandas.io.msgpack._packer.Packer._pack
TypeError: can't serialize True

I'm guessing this is because the isinstance check on line 136 of pandas.io.msgpack._packer.pyx returns False for numpy.bool_ .

Expected Output

I'd expect DataFrames with numpy bools in them to serialize just like ones with Python bools do.

Output of pd.show_versions()

[paste the output of pd.show_versions() here below this line]
pd.show_versions()

INSTALLED VERSIONS

commit: None
python: 2.7.14.final.0
python-bits: 64
OS: Darwin
OS-release: 17.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: de_DE.UTF-8
LOCALE: None.None

pandas: 0.21.0
pytest: 3.1.2
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.13.3
scipy: 0.19.1
pyarrow: 0.7.1
xarray: None
IPython: 5.3.0
sphinx: 1.6.2
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.2
feather: 0.4.0
matplotlib: 2.0.2
openpyxl: 2.4.7
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.8.0
bs4: 4.6.0
html5lib: 0.999
sqlalchemy: 1.1.11
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

[gfyoung]

@PhyNerd : Your diagnosis looks correct to me. That should be pretty easy to patch in light of that, so a PR would be most welcome!

[jreback]

you realize that storing non-integers co-mingled is completely inefficient as this is object dtype.

[PhyNerd]

Yes I do.
The Dataframes I want to export often have missing data, where fields are None in columns that are otherwise all np.bool_. In other cases users add mixed datatypes. There isn't much I can do about that.