ENH: Support mask properly in GroupBy.quantile

doc/source/whatsnew/v1.6.0.rst

@@ -28,6 +28,7 @@ enhancement2
 
 Other enhancements
 ^^^^^^^^^^^^^^^^^^
+- :meth:`.GroupBy.quantile` now preserving nullable dtypes instead of casting to numpy dtypes (:issue:`37493`)
 - :meth:`Series.add_suffix`, :meth:`DataFrame.add_suffix`, :meth:`Series.add_prefix` and :meth:`DataFrame.add_prefix` support an ``axis`` argument. If ``axis`` is set, the default behaviour of which axis to consider can be overwritten (:issue:`47819`)
 -
 

pandas/_libs/groupby.pyi

@@ -110,6 +110,7 @@ def group_quantile(
     sort_indexer: npt.NDArray[np.intp],  # const
     qs: npt.NDArray[np.float64],  # const
     interpolation: Literal["linear", "lower", "higher", "nearest", "midpoint"],
+    result_mask: np.ndarray | None = ...,
 ) -> None: ...
 def group_last(
     out: np.ndarray,  # rank_t[:, ::1]

pandas/_libs/groupby.pyx

@@ -1076,6 +1076,7 @@ def group_quantile(
     const intp_t[:] sort_indexer,
     const float64_t[:] qs,
     str interpolation,
+    uint8_t[:, ::1] result_mask=None,
 ) -> None:
     """
     Calculate the quantile per group.
@@ -1106,6 +1107,7 @@ def group_quantile(
         InterpolationEnumType interp
         float64_t q_val, q_idx, frac, val, next_val
         int64_t[::1] counts, non_na_counts
+        bint uses_result_mask = result_mask is not None
 
     assert values.shape[0] == N
 
@@ -1148,7 +1150,10 @@ def group_quantile(
 
             if non_na_sz == 0:
                 for k in range(nqs):
-                    out[i, k] = NaN
+                    if uses_result_mask:
+                        result_mask[i, k] = 1
+                    else:
+                        out[i, k] = NaN
             else:
                 for k in range(nqs):
                     q_val = qs[k]

pandas/core/groupby/groupby.py

@@ -46,6 +46,7 @@ class providing the base-class of operations.
 import pandas._libs.groupby as libgroupby
 from pandas._typing import (
     ArrayLike,
+    Dtype,
     IndexLabel,
     NDFrameT,
     PositionalIndexer,
@@ -92,6 +93,7 @@ class providing the base-class of operations.
     BooleanArray,
     Categorical,
     ExtensionArray,
+    FloatingArray,
 )
 from pandas.core.base import (
     PandasObject,
@@ -3247,14 +3249,17 @@ def quantile(
                 f"numeric_only={numeric_only} and dtype {self.obj.dtype}"
             )
 
-        def pre_processor(vals: ArrayLike) -> tuple[np.ndarray, np.dtype | None]:
+        def pre_processor(vals: ArrayLike) -> tuple[np.ndarray, Dtype | None]:
             if is_object_dtype(vals):
                 raise TypeError(
                     "'quantile' cannot be performed against 'object' dtypes!"
                 )
 
-            inference: np.dtype | None = None
-            if is_integer_dtype(vals.dtype):
+            inference: Dtype | None = None
+            if isinstance(vals, BaseMaskedArray) and is_numeric_dtype(vals.dtype):
+                out = vals.to_numpy(dtype=float, na_value=np.nan)
+                inference = vals.dtype
+            elif is_integer_dtype(vals.dtype):
                 if isinstance(vals, ExtensionArray):
                     out = vals.to_numpy(dtype=float, na_value=np.nan)
                 else:
@@ -3276,14 +3281,38 @@ def pre_processor(vals: ArrayLike) -> tuple[np.ndarray, np.dtype | None]:
 
             return out, inference
 
-        def post_processor(vals: np.ndarray, inference: np.dtype | None) -> np.ndarray:
+        def post_processor(
+            vals: np.ndarray,
+            inference: Dtype | None,
+            result_mask: np.ndarray | None,
+            orig_vals: ArrayLike,
+        ) -> ArrayLike:
             if inference:
                 # Check for edge case
-                if not (
+                if isinstance(orig_vals, BaseMaskedArray):
+                    assert result_mask is not None  # for mypy
+
+                    if interpolation in {"linear", "midpoint"} and not is_float_dtype(
+                        orig_vals
+                    ):
+                        return FloatingArray(vals, result_mask)
+                    else:
+                        # Item "ExtensionDtype" of "Union[ExtensionDtype, str,
+                        # dtype[Any], Type[object]]" has no attribute "numpy_dtype"
+                        # [union-attr]
+                        return type(orig_vals)(
+                            vals.astype(
+                                inference.numpy_dtype  # type: ignore[union-attr]
+                            ),
+                            result_mask,
+                        )
+
+                elif not (
                     is_integer_dtype(inference)
                     and interpolation in {"linear", "midpoint"}
                 ):
-                    vals = vals.astype(inference)
+                    assert isinstance(inference, np.dtype)  # for mypy
+                    return vals.astype(inference)
 
             return vals
 
@@ -3306,7 +3335,14 @@ def post_processor(vals: np.ndarray, inference: np.dtype | None) -> np.ndarray:
         labels_for_lexsort = np.where(ids == -1, na_label_for_sorting, ids)
 
         def blk_func(values: ArrayLike) -> ArrayLike:
-            mask = isna(values)
+            orig_vals = values
+            if isinstance(values, BaseMaskedArray):
+                mask = values._mask
+                result_mask = np.zeros((ngroups, nqs), dtype=np.bool_)
+            else:
+                mask = isna(values)
+                result_mask = None
+
             vals, inference = pre_processor(values)
 
             ncols = 1
@@ -3325,16 +3361,25 @@ def blk_func(values: ArrayLike) -> ArrayLike:
             sort_arr = np.lexsort(order).astype(np.intp, copy=False)
 
             if vals.ndim == 1:
-                func(out[0], values=vals, mask=mask, sort_indexer=sort_arr)
+                # Ea is always 1d
+                func(
+                    out[0],
+                    values=vals,
+                    mask=mask,
+                    sort_indexer=sort_arr,
+                    result_mask=result_mask,
+                )
             else:
                 for i in range(ncols):
                     func(out[i], values=vals[i], mask=mask[i], sort_indexer=sort_arr[i])
 
             if vals.ndim == 1:
                 out = out.ravel("K")
+                if result_mask is not None:
+                    result_mask = result_mask.ravel("K")
             else:
                 out = out.reshape(ncols, ngroups * nqs)
-            return post_processor(out, inference)
+            return post_processor(out, inference, result_mask, orig_vals)
 
         obj = self._obj_with_exclusions
         is_ser = obj.ndim == 1

pandas/tests/groupby/test_quantile.py

@@ -237,7 +237,7 @@ def test_groupby_quantile_nullable_array(values, q):
         idx = Index(["x", "y"], name="a")
         true_quantiles = [0.5]
 
-    expected = pd.Series(true_quantiles * 2, index=idx, name="b")
+    expected = pd.Series(true_quantiles * 2, index=idx, name="b", dtype="Float64")
     tm.assert_series_equal(result, expected)
 
 
@@ -266,14 +266,21 @@ def test_groupby_quantile_NA_float(any_float_dtype):
     df = DataFrame({"x": [1, 1], "y": [0.2, np.nan]}, dtype=any_float_dtype)
     result = df.groupby("x")["y"].quantile(0.5)
     exp_index = Index([1.0], dtype=any_float_dtype, name="x")
-    expected = pd.Series([0.2], dtype=float, index=exp_index, name="y")
-    tm.assert_series_equal(expected, result)
+
+    if any_float_dtype in ["Float32", "Float64"]:
+        expected_dtype = any_float_dtype
+    else:
+        expected_dtype = None
+
+    expected = pd.Series([0.2], dtype=expected_dtype, index=exp_index, name="y")
+    tm.assert_series_equal(result, expected)
 
     result = df.groupby("x")["y"].quantile([0.5, 0.75])
     expected = pd.Series(
         [0.2] * 2,
         index=pd.MultiIndex.from_product((exp_index, [0.5, 0.75]), names=["x", None]),
         name="y",
+        dtype=expected_dtype,
     )
     tm.assert_series_equal(result, expected)
 
@@ -283,12 +290,68 @@ def test_groupby_quantile_NA_int(any_int_ea_dtype):
     df = DataFrame({"x": [1, 1], "y": [2, 5]}, dtype=any_int_ea_dtype)
     result = df.groupby("x")["y"].quantile(0.5)
     expected = pd.Series(
-        [3.5], dtype=float, index=Index([1], name="x", dtype=any_int_ea_dtype), name="y"
+        [3.5],
+        dtype="Float64",
+        index=Index([1], name="x", dtype=any_int_ea_dtype),
+        name="y",
     )
     tm.assert_series_equal(expected, result)
 
     result = df.groupby("x").quantile(0.5)
-    expected = DataFrame({"y": 3.5}, index=Index([1], name="x", dtype=any_int_ea_dtype))
+    expected = DataFrame(
+        {"y": 3.5}, dtype="Float64", index=Index([1], name="x", dtype=any_int_ea_dtype)
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "interpolation, val1, val2", [("lower", 2, 2), ("higher", 2, 3), ("nearest", 2, 2)]
+)
+def test_groupby_quantile_all_na_group_masked(
+    interpolation, val1, val2, any_numeric_ea_dtype
+):
+    # GH#37493
+    df = DataFrame(
+        {"a": [1, 1, 1, 2], "b": [1, 2, 3, pd.NA]}, dtype=any_numeric_ea_dtype
+    )
+    result = df.groupby("a").quantile(q=[0.5, 0.7], interpolation=interpolation)
+    expected = DataFrame(
+        {"b": [val1, val2, pd.NA, pd.NA]},
+        dtype=any_numeric_ea_dtype,
+        index=pd.MultiIndex.from_arrays(
+            [pd.Series([1, 1, 2, 2], dtype=any_numeric_ea_dtype), [0.5, 0.7, 0.5, 0.7]],
+            names=["a", None],
+        ),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("interpolation", ["midpoint", "linear"])
+def test_groupby_quantile_all_na_group_masked_interp(
+    interpolation, any_numeric_ea_dtype
+):
+    # GH#37493
+    df = DataFrame(
+        {"a": [1, 1, 1, 2], "b": [1, 2, 3, pd.NA]}, dtype=any_numeric_ea_dtype
+    )
+    result = df.groupby("a").quantile(q=[0.5, 0.75], interpolation=interpolation)
+
+    if any_numeric_ea_dtype == "Float32":
+        expected_dtype = any_numeric_ea_dtype
+    else:
+        expected_dtype = "Float64"
+
+    expected = DataFrame(
+        {"b": [2.0, 2.5, pd.NA, pd.NA]},
+        dtype=expected_dtype,
+        index=pd.MultiIndex.from_arrays(
+            [
+                pd.Series([1, 1, 2, 2], dtype=any_numeric_ea_dtype),
+                [0.5, 0.75, 0.5, 0.75],
+            ],
+            names=["a", None],
+        ),
+    )
     tm.assert_frame_equal(result, expected)
 
 
@@ -298,7 +361,7 @@ def test_groupby_quantile_allNA_column(dtype):
     df = DataFrame({"x": [1, 1], "y": [pd.NA] * 2}, dtype=dtype)
     result = df.groupby("x")["y"].quantile(0.5)
     expected = pd.Series(
-        [np.nan], dtype=float, index=Index([1.0], dtype=dtype), name="y"
+        [np.nan], dtype=dtype, index=Index([1.0], dtype=dtype), name="y"
     )
     expected.index.name = "x"
     tm.assert_series_equal(expected, result)