[backport 2.3.x] String dtype: implement sum reduction (pandas-dev#59853

) (pandas-dev#60157)

String dtype: implement sum reduction (pandas-dev#59853)

(cherry picked from commit 2fdb16b)

doc/source/whatsnew/v2.3.0.rst

@@ -32,7 +32,7 @@ enhancement1
 Other enhancements
 ^^^^^^^^^^^^^^^^^^
 
--
+- The :meth:`~Series.sum` reduction is now implemented for ``StringDtype`` columns (:issue:`59853`)
 -
 
 .. ---------------------------------------------------------------------------

pandas/core/array_algos/masked_reductions.py

@@ -62,6 +62,10 @@ def _reductions(
         ):
             return libmissing.NA
 
+        if values.dtype == np.dtype(object):
+            # object dtype does not support `where` without passing an initial
+            values = values[~mask]
+            return func(values, axis=axis, **kwargs)
         return func(values, where=~mask, axis=axis, **kwargs)
 
 

pandas/core/arrays/arrow/array.py

@@ -69,6 +69,7 @@
     unpack_tuple_and_ellipses,
     validate_indices,
 )
+from pandas.core.nanops import check_below_min_count
 from pandas.core.strings.base import BaseStringArrayMethods
 
 from pandas.io._util import _arrow_dtype_mapping
@@ -1694,6 +1695,37 @@ def pyarrow_meth(data, skip_nulls, **kwargs):
                 denominator = pc.sqrt_checked(pc.count(self._pa_array))
                 return pc.divide_checked(numerator, denominator)
 
+        elif name == "sum" and (
+            pa.types.is_string(pa_type) or pa.types.is_large_string(pa_type)
+        ):
+
+            def pyarrow_meth(data, skip_nulls, min_count=0):  # type: ignore[misc]
+                mask = pc.is_null(data) if data.null_count > 0 else None
+                if skip_nulls:
+                    if min_count > 0 and check_below_min_count(
+                        (len(data),),
+                        None if mask is None else mask.to_numpy(),
+                        min_count,
+                    ):
+                        return pa.scalar(None, type=data.type)
+                    if data.null_count > 0:
+                        # binary_join returns null if there is any null ->
+                        # have to filter out any nulls
+                        data = data.filter(pc.invert(mask))
+                else:
+                    if mask is not None or check_below_min_count(
+                        (len(data),), None, min_count
+                    ):
+                        return pa.scalar(None, type=data.type)
+
+                if pa.types.is_large_string(data.type):
+                    # binary_join only supports string, not large_string
+                    data = data.cast(pa.string())
+                data_list = pa.ListArray.from_arrays(
+                    [0, len(data)], data.combine_chunks()
+                )[0]
+                return pc.binary_join(data_list, "")
+
         else:
             pyarrow_name = {
                 "median": "quantile",

pandas/core/arrays/string_.py

@@ -812,8 +812,8 @@ def _reduce(
             else:
                 return nanops.nanall(self._ndarray, skipna=skipna)
 
-        if name in ["min", "max"]:
-            result = getattr(self, name)(skipna=skipna, axis=axis)
+        if name in ["min", "max", "sum"]:
+            result = getattr(self, name)(skipna=skipna, axis=axis, **kwargs)
             if keepdims:
                 return self._from_sequence([result], dtype=self.dtype)
             return result
@@ -839,6 +839,20 @@ def max(self, axis=None, skipna: bool = True, **kwargs) -> Scalar:
         )
         return self._wrap_reduction_result(axis, result)
 
+    def sum(
+        self,
+        *,
+        axis: AxisInt | None = None,
+        skipna: bool = True,
+        min_count: int = 0,
+        **kwargs,
+    ) -> Scalar:
+        nv.validate_sum((), kwargs)
+        result = masked_reductions.sum(
+            values=self._ndarray, mask=self.isna(), skipna=skipna
+        )
+        return self._wrap_reduction_result(axis, result)
+
     def value_counts(self, dropna: bool = True) -> Series:
         from pandas.core.algorithms import value_counts_internal as value_counts
 

pandas/core/arrays/string_arrow.py

@@ -430,7 +430,11 @@ def _reduce(
                 return result.astype(np.bool_)
             return result
 
-        result = self._reduce_calc(name, skipna=skipna, keepdims=keepdims, **kwargs)
+        if name in ("min", "max", "sum", "argmin", "argmax"):
+            result = self._reduce_calc(name, skipna=skipna, keepdims=keepdims, **kwargs)
+        else:
+            raise TypeError(f"Cannot perform reduction '{name}' with string dtype")
+
         if name in ("argmin", "argmax") and isinstance(result, pa.Array):
             return self._convert_int_result(result)
         elif isinstance(result, pa.Array):

pandas/tests/apply/test_frame_apply.py

@@ -4,10 +4,6 @@
 import numpy as np
 import pytest
 
-from pandas._config import using_string_dtype
-
-from pandas.compat import HAS_PYARROW
-
 from pandas.core.dtypes.dtypes import CategoricalDtype
 
 import pandas as pd
@@ -1173,7 +1169,6 @@ def test_agg_with_name_as_column_name():
     tm.assert_series_equal(result, expected)
 
 
-@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
 def test_agg_multiple_mixed():
     # GH 20909
     mdf = DataFrame(
@@ -1202,9 +1197,6 @@ def test_agg_multiple_mixed():
     tm.assert_frame_equal(result, expected)
 
 
-@pytest.mark.xfail(
-    using_string_dtype() and not HAS_PYARROW, reason="TODO(infer_string)"
-)
 def test_agg_multiple_mixed_raises():
     # GH 20909
     mdf = DataFrame(
@@ -1294,7 +1286,6 @@ def test_agg_reduce(axis, float_frame):
     tm.assert_frame_equal(result, expected)
 
 
-@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
 def test_nuiscance_columns():
     # GH 15015
     df = DataFrame(
@@ -1471,7 +1462,6 @@ def test_apply_datetime_tz_issue(engine, request):
     tm.assert_series_equal(result, expected)
 
 
-@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)", strict=False)
 @pytest.mark.parametrize("df", [DataFrame({"A": ["a", None], "B": ["c", "d"]})])
 @pytest.mark.parametrize("method", ["min", "max", "sum"])
 def test_mixed_column_raises(df, method, using_infer_string):

pandas/tests/apply/test_invalid_arg.py

@@ -12,9 +12,6 @@
 import numpy as np
 import pytest
 
-from pandas._config import using_string_dtype
-
-from pandas.compat import HAS_PYARROW
 from pandas.errors import SpecificationError
 
 from pandas import (
@@ -212,10 +209,6 @@ def transform(row):
         data.apply(transform, axis=1)
 
 
-# we should raise a proper TypeError instead of propagating the pyarrow error
-@pytest.mark.xfail(
-    using_string_dtype() and not HAS_PYARROW, reason="TODO(infer_string)"
-)
 @pytest.mark.parametrize(
     "df, func, expected",
     tm.get_cython_table_params(
@@ -225,21 +218,25 @@ def transform(row):
 def test_agg_cython_table_raises_frame(df, func, expected, axis, using_infer_string):
     # GH 21224
     if using_infer_string:
-        import pyarrow as pa
+        if df.dtypes.iloc[0].storage == "pyarrow":
+            import pyarrow as pa
 
-        expected = (expected, pa.lib.ArrowNotImplementedError)
+            # TODO(infer_string)
+            # should raise a proper TypeError instead of propagating the pyarrow error
 
-    msg = "can't multiply sequence by non-int of type 'str'|has no kernel"
+            expected = (expected, pa.lib.ArrowNotImplementedError)
+        else:
+            expected = (expected, NotImplementedError)
+
+    msg = (
+        "can't multiply sequence by non-int of type 'str'|has no kernel|cannot perform"
+    )
     warn = None if isinstance(func, str) else FutureWarning
     with pytest.raises(expected, match=msg):
         with tm.assert_produces_warning(warn, match="using DataFrame.cumprod"):
             df.agg(func, axis=axis)
 
 
-# we should raise a proper TypeError instead of propagating the pyarrow error
-@pytest.mark.xfail(
-    using_string_dtype() and not HAS_PYARROW, reason="TODO(infer_string)"
-)
 @pytest.mark.parametrize(
     "series, func, expected",
     chain(
@@ -263,11 +260,15 @@ def test_agg_cython_table_raises_series(series, func, expected, using_infer_stri
         msg = r"Cannot convert \['a' 'b' 'c'\] to numeric"
 
     if using_infer_string:
-        import pyarrow as pa
-
-        expected = (expected, pa.lib.ArrowNotImplementedError)
-
-    msg = msg + "|does not support|has no kernel"
+        if series.dtype.storage == "pyarrow":
+            import pyarrow as pa
+
+            # TODO(infer_string)
+            # should raise a proper TypeError instead of propagating the pyarrow error
+            expected = (expected, pa.lib.ArrowNotImplementedError)
+        else:
+            expected = (expected, NotImplementedError)
+    msg = msg + "|does not support|has no kernel|Cannot perform|cannot perform"
     warn = None if isinstance(func, str) else FutureWarning
 
     with pytest.raises(expected, match=msg):

pandas/tests/arrays/string_/test_string.py

@@ -444,15 +444,13 @@ def test_astype_float(dtype, any_float_dtype):
 
 
 @pytest.mark.parametrize("skipna", [True, False])
-@pytest.mark.xfail(reason="Not implemented StringArray.sum")
 def test_reduce(skipna, dtype):
     arr = pd.Series(["a", "b", "c"], dtype=dtype)
     result = arr.sum(skipna=skipna)
     assert result == "abc"
 
 
 @pytest.mark.parametrize("skipna", [True, False])
-@pytest.mark.xfail(reason="Not implemented StringArray.sum")
 def test_reduce_missing(skipna, dtype):
     arr = pd.Series([None, "a", None, "b", "c", None], dtype=dtype)
     result = arr.sum(skipna=skipna)

pandas/tests/extension/test_arrow.py

@@ -459,10 +459,11 @@ def _supports_reduction(self, ser: pd.Series, op_name: str) -> bool:
                 pass
             else:
                 return False
+        elif pa.types.is_binary(pa_dtype) and op_name == "sum":
+            return False
         elif (
             pa.types.is_string(pa_dtype) or pa.types.is_binary(pa_dtype)
         ) and op_name in [
-            "sum",
             "mean",
             "median",
             "prod",
@@ -553,6 +554,7 @@ def test_reduce_series_boolean(
         return super().test_reduce_series_boolean(data, all_boolean_reductions, skipna)
 
     def _get_expected_reduction_dtype(self, arr, op_name: str, skipna: bool):
+        pa_type = arr._pa_array.type
         if op_name in ["max", "min"]:
             cmp_dtype = arr.dtype
         elif arr.dtype.name == "decimal128(7, 3)[pyarrow]":
@@ -562,6 +564,8 @@ def _get_expected_reduction_dtype(self, arr, op_name: str, skipna: bool):
                 cmp_dtype = "float64[pyarrow]"
         elif op_name in ["median", "var", "std", "mean", "skew"]:
             cmp_dtype = "float64[pyarrow]"
+        elif op_name == "sum" and pa.types.is_string(pa_type):
+            cmp_dtype = arr.dtype
         else:
             cmp_dtype = {
                 "i": "int64[pyarrow]",
@@ -585,26 +589,6 @@ def test_median_not_approximate(self, typ):
         result = pd.Series([1, 2], dtype=f"{typ}[pyarrow]").median()
         assert result == 1.5
 
-    def test_in_numeric_groupby(self, data_for_grouping):
-        dtype = data_for_grouping.dtype
-        if is_string_dtype(dtype):
-            df = pd.DataFrame(
-                {
-                    "A": [1, 1, 2, 2, 3, 3, 1, 4],
-                    "B": data_for_grouping,
-                    "C": [1, 1, 1, 1, 1, 1, 1, 1],
-                }
-            )
-
-            expected = pd.Index(["C"])
-            msg = re.escape(f"agg function failed [how->sum,dtype->{dtype}")
-            with pytest.raises(TypeError, match=msg):
-                df.groupby("A").sum()
-            result = df.groupby("A").sum(numeric_only=True).columns
-            tm.assert_index_equal(result, expected)
-        else:
-            super().test_in_numeric_groupby(data_for_grouping)
-
     def test_construct_from_string_own_name(self, dtype, request):
         pa_dtype = dtype.pyarrow_dtype
         if pa.types.is_decimal(pa_dtype):

pandas/tests/extension/test_string.py

@@ -191,7 +191,7 @@ def _get_expected_exception(
 
     def _supports_reduction(self, ser: pd.Series, op_name: str) -> bool:
         return (
-            op_name in ["min", "max"]
+            op_name in ["min", "max", "sum"]
             or ser.dtype.na_value is np.nan  # type: ignore[union-attr]
             and op_name in ("any", "all")
         )