Add method argument to DataFrame.quantile

python/cudf/cudf/_lib/cpp/quantiles.pxd

@@ -1,4 +1,4 @@
-# Copyright (c) 2020, NVIDIA CORPORATION.
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
 
 from libcpp cimport bool
 from libcpp.memory cimport unique_ptr

python/cudf/cudf/_lib/quantiles.pyx

@@ -20,7 +20,7 @@ from cudf._lib.cpp.column.column cimport column
 from cudf._lib.cpp.column.column_view cimport column_view
 from cudf._lib.cpp.quantiles cimport (
     quantile as cpp_quantile,
-    quantiles as cpp_quantiles,
+    quantiles as cpp_quantile_table,
 )
 from cudf._lib.cpp.table.table cimport table
 from cudf._lib.cpp.table.table_view cimport table_view
@@ -74,12 +74,14 @@ def quantile(
     return Column.from_unique_ptr(move(c_result))
 
 
-def quantiles(list source_columns,
-              vector[double] q,
-              object interp,
-              object is_input_sorted,
-              list column_order,
-              list null_precedence):
+def quantile_table(
+    list source_columns,
+    vector[double] q,
+    object interp,
+    object is_input_sorted,
+    list column_order,
+    list null_precedence,
+):
     cdef table_view c_input = table_view_from_columns(source_columns)
     cdef vector[double] c_q = q
     cdef interpolation c_interp = <interpolation>(
@@ -108,13 +110,13 @@ def quantiles(list source_columns,
 
     with nogil:
         c_result = move(
-            cpp_quantiles(
+            cpp_quantile_table(
                 c_input,
                 c_q,
                 c_interp,
                 c_is_input_sorted,
                 c_column_order,
-                c_null_precedence
+                c_null_precedence,
             )
         )
 

python/cudf/cudf/core/dataframe.py

@@ -5212,9 +5212,10 @@ def quantile(
         q=0.5,
         axis=0,
         numeric_only=True,
-        interpolation="linear",
+        interpolation=None,
         columns=None,
         exact=True,
+        method="single",
     ):
         """
         Return values at the given quantile.
@@ -5231,11 +5232,16 @@ def quantile(
         interpolation : {`linear`, `lower`, `higher`, `midpoint`, `nearest`}
             This parameter specifies the interpolation method to use,
             when the desired quantile lies between two data points i and j.
-            Default ``linear``.
+            Default is ``linear`` for ``method="single"``, and ``nearest``
+            for ``method="table"``.
         columns : list of str
             List of column names to include.
         exact : boolean
             Whether to use approximate or exact quantile algorithm.
+        method : {`single`, `table`}, default `single`
+            Whether to compute quantiles per-column ('single') or over all
+            columns ('table'). When 'table', the only allowed interpolation
+            methods are 'nearest', 'lower', and 'higher'.
 
         Returns
         -------
@@ -5288,39 +5294,62 @@ def quantile(
         if columns is None:
             columns = data_df._data.names
 
-        # Ensure that qs is non-scalar so that we always get a column back.
-        qs = [q] if is_scalar(q) else q
-        result = {}
-        for k in data_df._data.names:
-            if k in columns:
-                ser = data_df[k]
-                res = ser.quantile(
-                    qs,
-                    interpolation=interpolation,
-                    exact=exact,
-                    quant_index=False,
-                )._column
-                if len(res) == 0:
-                    res = column.column_empty_like(
-                        qs, dtype=ser.dtype, masked=True, newsize=len(qs)
-                    )
-                result[k] = res
+        if isinstance(q, numbers.Number):
+            q_is_number = True
+            qs = [float(q)]
+        elif pd.api.types.is_list_like(q):
+            q_is_number = False
+            qs = q
+        else:
+            msg = "`q` must be either a single element or list"
+            raise TypeError(msg)
 
-        result = DataFrame._from_data(result)
-        if isinstance(q, numbers.Number) and numeric_only:
-            result = result.fillna(np.nan).iloc[0]
-            result.index = data_df._data.to_pandas_index()
-            result.name = q
-            return result
+        if method == "table":
+            interpolation = interpolation or "nearest"
+            result = self._quantile_table(qs, interpolation.upper())
+
+            if q_is_number:
+                result = result.transpose()
+                return Series(
+                    data=result._columns[0], index=result.index, name=q
+                )
         else:
-            result.index = list(map(float, qs))
-            return result
+            # Ensure that qs is non-scalar so that we always get a column back.
+            interpolation = interpolation or "linear"
+            result = {}
+            for k in data_df._data.names:
+                if k in columns:
+                    ser = data_df[k]
+                    res = ser.quantile(
+                        qs,
+                        interpolation=interpolation,
+                        exact=exact,
+                        quant_index=False,
+                    )._column
+                    if len(res) == 0:
+                        res = column.column_empty_like(
+                            qs, dtype=ser.dtype, masked=True, newsize=len(qs)
+                        )
+                    result[k] = res
+            result = DataFrame._from_data(result)
+
+            if q_is_number and numeric_only:
+                result = result.fillna(np.nan).iloc[0]
+                result.index = data_df._data.to_pandas_index()
+                result.name = q
+                return result
+
+        result.index = list(map(float, qs))
+        return result
 
     @_cudf_nvtx_annotate
     def quantiles(self, q=0.5, interpolation="nearest"):
         """
         Return values at the given quantile.
 
+        This API is now deprecated. Please use ``DataFrame.quantile``
+        with ``method='table'``.
+
         Parameters
         ----------
         q : float or array-like
@@ -5334,25 +5363,13 @@ def quantiles(self, q=0.5, interpolation="nearest"):
         -------
         DataFrame
         """
-        if isinstance(q, numbers.Number):
-            q_is_number = True
-            q = [float(q)]
-        elif pd.api.types.is_list_like(q):
-            q_is_number = False
-        else:
-            msg = "`q` must be either a single element or list"
-            raise TypeError(msg)
-
-        result = self._quantiles(q, interpolation.upper())
+        warnings.warn(
+            "DataFrame.quantiles is now deprecated. "
+            "Please use DataFrame.quantile with `method='table'`.",
+            FutureWarning,
+        )
 
-        if q_is_number:
-            result = result.transpose()
-            return Series(
-                data=result._columns[0], index=result.index, name=q[0]
-            )
-        else:
-            result.index = as_index(q)
-            return result
+        return self.quantile(q=q, interpolation=interpolation, method="table")
 
     @_cudf_nvtx_annotate
     def isin(self, values):

python/cudf/cudf/core/frame.py

@@ -953,7 +953,7 @@ def _drop_na_columns(self, how="any", subset=None, thresh=None):
         return self[out_cols]
 
     @_cudf_nvtx_annotate
-    def _quantiles(
+    def _quantile_table(
         self,
         q,
         interpolation="LINEAR",
@@ -972,7 +972,7 @@ def _quantiles(
         ]
 
         return self._from_columns_like_self(
-            libcudf.quantiles.quantiles(
+            libcudf.quantiles.quantile_table(
                 [*self._columns],
                 q,
                 interpolation,

python/cudf/cudf/tests/test_quantiles.py

@@ -16,7 +16,7 @@ def test_single_q():
     gdf = cudf.from_pandas(pdf)
 
     pdf_q = pdf.quantile(q, interpolation="nearest")
-    gdf_q = gdf.quantiles(q, interpolation="nearest")
+    gdf_q = gdf.quantile(q, interpolation="nearest", method="table")
 
     assert_eq(pdf_q, gdf_q, check_index_type=False)
 
@@ -28,7 +28,7 @@ def test_with_index():
     gdf = cudf.from_pandas(pdf)
 
     pdf_q = pdf.quantile(q, interpolation="nearest")
-    gdf_q = gdf.quantiles(q, interpolation="nearest")
+    gdf_q = gdf.quantile(q, interpolation="nearest", method="table")
 
     assert_eq(pdf_q, gdf_q, check_index_type=False)
 
@@ -48,7 +48,7 @@ def test_with_multiindex():
     gdf = cudf.from_pandas(pdf)
 
     pdf_q = pdf.quantile(q, interpolation="nearest")
-    gdf_q = gdf.quantiles(q, interpolation="nearest")
+    gdf_q = gdf.quantile(q, interpolation="nearest", method="table")
 
     assert_eq(pdf_q, gdf_q, check_index_type=False)
 

python/dask_cudf/dask_cudf/sorting.py

@@ -48,7 +48,10 @@ def _quantile(a, q):
     n = len(a)
     if not len(a):
         return None, n
-    return (a.quantiles(q=q.tolist(), interpolation="nearest"), n)
+    return (
+        a.quantile(q=q.tolist(), interpolation="nearest", method="table"),
+        n,
+    )
 
 
 @_dask_cudf_nvtx_annotate
@@ -133,7 +136,7 @@ def _approximate_quantile(df, q):
     final_type = df._meta._constructor
 
     # Create metadata
-    meta = df._meta_nonempty.quantiles(q=q)
+    meta = df._meta_nonempty.quantile(q=q, method="table")
 
     # Define final action (create df with quantiles as index)
     def finalize_tsk(tsk):