Description
Pandas version checks
-
I have checked that this issue has not already been reported.
-
I have confirmed this bug exists on the latest version of pandas.
-
I have confirmed this bug exists on the main branch of pandas.
Reproducible Example
import pandas as pd
df = pd.DataFrame({'a': range(3), 'b': range(3), 'c': range(3)}).rename(columns={'b': 'a'})
print(df.query('c == 1'))Issue Description
Since pandas 2.2.1 this throws an unexpected error:
TypeError: dtype 'a int64 a int64 dtype: object' not understood
This is because DataFrame.query() calls DataFrame.eval() which in turn calls DataFrame._get_cleaned_column_resolvers().
The dict comprehension in DataFrame._get_cleaned_column_resolvers() was changed in version 2.2.1.
version 2.2.0
return {
clean_column_name(k): Series(
v, copy=False, index=self.index, name=k
).__finalize__(self)
for k, v in zip(self.columns, self._iter_column_arrays())
if not isinstance(k, int)
}
version 2.2.1
return {
clean_column_name(k): Series(
v, copy=False, index=self.index, name=k, dtype=self.dtypes[k]
).__finalize__(self)
for k, v in zip(self.columns, self._iter_column_arrays())
if not isinstance(k, int)
}
since the dtypes are now checked when the Series are created, this introduces the error described above, since for a duplicate
column name self.dtypes[k] returns a Series instead of single value.
Expected Behavior
- I would expect either the behavior prior to v2.2.1 where the above example would return:
>>> df.query('c == 1')
a a c
1 1 1 1
moreover, calling query() on column 'a' also works:
>>> df.query('a == 1')
a a c
1 1 1 1
or
2) If above behavior is unwanted, I would except better error handling, smt like:
>>> df.query('c == 1')
DuplicateColumnError: DataFrame.query() is not supported for DataFrames with duplicate column names
Installed Versions
INSTALLED VERSIONS
commit : bdc79c1
python : 3.11.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-165-generic
Version : #182-Ubuntu SMP Mon Oct 2 19:43:28 UTC 2023
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.2.1
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.8.2
setuptools : 69.1.1
pip : 23.0
Cython : None
pytest : 8.2.0
hypothesis : 6.100.4
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 5.2.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.3
IPython : None
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : 3.1.2
pandas_gbq : 0.19.2
pyarrow : 15.0.0
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 2.0.27
tables : None
tabulate : 0.9.0
xarray : None
xlrd : None
zstandard : None
tzdata : 2024.1
qtpy : None
pyqt5 : None