feat: recover struct column from exploded Series

bigframes/bigquery/__init__.py

@@ -271,6 +271,40 @@ def json_extract_array(
     return series._apply_unary_op(ops.JSONExtractArray(json_path=json_path))
 
 
+def struct(value: dataframe.DataFrame) -> series.Series:
+    """Takes a DataFrame and converts it into a Series of structs with each
+    struct entry corresponding to a DataFrame row and each struct field
+    corresponding to a DataFrame column
+
+    **Examples:**
+
+        >>> import bigframes.pandas as bpd
+        >>> import bigframes.bigquery as bbq
+        >>> import bigframes.series as series
+        >>> bpd.options.display.progress_bar = None
+
+        >>> srs = series.Series([{"version": 1, "project": "pandas"}, {"version": 2, "project": "numpy"},])
+        >>> df = srs.struct.explode()
+        >>> bbq.struct(df)
+        0    {'project': 'pandas', 'version': 1}
+        1     {'project': 'numpy', 'version': 2}
+        dtype: struct<project: string, version: int64>[pyarrow]
+
+        Args:
+            value (bigframes.dataframe.DataFrame):
+                The DataFrame to be converted to a Series of structs
+
+        Returns:
+            bigframes.series.Series: A new Series with struct entries representing rows of the original DataFrame
+    """
+    block = value._block
+    block, result_id = block.apply_nary_op(
+        block.value_columns, ops.StructOp(column_names=tuple(block.column_labels))
+    )
+    block = block.select_column(result_id)
+    return bigframes.series.Series(block)
+
+
 # Search functions defined from
 # https://cloud.google.com/bigquery/docs/reference/standard-sql/search_functions
 

bigframes/core/compile/scalar_op_compiler.py

@@ -1539,6 +1539,17 @@ def nary_remote_function_op_impl(
     return result
 
 
+@scalar_op_compiler.register_nary_op(ops.StructOp, pass_op=True)
+def struct_op_impl(
+    *values: ibis_types.Value, op: ops.StructOp
+) -> ibis_types.StructValue:
+    data = {}
+    for i, value in enumerate(values):
+        data[op.column_names[i]] = value
+
+    return ibis.struct(data)
+
+
 # Helpers
 def is_null(value) -> bool:
     # float NaN/inf should be treated as distinct from 'true' null values

bigframes/operations/__init__.py

@@ -867,6 +867,29 @@ def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionT
 case_when_op = CaseWhenOp()
 
 
+@dataclasses.dataclass(frozen=True)
+class StructOp(NaryOp):
+    name: typing.ClassVar[str] = "struct"
+    column_names: tuple[str]
+
+    def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType:
+        num_input_types = len(input_types)
+        # value1, value2, ...
+        assert num_input_types == len(self.column_names)
+        fields = []
+
+        for i in range(num_input_types):
+            fields.append(
+                (
+                    self.column_names[i],
+                    dtypes.bigframes_dtype_to_arrow_dtype(input_types[i]),
+                )
+            )
+        return pd.ArrowDtype(
+            pa.struct(fields)
+        )  # [(name1, value1), (name2, value2), ...]
+
+
 # Just parameterless unary ops for now
 # TODO: Parameter mappings
 NUMPY_TO_OP: typing.Final = {

tests/system/small/bigquery/test_struct.py

@@ -0,0 +1,61 @@
+# Copyright 2024 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.
+
+import pandas as pd
+import pytest
+
+import bigframes.bigquery as bbq
+import bigframes.series as series
+
+
+@pytest.mark.parametrize(
+    "columns_arg",
+    [
+        [
+            {"version": 1, "project": "pandas"},
+            {"version": 2, "project": "pandas"},
+            {"version": 1, "project": "numpy"},
+        ],
+        [
+            {"version": 1, "project": "pandas"},
+            {"version": None, "project": "pandas"},
+            {"version": 1, "project": "numpy"},
+        ],
+        [
+            {"array": [6, 4, 6], "project": "pandas"},
+            {"array": [6, 4, 7, 6], "project": "pandas"},
+            {"array": [7, 2, 3], "project": "numpy"},
+        ],
+        [
+            {"array": [6, 4, 6], "project": "pandas"},
+            {"array": [6, 4, 7, 6], "project": "pandas"},
+            {"array": [7, 2, 3], "project": "numpy"},
+        ],
+        [
+            {"struct": [{"x": 2, "y": 4}], "project": "pandas"},
+            {"struct": [{"x": 9, "y": 3}], "project": "pandas"},
+            {"struct": [{"x": 1, "y": 2}], "project": "numpy"},
+        ],
+    ],
+)
+def test_struct_from_dataframe(columns_arg):
+    srs = series.Series(
+        columns_arg,
+    )
+    pd.testing.assert_series_equal(
+        srs.to_pandas(),
+        bbq.struct(srs.struct.explode()).to_pandas(),
+        check_index_type=False,
+        check_dtype=False,
+    )