fix more tests

bigframes/dtypes.py

@@ -247,6 +247,7 @@ class SimpleDtypeInfo:
     "decimal128(38, 9)[pyarrow]",
     "decimal256(76, 38)[pyarrow]",
     "binary[pyarrow]",
+    "duration[us][pyarrow]",
 ]
 
 DTYPE_STRINGS = typing.get_args(DtypeString)
@@ -421,6 +422,8 @@ def is_bool_coercable(type_: ExpressionType) -> bool:
 # special case - both "Int64" and "int64[pyarrow]" are accepted
 BIGFRAMES_STRING_TO_BIGFRAMES["int64[pyarrow]"] = INT_DTYPE
 
+BIGFRAMES_STRING_TO_BIGFRAMES["duration[us][pyarrow]"] = TIMEDELTA_DTYPE
+
 # For the purposes of dataframe.memory_usage
 DTYPE_BYTE_SIZES = {
     type_info.dtype: type_info.logical_bytes for type_info in SIMPLE_TYPES

tests/system/small/pandas/core/methods/test_describe.py

@@ -21,7 +21,13 @@ def test_df_describe_non_temporal(scalars_dfs):
     pytest.importorskip("pandas", minversion="2.0.0")
     scalars_df, scalars_pandas_df = scalars_dfs
     # excluding temporal columns here because BigFrames cannot perform percentiles operations on them
-    unsupported_columns = ["datetime_col", "timestamp_col", "time_col", "date_col"]
+    unsupported_columns = [
+        "datetime_col",
+        "timestamp_col",
+        "time_col",
+        "date_col",
+        "duration_col",
+    ]
     bf_result = scalars_df.drop(columns=unsupported_columns).describe().to_pandas()
 
     modified_pd_df = scalars_pandas_df.drop(columns=unsupported_columns)

tests/system/small/test_dataframe_io.py

@@ -999,7 +999,7 @@ def test_to_sql_query_unnamed_index_included(
     scalars_df_default_index: bpd.DataFrame,
     scalars_pandas_df_default_index: pd.DataFrame,
 ):
-    bf_df = scalars_df_default_index.reset_index(drop=True)
+    bf_df = scalars_df_default_index.reset_index(drop=True).drop(columns="duration_col")
     sql, idx_ids, idx_labels = bf_df._to_sql_query(include_index=True)
     assert len(idx_labels) == 1
     assert len(idx_ids) == 1
@@ -1017,7 +1017,9 @@ def test_to_sql_query_named_index_included(
     scalars_df_default_index: bpd.DataFrame,
     scalars_pandas_df_default_index: pd.DataFrame,
 ):
-    bf_df = scalars_df_default_index.set_index("rowindex_2", drop=True)
+    bf_df = scalars_df_default_index.set_index("rowindex_2", drop=True).drop(
+        columns="duration_col"
+    )
     sql, idx_ids, idx_labels = bf_df._to_sql_query(include_index=True)
     assert len(idx_labels) == 1
     assert len(idx_ids) == 1
@@ -1034,7 +1036,7 @@ def test_to_sql_query_unnamed_index_excluded(
     scalars_df_default_index: bpd.DataFrame,
     scalars_pandas_df_default_index: pd.DataFrame,
 ):
-    bf_df = scalars_df_default_index.reset_index(drop=True)
+    bf_df = scalars_df_default_index.reset_index(drop=True).drop(columns="duration_col")
     sql, idx_ids, idx_labels = bf_df._to_sql_query(include_index=False)
     assert len(idx_labels) == 0
     assert len(idx_ids) == 0
@@ -1051,7 +1053,9 @@ def test_to_sql_query_named_index_excluded(
     scalars_df_default_index: bpd.DataFrame,
     scalars_pandas_df_default_index: pd.DataFrame,
 ):
-    bf_df = scalars_df_default_index.set_index("rowindex_2", drop=True)
+    bf_df = scalars_df_default_index.set_index("rowindex_2", drop=True).drop(
+        columns="duration_col"
+    )
     sql, idx_ids, idx_labels = bf_df._to_sql_query(include_index=False)
     assert len(idx_labels) == 0
     assert len(idx_ids) == 0

tests/system/small/test_session.py

@@ -1820,6 +1820,7 @@ def test_read_parquet_gcs(
     df_out = df_out.assign(
         datetime_col=df_out["datetime_col"].astype("timestamp[us][pyarrow]"),
         timestamp_col=df_out["timestamp_col"].astype("timestamp[us, tz=UTC][pyarrow]"),
+        duration_col=df_out["duration_col"].astype("duration[us][pyarrow]"),
     )
 
     # Make sure we actually have at least some values before comparing.
@@ -1868,7 +1869,8 @@ def test_read_parquet_gcs_compressed(
     # DATETIME gets loaded as TIMESTAMP in parquet. See:
     # https://cloud.google.com/bigquery/docs/exporting-data#parquet_export_details
     df_out = df_out.assign(
-        datetime_col=df_out["datetime_col"].astype("timestamp[us][pyarrow]")
+        datetime_col=df_out["datetime_col"].astype("timestamp[us][pyarrow]"),
+        duration_col=df_out["duration_col"].astype("duration[us][pyarrow]"),
     )
 
     # Make sure we actually have at least some values before comparing.
@@ -1926,9 +1928,23 @@ def test_read_json_gcs_bq_engine(session, scalars_dfs, gcs_folder):
 
     # The auto detects of BigQuery load job have restrictions to detect the bytes,
     # datetime, numeric and geometry types, so they're skipped here.
-    df = df.drop(columns=["bytes_col", "datetime_col", "numeric_col", "geography_col"])
+    df = df.drop(
+        columns=[
+            "bytes_col",
+            "datetime_col",
+            "numeric_col",
+            "geography_col",
+            "duration_col",
+        ]
+    )
     scalars_df = scalars_df.drop(
-        columns=["bytes_col", "datetime_col", "numeric_col", "geography_col"]
+        columns=[
+            "bytes_col",
+            "datetime_col",
+            "numeric_col",
+            "geography_col",
+            "duration_col",
+        ]
     )
     assert df.shape[0] == scalars_df.shape[0]
     pd.testing.assert_series_equal(
@@ -1962,11 +1978,15 @@ def test_read_json_gcs_default_engine(session, scalars_dfs, gcs_folder):
     # The auto detects of BigQuery load job have restrictions to detect the bytes,
     # numeric and geometry types, so they're skipped here.
     df = df.drop(columns=["bytes_col", "numeric_col", "geography_col"])
-    scalars_df = scalars_df.drop(columns=["bytes_col", "numeric_col", "geography_col"])
+    scalars_df = scalars_df.drop(
+        columns=["bytes_col", "numeric_col", "geography_col", "duration_col"]
+    )
 
     # pandas read_json does not respect the dtype overrides for these columns
     df = df.drop(columns=["date_col", "datetime_col", "time_col"])
-    scalars_df = scalars_df.drop(columns=["date_col", "datetime_col", "time_col"])
+    scalars_df = scalars_df.drop(
+        columns=["date_col", "datetime_col", "time_col", "duration_col"]
+    )
 
     assert df.shape[0] == scalars_df.shape[0]
     pd.testing.assert_series_equal(df.dtypes, scalars_df.dtypes)