BUG: .transform(...) with "first" and "last" fail when axis=1 (#46074)

rhshadrach · web-flow · commit e932ec966793 · 2022-02-26T16:58:57.000-05:00
diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst
@@ -382,6 +382,7 @@ Groupby/resample/rolling
 - Bug in :meth:`DataFrame.resample` ignoring ``closed="right"`` on :class:`TimedeltaIndex` (:issue:`45414`)
 - Bug in :meth:`.DataFrameGroupBy.transform` fails when ``func="size"`` and the input DataFrame has multiple columns (:issue:`27469`)
 - Bug in :meth:`.DataFrameGroupBy.size` and :meth:`.DataFrameGroupBy.transform` with ``func="size"`` produced incorrect results when ``axis=1`` (:issue:`45715`)
+- Bug in :meth:`.DataFrameGroupby.transform` fails when ``axis=1`` and ``func`` is ``"first"`` or ``"last"`` (:issue:`45986`)
 
 Reshaping
 ^^^^^^^^^
diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
@@ -472,9 +472,6 @@ def _transform_general(self, func: Callable, *args, **kwargs) -> Series:
         result.name = self.obj.name
         return result
 
-    def _can_use_transform_fast(self, func: str, result) -> bool:
-        return True
-
     def filter(self, func, dropna: bool = True, *args, **kwargs):
         """
         Return a copy of a Series excluding elements from groups that
@@ -1184,12 +1181,6 @@ def transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs):
             func, *args, engine=engine, engine_kwargs=engine_kwargs, **kwargs
         )
 
-    def _can_use_transform_fast(self, func: str, result) -> bool:
-        return func == "size" or (
-            isinstance(result, DataFrame)
-            and result.columns.equals(self._obj_with_exclusions.columns)
-        )
-
     def _define_paths(self, func, *args, **kwargs):
         if isinstance(func, str):
             fast_path = lambda group: getattr(group, func)(*args, **kwargs)
diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
@@ -1650,11 +1650,7 @@ def _transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs):
             with com.temp_setattr(self, "observed", True):
                 result = getattr(self, func)(*args, **kwargs)
 
-            if self._can_use_transform_fast(func, result):
-                return self._wrap_transform_fast_result(result)
-
-            # only reached for DataFrameGroupBy
-            return self._transform_general(func, *args, **kwargs)
+            return self._wrap_transform_fast_result(result)
 
     @final
     def _wrap_transform_fast_result(self, result: NDFrameT) -> NDFrameT:
diff --git a/pandas/tests/groupby/transform/test_transform.py b/pandas/tests/groupby/transform/test_transform.py
@@ -21,10 +21,7 @@
 )
 import pandas._testing as tm
 from pandas.core.groupby.base import maybe_normalize_deprecated_kernels
-from pandas.core.groupby.generic import (
-    DataFrameGroupBy,
-    SeriesGroupBy,
-)
+from pandas.core.groupby.generic import DataFrameGroupBy
 
 
 def assert_fp_equal(a, b):
@@ -195,10 +192,8 @@ def test_transform_axis_1_reducer(request, reduction_func):
     # GH#45715
     if reduction_func in (
         "corrwith",
-        "first",
         "idxmax",
         "idxmin",
-        "last",
         "ngroup",
         "nth",
     ):
@@ -418,45 +413,36 @@ def test_transform_select_columns(df):
     tm.assert_frame_equal(result, expected)
 
 
-@pytest.mark.parametrize("duplicates", [True, False])
-def test_transform_exclude_nuisance(df, duplicates):
+def test_transform_exclude_nuisance(df):
     # case that goes through _transform_item_by_item
 
-    if duplicates:
-        # make sure we work with duplicate columns GH#41427
-        df.columns = ["A", "C", "C", "D"]
+    df.columns = ["A", "B", "B", "D"]
 
     # this also tests orderings in transform between
     # series/frame to make sure it's consistent
     expected = {}
     grouped = df.groupby("A")
 
-    gbc = grouped["C"]
-    warn = FutureWarning if duplicates else None
-    with tm.assert_produces_warning(warn, match="Dropping invalid columns"):
-        expected["C"] = gbc.transform(np.mean)
-    if duplicates:
-        # squeeze 1-column DataFrame down to Series
-        expected["C"] = expected["C"]["C"]
+    gbc = grouped["B"]
+    with tm.assert_produces_warning(FutureWarning, match="Dropping invalid columns"):
+        expected["B"] = gbc.transform(lambda x: np.mean(x))
+    # squeeze 1-column DataFrame down to Series
+    expected["B"] = expected["B"]["B"]
 
-        assert isinstance(gbc.obj, DataFrame)
-        assert isinstance(gbc, DataFrameGroupBy)
-    else:
-        assert isinstance(gbc, SeriesGroupBy)
-        assert isinstance(gbc.obj, Series)
+    assert isinstance(gbc.obj, DataFrame)
+    assert isinstance(gbc, DataFrameGroupBy)
 
     expected["D"] = grouped["D"].transform(np.mean)
     expected = DataFrame(expected)
     with tm.assert_produces_warning(FutureWarning, match="Dropping invalid columns"):
-        result = df.groupby("A").transform(np.mean)
+        result = df.groupby("A").transform(lambda x: np.mean(x))
 
     tm.assert_frame_equal(result, expected)
 
 
 def test_transform_function_aliases(df):
-    with tm.assert_produces_warning(FutureWarning, match="Dropping invalid columns"):
-        result = df.groupby("A").transform("mean")
-        expected = df.groupby("A").transform(np.mean)
+    result = df.groupby("A").transform("mean")
+    expected = df.groupby("A").transform(np.mean)
     tm.assert_frame_equal(result, expected)
 
     result = df.groupby("A")["C"].transform("mean")
