Fix dataframe warnings on ChainedAssignmentError

[shunping]

• Fixed a flaky approximate quantile test.

  File "/runner/_work/beam/beam/sdks/python/test-suites/tox/py310/build/srcs/sdks/python/apache_beam/testing/util.py", line 202, in _equal
    raise BeamAssertException(msg)
  apache_beam.testing.util.BeamAssertException: Failed assert: [[(99.9, 499), (72.5, 225), (50.0, 0)]] == [[(99.9, 499), (22.5, 275), (50.0, 0)]], unexpected elements [[(99.9, 499), (22.5, 275), (50.0, 0)]], missing elements [[(99.9, 499), (72.5, 225), (50.0, 0)]] [while running 'checkGloballyWithKeyAndReversed/Match']
  self = <apache_beam.transforms.stats_test.ApproximateQuantilesTest testMethod=test_batched_quantiles>
  
       def test_batched_quantiles(self):
   >     with TestPipeline() as p:
  
  apache_beam/transforms/stats_test.py:482: 
  

• Fixed dataframe warnings on ChainedAssignmentError

  /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)
  

  /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))