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[BACKPORT] Fix output of df.groupby(as_index=False).size() (#2507) #2508

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Oct 12, 2021
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[BACKPORT] Fix output of df.groupby(as_index=False).size() (#2507) #2508

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16 changes: 12 additions & 4 deletions mars/dataframe/groupby/aggregation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -630,7 +630,7 @@ def _do_custom_agg(op, custom_reduction, *input_objs):
return concat_result

@staticmethod
def _do_predefined_agg(input_obj, agg_func, **kwds):
def _do_predefined_agg(input_obj, agg_func, single_func=False, **kwds):
ndim = getattr(input_obj, 'ndim', None) or input_obj.obj.ndim
if agg_func == 'str_concat':
agg_func = lambda x: x.str.cat(**kwds)
Expand All @@ -640,8 +640,14 @@ def _do_predefined_agg(input_obj, agg_func, **kwds):
agg_func.__name__ = func_name

if ndim == 2:
result = input_obj.agg([agg_func])
result.columns = result.columns.droplevel(-1)
if single_func:
result = input_obj.agg(agg_func)
if result.ndim == 1:
# when agg_func == size, agg only returns one single series.
result = result.to_frame(agg_func)
else:
result = input_obj.agg([agg_func])
result.columns = result.columns.droplevel(-1)
return result
else:
return input_obj.agg(agg_func)
Expand Down Expand Up @@ -704,7 +710,9 @@ def _wrapped_func(col):
if map_func_name == 'custom_reduction':
agg_dfs.extend(cls._do_custom_agg(op, custom_reduction, input_obj))
else:
agg_dfs.append(cls._do_predefined_agg(input_obj, map_func_name, **kwds))
single_func = map_func_name == op.raw_func
agg_dfs.append(cls._do_predefined_agg(
input_obj, map_func_name, single_func, **kwds))

if op._size_recorder_name is not None:
# record_size
Expand Down
5 changes: 5 additions & 0 deletions mars/dataframe/groupby/tests/test_groupby_execution.py
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Original file line number Diff line number Diff line change
Expand Up @@ -282,6 +282,11 @@ def test_dataframe_groupby_agg(setup):

# test as_index=False
for method in ['tree', 'shuffle']:
r = mdf.groupby('c2', as_index=False).agg('size', method=method)
pd.testing.assert_frame_equal(
r.execute().fetch().sort_values('c2', ignore_index=True),
raw.groupby('c2', as_index=False).agg('size').sort_values('c2', ignore_index=True))

r = mdf.groupby('c2', as_index=False).agg('mean', method=method)
pd.testing.assert_frame_equal(
r.execute().fetch().sort_values('c2', ignore_index=True),
Expand Down