shunping opened a new pull request, #38428:
URL: https://github.com/apache/beam/pull/38428
```
/Users/runner/work/beam/beam/sdks/python/apache_beam/dataframe/schemas.py:100:
FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in
certain cases, but when using Copy-on-Write (which will become the default
behaviour in pandas 3.0) this will never work to update the original DataFrame
or Series, because the intermediate object on which we are setting values will
behave as a copy.
A typical example is when you are setting values in a column of a
DataFrame, like:
df["col"][row_indexer] = value
Use `df.loc[row_indexer, "col"] = values` instead, to perform the
assignment in a single step and ensure this keeps updating the original `df`.
See the caveats in the documentation:
https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
proxy[name] = proxy[name].astype(dtype)
```
```
apache_beam/typehints/pandas_type_compatibility_test.py: 62 warnings
/Users/runner/work/beam/beam/sdks/python/target/.tox/py314-macos/lib/python3.14/site-packages/apache_beam/typehints/pandas_type_compatibility.py:227:
FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in
certain cases, but when using Copy-on-Write (which will become the default
behaviour in pandas 3.0) this will never work to update the original DataFrame
or Series, because the intermediate object on which we are setting values will
behave as a copy.
A typical example is when you are setting values in a column of a
DataFrame, like:
df["col"][row_indexer] = value
Use `df.loc[row_indexer, "col"] = values` instead, to perform the
assignment in a single step and ensure this keeps updating the original `df`.
See the caveats in the documentation:
https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
batch[column] = batch[column].astype(dtype_from_typehint(typehint))
```
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