Match pandas.Series.quantile behavior

python/cudf/cudf/core/column/numerical_base.py

@@ -115,6 +115,16 @@ def quantile(
             result = self._numeric_quantile(q, interpolation, exact)
         if return_scalar:
             scalar_result = result.element_indexing(0)
+            if interpolation in {"lower", "higher", "nearest"}:
+                try:
+                    new_scalar = self.dtype.type(scalar_result)
+                    scalar_result = (
+                        new_scalar
+                        if new_scalar == scalar_result
+                        else scalar_result
+                    )
+                except (TypeError, ValueError):
+                    pass
             return (
                 cudf.utils.dtypes._get_nan_for_dtype(self.dtype)
                 if scalar_result is NA

python/cudf/cudf/core/dataframe.py

@@ -5487,16 +5487,23 @@ def quantile(
         numeric_only : bool, default True
             If False, the quantile of datetime and timedelta data will be
             computed as well.
-        interpolation : {`linear`, `lower`, `higher`, `midpoint`, `nearest`}
+        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 is ``linear`` for ``method="single"``, and ``nearest``
+            Default is ``'linear'`` for ``method="single"``, and ``'nearest'``
             for ``method="table"``.
+
+                * linear: `i + (j - i) * fraction`, where `fraction` is the
+                fractional part of the index surrounded by `i` and `j`.
+                * lower: `i`.
+                * higher: `j`.
+                * nearest: `i` or `j` whichever is nearest.
+                * midpoint: (`i` + `j`) / 2.
         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`
+        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'.

python/cudf/cudf/core/series.py

@@ -3132,8 +3132,13 @@ def quantile(
         interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
             This optional parameter specifies the interpolation method to use,
             when the desired quantile lies between two data points i and j:
-        columns : list of str
-            List of column names to include.
+
+                * linear: `i + (j - i) * fraction`, where `fraction` is the
+                  fractional part of the index surrounded by `i` and `j`.
+                * lower: `i`.
+                * higher: `j`.
+                * nearest: `i` or `j` whichever is nearest.
+                * midpoint: (`i` + `j`) / 2.
         exact : boolean
             Whether to use approximate or exact quantile algorithm.
         quant_index : boolean

python/cudf/cudf/tests/test_quantiles.py

@@ -75,3 +75,18 @@ def test_quantile_q_type():
         ),
     ):
         gs.quantile(cudf.DataFrame())
+
+
+@pytest.mark.parametrize(
+    "interpolation", ["linear", "lower", "higher", "midpoint", "nearest"]
+)
+def test_quantile_type_int_float(interpolation):
+    data = [1, 3, 4]
+    psr = pd.Series(data)
+    gsr = cudf.Series(data)
+
+    expected = psr.quantile(0.5, interpolation=interpolation)
+    actual = gsr.quantile(0.5, interpolation=interpolation)
+
+    assert expected == actual
+    assert type(expected) == type(actual)