REF: re-raise AssertionError unchanged (pandas-dev#28959)

pandas/_libs/groupby.pyx

@@ -442,7 +442,7 @@ def _group_add(floating[:, :] out,
         floating[:, :] sumx, nobs
 
     if len(values) != len(labels):
-        raise AssertionError("len(index) != len(labels)")
+        raise ValueError("len(index) != len(labels)")
 
     nobs = np.zeros_like(out)
     sumx = np.zeros_like(out)
@@ -492,7 +492,7 @@ def _group_prod(floating[:, :] out,
         floating[:, :] prodx, nobs
 
     if not len(values) == len(labels):
-        raise AssertionError("len(index) != len(labels)")
+        raise ValueError("len(index) != len(labels)")
 
     nobs = np.zeros_like(out)
     prodx = np.ones_like(out)
@@ -542,7 +542,7 @@ def _group_var(floating[:, :] out,
     assert min_count == -1, "'min_count' only used in add and prod"
 
     if not len(values) == len(labels):
-        raise AssertionError("len(index) != len(labels)")
+        raise ValueError("len(index) != len(labels)")
 
     nobs = np.zeros_like(out)
     mean = np.zeros_like(out)
@@ -597,7 +597,7 @@ def _group_mean(floating[:, :] out,
     assert min_count == -1, "'min_count' only used in add and prod"
 
     if not len(values) == len(labels):
-        raise AssertionError("len(index) != len(labels)")
+        raise ValueError("len(index) != len(labels)")
 
     nobs = np.zeros_like(out)
     sumx = np.zeros_like(out)

pandas/core/groupby/generic.py

@@ -261,6 +261,8 @@ def aggregate(self, func=None, *args, **kwargs):
 
             try:
                 return self._python_agg_general(func, *args, **kwargs)
+            except AssertionError:
+                raise
             except Exception:
                 result = self._aggregate_named(func, *args, **kwargs)
 
@@ -887,6 +889,8 @@ def aggregate(self, func=None, *args, **kwargs):
                     result = self._aggregate_multiple_funcs(
                         [func], _level=_level, _axis=self.axis
                     )
+                except AssertionError:
+                    raise
                 except Exception:
                     result = self._aggregate_frame(func)
                 else:
@@ -1036,13 +1040,17 @@ def _aggregate_frame(self, func, *args, **kwargs):
                 for name, data in self:
                     fres = func(data, *args, **kwargs)
                     result[name] = self._try_cast(fres, data)
+            except AssertionError:
+                raise
             except Exception:
                 return self._aggregate_item_by_item(func, *args, **kwargs)
         else:
             for name in self.indices:
                 data = self.get_group(name, obj=obj)
                 try:
                     fres = func(data, *args, **kwargs)
+                except AssertionError:
+                    raise
                 except Exception:
                     wrapper = lambda x: func(x, *args, **kwargs)
                     result[name] = data.apply(wrapper, axis=axis)
@@ -1398,6 +1406,8 @@ def _choose_path(self, fast_path, slow_path, group):
         # if we make it here, test if we can use the fast path
         try:
             res_fast = fast_path(group)
+        except AssertionError:
+            raise
         except Exception:
             # Hard to know ex-ante what exceptions `fast_path` might raise
             return path, res
@@ -1422,6 +1432,8 @@ def _transform_item_by_item(self, obj, wrapper):
         for i, col in enumerate(obj):
             try:
                 output[col] = self[col].transform(wrapper)
+            except AssertionError:
+                raise
             except Exception:
                 pass
             else:

pandas/core/groupby/groupby.py

@@ -44,13 +44,7 @@ class providing the base-class of operations.
 from pandas.core import nanops
 import pandas.core.algorithms as algorithms
 from pandas.core.arrays import Categorical
-from pandas.core.base import (
-    DataError,
-    GroupByError,
-    PandasObject,
-    SelectionMixin,
-    SpecificationError,
-)
+from pandas.core.base import DataError, PandasObject, SelectionMixin
 import pandas.core.common as com
 from pandas.core.construction import extract_array
 from pandas.core.frame import DataFrame
@@ -862,8 +856,6 @@ def _cython_transform(self, how, numeric_only=True, **kwargs):
                 result, names = self.grouper.transform(obj.values, how, **kwargs)
             except NotImplementedError:
                 continue
-            except AssertionError as e:
-                raise GroupByError(str(e))
             if self._transform_should_cast(how):
                 output[name] = self._try_cast(result, obj)
             else:
@@ -890,12 +882,7 @@ def _cython_agg_general(self, how, alt=None, numeric_only=True, min_count=-1):
             if numeric_only and not is_numeric:
                 continue
 
-            try:
-                result, names = self.grouper.aggregate(
-                    obj.values, how, min_count=min_count
-                )
-            except AssertionError as e:
-                raise GroupByError(str(e))
+            result, names = self.grouper.aggregate(obj.values, how, min_count=min_count)
             output[name] = self._try_cast(result, obj)
 
         if len(output) == 0:
@@ -1353,8 +1340,8 @@ def f(self, **kwargs):
                 # try a cython aggregation if we can
                 try:
                     return self._cython_agg_general(alias, alt=npfunc, **kwargs)
-                except AssertionError as e:
-                    raise SpecificationError(str(e))
+                except AssertionError:
+                    raise
                 except DataError:
                     pass
                 except Exception:

pandas/core/groupby/ops.py

@@ -647,6 +647,8 @@ def _transform(
     def agg_series(self, obj, func):
         try:
             return self._aggregate_series_fast(obj, func)
+        except AssertionError:
+            raise
         except Exception:
             return self._aggregate_series_pure_python(obj, func)
 

pandas/core/resample.py

@@ -360,6 +360,8 @@ def _groupby_and_aggregate(self, how, grouper=None, *args, **kwargs):
                 result = grouped._aggregate_item_by_item(how, *args, **kwargs)
             else:
                 result = grouped.aggregate(how, *args, **kwargs)
+        except AssertionError:
+            raise
         except Exception:
 
             # we have a non-reducing function

pandas/tests/groupby/aggregate/test_other.py

@@ -19,7 +19,7 @@
     date_range,
     period_range,
 )
-from pandas.core.groupby.groupby import SpecificationError
+from pandas.core.base import SpecificationError
 import pandas.util.testing as tm
 
 from pandas.io.formats.printing import pprint_thing