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asv_bench/benchmarks/groupby.py

@@ -369,6 +369,18 @@ def time_category_size(self):
         self.draws.groupby(self.cats).size()
 
 
+class Shift:
+    def setup(self):
+        N = 18
+        self.df = DataFrame({"g": ["a", "b"] * 9, "v": list(range(N))})
+
+    def time_defaults(self):
+        self.df.groupby("g").shift()
+
+    def time_fill_value(self):
+        self.df.groupby("g").shift(fill_value=99)
+
+
 class FillNA:
     def setup(self):
         N = 100

doc/source/whatsnew/v1.3.2.rst

@@ -18,6 +18,7 @@ Fixed regressions
 - Regression in updating values of :class:`pandas.Series` using boolean index, created by using :meth:`pandas.DataFrame.pop` (:issue:`42530`)
 - Regression in :meth:`DataFrame.from_records` with empty records (:issue:`42456`)
 - Fixed regression in :meth:`DataFrame.shift` where TypeError occurred when shifting DataFrame created by concatenation of slices and fills with values (:issue:`42719`)
+- Regression in :meth:`DataFrame.agg` when the ``func`` argument returned lists and ``axis=1`` (:issue:`42727`)
 -
 
 .. ---------------------------------------------------------------------------

doc/source/whatsnew/v1.4.0.rst

@@ -35,6 +35,7 @@ Other enhancements
 -  Additional options added to :meth:`.Styler.bar` to control alignment and display, with keyword only arguments (:issue:`26070`, :issue:`36419`)
 - :meth:`Styler.bar` now validates the input argument ``width`` and ``height`` (:issue:`42511`)
 - :meth:`Series.ewm`, :meth:`DataFrame.ewm`, now support a ``method`` argument with a ``'table'`` option that performs the windowing operation over an entire :class:`DataFrame`. See :ref:`Window Overview <window.overview>` for performance and functional benefits (:issue:`42273`)
+- Added ``sparse_index`` and ``sparse_columns`` keyword arguments to :meth:`.Styler.to_html` (:issue:`41946`)
 - Added keyword argument ``environment`` to :meth:`.Styler.to_latex` also allowing a specific "longtable" entry with a separate jinja2 template (:issue:`41866`)
 -
 
@@ -168,6 +169,7 @@ Performance improvements
 - Performance improvement in :meth:`.GroupBy.sample`, especially when ``weights`` argument provided (:issue:`34483`)
 - Performance improvement in :meth:`.GroupBy.transform` for user-defined functions (:issue:`41598`)
 - Performance improvement in constructing :class:`DataFrame` objects (:issue:`42631`)
+- Performance improvement in :meth:`GroupBy.shift` when ``fill_value`` argument is provided (:issue:`26615`)
 
 .. ---------------------------------------------------------------------------
 
@@ -262,6 +264,7 @@ Groupby/resample/rolling
 - Fixed bug in :meth:`SeriesGroupBy.apply` where passing an unrecognized string argument failed to raise ``TypeError`` when the underlying ``Series`` is empty (:issue:`42021`)
 - Bug in :meth:`Series.rolling.apply`, :meth:`DataFrame.rolling.apply`, :meth:`Series.expanding.apply` and :meth:`DataFrame.expanding.apply` with ``engine="numba"`` where ``*args`` were being cached with the user passed function (:issue:`42287`)
 - Bug in :meth:`DataFrame.groupby.rolling.var` would calculate the rolling variance only on the first group (:issue:`42442`)
+- Bug in :meth:`GroupBy.shift` that would return the grouping columns if ``fill_value`` was not None (:issue:`41556`)
 
 Reshaping
 ^^^^^^^^^

pandas/core/apply.py

@@ -690,21 +690,28 @@ def agg(self):
         obj = self.obj
         axis = self.axis
 
+        # TODO: Avoid having to change state
+        self.obj = self.obj if self.axis == 0 else self.obj.T
+        self.axis = 0
+
+        result = None
+        try:
+            result = super().agg()
+        except TypeError as err:
+            exc = TypeError(
+                "DataFrame constructor called with "
+                f"incompatible data and dtype: {err}"
+            )
+            raise exc from err
+        finally:
+            self.obj = obj
+            self.axis = axis
+
         if axis == 1:
-            result = FrameRowApply(
-                obj.T,
-                self.orig_f,
-                self.raw,
-                self.result_type,
-                self.args,
-                self.kwargs,
-            ).agg()
             result = result.T if result is not None else result
-        else:
-            result = super().agg()
 
         if result is None:
-            result = obj.apply(self.orig_f, axis, args=self.args, **self.kwargs)
+            result = self.obj.apply(self.orig_f, axis, args=self.args, **self.kwargs)
 
         return result
 

pandas/core/construction.py

@@ -420,9 +420,9 @@ def extract_array(
                 return obj._values
             return obj
 
-        obj = obj.array
+        obj = obj._values
 
-    if extract_numpy and isinstance(obj, ABCPandasArray):
+    elif extract_numpy and isinstance(obj, ABCPandasArray):
         obj = obj.to_numpy()
 
     return obj

pandas/core/groupby/groupby.py

@@ -2822,6 +2822,7 @@ def _get_cythonized_result(
         result_is_index: bool = False,
         pre_processing=None,
         post_processing=None,
+        fill_value=None,
         **kwargs,
     ):
         """
@@ -2872,6 +2873,8 @@ def _get_cythonized_result(
             second argument, i.e. the signature should be
             (ndarray, Type). If `needs_nullable=True`, a third argument should be
             `nullable`, to allow for processing specific to nullable values.
+        fill_value : any, default None
+            The scalar value to use for newly introduced missing values.
         **kwargs : dict
             Extra arguments to be passed back to Cython funcs
 
@@ -2896,7 +2899,7 @@ def _get_cythonized_result(
         grouper = self.grouper
 
         ids, _, ngroups = grouper.group_info
-        output: dict[base.OutputKey, np.ndarray] = {}
+        output: dict[base.OutputKey, ArrayLike] = {}
 
         base_func = getattr(libgroupby, how)
         base_func = partial(base_func, labels=ids)
@@ -2911,6 +2914,7 @@ def blk_func(values: ArrayLike) -> ArrayLike:
             else:
                 result_sz = len(values)
 
+            result: ArrayLike
             result = np.zeros(result_sz, dtype=cython_dtype)
             if needs_2d:
                 result = result.reshape((-1, 1))
@@ -2946,7 +2950,7 @@ def blk_func(values: ArrayLike) -> ArrayLike:
                 result = result.reshape(-1)
 
             if result_is_index:
-                result = algorithms.take_nd(values, result)
+                result = algorithms.take_nd(values, result, fill_value=fill_value)
 
             if post_processing:
                 pp_kwargs = {}
@@ -3022,7 +3026,7 @@ def shift(self, periods=1, freq=None, axis=0, fill_value=None):
         tshift : Shift the time index, using the index’s frequency
             if available.
         """
-        if freq is not None or axis != 0 or not isna(fill_value):
+        if freq is not None or axis != 0:
             return self.apply(lambda x: x.shift(periods, freq, axis, fill_value))
 
         return self._get_cythonized_result(
@@ -3032,6 +3036,7 @@ def shift(self, periods=1, freq=None, axis=0, fill_value=None):
             needs_ngroups=True,
             result_is_index=True,
             periods=periods,
+            fill_value=fill_value,
         )
 
     @final

pandas/core/internals/blocks.py

@@ -1920,11 +1920,11 @@ def get_block_type(values, dtype: DtypeObj | None = None):
 
 
 def new_block(values, placement, *, ndim: int, klass=None) -> Block:
+    # caller is responsible for ensuring values is NOT a PandasArray
 
     if not isinstance(placement, BlockPlacement):
         placement = BlockPlacement(placement)
 
-    values, _ = extract_pandas_array(values, None, ndim)
     check_ndim(values, placement, ndim)
 
     if klass is None:

pandas/core/internals/managers.py

@@ -1779,11 +1779,6 @@ def create_block_manager_from_blocks(
     return mgr
 
 
-# We define this here so we can override it in tests.extension.test_numpy
-def _extract_array(obj):
-    return extract_array(obj, extract_numpy=True)
-
-
 def create_block_manager_from_arrays(
     arrays,
     names: Index,
@@ -1795,7 +1790,7 @@ def create_block_manager_from_arrays(
     # assert isinstance(axes, list)
     # assert all(isinstance(x, Index) for x in axes)
 
-    arrays = [_extract_array(x) for x in arrays]
+    arrays = [extract_array(x, extract_numpy=True) for x in arrays]
 
     try:
         blocks = _form_blocks(arrays, names, axes, consolidate)

pandas/core/reshape/tile.py

@@ -418,7 +418,11 @@ def _bins_to_cuts(
             bins = unique_bins
 
     side = "left" if right else "right"
-    ids = ensure_platform_int(bins.searchsorted(x, side=side))
+    # error: No overload variant of "searchsorted" of "ndarray" matches
+    # argument types "Any", "str"
+    ids = ensure_platform_int(
+        bins.searchsorted(x, side=side)  # type: ignore[call-overload]
+    )
 
     if include_lowest:
         ids[np.asarray(x) == bins[0]] = 1

pandas/core/strings/accessor.py

@@ -34,6 +34,7 @@
 from pandas.core.dtypes.missing import isna
 
 from pandas.core.base import NoNewAttributesMixin
+from pandas.core.construction import extract_array
 
 if TYPE_CHECKING:
     from pandas import (
@@ -213,10 +214,7 @@ def _validate(data):
         # see _libs/lib.pyx for list of inferred types
         allowed_types = ["string", "empty", "bytes", "mixed", "mixed-integer"]
 
-        # TODO: avoid kludge for tests.extension.test_numpy
-        from pandas.core.internals.managers import _extract_array
-
-        data = _extract_array(data)
+        data = extract_array(data)
 
         values = getattr(data, "categories", data)  # categorical / normal
 

pandas/io/formats/style.py

@@ -476,8 +476,8 @@ def to_latex(
             Defaults to ``pandas.options.styler.sparse.index`` value.
         sparse_columns : bool, optional
             Whether to sparsify the display of a hierarchical index. Setting to False
-            will display each explicit level element in a hierarchical key for each row.
-            Defaults to ``pandas.options.styler.sparse.columns`` value.
+            will display each explicit level element in a hierarchical key for each
+            column. Defaults to ``pandas.options.styler.sparse.columns`` value.
         multirow_align : {"c", "t", "b"}
             If sparsifying hierarchical MultiIndexes whether to align text centrally,
             at the top or bottom.
@@ -815,6 +815,8 @@ def to_html(
         *,
         table_uuid: str | None = None,
         table_attributes: str | None = None,
+        sparse_index: bool | None = None,
+        sparse_columns: bool | None = None,
         encoding: str | None = None,
         doctype_html: bool = False,
         exclude_styles: bool = False,
@@ -840,6 +842,18 @@ def to_html(
             ``<table .. <table_attributes> >``
 
             If not given defaults to Styler's preexisting value.
+        sparse_index : bool, optional
+            Whether to sparsify the display of a hierarchical index. Setting to False
+            will display each explicit level element in a hierarchical key for each row.
+            Defaults to ``pandas.options.styler.sparse.index`` value.
+
+            .. versionadded:: 1.4.0
+        sparse_columns : bool, optional
+            Whether to sparsify the display of a hierarchical index. Setting to False
+            will display each explicit level element in a hierarchical key for each
+            column. Defaults to ``pandas.options.styler.sparse.columns`` value.
+
+            .. versionadded:: 1.4.0
         encoding : str, optional
             Character encoding setting for file output, and HTML meta tags,
             defaults to "utf-8" if None.
@@ -866,8 +880,15 @@ def to_html(
         if table_attributes:
             self.set_table_attributes(table_attributes)
 
+        if sparse_index is None:
+            sparse_index = get_option("styler.sparse.index")
+        if sparse_columns is None:
+            sparse_columns = get_option("styler.sparse.columns")
+
         # Build HTML string..
-        html = self.render(
+        html = self._render_html(
+            sparse_index=sparse_index,
+            sparse_columns=sparse_columns,
             exclude_styles=exclude_styles,
             encoding=encoding if encoding else "utf-8",
             doctype_html=doctype_html,

pandas/tests/apply/test_frame_apply.py

@@ -644,13 +644,14 @@ def test_apply_dup_names_multi_agg():
     tm.assert_frame_equal(result, expected)
 
 
-def test_apply_nested_result_axis_1():
+@pytest.mark.parametrize("op", ["apply", "agg"])
+def test_apply_nested_result_axis_1(op):
     # GH 13820
     def apply_list(row):
         return [2 * row["A"], 2 * row["C"], 2 * row["B"]]
 
     df = DataFrame(np.zeros((4, 4)), columns=list("ABCD"))
-    result = df.apply(apply_list, axis=1)
+    result = getattr(df, op)(apply_list, axis=1)
     expected = Series(
         [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
     )

pandas/tests/extension/test_numpy.py

@@ -23,32 +23,17 @@
     ExtensionDtype,
     PandasDtype,
 )
-from pandas.core.dtypes.generic import ABCPandasArray
 
 import pandas as pd
 import pandas._testing as tm
 from pandas.core.arrays.numpy_ import PandasArray
-from pandas.core.internals import (
-    blocks,
-    managers,
-)
+from pandas.core.internals import blocks
 from pandas.tests.extension import base
 
 # TODO(ArrayManager) PandasArray
 pytestmark = td.skip_array_manager_not_yet_implemented
 
 
-def _extract_array_patched(obj):
-    if isinstance(obj, (pd.Index, pd.Series)):
-        obj = obj._values
-    if isinstance(obj, ABCPandasArray):
-        # TODO for reasons unclear, we get here in a couple of tests
-        #  with PandasArray._typ *not* patched
-        obj = obj.to_numpy()
-
-    return obj
-
-
 def _can_hold_element_patched(obj, element) -> bool:
     if isinstance(element, PandasArray):
         element = element.to_numpy()
@@ -98,7 +83,6 @@ def allow_in_pandas(monkeypatch):
     """
     with monkeypatch.context() as m:
         m.setattr(PandasArray, "_typ", "extension")
-        m.setattr(managers, "_extract_array", _extract_array_patched)
         m.setattr(blocks, "can_hold_element", _can_hold_element_patched)
         m.setattr(tm.asserters, "assert_attr_equal", _assert_attr_equal)
         yield

pandas/tests/groupby/test_groupby_shift_diff.py

@@ -55,7 +55,7 @@ def test_group_shift_with_fill_value():
         columns=["Z"],
         index=None,
     )
-    result = g.shift(-1, fill_value=0)[["Z"]]
+    result = g.shift(-1, fill_value=0)
 
     tm.assert_frame_equal(result, expected)
 

pandas/tests/internals/test_internals.py

@@ -1376,9 +1376,11 @@ def test_make_block_no_pandas_array(block_maker):
     # PandasArray, no dtype
     result = block_maker(arr, slice(len(arr)), ndim=arr.ndim)
     assert result.dtype.kind in ["i", "u"]
-    assert result.is_extension is False
 
     if block_maker is make_block:
+        # new_block requires caller to unwrap PandasArray
+        assert result.is_extension is False
+
         # PandasArray, PandasDtype
         result = block_maker(arr, slice(len(arr)), dtype=arr.dtype, ndim=arr.ndim)
         assert result.dtype.kind in ["i", "u"]

pandas/tests/io/formats/style/test_html.py

@@ -6,6 +6,7 @@
 from pandas import (
     DataFrame,
     MultiIndex,
+    option_context,
 )
 
 jinja2 = pytest.importorskip("jinja2")
@@ -429,3 +430,24 @@ def test_sticky_levels(styler_mi, index, columns):
 def test_sticky_raises(styler):
     with pytest.raises(ValueError, match="`axis` must be"):
         styler.set_sticky(axis="bad")
+
+
+@pytest.mark.parametrize(
+    "sparse_index, sparse_columns",
+    [(True, True), (True, False), (False, True), (False, False)],
+)
+def test_sparse_options(sparse_index, sparse_columns):
+    cidx = MultiIndex.from_tuples([("Z", "a"), ("Z", "b"), ("Y", "c")])
+    ridx = MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "c")])
+    df = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], index=ridx, columns=cidx)
+    styler = df.style
+
+    default_html = styler.to_html()  # defaults under pd.options to (True , True)
+
+    with option_context(
+        "styler.sparse.index", sparse_index, "styler.sparse.columns", sparse_columns
+    ):
+        html1 = styler.to_html()
+        assert (html1 == default_html) is (sparse_index and sparse_columns)
+    html2 = styler.to_html(sparse_index=sparse_index, sparse_columns=sparse_columns)
+    assert html1 == html2