CLN: remove redundant code related to Styler

pandas/core/indexing.py

@@ -2403,57 +2403,3 @@ def need_slice(obj) -> bool:
         or obj.stop is not None
         or (obj.step is not None and obj.step != 1)
     )
-
-
-def non_reducing_slice(slice_):
-    """
-    Ensure that a slice doesn't reduce to a Series or Scalar.
-
-    Any user-passed `subset` should have this called on it
-    to make sure we're always working with DataFrames.
-    """
-    # default to column slice, like DataFrame
-    # ['A', 'B'] -> IndexSlices[:, ['A', 'B']]
-    kinds = (ABCSeries, np.ndarray, Index, list, str)
-    if isinstance(slice_, kinds):
-        slice_ = IndexSlice[:, slice_]
-
-    def pred(part) -> bool:
-        """
-        Returns
-        -------
-        bool
-            True if slice does *not* reduce,
-            False if `part` is a tuple.
-        """
-        # true when slice does *not* reduce, False when part is a tuple,
-        # i.e. MultiIndex slice
-        if isinstance(part, tuple):
-            # GH#39421 check for sub-slice:
-            return any((isinstance(s, slice) or is_list_like(s)) for s in part)
-        else:
-            return isinstance(part, slice) or is_list_like(part)
-
-    if not is_list_like(slice_):
-        if not isinstance(slice_, slice):
-            # a 1-d slice, like df.loc[1]
-            slice_ = [[slice_]]
-        else:
-            # slice(a, b, c)
-            slice_ = [slice_]  # to tuplize later
-    else:
-        slice_ = [part if pred(part) else [part] for part in slice_]
-    return tuple(slice_)
-
-
-def maybe_numeric_slice(df, slice_, include_bool: bool = False):
-    """
-    Want nice defaults for background_gradient that don't break
-    with non-numeric data. But if slice_ is passed go with that.
-    """
-    if slice_ is None:
-        dtypes = [np.number]
-        if include_bool:
-            dtypes.append(bool)
-        slice_ = IndexSlice[:, df.select_dtypes(include=dtypes).columns]
-    return slice_

pandas/io/formats/style.py

@@ -37,6 +37,7 @@
 from pandas.util._decorators import doc
 
 from pandas.core.dtypes.common import is_float
+from pandas.core.dtypes.generic import ABCSeries
 
 import pandas as pd
 from pandas.api.types import (
@@ -47,10 +48,7 @@
 import pandas.core.common as com
 from pandas.core.frame import DataFrame
 from pandas.core.generic import NDFrame
-from pandas.core.indexing import (
-    maybe_numeric_slice,
-    non_reducing_slice,
-)
+from pandas.core.indexes.api import Index
 
 jinja2 = import_optional_dependency("jinja2", extra="DataFrame.style requires jinja2.")
 CSSSequence = Sequence[Tuple[str, Union[str, int, float]]]
@@ -625,7 +623,7 @@ def format(self, formatter, subset=None, na_rep: Optional[str] = None) -> Styler
             row_locs = range(len(self.data))
             col_locs = range(len(self.data.columns))
         else:
-            subset = non_reducing_slice(subset)
+            subset = _non_reducing_slice(subset)
             if len(subset) == 1:
                 subset = subset, self.data.columns
 
@@ -851,7 +849,7 @@ def _apply(
         **kwargs,
     ) -> Styler:
         subset = slice(None) if subset is None else subset
-        subset = non_reducing_slice(subset)
+        subset = _non_reducing_slice(subset)
         data = self.data.loc[subset]
         if axis is not None:
             result = data.apply(func, axis=axis, result_type="expand", **kwargs)
@@ -954,7 +952,7 @@ def _applymap(self, func: Callable, subset=None, **kwargs) -> Styler:
         func = partial(func, **kwargs)  # applymap doesn't take kwargs?
         if subset is None:
             subset = pd.IndexSlice[:]
-        subset = non_reducing_slice(subset)
+        subset = _non_reducing_slice(subset)
         result = self.data.loc[subset].applymap(func)
         self._update_ctx(result)
         return self
@@ -1304,7 +1302,7 @@ def hide_columns(self, subset) -> Styler:
         -------
         self : Styler
         """
-        subset = non_reducing_slice(subset)
+        subset = _non_reducing_slice(subset)
         hidden_df = self.data.loc[subset]
         self.hidden_columns = self.columns.get_indexer_for(hidden_df.columns)
         return self
@@ -1379,8 +1377,9 @@ def background_gradient(
         of the data is extended by ``low * (x.max() - x.min())`` and ``high *
         (x.max() - x.min())`` before normalizing.
         """
-        subset = maybe_numeric_slice(self.data, subset)
-        subset = non_reducing_slice(subset)
+        if subset is None:
+            subset = self.data.select_dtypes(include=np.number).columns
+
         self.apply(
             self._background_gradient,
             cmap=cmap,
@@ -1613,8 +1612,9 @@ def bar(
                 "(eg: color=['#d65f5f', '#5fba7d'])"
             )
 
-        subset = maybe_numeric_slice(self.data, subset)
-        subset = non_reducing_slice(subset)
+        if subset is None:
+            subset = self.data.select_dtypes(include=np.number).columns
+
         self.apply(
             self._bar,
             subset=subset,
@@ -2088,3 +2088,44 @@ def _maybe_convert_css_to_tuples(style: CSSProperties) -> CSSSequence:
                 f"for example 'attr: val;'. '{style}' was given."
             )
     return style
+
+
+def _non_reducing_slice(slice_):
+    """
+    Ensure that a slice doesn't reduce to a Series or Scalar.
+
+    Any user-passed `subset` should have this called on it
+    to make sure we're always working with DataFrames.
+    """
+    # default to column slice, like DataFrame
+    # ['A', 'B'] -> IndexSlices[:, ['A', 'B']]
+    kinds = (ABCSeries, np.ndarray, Index, list, str)
+    if isinstance(slice_, kinds):
+        slice_ = pd.IndexSlice[:, slice_]
+
+    def pred(part) -> bool:
+        """
+        Returns
+        -------
+        bool
+            True if slice does *not* reduce,
+            False if `part` is a tuple.
+        """
+        # true when slice does *not* reduce, False when part is a tuple,
+        # i.e. MultiIndex slice
+        if isinstance(part, tuple):
+            # GH#39421 check for sub-slice:
+            return any((isinstance(s, slice) or is_list_like(s)) for s in part)
+        else:
+            return isinstance(part, slice) or is_list_like(part)
+
+    if not is_list_like(slice_):
+        if not isinstance(slice_, slice):
+            # a 1-d slice, like df.loc[1]
+            slice_ = [[slice_]]
+        else:
+            # slice(a, b, c)
+            slice_ = [slice_]  # to tuplize later
+    else:
+        slice_ = [part if pred(part) else [part] for part in slice_]
+    return tuple(slice_)

pandas/tests/indexing/multiindex/test_slice.py

@@ -12,7 +12,6 @@
     Timestamp,
 )
 import pandas._testing as tm
-from pandas.core.indexing import non_reducing_slice
 from pandas.tests.indexing.common import _mklbl
 
 
@@ -769,59 +768,6 @@ def test_int_series_slicing(self, multiindex_year_month_day_dataframe_random_dat
         expected = ymd.reindex(s.index[5:])
         tm.assert_frame_equal(result, expected)
 
-    def test_non_reducing_slice_on_multiindex(self):
-        # GH 19861
-        dic = {
-            ("a", "d"): [1, 4],
-            ("a", "c"): [2, 3],
-            ("b", "c"): [3, 2],
-            ("b", "d"): [4, 1],
-        }
-        df = DataFrame(dic, index=[0, 1])
-        idx = pd.IndexSlice
-        slice_ = idx[:, idx["b", "d"]]
-        tslice_ = non_reducing_slice(slice_)
-
-        result = df.loc[tslice_]
-        expected = DataFrame({("b", "d"): [4, 1]})
-        tm.assert_frame_equal(result, expected)
-
-    @pytest.mark.parametrize(
-        "slice_",
-        [
-            pd.IndexSlice[:, :],
-            # check cols
-            pd.IndexSlice[:, pd.IndexSlice[["a"]]],  # inferred deeper need list
-            pd.IndexSlice[:, pd.IndexSlice[["a"], ["c"]]],  # inferred deeper need list
-            pd.IndexSlice[:, pd.IndexSlice["a", "c", :]],
-            pd.IndexSlice[:, pd.IndexSlice["a", :, "e"]],
-            pd.IndexSlice[:, pd.IndexSlice[:, "c", "e"]],
-            pd.IndexSlice[:, pd.IndexSlice["a", ["c", "d"], :]],  # check list
-            pd.IndexSlice[:, pd.IndexSlice["a", ["c", "d", "-"], :]],  # allow missing
-            pd.IndexSlice[:, pd.IndexSlice["a", ["c", "d", "-"], "e"]],  # no slice
-            # check rows
-            pd.IndexSlice[pd.IndexSlice[["U"]], :],  # inferred deeper need list
-            pd.IndexSlice[pd.IndexSlice[["U"], ["W"]], :],  # inferred deeper need list
-            pd.IndexSlice[pd.IndexSlice["U", "W", :], :],
-            pd.IndexSlice[pd.IndexSlice["U", :, "Y"], :],
-            pd.IndexSlice[pd.IndexSlice[:, "W", "Y"], :],
-            pd.IndexSlice[pd.IndexSlice[:, "W", ["Y", "Z"]], :],  # check list
-            pd.IndexSlice[pd.IndexSlice[:, "W", ["Y", "Z", "-"]], :],  # allow missing
-            pd.IndexSlice[pd.IndexSlice["U", "W", ["Y", "Z", "-"]], :],  # no slice
-            # check simultaneous
-            pd.IndexSlice[pd.IndexSlice[:, "W", "Y"], pd.IndexSlice["a", "c", :]],
-        ],
-    )
-    def test_non_reducing_multi_slice_on_multiindex(self, slice_):
-        # GH 33562
-        cols = pd.MultiIndex.from_product([["a", "b"], ["c", "d"], ["e", "f"]])
-        idxs = pd.MultiIndex.from_product([["U", "V"], ["W", "X"], ["Y", "Z"]])
-        df = DataFrame(np.arange(64).reshape(8, 8), columns=cols, index=idxs)
-
-        expected = df.loc[slice_]
-        result = df.loc[non_reducing_slice(slice_)]
-        tm.assert_frame_equal(result, expected)
-
     def test_loc_slice_negative_stepsize(self):
         # GH#38071
         mi = MultiIndex.from_product([["a", "b"], [0, 1]])

pandas/tests/indexing/test_indexing.py

@@ -23,10 +23,6 @@
     timedelta_range,
 )
 import pandas._testing as tm
-from pandas.core.indexing import (
-    maybe_numeric_slice,
-    non_reducing_slice,
-)
 from pandas.tests.indexing.common import _mklbl
 from pandas.tests.indexing.test_floats import gen_obj
 
@@ -794,53 +790,6 @@ def test_range_in_series_indexing(self, size):
         s.loc[range(2)] = 43
         tm.assert_series_equal(s.loc[range(2)], Series(43.0, index=[0, 1]))
 
-    @pytest.mark.parametrize(
-        "slc",
-        [
-            pd.IndexSlice[:, :],
-            pd.IndexSlice[:, 1],
-            pd.IndexSlice[1, :],
-            pd.IndexSlice[[1], [1]],
-            pd.IndexSlice[1, [1]],
-            pd.IndexSlice[[1], 1],
-            pd.IndexSlice[1],
-            pd.IndexSlice[1, 1],
-            slice(None, None, None),
-            [0, 1],
-            np.array([0, 1]),
-            Series([0, 1]),
-        ],
-    )
-    def test_non_reducing_slice(self, slc):
-        df = DataFrame([[0, 1], [2, 3]])
-
-        tslice_ = non_reducing_slice(slc)
-        assert isinstance(df.loc[tslice_], DataFrame)
-
-    @pytest.mark.parametrize("box", [list, Series, np.array])
-    def test_list_slice(self, box):
-        # like dataframe getitem
-        subset = box(["A"])
-
-        df = DataFrame({"A": [1, 2], "B": [3, 4]}, index=["A", "B"])
-        expected = pd.IndexSlice[:, ["A"]]
-
-        result = non_reducing_slice(subset)
-        tm.assert_frame_equal(df.loc[result], df.loc[expected])
-
-    def test_maybe_numeric_slice(self):
-        df = DataFrame({"A": [1, 2], "B": ["c", "d"], "C": [True, False]})
-        result = maybe_numeric_slice(df, slice_=None)
-        expected = pd.IndexSlice[:, ["A"]]
-        assert result == expected
-
-        result = maybe_numeric_slice(df, None, include_bool=True)
-        expected = pd.IndexSlice[:, ["A", "C"]]
-        assert all(result[1] == expected[1])
-        result = maybe_numeric_slice(df, [1])
-        expected = [1]
-        assert result == expected
-
     def test_partial_boolean_frame_indexing(self):
         # GH 17170
         df = DataFrame(