googleapis · gcf-merge-on-green · Oct 27, 2023 · Oct 13, 2023 · Oct 13, 2023 · Oct 13, 2023
@@ -21,6 +21,7 @@
 import bigframes.core as core
 import bigframes.core.blocks as blocks
 import bigframes.core.ordering as ordering
+import bigframes.core.window_spec as windows
 import bigframes.operations as ops
 import bigframes.operations.aggregations as agg_ops
 
@@ -68,21 +69,21 @@ def indicate_duplicates(
     if keep == "first":
         # Count how many copies occur up to current copy of value
         # Discard this value if there are copies BEFORE
-        window_spec = core.WindowSpec(
+        window_spec = windows.WindowSpec(
             grouping_keys=tuple(columns),
             following=0,
         )
     elif keep == "last":
         # Count how many copies occur up to current copy of values
         # Discard this value if there are copies AFTER
-        window_spec = core.WindowSpec(
+        window_spec = windows.WindowSpec(
             grouping_keys=tuple(columns),
             preceding=0,
         )
     else:  # keep == False
         # Count how many copies of the value occur in entire series.
         # Discard this value if there are copies ANYWHERE
-        window_spec = core.WindowSpec(grouping_keys=tuple(columns))
+        window_spec = windows.WindowSpec(grouping_keys=tuple(columns))
     block, dummy = block.create_constant(1)
     block, val_count_col_id = block.apply_window_op(
         dummy,
@@ -131,7 +132,7 @@ def value_counts(
     )
     count_id = agg_ids[0]
     if normalize:
-        unbound_window = core.WindowSpec()
+        unbound_window = windows.WindowSpec()
         block, total_count_id = block.apply_window_op(
             count_id, agg_ops.sum_op, unbound_window
         )
@@ -153,7 +154,7 @@ def value_counts(
 
 def pct_change(block: blocks.Block, periods: int = 1) -> blocks.Block:
     column_labels = block.column_labels
-    window_spec = core.WindowSpec(
+    window_spec = windows.WindowSpec(
         preceding=periods if periods > 0 else None,
         following=-periods if periods < 0 else None,
     )
@@ -195,7 +196,7 @@ def rank(
             ops.isnull_op,
         )
         nullity_col_ids.append(nullity_col_id)
-        window = core.WindowSpec(
+        window = windows.WindowSpec(
             # BigQuery has syntax to reorder nulls with "NULLS FIRST/LAST", but that is unavailable through ibis presently, so must order on a separate nullity expression first.
             ordering=(
                 ordering.OrderingColumnReference(
@@ -229,7 +230,7 @@ def rank(
             block, result_id = block.apply_window_op(
                 rownum_col_ids[i],
                 agg_op,
-                window_spec=core.WindowSpec(grouping_keys=[columns[i]]),
+                window_spec=windows.WindowSpec(grouping_keys=(columns[i],)),
                 skip_reproject_unsafe=(i < (len(columns) - 1)),
             )
             post_agg_rownum_col_ids.append(result_id)
@@ -311,7 +312,7 @@ def nsmallest(
         block, counter = block.apply_window_op(
             column_ids[0],
             agg_ops.rank_op,
-            window_spec=core.WindowSpec(ordering=order_refs),
+            window_spec=windows.WindowSpec(ordering=tuple(order_refs)),
         )
         block, condition = block.apply_unary_op(
             counter, ops.partial_right(ops.le_op, n)
@@ -343,7 +344,7 @@ def nlargest(
         block, counter = block.apply_window_op(
             column_ids[0],
             agg_ops.rank_op,
-            window_spec=core.WindowSpec(ordering=order_refs),
+            window_spec=windows.WindowSpec(ordering=tuple(order_refs)),
         )
         block, condition = block.apply_unary_op(
             counter, ops.partial_right(ops.le_op, n)
@@ -440,14 +441,14 @@ def _mean_delta_to_power(
     grouping_column_ids: typing.Sequence[str],
 ) -> typing.Tuple[blocks.Block, typing.Sequence[str]]:
     """Calculate (x-mean(x))^n. Useful for calculating moment statistics such as skew and kurtosis."""
-    window = core.WindowSpec(grouping_keys=grouping_column_ids)
+    window = windows.WindowSpec(grouping_keys=tuple(grouping_column_ids))
     block, mean_ids = block.multi_apply_window_op(column_ids, agg_ops.mean_op, window)
     delta_ids = []
     cube_op = ops.partial_right(ops.pow_op, n_power)
     for val_id, mean_val_id in zip(column_ids, mean_ids):
         block, delta_id = block.apply_binary_op(val_id, mean_val_id, ops.sub_op)
         block, delta_power_id = block.apply_unary_op(delta_id, cube_op)
-        block = block.drop_columns(delta_id)
+        block = block.drop_columns([delta_id])
         delta_ids.append(delta_power_id)
     return block, delta_ids
 
@@ -645,7 +646,7 @@ def _idx_extrema(
                 for idx_col in original_block.index_columns
             ],
         ]
-        window_spec = core.WindowSpec(ordering=order_refs)
+        window_spec = windows.WindowSpec(ordering=tuple(order_refs))
         idx_col = original_block.index_columns[0]
         block, result_col = block.apply_window_op(
             idx_col, agg_ops.first_op, window_spec

@@ -35,7 +35,6 @@
 import bigframes.core as core
 import bigframes.core.guid as guid
 import bigframes.core.indexes as indexes
-import bigframes.core.joins as joins
 import bigframes.core.joins.name_resolution as join_names
 import bigframes.core.ordering as ordering
 import bigframes.core.utils
@@ -378,7 +377,7 @@ def _to_dataframe(self, result) -> pd.DataFrame:
         """Convert BigQuery data to pandas DataFrame with specific dtypes."""
         dtypes = dict(zip(self.index_columns, self.index_dtypes))
         dtypes.update(zip(self.value_columns, self.dtypes))
-        return self._expr._session._rows_to_dataframe(result, dtypes)
+        return self._expr.session._rows_to_dataframe(result, dtypes)
 
     def to_pandas(
         self,
@@ -422,7 +421,7 @@ def to_pandas_batches(self):
         dtypes.update(zip(self.value_columns, self.dtypes))
         results_iterator, _ = self._expr.start_query()
         for arrow_table in results_iterator.to_arrow_iterable(
-            bqstorage_client=self._expr._session.bqstoragereadclient
+            bqstorage_client=self._expr.session.bqstoragereadclient
         ):
             df = bigframes.session._io.pandas.arrow_to_pandas(arrow_table, dtypes)
             self._copy_index_to_pandas(df)
@@ -454,7 +453,9 @@ def _compute_and_count(
 
         results_iterator, query_job = expr.start_query(max_results=max_results)
 
-        table_size = expr._get_table_size(query_job.destination) / _BYTES_TO_MEGABYTES
+        table_size = (
+            expr.session._get_table_size(query_job.destination) / _BYTES_TO_MEGABYTES
+        )
         fraction = (
             max_download_size / table_size
             if (max_download_size is not None) and (table_size != 0)
@@ -819,7 +820,9 @@ def aggregate_all_and_stack(
         axis: int | str = 0,
         value_col_id: str = "values",
         dropna: bool = True,
-        dtype=pd.Float64Dtype(),
+        dtype: typing.Union[
+            bigframes.dtypes.Dtype, typing.Tuple[bigframes.dtypes.Dtype, ...]
+        ] = pd.Float64Dtype(),
     ) -> Block:
         axis_n = utils.get_axis_number(axis)
         if axis_n == 0:
@@ -829,7 +832,7 @@ def aggregate_all_and_stack(
             result_expr = self.expr.aggregate(aggregations, dropna=dropna).unpivot(
                 row_labels=self.column_labels.to_list(),
                 index_col_ids=["index"],
-                unpivot_columns=[(value_col_id, self.value_columns)],
+                unpivot_columns=tuple([(value_col_id, tuple(self.value_columns))]),
                 dtype=dtype,
             )
             return Block(result_expr, index_columns=["index"], column_labels=[None])
@@ -841,7 +844,7 @@ def aggregate_all_and_stack(
             stacked_expr = expr_with_offsets.unpivot(
                 row_labels=self.column_labels.to_list(),
                 index_col_ids=[guid.generate_guid()],
-                unpivot_columns=[(value_col_id, self.value_columns)],
+                unpivot_columns=[(value_col_id, tuple(self.value_columns))],
                 passthrough_columns=[*self.index_columns, offset_col],
                 dtype=dtype,
             )
@@ -1029,13 +1032,13 @@ def summarize(
             for col_id in column_ids
         ]
         columns = [
-            (col_id, [f"{col_id}-{stat.name}" for stat in stats])
+            (col_id, tuple(f"{col_id}-{stat.name}" for stat in stats))
             for col_id in column_ids
         ]
         expr = self.expr.aggregate(aggregations).unpivot(
             labels,
-            unpivot_columns=columns,
-            index_col_ids=[label_col_id],
+            unpivot_columns=tuple(columns),
+            index_col_ids=tuple([label_col_id]),
         )
         labels = self._get_labels_for_columns(column_ids)
         return Block(expr, column_labels=labels, index_columns=[label_col_id])
@@ -1342,7 +1345,7 @@ def stack(self, how="left", levels: int = 1):
             passthrough_columns=self.index_columns,
             unpivot_columns=unpivot_columns,
             index_col_ids=added_index_columns,
-            dtype=dtypes,
+            dtype=tuple(dtypes),
             how=how,
         )
         new_index_level_names = self.column_labels.names[-levels:]
@@ -1382,7 +1385,7 @@ def _create_stack_column(
                     dtype = self._column_type(input_id)
             input_columns.append(input_id)
             # Input column i is the first one that
-        return input_columns, dtype or pd.Float64Dtype()
+        return tuple(input_columns), dtype or pd.Float64Dtype()
 
     def _column_type(self, col_id: str) -> bigframes.dtypes.Dtype:
         col_offset = self.value_columns.index(col_id)
@@ -1497,8 +1500,7 @@ def merge(
         sort: bool,
         suffixes: tuple[str, str] = ("_x", "_y"),
     ) -> Block:
-        joined_expr = joins.join_by_column(
-            self.expr,
+        joined_expr = self.expr.join(
             left_join_ids,
             other.expr,
             right_join_ids,
@@ -1708,7 +1710,7 @@ def _is_monotonic(
         return result
 
 
-def block_from_local(data, session=None) -> Block:
+def block_from_local(data) -> Block:
     pd_data = pd.DataFrame(data)
     columns = pd_data.columns
 
@@ -1730,7 +1732,7 @@ def block_from_local(data, session=None) -> Block:
     )
     index_ids = pd_data.columns[: len(index_labels)]
 
-    keys_expr = core.ArrayValue.mem_expr_from_pandas(pd_data, session)
+    keys_expr = core.ArrayValue.from_pandas(pd_data)
     return Block(
         keys_expr,
         column_labels=columns,

@@ -0,0 +1,21 @@
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from bigframes.core.compile.compiled import CompiledArrayValue
+from bigframes.core.compile.compiler import compile_node
+
+__all__ = [
+    "compile_node",
+    "CompiledArrayValue",
+]