🦉 Updates from OwlBot post-processor

See https://github.com/googleapis/repo-automation-bots/blob/main/packages/owl-bot/README.md

Author: gcf-owl-bot[bot]

bigframes/pandas/__init__.py

@@ -39,6 +39,7 @@
 from bigframes.pandas.core.api import to_timedelta
 from bigframes.pandas.io.api import (
     from_glob_path,
+    read_arrow,
     read_csv,
     read_gbq,
     read_gbq_function,
@@ -50,7 +51,6 @@
     read_pandas,
     read_parquet,
     read_pickle,
-    read_arrow,
 )
 import bigframes.series
 import bigframes.session

bigframes/session/__init__.py

@@ -1031,7 +1031,10 @@ def _read_pandas(
         if is_inline:
             if final_engine == "bigquery_inline":
                 # Ensure inline data isn't too large if specified directly
-                if pandas_dataframe.memory_usage(deep=True).sum() > bigframes.constants.MAX_INLINE_BYTES:
+                if (
+                    pandas_dataframe.memory_usage(deep=True).sum()
+                    > bigframes.constants.MAX_INLINE_BYTES
+                ):
                     raise ValueError(
                         f"DataFrame size ({pandas_dataframe.memory_usage(deep=True).sum()} bytes) "
                         f"exceeds the maximum allowed for inline data "
@@ -1058,11 +1061,10 @@ def _read_pandas_inline(
         local_block = blocks.Block.from_local(pandas_dataframe, self)
         return dataframe.DataFrame(local_block)
 
-    def _read_arrow_inline(
-        self, arrow_table: pyarrow.Table
-    ) -> dataframe.DataFrame:
+    def _read_arrow_inline(self, arrow_table: pyarrow.Table) -> dataframe.DataFrame:
         """Creates a BigFrames DataFrame from an in-memory pyarrow Table by inlining data."""
         import bigframes.dataframe as dataframe
+
         # Assuming Block.from_local can handle pandas DataFrame.
         # If Block.from_local is enhanced to take pyarrow.Table directly,
         # this conversion can be removed.

tests/system/small/test_read_arrow.py

@@ -34,9 +34,7 @@ def test_read_arrow_basic(session):
         pa.array([0.1, 0.2, 0.3], type=pa.float64()),
         pa.array(["foo", "bar", "baz"], type=pa.string()),
     ]
-    arrow_table = pa.Table.from_arrays(
-        data, names=["ints", "floats", "strings"]
-    )
+    arrow_table = pa.Table.from_arrays(data, names=["ints", "floats", "strings"])
 
     bf_df = bpd.read_arrow(arrow_table)
 
@@ -142,13 +140,17 @@ def test_read_arrow_all_types(session):
     bf_pd_df = bf_df.to_pandas()
 
     for col in ["int_col", "float_col"]:
-            bf_pd_df[col] = bf_pd_df[col].astype(pd_expected[col].dtype)
+        bf_pd_df[col] = bf_pd_df[col].astype(pd_expected[col].dtype)
 
     bf_pd_df["str_col"] = bf_pd_df["str_col"].astype(pandas.ArrowDtype(pa.string()))
     bf_pd_df["ts_col"] = pandas.to_datetime(bf_pd_df["ts_col"], utc=True)
-    bf_pd_df["date_col"] = bf_pd_df["date_col"].apply(lambda x: x.date() if hasattr(x, 'date') and x is not pandas.NaT else x)
+    bf_pd_df["date_col"] = bf_pd_df["date_col"].apply(
+        lambda x: x.date() if hasattr(x, "date") and x is not pandas.NaT else x
+    )
     bf_pd_df["bool_col"] = bf_pd_df["bool_col"].astype(pandas.ArrowDtype(pa.bool_()))
-    pd_expected["bool_col"] = pd_expected["bool_col"].astype(pandas.ArrowDtype(pa.bool_()))
+    pd_expected["bool_col"] = pd_expected["bool_col"].astype(
+        pandas.ArrowDtype(pa.bool_())
+    )
 
     pandas.testing.assert_frame_equal(
         bf_pd_df, pd_expected, check_dtype=False, rtol=1e-5
@@ -193,10 +195,18 @@ def test_read_arrow_list_types(session):
     bf_pd_df = bf_df.to_pandas()
 
     # Explicitly cast to ArrowDtype for comparison as pandas might default to object
-    pd_expected["list_int_col"] = pd_expected["list_int_col"].astype(pandas.ArrowDtype(pa.list_(pa.int64())))
-    pd_expected["list_str_col"] = pd_expected["list_str_col"].astype(pandas.ArrowDtype(pa.list_(pa.string())))
-    bf_pd_df["list_int_col"] = bf_pd_df["list_int_col"].astype(pandas.ArrowDtype(pa.list_(pa.int64())))
-    bf_pd_df["list_str_col"] = bf_pd_df["list_str_col"].astype(pandas.ArrowDtype(pa.list_(pa.string())))
+    pd_expected["list_int_col"] = pd_expected["list_int_col"].astype(
+        pandas.ArrowDtype(pa.list_(pa.int64()))
+    )
+    pd_expected["list_str_col"] = pd_expected["list_str_col"].astype(
+        pandas.ArrowDtype(pa.list_(pa.string()))
+    )
+    bf_pd_df["list_int_col"] = bf_pd_df["list_int_col"].astype(
+        pandas.ArrowDtype(pa.list_(pa.int64()))
+    )
+    bf_pd_df["list_str_col"] = bf_pd_df["list_str_col"].astype(
+        pandas.ArrowDtype(pa.list_(pa.string()))
+    )
 
     pandas.testing.assert_frame_equal(bf_pd_df, pd_expected, check_dtype=True)
 
@@ -214,7 +224,9 @@ def test_read_arrow_engine_streaming(session):
     assert str(bf_df.dtypes["event"]) == "string[pyarrow]"
     pd_expected = arrow_table.to_pandas()
     bf_pd_df = bf_df.to_pandas()
-    pandas.testing.assert_frame_equal(bf_pd_df.astype(pd_expected.dtypes), pd_expected, check_dtype=False)
+    pandas.testing.assert_frame_equal(
+        bf_pd_df.astype(pd_expected.dtypes), pd_expected, check_dtype=False
+    )
 
 
 def test_read_arrow_engine_write(session):
@@ -230,7 +242,9 @@ def test_read_arrow_engine_write(session):
     assert str(bf_df.dtypes["status"]) == "string[pyarrow]"
     pd_expected = arrow_table.to_pandas()
     bf_pd_df = bf_df.to_pandas()
-    pandas.testing.assert_frame_equal(bf_pd_df.astype(pd_expected.dtypes), pd_expected, check_dtype=False)
+    pandas.testing.assert_frame_equal(
+        bf_pd_df.astype(pd_expected.dtypes), pd_expected, check_dtype=False
+    )
 
 
 def test_read_arrow_no_columns_empty_rows(session):
@@ -241,7 +255,14 @@ def test_read_arrow_no_columns_empty_rows(session):
 
 
 def test_read_arrow_special_column_names(session):
-    col_names = ["col with space", "col/slash", "col.dot", "col:colon", "col(paren)", "col[bracket]"]
+    col_names = [
+        "col with space",
+        "col/slash",
+        "col.dot",
+        "col:colon",
+        "col(paren)",
+        "col[bracket]",
+    ]
     # BigQuery normalizes column names by replacing special characters with underscores.
     # Exception: dots are not allowed and usually cause errors or are handled by specific client libraries.
     # BigFrames aims to map to valid BigQuery column names.
@@ -267,10 +288,10 @@ def test_read_arrow_special_column_names(session):
     expected_bq_names = [
         "col_with_space",
         "col_slash",
-        "col_dot", # BQ might error on dots or replace them. Let's assume replacement for now.
+        "col_dot",  # BQ might error on dots or replace them. Let's assume replacement for now.
         "col_colon",
         "col_paren_",
-        "col_bracket_"
+        "col_bracket_",
     ]
     # Update: Based on typical BigQuery behavior, dots are not allowed.
     # However, BigFrames might handle this by replacing dots with underscores before sending to BQ,
@@ -292,8 +313,8 @@ def test_read_arrow_special_column_names(session):
     # And arrow_table.to_pandas() will use the original names.
     # We then rename bf_pd_df columns to match pd_expected for data comparison.
 
-    pd_expected = arrow_table.to_pandas() # Has original names
-    bf_pd_df = bf_df.to_pandas() # Has BQ/BF names
+    pd_expected = arrow_table.to_pandas()  # Has original names
+    bf_pd_df = bf_df.to_pandas()  # Has BQ/BF names
 
     assert len(bf_pd_df.columns) == len(pd_expected.columns)