CLN: Enforce deprecation of passing a dict to SeriesGroupBy.agg (#57757)

doc/source/whatsnew/v3.0.0.rst

@@ -201,6 +201,7 @@ Removal of prior version deprecations/changes
 - Changed the default value of ``observed`` in :meth:`DataFrame.groupby` and :meth:`Series.groupby` to ``True`` (:issue:`51811`)
 - Enforced deprecation disallowing parsing datetimes with mixed time zones unless user passes ``utc=True`` to :func:`to_datetime` (:issue:`57275`)
 - Enforced deprecation of ``axis=None`` acting the same as ``axis=0`` in the DataFrame reductions ``sum``, ``prod``, ``std``, ``var``, and ``sem``, passing ``axis=None`` will now reduce over both axes; this is particularly the case when doing e.g. ``numpy.sum(df)`` (:issue:`21597`)
+- Enforced deprecation of passing a dictionary to :meth:`SeriesGroupBy.agg` (:issue:`52268`)
 - Enforced silent-downcasting deprecation for :ref:`all relevant methods <whatsnew_220.silent_downcasting>` (:issue:`54710`)
 - In :meth:`DataFrame.stack`, the default value of ``future_stack`` is now ``True``; specifying ``False`` will raise a ``FutureWarning`` (:issue:`55448`)
 - Methods ``apply``, ``agg``, and ``transform`` will no longer replace NumPy functions (e.g. ``np.sum``) and built-in functions (e.g. ``min``) with the equivalent pandas implementation; use string aliases (e.g. ``"sum"`` and ``"min"``) if you desire to use the pandas implementation (:issue:`53974`)

pandas/core/groupby/generic.py

@@ -384,23 +384,9 @@ def _python_agg_general(self, func, *args, **kwargs):
 
     def _aggregate_multiple_funcs(self, arg, *args, **kwargs) -> DataFrame:
         if isinstance(arg, dict):
-            if self.as_index:
-                # GH 15931
-                raise SpecificationError("nested renamer is not supported")
-            else:
-                # GH#50684 - This accidentally worked in 1.x
-                msg = (
-                    "Passing a dictionary to SeriesGroupBy.agg is deprecated "
-                    "and will raise in a future version of pandas. Pass a list "
-                    "of aggregations instead."
-                )
-                warnings.warn(
-                    message=msg,
-                    category=FutureWarning,
-                    stacklevel=find_stack_level(),
-                )
-                arg = list(arg.items())
-        elif any(isinstance(x, (tuple, list)) for x in arg):
+            raise SpecificationError("nested renamer is not supported")
+
+        if any(isinstance(x, (tuple, list)) for x in arg):
             arg = [(x, x) if not isinstance(x, (tuple, list)) else x for x in arg]
         else:
             # list of functions / function names

pandas/tests/groupby/aggregate/test_aggregate.py

@@ -1015,10 +1015,9 @@ def test_groupby_as_index_agg(df):
 
     expected3 = grouped["C"].sum()
     expected3 = DataFrame(expected3).rename(columns={"C": "Q"})
-    msg = "Passing a dictionary to SeriesGroupBy.agg is deprecated"
-    with tm.assert_produces_warning(FutureWarning, match=msg):
-        result3 = grouped["C"].agg({"Q": "sum"})
-    tm.assert_frame_equal(result3, expected3)
+    msg = "nested renamer is not supported"
+    with pytest.raises(SpecificationError, match=msg):
+        grouped["C"].agg({"Q": "sum"})
 
     # GH7115 & GH8112 & GH8582
     df = DataFrame(

pandas/tests/groupby/test_grouping.py

@@ -9,6 +9,8 @@
 import numpy as np
 import pytest
 
+from pandas.errors import SpecificationError
+
 import pandas as pd
 from pandas import (
     CategoricalIndex,
@@ -530,12 +532,10 @@ def test_multiindex_negative_level(self, multiindex_dataframe_random_data):
         ).sum()
         tm.assert_frame_equal(result, expected)
 
-    def test_multifunc_select_col_integer_cols(self, df):
+    def test_agg_with_dict_raises(self, df):
         df.columns = np.arange(len(df.columns))
-
-        # it works!
-        msg = "Passing a dictionary to SeriesGroupBy.agg is deprecated"
-        with tm.assert_produces_warning(FutureWarning, match=msg):
+        msg = "nested renamer is not supported"
+        with pytest.raises(SpecificationError, match=msg):
             df.groupby(1, as_index=False)[2].agg({"Q": np.mean})
 
     def test_multiindex_columns_empty_level(self):