BUG: concat behaves differently for bool/boolean dtypes than it does for int64/Int64 · Issue #42800 · pandas-dev/pandas (original) (raw)

Code Sample, a copy-pastable example

import pandas as pd

int1 = pd.Series([1, 2], dtype='int64') int2 = pd.Series([pd.NA, 4], dtype='Int64')

pd.concat([int1, int2])

0       1
1       2
0    <NA>
1       4
dtype: Int64

bool1 = pd.Series([True, False], dtype='bool') bool2 = pd.Series([pd.NA, True], dtype='boolean')

pd.concat([bool1, bool2])

0     True
1    False
0     <NA>
1     True
dtype: object

Problem description

When running concat with two series, one of int64 dtype and one of Int64 dtype containing pd.NA, the resulting series has a dtype of Int64, which makes sense as this type is able to contain data from both. However, performing this same test on series with types of bool and boolean, results in a series with a dtype of object. It seems for consistency with the other behavior the series that results from concat should be boolean and not object.

Expected Output

The series resulting from running concat with a bool series and a boolean series, should be a series with a boolean dtype instead of object.

0     True
1    False
0     <NA>
1     True
dtype: boolean

Output of pd.show_versions()

INSTALLED VERSIONS

commit : 2cb9652
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Tue Jan 12 22:04:47 PST 2021; root:xnu-4903.278.56~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 1.2.4
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 40.8.0
Cython : None
pytest : 5.2.0
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 : 0.8.5
fastparquet : 0.5.0
gcsfs : None
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.52.0