REF: Dispatch string methods to ExtensionArray

ci/code_checks.sh

@@ -335,7 +335,7 @@ if [[ -z "$CHECK" || "$CHECK" == "doctests" ]]; then
     RET=$(($RET + $?)) ; echo $MSG "DONE"
 
     MSG='Doctests strings.py' ; echo $MSG
-    pytest -q --doctest-modules pandas/core/strings.py
+    pytest -q --doctest-modules pandas/core/strings/
     RET=$(($RET + $?)) ; echo $MSG "DONE"
 
     # Directories

pandas/core/arrays/categorical.py

@@ -51,6 +51,7 @@
 from pandas.core.missing import interpolate_2d
 from pandas.core.ops.common import unpack_zerodim_and_defer
 from pandas.core.sorting import nargsort
+from pandas.core.strings.object_array import ObjectStringArrayMixin
 
 from pandas.io.formats import console
 
@@ -176,7 +177,7 @@ def contains(cat, key, container):
         return any(loc_ in container for loc_ in loc)
 
 
-class Categorical(NDArrayBackedExtensionArray, PandasObject):
+class Categorical(NDArrayBackedExtensionArray, PandasObject, ObjectStringArrayMixin):
     """
     Represent a categorical variable in classic R / S-plus fashion.
 
@@ -2305,6 +2306,25 @@ def replace(self, to_replace, value, inplace: bool = False):
         if not inplace:
             return cat
 
+    # ------------------------------------------------------------------------
+    # String methods interface
+    def _str_map(self, f, na_value=np.nan, dtype=np.dtype(object)):
+        # Optimization to apply the callable `f` to the categories once
+        # and rebuild the result by `take`ing from the result with the codes.
+        # Returns the same type as the object-dtype implementation though.
+        from pandas.core.arrays import PandasArray
+
+        categories = self.categories
+        codes = self.codes
+        result = PandasArray(categories.to_numpy())._str_map(f, na_value, dtype)
+        return take_1d(result, codes, fill_value=na_value)
+
+    def _str_get_dummies(self, sep="|"):
+        # sep may not be in categories. Just bail on this.
+        from pandas.core.arrays import PandasArray
+
+        return PandasArray(self.astype(str))._str_get_dummies(sep)
+
 
 # The Series.cat accessor
 

pandas/core/arrays/numpy_.py

@@ -16,6 +16,7 @@
 from pandas.core.array_algos import masked_reductions
 from pandas.core.arrays._mixins import NDArrayBackedExtensionArray
 from pandas.core.arrays.base import ExtensionOpsMixin
+from pandas.core.strings.object_array import ObjectStringArrayMixin
 
 
 class PandasDtype(ExtensionDtype):
@@ -114,7 +115,10 @@ def itemsize(self) -> int:
 
 
 class PandasArray(
-    NDArrayBackedExtensionArray, ExtensionOpsMixin, NDArrayOperatorsMixin
+    NDArrayBackedExtensionArray,
+    ExtensionOpsMixin,
+    NDArrayOperatorsMixin,
+    ObjectStringArrayMixin,
 ):
     """
     A pandas ExtensionArray for NumPy data.
@@ -376,6 +380,10 @@ def arithmetic_method(self, other):
 
     _create_comparison_method = _create_arithmetic_method
 
+    # ------------------------------------------------------------------------
+    # String methods interface
+    _str_na_value = np.nan
+
 
 PandasArray._add_arithmetic_ops()
 PandasArray._add_comparison_ops()

pandas/core/arrays/string_.py

@@ -6,8 +6,14 @@
 from pandas._libs import lib, missing as libmissing
 
 from pandas.core.dtypes.base import ExtensionDtype, register_extension_dtype
-from pandas.core.dtypes.common import pandas_dtype
-from pandas.core.dtypes.inference import is_array_like
+from pandas.core.dtypes.common import (
+    is_array_like,
+    is_bool_dtype,
+    is_integer_dtype,
+    is_object_dtype,
+    is_string_dtype,
+    pandas_dtype,
+)
 
 from pandas import compat
 from pandas.core import ops
@@ -347,6 +353,58 @@ def _add_arithmetic_ops(cls):
         cls.__rmul__ = cls._create_arithmetic_method(ops.rmul)
 
     _create_comparison_method = _create_arithmetic_method
+    # ------------------------------------------------------------------------
+    # String methods interface
+    _str_na_value = StringDtype.na_value
+
+    def _str_map(self, f, na_value=None, dtype=None):
+        from pandas.arrays import BooleanArray, IntegerArray, StringArray
+        from pandas.core.arrays.string_ import StringDtype
+
+        if dtype is None:
+            dtype = StringDtype()
+        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):
+            constructor: Union[Type[IntegerArray], Type[BooleanArray]]
+            if is_integer_dtype(dtype):
+                constructor = IntegerArray
+            else:
+                constructor = BooleanArray
+
+            na_value_is_na = isna(na_value)
+            if na_value_is_na:
+                na_value = 1
+            result = lib.map_infer_mask(
+                arr,
+                f,
+                mask.view("uint8"),
+                convert=False,
+                na_value=na_value,
+                dtype=np.dtype(dtype),
+            )
+
+            if not na_value_is_na:
+                mask[:] = False
+
+            return constructor(result, mask)
+
+        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
+            )
+            return StringArray(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"))
 
 
 StringArray._add_arithmetic_ops()