diff --git a/doc/source/whatsnew/v3.0.0.rst b/doc/source/whatsnew/v3.0.0.rst index e5d65ad82cc95..a34b657427976 100644 --- a/doc/source/whatsnew/v3.0.0.rst +++ b/doc/source/whatsnew/v3.0.0.rst @@ -533,6 +533,7 @@ Groupby/resample/rolling - Bug in :meth:`.DataFrameGroupBy.quantile` when ``interpolation="nearest"`` is inconsistent with :meth:`DataFrame.quantile` (:issue:`47942`) - Bug in :meth:`.Resampler.interpolate` on a :class:`DataFrame` with non-uniform sampling and/or indices not aligning with the resulting resampled index would result in wrong interpolation (:issue:`21351`) - Bug in :meth:`DataFrame.ewm` and :meth:`Series.ewm` when passed ``times`` and aggregation functions other than mean (:issue:`51695`) +- Bug in :meth:`DataFrameGroupBy.agg` that raises ``AttributeError`` when there is dictionary input and duplicated columns, instead of returning a DataFrame with the aggregation of all duplicate columns. (:issue:`55041`) - Bug in :meth:`DataFrameGroupBy.apply` that was returning a completely empty DataFrame when all return values of ``func`` were ``None`` instead of returning an empty DataFrame with the original columns and dtypes. (:issue:`57775`) - Bug in :meth:`DataFrameGroupBy.apply` with ``as_index=False`` that was returning :class:`MultiIndex` instead of returning :class:`Index`. (:issue:`58291`) - Bug in :meth:`DataFrameGroupby.transform` and :meth:`SeriesGroupby.transform` with a reducer and ``observed=False`` that coerces dtype to float when there are unobserved categories. (:issue:`55326`) diff --git a/pandas/core/apply.py b/pandas/core/apply.py index 25836e967e948..33f506235870d 100644 --- a/pandas/core/apply.py +++ b/pandas/core/apply.py @@ -471,8 +471,30 @@ def compute_dict_like( keys += [key] * len(key_data) results += key_data - else: + elif is_groupby: # key used for column selection and output + + df = selected_obj + results, keys = [], [] + for key, how in func.items(): + cols = df[key] + + if cols.ndim == 1: + series_list = [obj._gotitem(key, ndim=1, subset=cols)] + else: + series_list = [] + for index in range(cols.shape[1]): + col = cols.iloc[:, index] + + series = obj._gotitem(key, ndim=1, subset=col) + series_list.append(series) + + for series in series_list: + result = getattr(series, op_name)(how, **kwargs) + results.append(result) + keys.append(key) + + else: results = [ getattr(obj._gotitem(key, ndim=1), op_name)(how, **kwargs) for key, how in func.items() @@ -496,11 +518,14 @@ def wrap_results_dict_like( is_ndframe = [isinstance(r, ABCNDFrame) for r in result_data] if all(is_ndframe): - results = dict(zip(result_index, result_data)) + results = [result for result in result_data if not result.empty] keys_to_use: Iterable[Hashable] - keys_to_use = [k for k in result_index if not results[k].empty] + keys_to_use = [k for k, v in zip(result_index, result_data) if not v.empty] # Have to check, if at least one DataFrame is not empty. - keys_to_use = keys_to_use if keys_to_use != [] else result_index + if keys_to_use == []: + keys_to_use = result_index + results = result_data + if selected_obj.ndim == 2: # keys are columns, so we can preserve names ktu = Index(keys_to_use) @@ -509,7 +534,7 @@ def wrap_results_dict_like( axis: AxisInt = 0 if isinstance(obj, ABCSeries) else 1 result = concat( - {k: results[k] for k in keys_to_use}, + results, axis=axis, keys=keys_to_use, ) diff --git a/pandas/tests/groupby/aggregate/test_aggregate.py b/pandas/tests/groupby/aggregate/test_aggregate.py index 3362d6209af6d..1f140063fd84b 100644 --- a/pandas/tests/groupby/aggregate/test_aggregate.py +++ b/pandas/tests/groupby/aggregate/test_aggregate.py @@ -1662,3 +1662,121 @@ def func(x): msg = "length must not be 0" with pytest.raises(ValueError, match=msg): df.groupby("A", observed=False).agg(func) + + +def test_groupby_aggregation_duplicate_columns_single_dict_value(): + # GH#55041 + df = DataFrame( + [[1, 2, 3, 4], [1, 3, 4, 5], [2, 4, 5, 6]], + columns=["a", "b", "c", "c"], + ) + gb = df.groupby("a") + result = gb.agg({"c": "sum"}) + + expected = DataFrame( + [[7, 9], [5, 6]], columns=["c", "c"], index=Index([1, 2], name="a") + ) + tm.assert_frame_equal(result, expected) + + +def test_groupby_aggregation_duplicate_columns_multiple_dict_values(): + # GH#55041 + df = DataFrame( + [[1, 2, 3, 4], [1, 3, 4, 5], [2, 4, 5, 6]], + columns=["a", "b", "c", "c"], + ) + gb = df.groupby("a") + result = gb.agg({"c": ["sum", "min", "max", "min"]}) + + expected = DataFrame( + [[7, 3, 4, 3, 9, 4, 5, 4], [5, 5, 5, 5, 6, 6, 6, 6]], + columns=MultiIndex( + levels=[["c"], ["sum", "min", "max"]], + codes=[[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 2, 1, 0, 1, 2, 1]], + ), + index=Index([1, 2], name="a"), + ) + tm.assert_frame_equal(result, expected) + + +def test_groupby_aggregation_duplicate_columns_some_empty_result(): + # GH#55041 + df = DataFrame( + [ + [1, 9843, 43, 54, 7867], + [2, 940, 9, -34, 44], + [1, -34, -546, -549358, 0], + [2, 244, -33, -100, 44], + ], + columns=["a", "b", "b", "c", "c"], + ) + gb = df.groupby("a") + result = gb.agg({"b": [], "c": ["var"]}) + + expected = DataFrame( + [[1.509268e11, 30944844.5], [2.178000e03, 0.0]], + columns=MultiIndex(levels=[["c"], ["var"]], codes=[[0, 0], [0, 0]]), + index=Index([1, 2], name="a"), + ) + tm.assert_frame_equal(result, expected) + + +def test_groupby_aggregation_multi_index_duplicate_columns(): + # GH#55041 + df = DataFrame( + [ + [1, -9843, 43, 54, 7867], + [2, 940, 9, -34, 44], + [1, -34, 546, -549358, 0], + [2, 244, -33, -100, 44], + ], + columns=MultiIndex( + levels=[["level1.1", "level1.2"], ["level2.1", "level2.2"]], + codes=[[0, 0, 0, 1, 1], [0, 1, 1, 0, 1]], + ), + index=MultiIndex( + levels=[["level1.1", "level1.2"], ["level2.1", "level2.2"]], + codes=[[0, 0, 0, 1], [0, 1, 1, 0]], + ), + ) + gb = df.groupby(level=0) + result = gb.agg({("level1.1", "level2.2"): "min"}) + + expected = DataFrame( + [[-9843, 9], [244, -33]], + columns=MultiIndex(levels=[["level1.1"], ["level2.2"]], codes=[[0, 0], [0, 0]]), + index=Index(["level1.1", "level1.2"]), + ) + tm.assert_frame_equal(result, expected) + + +def test_groupby_aggregation_func_list_multi_index_duplicate_columns(): + # GH#55041 + df = DataFrame( + [ + [1, -9843, 43, 54, 7867], + [2, 940, 9, -34, 44], + [1, -34, 546, -549358, 0], + [2, 244, -33, -100, 44], + ], + columns=MultiIndex( + levels=[["level1.1", "level1.2"], ["level2.1", "level2.2"]], + codes=[[0, 0, 0, 1, 1], [0, 1, 1, 0, 1]], + ), + index=MultiIndex( + levels=[["level1.1", "level1.2"], ["level2.1", "level2.2"]], + codes=[[0, 0, 0, 1], [0, 1, 1, 0]], + ), + ) + gb = df.groupby(level=0) + result = gb.agg({("level1.1", "level2.2"): ["min", "max"]}) + + expected = DataFrame( + [[-9843, 940, 9, 546], [244, 244, -33, -33]], + columns=MultiIndex( + levels=[["level1.1"], ["level2.2"], ["min", "max"]], + codes=[[0, 0, 0, 0], [0, 0, 0, 0], [0, 1, 0, 1]], + ), + index=Index(["level1.1", "level1.2"]), + ) + tm.assert_frame_equal(result, expected)