Merge branch 'main' into refactor-isnull-op

Author: tswast

bigframes/core/compile/polars/compiler.py

@@ -638,7 +638,7 @@ def _aggregate(
     def compile_explode(self, node: nodes.ExplodeNode):
         assert node.offsets_col is None
         df = self.compile_node(node.child)
-        cols = [pl.col(col.id.sql) for col in node.column_ids]
+        cols = [col.id.sql for col in node.column_ids]
         return df.explode(cols)
 
     @compile_node.register

tests/system/small/ml/test_llm.py

@@ -251,6 +251,7 @@ def __eq__(self, other):
         return self.equals(other)
 
 
+@pytest.mark.skip("b/436340035 test failed")
 @pytest.mark.parametrize(
     (
         "model_class",
@@ -393,6 +394,7 @@ def test_text_generator_retry_success(
         )
 
 
+@pytest.mark.skip("b/436340035 test failed")
 @pytest.mark.parametrize(
     (
         "model_class",
@@ -509,6 +511,7 @@ def test_text_generator_retry_no_progress(session, model_class, options, bq_conn
         )
 
 
+@pytest.mark.skip("b/436340035 test failed")
 def test_text_embedding_generator_retry_success(session, bq_connection):
     # Requests.
     df0 = EqCmpAllDataFrame(
@@ -790,13 +793,14 @@ def test_gemini_preview_model_warnings(model_name):
         llm.GeminiTextGenerator(model_name=model_name)
 
 
+# b/436340035 temp disable the test to unblock presumbit
 @pytest.mark.parametrize(
     "model_class",
     [
         llm.TextEmbeddingGenerator,
         llm.MultimodalEmbeddingGenerator,
         llm.GeminiTextGenerator,
-        llm.Claude3TextGenerator,
+        # llm.Claude3TextGenerator,
     ],
 )
 def test_text_embedding_generator_no_default_model_warning(model_class):

tests/unit/test_dataframe_polars.py

@@ -1198,6 +1198,7 @@ def test_df_fillna(scalars_dfs, col, fill_value):
     pd.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False)
 
 
+@pytest.mark.skip("b/436316698 unit test failed for python 3.12")
 def test_df_ffill(scalars_dfs):
     scalars_df, scalars_pandas_df = scalars_dfs
     bf_result = scalars_df[["int64_col", "float64_col"]].ffill(limit=1).to_pandas()