Merge branch 'pandas-dev:main' into Fix#58748

Author: SiddheshBangar

ci/code_checks.sh

@@ -440,7 +440,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.errors.UnsortedIndexError SA01" \
         -i "pandas.errors.UnsupportedFunctionCall SA01" \
         -i "pandas.errors.ValueLabelTypeMismatch SA01" \
-        -i "pandas.get_option SA01" \
         -i "pandas.infer_freq SA01" \
         -i "pandas.interval_range RT03" \
         -i "pandas.io.formats.style.Styler.apply RT03" \

doc/source/user_guide/merging.rst

@@ -763,7 +763,7 @@ Joining a single Index to a MultiIndex
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 You can join a :class:`DataFrame` with a :class:`Index` to a :class:`DataFrame` with a :class:`MultiIndex` on a level.
-The ``name`` of the :class:`Index` with match the level name of the :class:`MultiIndex`.
+The ``name`` of the :class:`Index` will match the level name of the :class:`MultiIndex`.
 
 ..  ipython:: python
 

pandas/_config/config.py

@@ -157,6 +157,12 @@ def get_option(pat: str) -> Any:
     ------
     OptionError : if no such option exists
 
+    See Also
+    --------
+    set_option : Set the value of the specified option or options.
+    reset_option : Reset one or more options to their default value.
+    describe_option : Print the description for one or more registered options.
+
     Notes
     -----
     For all available options, please view the :ref:`User Guide <options.available>`

pandas/core/reshape/reshape.py

@@ -925,27 +925,99 @@ def _reorder_for_extension_array_stack(
 def stack_v3(frame: DataFrame, level: list[int]) -> Series | DataFrame:
     if frame.columns.nunique() != len(frame.columns):
         raise ValueError("Columns with duplicate values are not supported in stack")
-
-    # If we need to drop `level` from columns, it needs to be in descending order
     set_levels = set(level)
-    drop_levnums = sorted(level, reverse=True)
     stack_cols = frame.columns._drop_level_numbers(
         [k for k in range(frame.columns.nlevels - 1, -1, -1) if k not in set_levels]
     )
+
+    result = stack_reshape(frame, level, set_levels, stack_cols)
+
+    # Construct the correct MultiIndex by combining the frame's index and
+    # stacked columns.
+    ratio = 0 if frame.empty else len(result) // len(frame)
+
+    index_levels: list | FrozenList
+    if isinstance(frame.index, MultiIndex):
+        index_levels = frame.index.levels
+        index_codes = list(np.tile(frame.index.codes, (1, ratio)))
+    else:
+        codes, uniques = factorize(frame.index, use_na_sentinel=False)
+        index_levels = [uniques]
+        index_codes = list(np.tile(codes, (1, ratio)))
+
     if len(level) > 1:
         # Arrange columns in the order we want to take them, e.g. level=[2, 0, 1]
         sorter = np.argsort(level)
         assert isinstance(stack_cols, MultiIndex)
         ordered_stack_cols = stack_cols._reorder_ilevels(sorter)
     else:
         ordered_stack_cols = stack_cols
-
-    stack_cols_unique = stack_cols.unique()
     ordered_stack_cols_unique = ordered_stack_cols.unique()
+    if isinstance(ordered_stack_cols, MultiIndex):
+        column_levels = ordered_stack_cols.levels
+        column_codes = ordered_stack_cols.drop_duplicates().codes
+    else:
+        column_levels = [ordered_stack_cols_unique]
+        column_codes = [factorize(ordered_stack_cols_unique, use_na_sentinel=False)[0]]
+
+    # error: Incompatible types in assignment (expression has type "list[ndarray[Any,
+    # dtype[Any]]]", variable has type "FrozenList")
+    column_codes = [np.repeat(codes, len(frame)) for codes in column_codes]  # type: ignore[assignment]
+    result.index = MultiIndex(
+        levels=index_levels + column_levels,
+        codes=index_codes + column_codes,
+        names=frame.index.names + list(ordered_stack_cols.names),
+        verify_integrity=False,
+    )
+
+    # sort result, but faster than calling sort_index since we know the order we need
+    len_df = len(frame)
+    n_uniques = len(ordered_stack_cols_unique)
+    indexer = np.arange(n_uniques)
+    idxs = np.tile(len_df * indexer, len_df) + np.repeat(np.arange(len_df), n_uniques)
+    result = result.take(idxs)
+
+    # Reshape/rename if needed and dropna
+    if result.ndim == 2 and frame.columns.nlevels == len(level):
+        if len(result.columns) == 0:
+            result = Series(index=result.index)
+        else:
+            result = result.iloc[:, 0]
+    if result.ndim == 1:
+        result.name = None
+
+    return result
+
+
+def stack_reshape(
+    frame: DataFrame, level: list[int], set_levels: set[int], stack_cols: Index
+) -> Series | DataFrame:
+    """Reshape the data of a frame for stack.
+
+    This function takes care of most of the work that stack needs to do. Caller
+    will sort the result once the appropriate index is set.
+
+    Parameters
+    ----------
+    frame: DataFrame
+        DataFrame that is to be stacked.
+    level: list of ints.
+        Levels of the columns to stack.
+    set_levels: set of ints.
+        Same as level, but as a set.
+    stack_cols: Index.
+        Columns of the result when the DataFrame is stacked.
+
+    Returns
+    -------
+    The data of behind the stacked DataFrame.
+    """
+    # If we need to drop `level` from columns, it needs to be in descending order
+    drop_levnums = sorted(level, reverse=True)
 
     # Grab data for each unique index to be stacked
     buf = []
-    for idx in stack_cols_unique:
+    for idx in stack_cols.unique():
         if len(frame.columns) == 1:
             data = frame.copy()
         else:
@@ -972,10 +1044,8 @@ def stack_v3(frame: DataFrame, level: list[int]) -> Series | DataFrame:
                 data.columns = RangeIndex(len(data.columns))
         buf.append(data)
 
-    result: Series | DataFrame
     if len(buf) > 0 and not frame.empty:
         result = concat(buf, ignore_index=True)
-        ratio = len(result) // len(frame)
     else:
         # input is empty
         if len(level) < frame.columns.nlevels:
@@ -984,54 +1054,11 @@ def stack_v3(frame: DataFrame, level: list[int]) -> Series | DataFrame:
         else:
             new_columns = [0]
         result = DataFrame(columns=new_columns, dtype=frame._values.dtype)
-        ratio = 0
 
     if len(level) < frame.columns.nlevels:
         # concat column order may be different from dropping the levels
         desired_columns = frame.columns._drop_level_numbers(drop_levnums).unique()
         if not result.columns.equals(desired_columns):
             result = result[desired_columns]
 
-    # Construct the correct MultiIndex by combining the frame's index and
-    # stacked columns.
-    index_levels: list | FrozenList
-    if isinstance(frame.index, MultiIndex):
-        index_levels = frame.index.levels
-        index_codes = list(np.tile(frame.index.codes, (1, ratio)))
-    else:
-        codes, uniques = factorize(frame.index, use_na_sentinel=False)
-        index_levels = [uniques]
-        index_codes = list(np.tile(codes, (1, ratio)))
-    if isinstance(ordered_stack_cols, MultiIndex):
-        column_levels = ordered_stack_cols.levels
-        column_codes = ordered_stack_cols.drop_duplicates().codes
-    else:
-        column_levels = [ordered_stack_cols.unique()]
-        column_codes = [factorize(ordered_stack_cols_unique, use_na_sentinel=False)[0]]
-    # error: Incompatible types in assignment (expression has type "list[ndarray[Any,
-    # dtype[Any]]]", variable has type "FrozenList")
-    column_codes = [np.repeat(codes, len(frame)) for codes in column_codes]  # type: ignore[assignment]
-    result.index = MultiIndex(
-        levels=index_levels + column_levels,
-        codes=index_codes + column_codes,
-        names=frame.index.names + list(ordered_stack_cols.names),
-        verify_integrity=False,
-    )
-
-    # sort result, but faster than calling sort_index since we know the order we need
-    len_df = len(frame)
-    n_uniques = len(ordered_stack_cols_unique)
-    indexer = np.arange(n_uniques)
-    idxs = np.tile(len_df * indexer, len_df) + np.repeat(np.arange(len_df), n_uniques)
-    result = result.take(idxs)
-
-    # Reshape/rename if needed and dropna
-    if result.ndim == 2 and frame.columns.nlevels == len(level):
-        if len(result.columns) == 0:
-            result = Series(index=result.index)
-        else:
-            result = result.iloc[:, 0]
-    if result.ndim == 1:
-        result.name = None
-
     return result

pandas/tests/groupby/test_apply.py

@@ -322,6 +322,8 @@ def test_groupby_as_index_apply():
     tm.assert_index_equal(res_as_apply, exp_as_apply)
     tm.assert_index_equal(res_not_as_apply, exp_not_as_apply)
 
+
+def test_groupby_as_index_apply_str():
     ind = Index(list("abcde"))
     df = DataFrame([[1, 2], [2, 3], [1, 4], [1, 5], [2, 6]], index=ind)
     msg = "DataFrameGroupBy.apply operated on the grouping columns"
@@ -379,8 +381,8 @@ def f(piece):
             {"value": piece, "demeaned": piece - piece.mean(), "logged": logged}
         )
 
-    dr = bdate_range("1/1/2000", periods=100)
-    ts = Series(np.random.default_rng(2).standard_normal(100), index=dr)
+    dr = bdate_range("1/1/2000", periods=10)
+    ts = Series(np.random.default_rng(2).standard_normal(10), index=dr)
 
     grouped = ts.groupby(lambda x: x.month, group_keys=False)
     result = grouped.apply(f)
@@ -639,13 +641,13 @@ def reindex_helper(x):
 def test_apply_corner_cases():
     # #535, can't use sliding iterator
 
-    N = 1000
+    N = 10
     labels = np.random.default_rng(2).integers(0, 100, size=N)
     df = DataFrame(
         {
             "key": labels,
             "value1": np.random.default_rng(2).standard_normal(N),
-            "value2": ["foo", "bar", "baz", "qux"] * (N // 4),
+            "value2": ["foo", "bar", "baz", "qux", "a"] * (N // 5),
         }
     )
 
@@ -680,6 +682,8 @@ def test_apply_numeric_coercion_when_datetime():
         result = df.groupby(["Number"]).apply(lambda x: x.iloc[0])
     tm.assert_series_equal(result["Str"], expected["Str"])
 
+
+def test_apply_numeric_coercion_when_datetime_getitem():
     # GH 15421
     df = DataFrame(
         {"A": [10, 20, 30], "B": ["foo", "3", "4"], "T": [pd.Timestamp("12:31:22")] * 3}
@@ -695,6 +699,8 @@ def get_B(g):
     expected.index = df.A
     tm.assert_series_equal(result, expected)
 
+
+def test_apply_numeric_coercion_when_datetime_with_nat():
     # GH 14423
     def predictions(tool):
         out = Series(index=["p1", "p2", "useTime"], dtype=object)
@@ -843,10 +849,24 @@ def test_func(x):
     tm.assert_frame_equal(result, expected)
 
 
-def test_groupby_apply_none_first():
+@pytest.mark.parametrize(
+    "in_data, out_idx, out_data",
+    [
+        [
+            {"groups": [1, 1, 1, 2], "vars": [0, 1, 2, 3]},
+            [[1, 1], [0, 2]],
+            {"groups": [1, 1], "vars": [0, 2]},
+        ],
+        [
+            {"groups": [1, 2, 2, 2], "vars": [0, 1, 2, 3]},
+            [[2, 2], [1, 3]],
+            {"groups": [2, 2], "vars": [1, 3]},
+        ],
+    ],
+)
+def test_groupby_apply_none_first(in_data, out_idx, out_data):
     # GH 12824. Tests if apply returns None first.
-    test_df1 = DataFrame({"groups": [1, 1, 1, 2], "vars": [0, 1, 2, 3]})
-    test_df2 = DataFrame({"groups": [1, 2, 2, 2], "vars": [0, 1, 2, 3]})
+    test_df1 = DataFrame(in_data)
 
     def test_func(x):
         if x.shape[0] < 2:
@@ -856,14 +876,9 @@ def test_func(x):
     msg = "DataFrameGroupBy.apply operated on the grouping columns"
     with tm.assert_produces_warning(DeprecationWarning, match=msg):
         result1 = test_df1.groupby("groups").apply(test_func)
-    with tm.assert_produces_warning(DeprecationWarning, match=msg):
-        result2 = test_df2.groupby("groups").apply(test_func)
-    index1 = MultiIndex.from_arrays([[1, 1], [0, 2]], names=["groups", None])
-    index2 = MultiIndex.from_arrays([[2, 2], [1, 3]], names=["groups", None])
-    expected1 = DataFrame({"groups": [1, 1], "vars": [0, 2]}, index=index1)
-    expected2 = DataFrame({"groups": [2, 2], "vars": [1, 3]}, index=index2)
+    index1 = MultiIndex.from_arrays(out_idx, names=["groups", None])
+    expected1 = DataFrame(out_data, index=index1)
     tm.assert_frame_equal(result1, expected1)
-    tm.assert_frame_equal(result2, expected2)
 
 
 def test_groupby_apply_return_empty_chunk():
@@ -883,18 +898,16 @@ def test_groupby_apply_return_empty_chunk():
     tm.assert_series_equal(result, expected)
 
 
-def test_apply_with_mixed_types():
+@pytest.mark.parametrize("meth", ["apply", "transform"])
+def test_apply_with_mixed_types(meth):
     # gh-20949
     df = DataFrame({"A": "a a b".split(), "B": [1, 2, 3], "C": [4, 6, 5]})
     g = df.groupby("A", group_keys=False)
 
-    result = g.transform(lambda x: x / x.sum())
+    result = getattr(g, meth)(lambda x: x / x.sum())
     expected = DataFrame({"B": [1 / 3.0, 2 / 3.0, 1], "C": [0.4, 0.6, 1.0]})
     tm.assert_frame_equal(result, expected)
 
-    result = g.apply(lambda x: x / x.sum())
-    tm.assert_frame_equal(result, expected)
-
 
 def test_func_returns_object():
     # GH 28652
@@ -1106,7 +1119,7 @@ def test_apply_function_with_indexing_return_column():
 
 @pytest.mark.parametrize(
     "udf",
-    [(lambda x: x.copy()), (lambda x: x.copy().rename(lambda y: y + 1))],
+    [lambda x: x.copy(), lambda x: x.copy().rename(lambda y: y + 1)],
 )
 @pytest.mark.parametrize("group_keys", [True, False])
 def test_apply_result_type(group_keys, udf):
@@ -1214,7 +1227,7 @@ def test_apply_with_date_in_multiindex_does_not_convert_to_timestamp():
     expected = df.iloc[[0, 2, 3]]
     expected = expected.reset_index()
     expected.index = MultiIndex.from_frame(expected[["A", "B", "idx"]])
-    expected = expected.drop(columns="idx")
+    expected = expected.drop(columns=["idx"])
 
     tm.assert_frame_equal(result, expected)
     for val in result.index.levels[1]: