ENH: ArrayManager.quantile

pandas/core/frame.py

@@ -9650,15 +9650,11 @@ def quantile(
                 return self._constructor([], index=q, columns=cols)
             return self._constructor_sliced([], index=cols, name=q, dtype=np.float64)
 
-        result = data._mgr.quantile(
+        res = data._mgr.quantile(
             qs=q, axis=1, interpolation=interpolation, transposed=is_transposed
         )
 
-        result = self._constructor(result)
-
-        if is_transposed:
-            result = result.T
-
+        result = self._constructor(res)
         return result
 
     @doc(NDFrame.asfreq, **_shared_doc_kwargs)

pandas/core/internals/array_manager.py

@@ -55,6 +55,7 @@
 )
 
 import pandas.core.algorithms as algos
+from pandas.core.array_algos.quantile import quantile_compat
 from pandas.core.arrays import (
     DatetimeArray,
     ExtensionArray,
@@ -75,6 +76,7 @@
 from pandas.core.internals.blocks import make_block
 
 if TYPE_CHECKING:
+    from pandas import Float64Index
     from pandas.core.internals.managers import SingleBlockManager
 
 
@@ -433,7 +435,28 @@ def apply_with_block(self: T, f, align_keys=None, **kwargs) -> T:
 
         return type(self)(result_arrays, self._axes)
 
-    # TODO quantile
+    def quantile(
+        self,
+        *,
+        qs: Float64Index,
+        axis: int = 0,
+        transposed: bool = False,
+        interpolation="linear",
+    ) -> ArrayManager:
+
+        arrs = [
+            x if not isinstance(x, np.ndarray) else np.atleast_2d(x)
+            for x in self.arrays
+        ]
+        assert axis == 1
+        new_arrs = [quantile_compat(x, qs, interpolation, axis=axis) for x in arrs]
+        for i, arr in enumerate(new_arrs):
+            if arr.ndim == 2:
+                assert arr.shape[0] == 1, arr.shape
+                new_arrs[i] = arr[0]
+
+        axes = [qs, self._axes[1]]
+        return type(self)(new_arrs, axes)
 
     def isna(self, func) -> ArrayManager:
         return self.apply("apply", func=func)

pandas/core/internals/managers.py

@@ -554,13 +554,6 @@ def quantile(
             for blk in self.blocks
         ]
 
-        if transposed:
-            new_axes = new_axes[::-1]
-            blocks = [
-                b.make_block(b.values.T, placement=np.arange(b.shape[1]))
-                for b in blocks
-            ]
-
         return type(self)(blocks, new_axes)
 
     def isna(self, func) -> BlockManager:

pandas/tests/frame/methods/test_quantile.py

@@ -524,6 +524,7 @@ def test_quantile_empty_no_columns(self):
         expected.columns.name = "captain tightpants"
         tm.assert_frame_equal(result, expected)
 
+    @td.skip_array_manager_invalid_test
     def test_quantile_item_cache(self):
         # previous behavior incorrect retained an invalid _item_cache entry
         df = DataFrame(np.random.randn(4, 3), columns=["A", "B", "C"])
@@ -608,12 +609,18 @@ def test_quantile_ea_with_na(self, index, frame_or_series):
         expected = frame_or_series(expected)
         tm.assert_equal(result, expected)
 
+    # TODO: filtering can be removed after GH#39763 is fixed
+    @pytest.mark.filterwarnings("ignore:Using .astype to convert:FutureWarning")
     def test_quantile_ea_all_na(self, index, frame_or_series):
 
         obj = frame_or_series(index).copy()
 
         obj.iloc[:] = index._na_value
 
+        # TODO: this casting should be unnecessary after GH#39763 is fixed
+        obj[:] = obj.astype(index.dtype)
+        assert np.all(obj.dtypes == index.dtype)
+
         # result should be invariant to shuffling
         indexer = np.arange(len(index), dtype=np.intp)
         np.random.shuffle(indexer)

pandas/tests/series/methods/test_quantile.py

@@ -1,8 +1,6 @@
 import numpy as np
 import pytest
 
-import pandas.util._test_decorators as td
-
 from pandas.core.dtypes.common import is_integer
 
 import pandas as pd