From e01b57adac056901c63b2dd74a95fbe488c1df89 Mon Sep 17 00:00:00 2001
From: jbrockmendel <jbrockmendel@gmail.com>
Date: Wed, 23 Oct 2019 14:25:20 -0700
Subject: [PATCH] CLN: AttributeError in _wrap_applied_output

---
 pandas/core/base.py            |  4 ----
 pandas/core/groupby/generic.py | 19 ++++++++++++++-----
 pandas/core/groupby/ops.py     | 10 ++++++++--
 3 files changed, 22 insertions(+), 11 deletions(-)

diff --git a/pandas/core/base.py b/pandas/core/base.py
index 5ae3926952a67..9586d49c555ff 100644
--- a/pandas/core/base.py
+++ b/pandas/core/base.py
@@ -571,8 +571,6 @@ def _aggregate_multiple_funcs(self, arg, _level, _axis):
 
                 except (TypeError, DataError):
                     pass
-                except SpecificationError:
-                    raise
                 else:
                     results.append(new_res)
 
@@ -591,8 +589,6 @@ def _aggregate_multiple_funcs(self, arg, _level, _axis):
                 except ValueError:
                     # cannot aggregate
                     continue
-                except SpecificationError:
-                    raise
                 else:
                     results.append(new_res)
                     keys.append(col)
diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index c766fcaa4f849..58ede6307525c 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -475,7 +475,7 @@ def _transform_fast(self, func, func_nm):
             out = self._try_cast(out, self.obj)
         return Series(out, index=self.obj.index, name=self.obj.name)
 
-    def filter(self, func, dropna=True, *args, **kwargs):  # noqa
+    def filter(self, func, dropna=True, *args, **kwargs):
         """
         Return a copy of a Series excluding elements from groups that
         do not satisfy the boolean criterion specified by func.
@@ -1230,7 +1230,7 @@ def first_not_none(values):
                         return self._concat_objects(keys, values, not_indexed_same=True)
 
                 try:
-                    if self.axis == 0:
+                    if self.axis == 0 and isinstance(v, ABCSeries):
                         # GH6124 if the list of Series have a consistent name,
                         # then propagate that name to the result.
                         index = v.index.copy()
@@ -1266,15 +1266,24 @@ def first_not_none(values):
                                 axis=self.axis,
                             ).unstack()
                             result.columns = index
-                    else:
+                    elif isinstance(v, ABCSeries):
                         stacked_values = np.vstack([np.asarray(v) for v in values])
                         result = DataFrame(
                             stacked_values.T, index=v.index, columns=key_index
                         )
+                    else:
+                        # GH#1738: values is list of arrays of unequal lengths
+                        #  fall through to the outer else clause
+                        # TODO: sure this is right?  we used to do this
+                        #  after raising AttributeError above
+                        return Series(
+                            values, index=key_index, name=self._selection_name
+                        )
 
-                except (ValueError, AttributeError):
+                except ValueError:
+                    # TODO: not reached in tests; is this still needed?
                     # GH1738: values is list of arrays of unequal lengths fall
-                    # through to the outer else caluse
+                    # through to the outer else clause
                     return Series(values, index=key_index, name=self._selection_name)
 
                 # if we have date/time like in the original, then coerce dates
diff --git a/pandas/core/groupby/ops.py b/pandas/core/groupby/ops.py
index e6f4f2f056058..8bebe5e8161a5 100644
--- a/pandas/core/groupby/ops.py
+++ b/pandas/core/groupby/ops.py
@@ -198,6 +198,9 @@ def apply(self, f, data, axis=0):
             f_name not in base.plotting_methods
             and hasattr(splitter, "fast_apply")
             and axis == 0
+            # with MultiIndex, apply_frame_axis0 would raise InvalidApply
+            # TODO: can we make this check prettier?
+            and not splitter._get_sorted_data().index._has_complex_internals
         ):
             try:
                 result_values, mutated = splitter.fast_apply(f, group_keys)
@@ -207,11 +210,14 @@ def apply(self, f, data, axis=0):
                 if len(result_values) == len(group_keys):
                     return group_keys, result_values, mutated
 
-            except libreduction.InvalidApply:
+            except libreduction.InvalidApply as err:
                 # Cannot fast apply on MultiIndex (_has_complex_internals).
                 # This Exception is also raised if `f` triggers an exception
                 # but it is preferable to raise the exception in Python.
-                pass
+                if "Let this error raise above us" not in str(err):
+                    # TODO: can we infer anything about whether this is
+                    #  worth-retrying in pure-python?
+                    raise
             except TypeError as err:
                 if "Cannot convert" in str(err):
                     # via apply_frame_axis0 if we pass a non-ndarray