BUG: warning from typing library if you subclass a DataFrame using Generic with python 3.11

[Dr-Irv]

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
from typing import TypeVar, Generic

T = TypeVar("T")

class MyDataFrame(pd.DataFrame, Generic[T]):
    ...

def func() -> MyDataFrame[int]:
    return MyDataFrame[int]({"foo": [1, 2, 3]})

x = func()
print(x)

Issue Description

With pandas 1.5.1 and python 3.10, the code above gives no warning at runtime.

With pandas 1.5.1 and python 3.11, you get a warning from the typing library at runtime:

C:\Anaconda3\envs\pandasstubs311\Lib\typing.py:1253: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
  result.__orig_class__ = self

We have testing code in pandas-stubs that uses this, because it was reported by the people developing the pandera library.

Expected Behavior

No warning.

We're doing something in pandas that doesn't allow a Generic subclass to be created with python 3.11.

Installed Versions

INSTALLED VERSIONS

commit : 91111fd
python : 3.11.0.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19043
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252

pandas : 1.5.1
numpy : 1.23.4
pytz : 2022.6
dateutil : 2.8.2
setuptools : 65.5.1
pip : 22.3.1
Cython : None
pytest : 7.2.0
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.6.2
numba : None
numexpr : None
odfpy : None
openpyxl : 3.0.10
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : 1.0.10
s3fs : None
scipy : 1.9.3
snappy : None
sqlalchemy : 1.4.43
tables : None
tabulate : 0.9.0
xarray : 2022.11.0
xlrd : 2.0.1
xlwt : None
zstandard : None
tzdata : None

[Dr-Irv]

Seems like is_list_like() is not handling a generic alias correctly:

import pandas as pd

from pandas._libs import lib
import sys

is_list_like = lib.is_list_like


from typing import TypeVar, Generic

T = TypeVar("T")


class Base:
    def __init__(self, x: int):
        self._x = x


class Gen(Base, Generic[T]):
    ...


fooc = Gen[float]
print(type(fooc))
print(sys.version)
print("pandas version ", pd.__version__)
print(is_list_like(fooc))

With python 3.10, output is

<class 'typing._GenericAlias'>
3.10.4 | packaged by conda-forge | (main, Mar 30 2022, 08:38:02) [MSC v.1916 64 bit (AMD64)]
pandas version  1.5.1
False

With python 3.11, output is

<class 'typing._GenericAlias'>
3.11.0 | packaged by conda-forge | (main, Oct 25 2022, 06:12:32) [MSC v.1929 64 bit (AMD64)]
pandas version  1.5.1
True

[sagitta42]

Hi everyone, I'm getting a warning UserWarning: Pandas doesn't allow columns to be created via a new attribute name when assigning a dictionary as an attribute to a class inheriting from DataFrame.
I have not found this issue being reported.

Version checks: python version 3.10.7, pandas 1.5.3

Example:

import pandas as pd

class MyClass(pd.DataFrame):
    def __init__(self, dct_frame, dct_settings):
        pd.DataFrame.__init__(self, dct_frame)
        self.settings = dct_settings


mc = MyClass({'a': [1,2], 'b': [3,4]}, {'plot_style': 'param_per_chanenl'})

Warning message:

UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
  self.settings = dct_settings

Is there a way to combat this? Or should I find another way of structuring my class to avoid this?
I'm an amateur and don't know how to structure a comment in an issue thread properly, but didn't know where else to ask, as I'm not finding discussions on this, so I want to apologize for bothering.

[Dr-Irv]

Because pandas allows referring to columns as an attribute, you can't add attributes to a subclass. You can store your additional attributes in Dataframe.attrs

The following should work:

import pandas as pd

class MyClass(pd.DataFrame):
    def __init__(self, dct_frame, dct_settings):
        pd.DataFrame.__init__(self, dct_frame)
        self.attrs["settings"] = dct_settings

[sagitta42]

Thanks for the lightning fast response! It did not flash the warning when creating string attributes in the subclass, but did for a dict attribute. I will follow your suggestion.