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fix: fix read_gbq_function issue in dataframe apply method #1174

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fix: fix read_gbq_function issue in dataframe apply method #1174

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f9b885e
fix: fix read_gbq_function issue in dataframe apply method
jialuoo Nov 25, 2024
e43636d
add a test
jialuoo Dec 2, 2024
1fe9662
Merge branch 'main' into fix-df-apply
jialuoo Dec 4, 2024
baa3bd2
Merge branch 'main' into fix-df-apply
jialuoo Dec 5, 2024
01e132f
fix the test
jialuoo Dec 5, 2024
80978e5
resolve the comments
jialuoo Dec 13, 2024
f61ef7e
🦉 Updates from OwlBot post-processor
gcf-owl-bot[bot] Dec 13, 2024
3e4c846
quick fix
jialuoo Dec 13, 2024
aedff6c
Merge branch 'main' into fix-df-apply
jialuoo Dec 13, 2024
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11 changes: 10 additions & 1 deletion bigframes/dataframe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3959,7 +3959,16 @@ def apply(self, func, *, axis=0, args: typing.Tuple = (), **kwargs):
return result_series

# Per-column apply
results = {name: func(col, *args, **kwargs) for name, col in self.items()}
if hasattr(func, "bigframes_remote_function"):
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@TrevorBergeron could you please review if this element-wise proxy for series-wise application doesn't have any blind sight?

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Isn't dataframe.apply for column or series-wise application? Why do we need to support applying as an element-wise operator? isn't this what dataframe.map is for?

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For scalar ops the end result is the same. I think we don't lose anything by being lax for user convenience. Note that this API has other non-pandas-like extended behavior

python-bigquery-dataframes/bigframes/dataframe.py

Line 3933 in e0a8288

# This is a special case where we are providing not-pandas-like

if args or kwargs:
warnings.warn(
"The args and kwargs are not supported in the remote function.",
category=bigframes.exceptions.ArgsAndKwargsNotSupportedWarning,
)
results = {name: col.apply(func) for name, col in self.items()}
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else:
results = {name: func(col, *args, **kwargs) for name, col in self.items()}
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if all(
[
isinstance(val, bigframes.series.Series) or utils.is_list_like(val)
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4 changes: 4 additions & 0 deletions bigframes/exceptions.py
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Expand Up @@ -77,3 +77,7 @@ class ApiDeprecationWarning(FutureWarning):

class BadIndexerKeyWarning(Warning):
"""The indexer key is not used correctly."""


class ArgsAndKwargsNotSupportedWarning(Warning):
"""The args and kwargs are not supported."""
24 changes: 24 additions & 0 deletions tests/system/small/test_remote_function.py
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Expand Up @@ -839,6 +839,30 @@ def test_read_gbq_function_enforces_explicit_types(
)


@pytest.mark.flaky(retries=2, delay=120)
def test_df_apply(session, scalars_dfs):
scalars_df, scalars_pandas_df = scalars_dfs
bdf = bigframes.pandas.DataFrame(
{
"Column1": scalars_df["string_col"],
"Column2": scalars_df["string_col"],
}
)

func_ref = session.read_gbq_function("bqutil.fn.cw_lower_case_ascii_only")
bf_result = bdf.apply(func_ref).to_pandas()
# The str.lower() method has the same functionality as func_ref.
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This is true for ASCII only, we should say that in the comment

pd_result = pd.DataFrame(
{
"Column1": scalars_pandas_df["string_col"].str.lower(),
"Column2": scalars_pandas_df["string_col"].str.lower(),
}
)

pd.testing.assert_series_equal(pd_result["Column1"], bf_result["Column1"])
pd.testing.assert_series_equal(pd_result["Column2"], bf_result["Column2"])


@pytest.mark.flaky(retries=2, delay=120)
def test_read_gbq_function_multiple_inputs_not_a_row_processor(session):
with pytest.raises(ValueError) as context:
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