Description
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I have checked that this issue has not already been reported.
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I have confirmed this bug exists on the latest version of pandas.
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(optional) I have confirmed this bug exists on the master branch of pandas. (exists on the version I checked out 2 weeks ago)
I observed the following behavior:
When I define my DataFrame with a numeric column like
import pandas as pd
df = pd.DataFrame({"a": [1, 2]})
and filter with isin() for "1" like
df["a"].isin(["1"])
I get
0 True
1 False
Name: a, dtype: bool
which results probably from implicit type casting?
But when my column has datatype object like
df = pd.DataFrame({"a": [1, "2"]})
and filter again with isin()
df["a"].isin(["1"])
I get the following:
0 False
1 False
Name: a, dtype: bool
That was the output I expected.
This seems a bit inconsistent. Is this the expected behavior? If not, I would look into this, if I have a bit of time in the coming weeks. This may be loosely related to #19356 .
Output of pd.show_versions()
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-99-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : 0.29.15
pytest : 5.4.1
hypothesis : 5.8.3
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.8
numba : 0.49.0