Code Sample, a copy-pastable example if possible
dtype = pd.api.types.CategoricalDtype(categories=["A","B"])
ds1 = pd.Series(data="A", index=[1,2], dtype=dtype)
ds2 = pd.Series(data="A", dtype=dtype)
Problem description
In the above ds1 has lost the category "B" while ds2 has retained it.
Expected Output
Both ds1 and ds2 should have the same dtype.
Output of pd.show_versions()
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-43-Microsoft
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.22.0
pytest: None
pip: 9.0.1
setuptools: 20.7.0
Cython: None
numpy: 1.13.3
scipy: 1.0.0
pyarrow: None
xarray: None
IPython: 6.2.1
sphinx: None
patsy: None
dateutil: 2.6.1
pytz: 2018.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.1.1
openpyxl: 2.5.0
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 1.0b10
sqlalchemy: None
pymysql: None
psycopg2: 2.7.4 (dt dec pq3 ext lo64)
jinja2: 2.8
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
Comment From: jschendel
Looks like this was fixed on master by #19717:
In [2]: pd.__version__
Out[2]: '0.23.0.dev0+428.gaedbd94'
In [3]: dtype = pd.api.types.CategoricalDtype(categories=["A","B"])
In [4]: ds1 = pd.Series(data="A", index=[1,2], dtype=dtype)
In [5]: ds1.dtype
Out[5]: CategoricalDtype(categories=['A', 'B'], ordered=False)
In [6]: ds1
Out[6]:
1 A
2 A
dtype: category
Categories (2, object): [A, B]