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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I have confirmed this bug exists on the master branch of pandas.
Reproducible Example
df1 = pd.DataFrame()
df1[0] = pd.arrays.IntervalArray.from_tuples([(1.0, 2.0)])
df2 = pd.DataFrame()
df2[0] = pd.arrays.IntervalArray.from_tuples([(1.0000000000001, 2.0)])
# Using very large tolerances do not propagate to the interval dtype, since this assert fails:
pd.testing.assert_frame_equal(df1, df2, check_exact=False, rtol=10., atol=10.)
Issue Description
It seems that pd.testing.assert_frame_equal
does not propagate chosen tolerances for interval
dtype comparison.
Expected Behavior
df1 = pd.DataFrame()
df1[0] = pd.arrays.IntervalArray.from_tuples([(1.0, 2.0)])
df2 = pd.DataFrame()
df2[0] = pd.arrays.IntervalArray.from_tuples([(1.0000000000001, 2.0)])
This test should pass...
pd.testing.assert_frame_equal(df1, df2, check_exact=False, rtol=10., atol=10.)
Installed Versions
pandas : 1.2.4
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0.post20210125
Cython : 0.29.23
pytest : 6.2.3
hypothesis : None
sphinx : 4.0.1
blosc : None
feather : None
xlsxwriter : 1.3.8
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.9.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.4.7
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1