feature #58141: Consistent naming conventions for string dtype aliases

pandas/__init__.py

@@ -42,6 +42,25 @@
 # let init-time option registration happen
 import pandas.core.config_init  # pyright: ignore[reportUnusedImport] # noqa: F401
 
+# Import the new factory functions
+from pandas.core.dtypes.factory import (
+    boolean,
+    categorical,
+    date,
+    datetime,
+    decimal,
+    duration,
+    floating,
+    integer,
+    interval,
+    list,
+    map,
+    period,
+    sparse,
+    string,
+    struct,
+)
+
 from pandas.core.api import (
     # dtype
     ArrowDtype,
@@ -281,24 +300,35 @@
     "array",
     "arrays",
     "bdate_range",
+    "boolean",
+    "categorical",
     "concat",
     "crosstab",
     "cut",
+    "date",
     "date_range",
+    "datetime",
+    "decimal",
     "describe_option",
+    "duration",
     "errors",
     "eval",
     "factorize",
+    "floating",
     "from_dummies",
     "get_dummies",
     "get_option",
     "infer_freq",
+    "integer",
+    "interval",
     "interval_range",
     "io",
     "isna",
     "isnull",
     "json_normalize",
+    "list",
     "lreshape",
+    "map",
     "melt",
     "merge",
     "merge_asof",
@@ -308,6 +338,7 @@
     "offsets",
     "option_context",
     "options",
+    "period",
     "period_range",
     "pivot",
     "pivot_table",
@@ -337,6 +368,9 @@
     "set_eng_float_format",
     "set_option",
     "show_versions",
+    "sparse",
+    "string",
+    "struct",
     "test",
     "testing",
     "timedelta_range",

pandas/core/dtypes/dtypes.py

@@ -16,6 +16,7 @@
     TYPE_CHECKING,
     Any,
     cast,
+    List,
 )
 import warnings
 import zoneinfo
@@ -675,7 +676,7 @@ def _is_boolean(self) -> bool:
 
         return is_bool_dtype(self.categories)
 
-    def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
+    def _get_common_dtype(self, dtypes: List[DtypeObj]) -> DtypeObj | None:
         # check if we have all categorical dtype with identical categories
         if all(isinstance(x, CategoricalDtype) for x in dtypes):
             first = dtypes[0]
@@ -966,7 +967,7 @@ def __setstate__(self, state) -> None:
         self._tz = state["tz"]
         self._unit = state["unit"]
 
-    def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
+    def _get_common_dtype(self, dtypes: List[DtypeObj]) -> DtypeObj | None:
         if all(isinstance(t, DatetimeTZDtype) and t.tz == self.tz for t in dtypes):
             np_dtype = np.max([cast(DatetimeTZDtype, t).base for t in [self, *dtypes]])
             unit = np.datetime_data(np_dtype)[0]
@@ -1479,7 +1480,7 @@ def __from_arrow__(self, array: pa.Array | pa.ChunkedArray) -> IntervalArray:
             )
         return IntervalArray._concat_same_type(results)
 
-    def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
+    def _get_common_dtype(self, dtypes: List[DtypeObj]) -> DtypeObj | None:
         if not all(isinstance(x, IntervalDtype) for x in dtypes):
             return None
 
@@ -1677,7 +1678,7 @@ def from_numpy_dtype(cls, dtype: np.dtype) -> BaseMaskedDtype:
         else:
             raise NotImplementedError(dtype)
 
-    def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
+    def _get_common_dtype(self, dtypes: List[DtypeObj]) -> DtypeObj | None:
         # We unwrap any masked dtypes, find the common dtype we would use
         #  for that, then re-mask the result.
         from pandas.core.dtypes.cast import find_common_type
@@ -2104,7 +2105,7 @@ def _subtype_with_str(self):
             return type(self.fill_value)
         return self.subtype
 
-    def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
+    def _get_common_dtype(self, dtypes: List[DtypeObj]) -> DtypeObj | None:
         # TODO for now only handle SparseDtypes and numpy dtypes => extend
         # with other compatible extension dtypes
         from pandas.core.dtypes.cast import np_find_common_type
@@ -2419,7 +2420,7 @@ def _is_boolean(self) -> bool:
         """
         return pa.types.is_boolean(self.pyarrow_dtype)
 
-    def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
+    def _get_common_dtype(self, dtypes: List[DtypeObj]) -> DtypeObj | None:
         # We unwrap any masked dtypes, find the common dtype we would use
         #  for that, then re-mask the result.
         # Mirrors BaseMaskedDtype