feat: Add bigframes.bigquery.rand() function

bigframes/bigquery/__init__.py

@@ -57,6 +57,7 @@
     to_json,
     to_json_string,
 )
+from bigframes.bigquery._operations.mathematical import rand
 from bigframes.bigquery._operations.search import create_vector_index, vector_search
 from bigframes.bigquery._operations.sql import sql_scalar
 from bigframes.bigquery._operations.struct import struct
@@ -97,6 +98,8 @@
     parse_json,
     to_json,
     to_json_string,
+    # mathematical ops
+    rand,
     # search ops
     create_vector_index,
     vector_search,
@@ -148,6 +151,8 @@
     "parse_json",
     "to_json",
     "to_json_string",
+    # mathematical ops
+    "rand",
     # search ops
     "create_vector_index",
     "vector_search",

bigframes/bigquery/_operations/mathematical.py

@@ -0,0 +1,68 @@
+# Copyright 2025 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 __future__ import annotations
+
+from typing import Union
+
+from bigframes import dataframe
+from bigframes import dtypes
+from bigframes import operations as ops
+from bigframes import series
+
+
+def rand(input_data: Union[series.Series, dataframe.DataFrame]) -> series.Series:
+    """
+    Generates a pseudo-random value of type FLOAT64 in the range of [0, 1),
+    inclusive of 0 and exclusive of 1.
+
+    .. warning::
+        This method introduces non-determinism to the expression. Reading the
+        same column twice may result in different results.
+
+    **Examples:**
+
+        >>> import bigframes.pandas as bpd
+        >>> import bigframes.bigquery as bbq
+        >>> df = bpd.DataFrame({"a": [1, 2, 3]})
+        >>> df['random'] = bbq.rand(df)
+        >>> # Resulting column 'random' will contain random floats between 0 and 1.
+
+    Args:
+        input_data (bigframes.pandas.Series or bigframes.pandas.DataFrame):
+            A Series or DataFrame to determine the number of rows and the index
+            of the result. The actual values in this input are ignored.
+
+    Returns:
+        bigframes.pandas.Series: A new Series of random float values.
+    """
+    if isinstance(input_data, dataframe.DataFrame):
+        if len(input_data.columns) == 0:
+            raise ValueError("Input DataFrame must have at least one column.")
+        # Use the first column as anchor
+        anchor = input_data.iloc[:, 0]
+    elif isinstance(input_data, series.Series):
+        anchor = input_data
+    else:
+        raise TypeError(
+            f"Unsupported type {type(input_data)}. "
+            "Expected bigframes.pandas.Series or bigframes.pandas.DataFrame."
+        )
+
+    op = ops.SqlScalarOp(
+        _output_type=dtypes.FLOAT_DTYPE,
+        sql_template="RAND()",
+        is_deterministic=False,
+    )
+    return anchor._apply_nary_op(op, [])

bigframes/operations/generic_ops.py

@@ -443,10 +443,15 @@ class SqlScalarOp(base_ops.NaryOp):
     name: typing.ClassVar[str] = "sql_scalar"
     _output_type: dtypes.ExpressionType
     sql_template: str
+    is_deterministic: bool = True
 
     def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType:
         return self._output_type
 
+    @property
+    def deterministic(self) -> bool:
+        return self.is_deterministic
+
 
 @dataclasses.dataclass(frozen=True)
 class PyUdfOp(base_ops.NaryOp):

tests/system/small/bigquery/test_mathematical.py

@@ -0,0 +1,36 @@
+# Copyright 2025 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 bigframes.bigquery as bbq
+
+
+def test_rand(scalars_df_index):
+    df = scalars_df_index
+
+    # Apply rand
+    result = bbq.rand(df)
+
+    # Eagerly evaluate
+    result_pd = result.to_pandas()
+
+    # Check length
+    assert len(result_pd) == len(df)
+
+    # Check values in [0, 1)
+    assert (result_pd >= 0).all()
+    assert (result_pd < 1).all()
+
+    # Check not all values are equal (unlikely collision for random)
+    if len(result_pd) > 1:
+        assert result_pd.nunique() > 1

tests/unit/bigquery/test_mathematical.py

@@ -0,0 +1,57 @@
+# Copyright 2025 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 unittest.mock as mock
+
+import bigframes.bigquery as bbq
+import bigframes.dataframe as dataframe
+import bigframes.dtypes as dtypes
+import bigframes.operations as ops
+import bigframes.series as series
+
+
+def test_rand_calls_apply_nary_op():
+    mock_series = mock.create_autospec(series.Series, instance=True)
+
+    bbq.rand(mock_series)
+
+    mock_series._apply_nary_op.assert_called_once()
+    args, _ = mock_series._apply_nary_op.call_args
+    op = args[0]
+    assert isinstance(op, ops.SqlScalarOp)
+    assert op.sql_template == "RAND()"
+    assert op._output_type == dtypes.FLOAT_DTYPE
+    assert op.deterministic is False
+    assert args[1] == []
+
+
+def test_rand_with_dataframe():
+    mock_df = mock.create_autospec(dataframe.DataFrame, instance=True)
+    # mock columns length > 0
+    mock_df.columns = ["col1"]
+    # mock iloc to return a series
+    mock_series = mock.create_autospec(series.Series, instance=True)
+    # Configure mock_df.iloc to return mock_series when indexed
+    # iloc is indexable, so we mock __getitem__
+    mock_indexer = mock.MagicMock()
+    mock_indexer.__getitem__.return_value = mock_series
+    type(mock_df).iloc = mock.PropertyMock(return_value=mock_indexer)
+
+    bbq.rand(mock_df)
+
+    mock_series._apply_nary_op.assert_called_once()
+    args, _ = mock_series._apply_nary_op.call_args
+    op = args[0]
+    assert isinstance(op, ops.SqlScalarOp)
+    assert op.sql_template == "RAND()"