Description
Code Sample, a copy-pastable example if possible
>>> import pandas as pd
>>> import numpy as np
>>> s = pd.Series(["a","b","c","a", np.nan], dtype="category")
>>> s
0 a
1 b
2 c
3 a
4 NaN
dtype: category
Categories (3, object): [a, b, c]
# `na=False` kwarg should make all missing values False, but this seems broken for categoricals
>>> s.str.contains("a", na=False)
0 True
1 False
2 False
3 True
4 NaN
dtype: object
# compare that to object series
>>> s2 = pd.Series(["a","b","c","a", np.nan])
>>> s2
0 a
1 b
2 c
3 a
4 NaN
dtype: object
>>> s2.str.contains("a", na=False)
0 True
1 False
2 False
3 True
4 False
dtype: bool
Problem description
.str.contains(..., na=False)
should make missing values False when the calling series is of type categorical just like it does for object series.
Expected Output
>>> s = pd.Series(["a","b","c","a", np.nan], dtype="category")
>>> s2 = pd.Series(["a","b","c","a", np.nan])
>>> (s.str.contains("a", na=False) == s2.str.contains("a", na=False)).all()
True
Output of pd.show_versions()
INSTALLED VERSIONS
commit: None
python: 3.7.0.final.0
python-bits: 64
OS: Darwin
OS-release: 17.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.23.3
pytest: None
pip: 18.0
setuptools: 40.0.0
Cython: None
numpy: 1.15.0
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None