ENH: ArrayManager.quantile (#40189)

pandas/core/array_algos/quantile.py

@@ -1,11 +1,50 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
 import numpy as np
 
 from pandas._libs import lib
+from pandas._typing import ArrayLike
 
-from pandas.core.dtypes.common import is_list_like
+from pandas.core.dtypes.common import (
+    is_list_like,
+    is_sparse,
+)
+from pandas.core.dtypes.missing import (
+    isna,
+    na_value_for_dtype,
+)
 
 from pandas.core.nanops import nanpercentile
 
+if TYPE_CHECKING:
+    from pandas.core.arrays import ExtensionArray
+
+
+def quantile_compat(values: ArrayLike, qs, interpolation: str, axis: int) -> ArrayLike:
+    """
+    Compute the quantiles of the given values for each quantile in `qs`.
+
+    Parameters
+    ----------
+    values : np.ndarray or ExtensionArray
+    qs : a scalar or list of the quantiles to be computed
+    interpolation : str
+    axis : int
+
+    Returns
+    -------
+    np.ndarray or ExtensionArray
+    """
+    if isinstance(values, np.ndarray):
+        fill_value = na_value_for_dtype(values.dtype, compat=False)
+        mask = isna(values)
+        result = quantile_with_mask(values, mask, fill_value, qs, interpolation, axis)
+    else:
+        result = quantile_ea_compat(values, qs, interpolation, axis)
+    return result
+
 
 def quantile_with_mask(
     values: np.ndarray,
@@ -75,3 +114,50 @@ def quantile_with_mask(
         result = lib.item_from_zerodim(result)
 
     return result
+
+
+def quantile_ea_compat(
+    values: ExtensionArray, qs, interpolation: str, axis: int
+) -> ExtensionArray:
+    """
+    ExtensionArray compatibility layer for quantile_with_mask.
+
+    We pretend that an ExtensionArray with shape (N,) is actually (1, N,)
+    for compatibility with non-EA code.
+
+    Parameters
+    ----------
+    values : ExtensionArray
+    qs : a scalar or list of the quantiles to be computed
+    interpolation: str
+    axis : int
+
+    Returns
+    -------
+    ExtensionArray
+    """
+    # TODO(EA2D): make-believe not needed with 2D EAs
+    orig = values
+
+    # asarray needed for Sparse, see GH#24600
+    mask = np.asarray(values.isna())
+    mask = np.atleast_2d(mask)
+
+    values, fill_value = values._values_for_factorize()
+    values = np.atleast_2d(values)
+
+    result = quantile_with_mask(values, mask, fill_value, qs, interpolation, axis)
+
+    if not is_sparse(orig.dtype):
+        # shape[0] should be 1 as long as EAs are 1D
+
+        if result.ndim == 1:
+            # i.e. qs was originally a scalar
+            assert result.shape == (1,), result.shape
+            result = type(orig)._from_factorized(result, orig)
+
+        else:
+            assert result.shape == (1, len(qs)), result.shape
+            result = type(orig)._from_factorized(result[0], orig)
+
+    return result

pandas/core/frame.py

@@ -9661,9 +9661,8 @@ def quantile(
         q = Index(q, dtype=np.float64)
         data = self._get_numeric_data() if numeric_only else self
         axis = self._get_axis_number(axis)
-        is_transposed = axis == 1
 
-        if is_transposed:
+        if axis == 1:
             data = data.T
 
         if len(data.columns) == 0:
@@ -9673,15 +9672,9 @@ def quantile(
                 return self._constructor([], index=q, columns=cols)
             return self._constructor_sliced([], index=cols, name=q, dtype=np.float64)
 
-        result = data._mgr.quantile(
-            qs=q, axis=1, interpolation=interpolation, transposed=is_transposed
-        )
-
-        result = self._constructor(result)
-
-        if is_transposed:
-            result = result.T
+        res = data._mgr.quantile(qs=q, axis=1, interpolation=interpolation)
 
+        result = self._constructor(res)
         return result
 
     @doc(NDFrame.asfreq, **_shared_doc_kwargs)

pandas/core/internals/array_manager.py

@@ -4,6 +4,7 @@
 from __future__ import annotations
 
 from typing import (
+    TYPE_CHECKING,
     Any,
     Callable,
     List,
@@ -56,6 +57,7 @@
 )
 
 import pandas.core.algorithms as algos
+from pandas.core.array_algos.quantile import quantile_compat
 from pandas.core.array_algos.take import take_nd
 from pandas.core.arrays import (
     DatetimeArray,
@@ -82,6 +84,10 @@
 )
 from pandas.core.internals.blocks import make_block
 
+if TYPE_CHECKING:
+    from pandas import Float64Index
+
+
 T = TypeVar("T", bound="ArrayManager")
 
 
@@ -448,7 +454,28 @@ 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/blocks.py

@@ -81,7 +81,7 @@
     setitem_datetimelike_compat,
     validate_putmask,
 )
-from pandas.core.array_algos.quantile import quantile_with_mask
+from pandas.core.array_algos.quantile import quantile_compat
 from pandas.core.array_algos.replace import (
     compare_or_regex_search,
     replace_regex,
@@ -1458,11 +1458,7 @@ def quantile(
         assert axis == 1  # only ever called this way
         assert is_list_like(qs)  # caller is responsible for this
 
-        fill_value = self.fill_value
-        values = self.values
-        mask = np.asarray(isna(values))
-
-        result = quantile_with_mask(values, mask, fill_value, qs, interpolation, axis)
+        result = quantile_compat(self.values, qs, interpolation, axis)
 
         return make_block(result, placement=self.mgr_locs, ndim=2)
 
@@ -1836,24 +1832,6 @@ def _unstack(self, unstacker, fill_value, new_placement):
         ]
         return blocks, mask
 
-    def quantile(self, qs, interpolation="linear", axis: int = 0) -> Block:
-        # asarray needed for Sparse, see GH#24600
-        mask = np.asarray(isna(self.values))
-        mask = np.atleast_2d(mask)
-
-        values, fill_value = self.values._values_for_factorize()
-
-        values = np.atleast_2d(values)
-
-        result = quantile_with_mask(values, mask, fill_value, qs, interpolation, axis)
-
-        if not is_sparse(self.dtype):
-            # shape[0] should be 1 as long as EAs are 1D
-            assert result.shape == (1, len(qs)), result.shape
-            result = type(self.values)._from_factorized(result[0], self.values)
-
-        return make_block(result, placement=self.mgr_locs, ndim=2)
-
 
 class HybridMixin:
     """

pandas/core/internals/managers.py

@@ -521,7 +521,6 @@ def quantile(
         *,
         qs: Float64Index,
         axis: int = 0,
-        transposed: bool = False,
         interpolation="linear",
     ) -> BlockManager:
         """
@@ -534,8 +533,6 @@ def quantile(
         axis: reduction axis, default 0
         consolidate: bool, default True. Join together blocks having same
             dtype
-        transposed: bool, default False
-            we are holding transposed data
         interpolation : type of interpolation, default 'linear'
         qs : list of the quantiles to be computed
 
@@ -557,13 +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_describe.py

@@ -1,8 +1,6 @@
 import numpy as np
 import pytest
 
-import pandas.util._test_decorators as td
-
 import pandas as pd
 from pandas import (
     Categorical,
@@ -13,9 +11,6 @@
 )
 import pandas._testing as tm
 
-# TODO(ArrayManager) quantile is needed for describe()
-pytestmark = td.skip_array_manager_not_yet_implemented
-
 
 class TestDataFrameDescribe:
     def test_describe_bool_in_mixed_frame(self):

pandas/tests/frame/methods/test_quantile.py

@@ -1,8 +1,6 @@
 import numpy as np
 import pytest
 
-import pandas.util._test_decorators as td
-
 import pandas as pd
 from pandas import (
     DataFrame,
@@ -11,8 +9,6 @@
 )
 import pandas._testing as tm
 
-pytestmark = td.skip_array_manager_not_yet_implemented
-
 
 class TestDataFrameQuantile:
     @pytest.mark.parametrize(
@@ -526,12 +522,13 @@ def test_quantile_empty_no_columns(self):
         expected.columns.name = "captain tightpants"
         tm.assert_frame_equal(result, expected)
 
-    def test_quantile_item_cache(self):
+    def test_quantile_item_cache(self, using_array_manager):
         # previous behavior incorrect retained an invalid _item_cache entry
         df = DataFrame(np.random.randn(4, 3), columns=["A", "B", "C"])
         df["D"] = df["A"] * 2
         ser = df["A"]
-        assert len(df._mgr.blocks) == 2
+        if not using_array_manager:
+            assert len(df._mgr.blocks) == 2
 
         df.quantile(numeric_only=False)
         ser.values[0] = 99
@@ -610,12 +607,18 @@ 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(ArrayManager): 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/groupby/aggregate/test_aggregate.py

@@ -10,7 +10,6 @@
 import pytest
 
 from pandas.errors import PerformanceWarning
-import pandas.util._test_decorators as td
 
 from pandas.core.dtypes.common import is_integer_dtype
 
@@ -46,7 +45,6 @@ def test_agg_regression1(tsframe):
     tm.assert_frame_equal(result, expected)
 
 
-@td.skip_array_manager_not_yet_implemented  # TODO(ArrayManager) quantile/describe
 def test_agg_must_agg(df):
     grouped = df.groupby("A")["C"]
 

pandas/tests/groupby/test_apply.py

@@ -318,7 +318,6 @@ def test_groupby_as_index_apply(df):
     tm.assert_index_equal(res, ind)
 
 
-@td.skip_array_manager_not_yet_implemented  # TODO(ArrayManager) quantile
 def test_apply_concat_preserve_names(three_group):
     grouped = three_group.groupby(["A", "B"])
 

pandas/tests/groupby/test_categorical.py

@@ -83,7 +83,6 @@ def get_stats(group):
     assert result.index.names[0] == "C"
 
 
-@td.skip_array_manager_not_yet_implemented  # TODO(ArrayManager) quantile
 def test_basic():
 
     cats = Categorical(
@@ -540,7 +539,6 @@ def test_dataframe_categorical_ordered_observed_sort(ordered, observed, sort):
         assert False, msg
 
 
-@td.skip_array_manager_not_yet_implemented  # TODO(ArrayManager) quantile
 def test_datetime():
     # GH9049: ensure backward compatibility
     levels = pd.date_range("2014-01-01", periods=4)
@@ -606,7 +604,6 @@ def test_categorical_index():
     tm.assert_frame_equal(result, expected)
 
 
-@td.skip_array_manager_not_yet_implemented  # TODO(ArrayManager) quantile
 def test_describe_categorical_columns():
     # GH 11558
     cats = CategoricalIndex(
@@ -621,7 +618,6 @@ def test_describe_categorical_columns():
     tm.assert_categorical_equal(result.stack().columns.values, cats.values)
 
 
-@td.skip_array_manager_not_yet_implemented  # TODO(ArrayManager) quantile
 def test_unstack_categorical():
     # GH11558 (example is taken from the original issue)
     df = DataFrame(