diff --git a/doc/source/whatsnew/v2.3.0.rst b/doc/source/whatsnew/v2.3.0.rst index 230332319e0ac..ac74e6a8e5f77 100644 --- a/doc/source/whatsnew/v2.3.0.rst +++ b/doc/source/whatsnew/v2.3.0.rst @@ -119,6 +119,7 @@ Conversion Strings ^^^^^^^ +- Bug in :meth:`.DataFrameGroupBy.min`, :meth:`.DataFrameGroupBy.max`, :meth:`.Resampler.min`, :meth:`.Resampler.max` on string input of all NA values would return float dtype; now returns string (:issue:`60810`) - Bug in :meth:`DataFrame.sum` with ``axis=1``, :meth:`.DataFrameGroupBy.sum` or :meth:`.SeriesGroupBy.sum` with ``skipna=True``, and :meth:`.Resampler.sum` on :class:`StringDtype` with all NA values resulted in ``0`` and is now the empty string ``""`` (:issue:`60229`) - Bug in :meth:`Series.__pos__` and :meth:`DataFrame.__pos__` did not raise for :class:`StringDtype` with ``storage="pyarrow"`` (:issue:`60710`) - Bug in :meth:`Series.rank` for :class:`StringDtype` with ``storage="pyarrow"`` incorrectly returning integer results in case of ``method="average"`` and raising an error if it would truncate results (:issue:`59768`) diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py index d0c0ed29b6d44..f24a84648a004 100644 --- a/pandas/core/groupby/groupby.py +++ b/pandas/core/groupby/groupby.py @@ -81,6 +81,7 @@ class providing the base-class of operations. is_numeric_dtype, is_object_dtype, is_scalar, + is_string_dtype, needs_i8_conversion, pandas_dtype, ) @@ -1723,8 +1724,13 @@ def _agg_py_fallback( # preserve the kind of exception that raised raise type(err)(msg) from err - if ser.dtype == object: + dtype = ser.dtype + if dtype == object: res_values = res_values.astype(object, copy=False) + elif is_string_dtype(dtype): + # mypy doesn't infer dtype is an ExtensionDtype + string_array_cls = dtype.construct_array_type() # type: ignore[union-attr] + res_values = string_array_cls._from_sequence(res_values, dtype=dtype) # If we are DataFrameGroupBy and went through a SeriesGroupByPath # then we need to reshape diff --git a/pandas/tests/groupby/test_reductions.py b/pandas/tests/groupby/test_reductions.py index 45047fe004aa0..014558bbf4bba 100644 --- a/pandas/tests/groupby/test_reductions.py +++ b/pandas/tests/groupby/test_reductions.py @@ -20,6 +20,7 @@ isna, ) import pandas._testing as tm +from pandas.tests.groupby import get_groupby_method_args from pandas.util import _test_decorators as td @@ -956,17 +957,95 @@ def test_min_empty_string_dtype(func, string_dtype_no_object): @pytest.mark.parametrize("min_count", [0, 1]) -def test_string_dtype_empty_sum(string_dtype_no_object, skipna, min_count): - # https://github.com/pandas-dev/pandas/issues/60229 +@pytest.mark.parametrize("test_series", [True, False]) +def test_string_dtype_all_na( + string_dtype_no_object, reduction_func, skipna, min_count, test_series +): + # https://github.com/pandas-dev/pandas/issues/60985 + if reduction_func == "corrwith": + # corrwith is deprecated. + return + dtype = string_dtype_no_object + + if reduction_func in [ + "any", + "all", + "idxmin", + "idxmax", + "mean", + "median", + "std", + "var", + ]: + kwargs = {"skipna": skipna} + elif reduction_func in ["kurt"]: + kwargs = {"min_count": min_count} + elif reduction_func in ["count", "nunique", "quantile", "sem", "size"]: + kwargs = {} + else: + kwargs = {"skipna": skipna, "min_count": min_count} + + expected_dtype, expected_value = dtype, pd.NA + if reduction_func in ["all", "any"]: + expected_dtype = "bool" + # TODO: For skipna=False, bool(pd.NA) raises; should groupby? + expected_value = not skipna if reduction_func == "any" else True + elif reduction_func in ["count", "nunique", "size"]: + # TODO: Should be more consistent - return Int64 when dtype.na_value is pd.NA? + if ( + test_series + and reduction_func == "size" + and dtype.storage == "pyarrow" + and dtype.na_value is pd.NA + ): + expected_dtype = "Int64" + else: + expected_dtype = "int64" + expected_value = 1 if reduction_func == "size" else 0 + elif reduction_func in ["idxmin", "idxmax"]: + expected_dtype, expected_value = "float64", np.nan + elif not skipna or min_count > 0: + expected_value = pd.NA + elif reduction_func == "sum": + # https://github.com/pandas-dev/pandas/pull/60936 + expected_value = "" + df = DataFrame({"a": ["x"], "b": [pd.NA]}, dtype=dtype) - gb = df.groupby("a") - result = gb.sum(skipna=skipna, min_count=min_count) - value = "" if skipna and min_count == 0 else pd.NA - expected = DataFrame( - {"b": value}, index=pd.Index(["x"], name="a", dtype=dtype), dtype=dtype - ) - tm.assert_frame_equal(result, expected) + obj = df["b"] if test_series else df + args = get_groupby_method_args(reduction_func, obj) + gb = obj.groupby(df["a"]) + method = getattr(gb, reduction_func) + + if reduction_func in [ + "mean", + "median", + "kurt", + "prod", + "quantile", + "sem", + "skew", + "std", + "var", + ]: + msg = f"dtype '{dtype}' does not support operation '{reduction_func}'" + with pytest.raises(TypeError, match=msg): + method(*args, **kwargs) + return + elif reduction_func in ["idxmin", "idxmax"] and not skipna: + msg = f"{reduction_func} with skipna=False encountered an NA value." + with pytest.raises(ValueError, match=msg): + method(*args, **kwargs) + return + + result = method(*args, **kwargs) + index = pd.Index(["x"], name="a", dtype=dtype) + if test_series or reduction_func == "size": + name = None if not test_series and reduction_func == "size" else "b" + expected = Series(expected_value, index=index, dtype=expected_dtype, name=name) + else: + expected = DataFrame({"b": expected_value}, index=index, dtype=expected_dtype) + tm.assert_equal(result, expected) def test_max_nan_bug(): diff --git a/pandas/tests/resample/test_resampler_grouper.py b/pandas/tests/resample/test_resampler_grouper.py index 7870c5a9d3e17..286625b8ce470 100644 --- a/pandas/tests/resample/test_resampler_grouper.py +++ b/pandas/tests/resample/test_resampler_grouper.py @@ -3,8 +3,6 @@ import numpy as np import pytest -from pandas._config import using_string_dtype - from pandas.compat import is_platform_windows import pandas as pd @@ -462,7 +460,6 @@ def test_empty(keys): tm.assert_frame_equal(result, expected) -@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)") @pytest.mark.parametrize("consolidate", [True, False]) def test_resample_groupby_agg_object_dtype_all_nan(consolidate): # https://github.com/pandas-dev/pandas/issues/39329