googleapis · TrevorBergeron · Feb 28, 2025 · Feb 26, 2025 · Feb 27, 2025 · Feb 27, 2025
@@ -26,6 +26,7 @@
 import bigframes.core.expression as ex
 import bigframes.core.ordering as ordering
 import bigframes.core.window_spec as windows
+import bigframes.dtypes
 import bigframes.dtypes as dtypes
 import bigframes.operations as ops
 import bigframes.operations.aggregations as agg_ops
@@ -409,6 +410,8 @@ def rank(
     method: str = "average",
     na_option: str = "keep",
     ascending: bool = True,
+    grouping_cols: tuple[str, ...] = (),
+    columns: tuple[str, ...] = (),
 ):
     if method not in ["average", "min", "max", "first", "dense"]:
         raise ValueError(
@@ -417,8 +420,8 @@ def rank(
     if na_option not in ["keep", "top", "bottom"]:
         raise ValueError("na_option must be one of 'keep', 'top', or 'bottom'")
 
-    columns = block.value_columns
-    labels = block.column_labels
+    columns = columns or tuple(col for col in block.value_columns)
+    labels = [block.col_id_to_label[id] for id in columns]
     # Step 1: Calculate row numbers for each row
     # Identify null values to be treated according to na_option param
     rownum_col_ids = []
@@ -442,9 +445,13 @@ def rank(
         block, rownum_id = block.apply_window_op(
             col if na_option == "keep" else nullity_col_id,
             agg_ops.dense_rank_op if method == "dense" else agg_ops.count_op,
-            window_spec=windows.unbound(ordering=window_ordering)
+            window_spec=windows.unbound(
+                grouping_keys=grouping_cols, ordering=window_ordering
+            )
             if method == "dense"
-            else windows.rows(following=0, ordering=window_ordering),
+            else windows.rows(
+                following=0, ordering=window_ordering, grouping_keys=grouping_cols
+            ),
             skip_reproject_unsafe=(col != columns[-1]),
         )
         rownum_col_ids.append(rownum_id)
@@ -462,12 +469,32 @@ def rank(
             block, result_id = block.apply_window_op(
                 rownum_col_ids[i],
                 agg_op,
-                window_spec=windows.unbound(grouping_keys=(columns[i],)),
+                window_spec=windows.unbound(grouping_keys=(columns[i], *grouping_cols)),
                 skip_reproject_unsafe=(i < (len(columns) - 1)),
             )
             post_agg_rownum_col_ids.append(result_id)
         rownum_col_ids = post_agg_rownum_col_ids
 
+    # Pandas masks all values where any grouping column is null
+    # Note: we use pd.NA instead of float('nan')
+    if grouping_cols:
+        predicate = functools.reduce(
+            ops.and_op.as_expr,
+            [ops.notnull_op.as_expr(column_id) for column_id in grouping_cols],
+        )
+        block = block.project_exprs(
+            [
+                ops.where_op.as_expr(
+                    ex.deref(col),
+                    predicate,
+                    ex.const(None),
+                )
+                for col in rownum_col_ids
+            ],
+            labels=labels,
+        )
+        rownum_col_ids = list(block.value_columns[-len(rownum_col_ids) :])
+
     # Step 3: post processing: mask null values and cast to float
     if method in ["min", "max", "first", "dense"]:
         # Pandas rank always produces Float64, so must cast for aggregation types that produce ints

@@ -397,6 +397,7 @@ def literal_to_ibis_scalar(
             )
         # "correct" way would be to use ibis.array, but this produces invalid BQ SQL syntax
         return tuple(literal)
+
     if not pd.api.types.is_list_like(literal) and pd.isna(literal):
         if ibis_dtype:
             return bigframes_vendored.ibis.null().cast(ibis_dtype)

@@ -174,6 +174,20 @@ def median(self, numeric_only: bool = False, *, exact: bool = True) -> df.DataFr
             return self.quantile(0.5)
         return self._aggregate_all(agg_ops.median_op, numeric_only=True)
 
+    def rank(
+        self, method="average", ascending: bool = True, na_option: str = "keep"
+    ) -> df.DataFrame:
+        return df.DataFrame(
+            block_ops.rank(
+                self._block,
+                method,
+                na_option,
+                ascending,
+                grouping_cols=tuple(self._by_col_ids),
+                columns=tuple(self._selected_cols),
+            )
+        )
+
     def quantile(
         self, q: Union[float, Sequence[float]] = 0.5, *, numeric_only: bool = False
     ) -> df.DataFrame:
@@ -574,6 +588,20 @@ def sum(self, *args) -> series.Series:
     def mean(self, *args) -> series.Series:
         return self._aggregate(agg_ops.mean_op)
 
+    def rank(
+        self, method="average", ascending: bool = True, na_option: str = "keep"
+    ) -> series.Series:
+        return series.Series(
+            block_ops.rank(
+                self._block,
+                method,
+                na_option,
+                ascending,
+                grouping_cols=tuple(self._by_col_ids),
+                columns=(self._value_column,),
+            )
+        )
+
     def median(
         self,
         *args,

@@ -16,7 +16,7 @@
 import pytest
 
 import bigframes.pandas as bpd
-from tests.system.utils import assert_pandas_df_equal
+from tests.system.utils import assert_pandas_df_equal, skip_legacy_pandas
 
 # =================
 # DataFrame.groupby
@@ -94,6 +94,72 @@ def test_dataframe_groupby_quantile(scalars_df_index, scalars_pandas_df_index, q
     )
 
 
+@skip_legacy_pandas
+@pytest.mark.parametrize(
+    ("na_option", "method", "ascending"),
+    [
+        (
+            "keep",
+            "average",
+            True,
+        ),
+        (
+            "top",
+            "min",
+            False,
+        ),
+        (
+            "bottom",
+            "max",
+            False,
+        ),
+        (
+            "top",
+            "first",
+            False,
+        ),
+        (
+            "bottom",
+            "dense",
+            False,
+        ),
+    ],
+)
+def test_dataframe_groupby_rank(
+    scalars_df_index,
+    scalars_pandas_df_index,
+    na_option,
+    method,
+    ascending,
+):
+    col_names = ["int64_too", "float64_col", "int64_col", "string_col"]
+    bf_result = (
+        scalars_df_index[col_names]
+        .groupby("string_col")
+        .rank(
+            na_option=na_option,
+            method=method,
+            ascending=ascending,
+        )
+    ).to_pandas()
+    pd_result = (
+        (
+            scalars_pandas_df_index[col_names]
+            .groupby("string_col")
+            .rank(
+                na_option=na_option,
+                method=method,
+                ascending=ascending,
+            )
+        )
+        .astype("float64")
+        .astype("Float64")
+    )
+    pd.testing.assert_frame_equal(
+        pd_result, bf_result, check_dtype=False, check_index_type=False
+    )
+
+
 @pytest.mark.parametrize(
     ("operator"),
     [
@@ -534,6 +600,72 @@ def test_series_groupby_agg_list(scalars_df_index, scalars_pandas_df_index):
     )
 
 
+@skip_legacy_pandas
+@pytest.mark.parametrize(
+    ("na_option", "method", "ascending"),
+    [
+        (
+            "keep",
+            "average",
+            True,
+        ),
+        (
+            "top",
+            "min",
+            False,
+        ),
+        (
+            "bottom",
+            "max",
+            False,
+        ),
+        (
+            "top",
+            "first",
+            False,
+        ),
+        (
+            "bottom",
+            "dense",
+            False,
+        ),
+    ],
+)
+def test_series_groupby_rank(
+    scalars_df_index,
+    scalars_pandas_df_index,
+    na_option,
+    method,
+    ascending,
+):
+    col_names = ["int64_col", "string_col"]
+    bf_result = (
+        scalars_df_index[col_names]
+        .groupby("string_col")["int64_col"]
+        .rank(
+            na_option=na_option,
+            method=method,
+            ascending=ascending,
+        )
+    ).to_pandas()
+    pd_result = (
+        (
+            scalars_pandas_df_index[col_names]
+            .groupby("string_col")["int64_col"]
+            .rank(
+                na_option=na_option,
+                method=method,
+                ascending=ascending,
+            )
+        )
+        .astype("float64")
+        .astype("Float64")
+    )
+    pd.testing.assert_series_equal(
+        pd_result, bf_result, check_dtype=False, check_index_type=False
+    )
+
+
 @pytest.mark.parametrize("dropna", [True, False])
 def test_series_groupby_head(scalars_df_index, scalars_pandas_df_index, dropna):
     bf_result = (

@@ -363,6 +363,77 @@ def var(
         """
         raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
 
+    def rank(
+        self,
+        method: str = "average",
+        ascending: bool = True,
+        na_option: str = "keep",
+    ):
+        """
+        Provide the rank of values within each group.
+
+        **Examples:**
+
+            >>> import bigframes.pandas as bpd
+            >>> import numpy as np
+            >>> bpd.options.display.progress_bar = None
+
+            >>> df = bpd.DataFrame(
+            ...     {
+            ...         "group": ["a", "a", "a", "a", "a", "b", "b", "b", "b", "b"],
+            ...         "value": [2, 4, 2, 3, 5, 1, 2, 4, 1, 5],
+            ...     }
+            ... )
+            >>> df
+            group  value
+            0     a      2
+            1     a      4
+            2     a      2
+            3     a      3
+            4     a      5
+            5     b      1
+            6     b      2
+            7     b      4
+            8     b      1
+            9     b      5
+            <BLANKLINE>
+            [10 rows x 2 columns]
+            >>> for method in ['average', 'min', 'max', 'dense', 'first']:
+            ...     df[f'{method}_rank'] = df.groupby('group')['value'].rank(method)
+            >>> df
+            group  value  average_rank  min_rank  max_rank  dense_rank  first_rank
+            0     a      2           1.5       1.0       2.0         1.0         1.0
+            1     a      4           4.0       4.0       4.0         3.0         4.0
+            2     a      2           1.5       1.0       2.0         1.0         2.0
+            3     a      3           3.0       3.0       3.0         2.0         3.0
+            4     a      5           5.0       5.0       5.0         4.0         5.0
+            5     b      1           1.5       1.0       2.0         1.0         1.0
+            6     b      2           3.0       3.0       3.0         2.0         3.0
+            7     b      4           4.0       4.0       4.0         3.0         4.0
+            8     b      1           1.5       1.0       2.0         1.0         2.0
+            9     b      5           5.0       5.0       5.0         4.0         5.0
+            <BLANKLINE>
+            [10 rows x 7 columns]
+
+        Args:
+            method ({'average', 'min', 'max', 'first', 'dense'}, default 'average'):
+                * average: average rank of group.
+                * min: lowest rank in group.
+                * max: highest rank in group.
+                * first: ranks assigned in order they appear in the array.
+                * dense: like 'min', but rank always increases by 1 between groups.
+            ascending (bool, default True):
+                False for ranks by high (1) to low (N).
+            na_option ({'keep', 'top', 'bottom'}, default 'keep'):
+                * keep: leave NA values where they are.
+                * top: smallest rank if ascending.
+                * bottom: smallest rank if descending.
+
+        Returns:
+            DataFrame with ranking of values within each group
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
     def skew(
         self,
         *,