Implement Arrow String Array that is compatible with NumPy semantics

pandas/conftest.py

@@ -1321,6 +1321,7 @@ def nullable_string_dtype(request):
     params=[
         "python",
         pytest.param("pyarrow", marks=td.skip_if_no("pyarrow")),
+        pytest.param("pyarrow_numpy", marks=td.skip_if_no("pyarrow")),
     ]
 )
 def string_storage(request):
@@ -1329,6 +1330,7 @@ def string_storage(request):
 
     * 'python'
     * 'pyarrow'
+    * 'pyarrow_numpy'
     """
     return request.param
 
@@ -1380,6 +1382,7 @@ def object_dtype(request):
         "object",
         "string[python]",
         pytest.param("string[pyarrow]", marks=td.skip_if_no("pyarrow")),
+        pytest.param("string[pyarrow_numpy]", marks=td.skip_if_no("pyarrow")),
     ]
 )
 def any_string_dtype(request):
@@ -2000,4 +2003,4 @@ def warsaw(request) -> str:
 
 @pytest.fixture()
 def arrow_string_storage():
-    return ("pyarrow",)
+    return ("pyarrow", "pyarrow_numpy")

pandas/core/arrays/arrow/array.py

@@ -506,7 +506,10 @@ def __getitem__(self, item: PositionalIndexer):
         if isinstance(item, np.ndarray):
             if not len(item):
                 # Removable once we migrate StringDtype[pyarrow] to ArrowDtype[string]
-                if self._dtype.name == "string" and self._dtype.storage == "pyarrow":
+                if self._dtype.name == "string" and self._dtype.storage in (
+                    "pyarrow",
+                    "pyarrow_numpy",
+                ):
                     pa_dtype = pa.string()
                 else:
                     pa_dtype = self._dtype.pyarrow_dtype

pandas/core/arrays/string_.py

@@ -76,7 +76,7 @@ class StringDtype(StorageExtensionDtype):
 
     Parameters
     ----------
-    storage : {"python", "pyarrow"}, optional
+    storage : {"python", "pyarrow", "pyarrow_numpy"}, optional
         If not given, the value of ``pd.options.mode.string_storage``.
 
     Attributes
@@ -108,11 +108,11 @@ def na_value(self) -> libmissing.NAType:
     def __init__(self, storage=None) -> None:
         if storage is None:
             storage = get_option("mode.string_storage")
-        if storage not in {"python", "pyarrow"}:
+        if storage not in {"python", "pyarrow", "pyarrow_numpy"}:
             raise ValueError(
                 f"Storage must be 'python' or 'pyarrow'. Got {storage} instead."
             )
-        if storage == "pyarrow" and pa_version_under7p0:
+        if storage in ("pyarrow", "pyarrow_numpy") and pa_version_under7p0:
             raise ImportError(
                 "pyarrow>=7.0.0 is required for PyArrow backed StringArray."
             )
@@ -160,6 +160,8 @@ def construct_from_string(cls, string):
             return cls(storage="python")
         elif string == "string[pyarrow]":
             return cls(storage="pyarrow")
+        elif string == "string[pyarrow_numpy]":
+            return cls(storage="pyarrow_numpy")
         else:
             raise TypeError(f"Cannot construct a '{cls.__name__}' from '{string}'")
 
@@ -176,12 +178,17 @@ def construct_array_type(  # type: ignore[override]
         -------
         type
         """
-        from pandas.core.arrays.string_arrow import ArrowStringArray
+        from pandas.core.arrays.string_arrow import (
+            ArrowStringArray,
+            ArrowStringArrayNumpySemantics,
+        )
 
         if self.storage == "python":
             return StringArray
-        else:
+        elif self.storage == "pyarrow":
             return ArrowStringArray
+        else:
+            return ArrowStringArrayNumpySemantics
 
     def __from_arrow__(
         self, array: pyarrow.Array | pyarrow.ChunkedArray
@@ -193,6 +200,10 @@ def __from_arrow__(
             from pandas.core.arrays.string_arrow import ArrowStringArray
 
             return ArrowStringArray(array)
+        elif self.storage == "pyarrow_numpy":
+            from pandas.core.arrays.string_arrow import ArrowStringArrayNumpySemantics
+
+            return ArrowStringArrayNumpySemantics(array)
         else:
             import pyarrow
 

pandas/core/arrays/string_arrow.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+from functools import partial
 import re
 from typing import (
     TYPE_CHECKING,
@@ -27,6 +28,7 @@
 )
 from pandas.core.dtypes.missing import isna
 
+from pandas.core.arrays._arrow_string_mixins import ArrowStringArrayMixin
 from pandas.core.arrays.arrow import ArrowExtensionArray
 from pandas.core.arrays.boolean import BooleanDtype
 from pandas.core.arrays.integer import Int64Dtype
@@ -113,10 +115,11 @@ class ArrowStringArray(ObjectStringArrayMixin, ArrowExtensionArray, BaseStringAr
     # error: Incompatible types in assignment (expression has type "StringDtype",
     # base class "ArrowExtensionArray" defined the type as "ArrowDtype")
     _dtype: StringDtype  # type: ignore[assignment]
+    _storage = "pyarrow"
 
     def __init__(self, values) -> None:
         super().__init__(values)
-        self._dtype = StringDtype(storage="pyarrow")
+        self._dtype = StringDtype(storage=self._storage)
 
         if not pa.types.is_string(self._pa_array.type) and not (
             pa.types.is_dictionary(self._pa_array.type)
@@ -144,7 +147,10 @@ def _from_sequence(cls, scalars, dtype: Dtype | None = None, copy: bool = False)
 
         if dtype and not (isinstance(dtype, str) and dtype == "string"):
             dtype = pandas_dtype(dtype)
-            assert isinstance(dtype, StringDtype) and dtype.storage == "pyarrow"
+            assert isinstance(dtype, StringDtype) and dtype.storage in (
+                "pyarrow",
+                "pyarrow_numpy",
+            )
 
         if isinstance(scalars, BaseMaskedArray):
             # avoid costly conversion to object dtype in ensure_string_array and
@@ -178,6 +184,10 @@ def insert(self, loc: int, item) -> ArrowStringArray:
             raise TypeError("Scalar must be NA or str")
         return super().insert(loc, item)
 
+    @staticmethod
+    def _result_converter(values, **kwargs):
+        return BooleanDtype().__from_arrow__(values)
+
     def _maybe_convert_setitem_value(self, value):
         """Maybe convert value to be pyarrow compatible."""
         if is_scalar(value):
@@ -313,7 +323,7 @@ def _str_contains(
             result = pc.match_substring_regex(self._pa_array, pat, ignore_case=not case)
         else:
             result = pc.match_substring(self._pa_array, pat, ignore_case=not case)
-        result = BooleanDtype().__from_arrow__(result)
+        result = self._result_converter(result, na=na)
         if not isna(na):
             result[isna(result)] = bool(na)
         return result
@@ -322,7 +332,7 @@ def _str_startswith(self, pat: str, na=None):
         result = pc.starts_with(self._pa_array, pattern=pat)
         if not isna(na):
             result = result.fill_null(na)
-        result = BooleanDtype().__from_arrow__(result)
+        result = self._result_converter(result)
         if not isna(na):
             result[isna(result)] = bool(na)
         return result
@@ -331,7 +341,7 @@ def _str_endswith(self, pat: str, na=None):
         result = pc.ends_with(self._pa_array, pattern=pat)
         if not isna(na):
             result = result.fill_null(na)
-        result = BooleanDtype().__from_arrow__(result)
+        result = self._result_converter(result)
         if not isna(na):
             result[isna(result)] = bool(na)
         return result
@@ -369,39 +379,39 @@ def _str_fullmatch(
 
     def _str_isalnum(self):
         result = pc.utf8_is_alnum(self._pa_array)
-        return BooleanDtype().__from_arrow__(result)
+        return self._result_converter(result)
 
     def _str_isalpha(self):
         result = pc.utf8_is_alpha(self._pa_array)
-        return BooleanDtype().__from_arrow__(result)
+        return self._result_converter(result)
 
     def _str_isdecimal(self):
         result = pc.utf8_is_decimal(self._pa_array)
-        return BooleanDtype().__from_arrow__(result)
+        return self._result_converter(result)
 
     def _str_isdigit(self):
         result = pc.utf8_is_digit(self._pa_array)
-        return BooleanDtype().__from_arrow__(result)
+        return self._result_converter(result)
 
     def _str_islower(self):
         result = pc.utf8_is_lower(self._pa_array)
-        return BooleanDtype().__from_arrow__(result)
+        return self._result_converter(result)
 
     def _str_isnumeric(self):
         result = pc.utf8_is_numeric(self._pa_array)
-        return BooleanDtype().__from_arrow__(result)
+        return self._result_converter(result)
 
     def _str_isspace(self):
         result = pc.utf8_is_space(self._pa_array)
-        return BooleanDtype().__from_arrow__(result)
+        return self._result_converter(result)
 
     def _str_istitle(self):
         result = pc.utf8_is_title(self._pa_array)
-        return BooleanDtype().__from_arrow__(result)
+        return self._result_converter(result)
 
     def _str_isupper(self):
         result = pc.utf8_is_upper(self._pa_array)
-        return BooleanDtype().__from_arrow__(result)
+        return self._result_converter(result)
 
     def _str_len(self):
         result = pc.utf8_length(self._pa_array)
@@ -433,3 +443,114 @@ def _str_rstrip(self, to_strip=None):
         else:
             result = pc.utf8_rtrim(self._pa_array, characters=to_strip)
         return type(self)(result)
+
+
+class ArrowStringArrayNumpySemantics(ArrowStringArray):
+    _storage = "pyarrow_numpy"
+
+    @staticmethod
+    def _result_converter(values, na=None):
+        if not isna(na):
+            values = values.fill_null(bool(na))
+        return ArrowExtensionArray(values).to_numpy(na_value=np.nan)
+
+    def __getattribute__(self, item):
+        # ArrowStringArray and we both inherit from ArrowExtensionArray, which
+        # creates inheritance problems (Diamnond inheritance)
+        if item in ArrowStringArrayMixin.__dict__ and item != "_pa_array":
+            return partial(getattr(ArrowStringArrayMixin, item), self)
+        return super().__getattribute__(item)
+
+    def _str_map(
+        self, f, na_value=None, dtype: Dtype | None = None, convert: bool = True
+    ):
+        if dtype is None:
+            dtype = self.dtype
+        if na_value is None:
+            na_value = self.dtype.na_value
+
+        mask = isna(self)
+        arr = np.asarray(self)
+
+        if is_integer_dtype(dtype) or is_bool_dtype(dtype):
+            if is_integer_dtype(dtype):
+                na_value = np.nan
+            else:
+                na_value = False
+            try:
+                result = lib.map_infer_mask(
+                    arr,
+                    f,
+                    mask.view("uint8"),
+                    convert=False,
+                    na_value=na_value,
+                    dtype=np.dtype(dtype),  # type: ignore[arg-type]
+                )
+                return result
+
+            except ValueError:
+                result = lib.map_infer_mask(
+                    arr,
+                    f,
+                    mask.view("uint8"),
+                    convert=False,
+                    na_value=na_value,
+                )
+                if convert and result.dtype == object:
+                    result = lib.maybe_convert_objects(result)
+                return result
+
+        elif is_string_dtype(dtype) and not is_object_dtype(dtype):
+            # i.e. StringDtype
+            result = lib.map_infer_mask(
+                arr, f, mask.view("uint8"), convert=False, na_value=na_value
+            )
+            result = pa.array(result, mask=mask, type=pa.string(), from_pandas=True)
+            return type(self)(result)
+        else:
+            # This is when the result type is object. We reach this when
+            # -> We know the result type is truly object (e.g. .encode returns bytes
+            #    or .findall returns a list).
+            # -> We don't know the result type. E.g. `.get` can return anything.
+            return lib.map_infer_mask(arr, f, mask.view("uint8"))
+
+    def _convert_int_dtype(self, result):
+        if result.dtype == np.int32:
+            result = result.astype(np.int64)
+        return result
+
+    def _str_count(self, pat: str, flags: int = 0):
+        if flags:
+            return super()._str_count(pat, flags)
+        result = pc.count_substring_regex(self._pa_array, pat).to_numpy()
+        return self._convert_int_dtype(result)
+
+    def _str_len(self):
+        result = pc.utf8_length(self._pa_array).to_numpy()
+        return self._convert_int_dtype(result)
+
+    def _str_find(self, sub: str, start: int = 0, end: int | None = None):
+        if start != 0 and end is not None:
+            slices = pc.utf8_slice_codeunits(self._pa_array, start, stop=end)
+            result = pc.find_substring(slices, sub)
+            not_found = pc.equal(result, -1)
+            offset_result = pc.add(result, end - start)
+            result = pc.if_else(not_found, result, offset_result)
+        elif start == 0 and end is None:
+            slices = self._pa_array
+            result = pc.find_substring(slices, sub)
+        else:
+            return super()._str_find(sub, start, end)
+        return self._convert_int_dtype(result.to_numpy())
+
+    def _cmp_method(self, other, op):
+        result = super()._cmp_method(other, op)
+        return result.to_numpy(np.bool_, na_value=False)
+
+    def value_counts(self, dropna: bool = True):
+        from pandas import Series
+
+        result = super().value_counts(dropna)
+        return Series(
+            result._values.to_numpy(), index=result.index, name=result.name, copy=False
+        )

pandas/core/config_init.py

@@ -500,7 +500,7 @@ def use_inf_as_na_cb(key) -> None:
         "string_storage",
         "python",
         string_storage_doc,
-        validator=is_one_of_factory(["python", "pyarrow"]),
+        validator=is_one_of_factory(["python", "pyarrow", "pyarrow_numpy"]),
     )
 
 

pandas/core/strings/accessor.py

@@ -145,7 +145,9 @@ def _map_and_wrap(name: str | None, docstring: str | None):
     @forbid_nonstring_types(["bytes"], name=name)
     def wrapper(self):
         result = getattr(self._data.array, f"_str_{name}")()
-        return self._wrap_result(result)
+        return self._wrap_result(
+            result, returns_string=name not in ("isnumeric", "isdecimal")
+        )
 
     wrapper.__doc__ = docstring
     return wrapper