REF (string dtype): rename using_pyarrow_string_dtype to using_string_dtype (#59320)

Author: jorisvandenbossche

pandas/_config/__init__.py

@@ -52,6 +52,6 @@ def using_nullable_dtypes() -> bool:
     return _mode_options["nullable_dtypes"]
 
 
-def using_pyarrow_string_dtype() -> bool:
+def using_string_dtype() -> bool:
     _mode_options = _global_config["future"]
     return _mode_options["infer_string"]

pandas/_libs/lib.pyx

@@ -37,7 +37,7 @@ from cython cimport (
     floating,
 )
 
-from pandas._config import using_pyarrow_string_dtype
+from pandas._config import using_string_dtype
 
 from pandas._libs.missing import check_na_tuples_nonequal
 
@@ -2725,7 +2725,7 @@ def maybe_convert_objects(ndarray[object] objects,
         seen.object_ = True
 
     elif seen.str_:
-        if using_pyarrow_string_dtype() and is_string_array(objects, skipna=True):
+        if using_string_dtype() and is_string_array(objects, skipna=True):
             from pandas.core.arrays.string_ import StringDtype
 
             dtype = StringDtype(storage="pyarrow_numpy")

pandas/core/construction.py

@@ -19,7 +19,7 @@
 import numpy as np
 from numpy import ma
 
-from pandas._config import using_pyarrow_string_dtype
+from pandas._config import using_string_dtype
 
 from pandas._libs import lib
 from pandas._libs.tslibs import (
@@ -566,11 +566,7 @@ def sanitize_array(
     if not is_list_like(data):
         if index is None:
             raise ValueError("index must be specified when data is not list-like")
-        if (
-            isinstance(data, str)
-            and using_pyarrow_string_dtype()
-            and original_dtype is None
-        ):
+        if isinstance(data, str) and using_string_dtype() and original_dtype is None:
             from pandas.core.arrays.string_ import StringDtype
 
             dtype = StringDtype("pyarrow_numpy")
@@ -604,14 +600,10 @@ def sanitize_array(
             subarr = data
             if data.dtype == object:
                 subarr = maybe_infer_to_datetimelike(data)
-                if (
-                    object_index
-                    and using_pyarrow_string_dtype()
-                    and is_string_dtype(subarr)
-                ):
+                if object_index and using_string_dtype() and is_string_dtype(subarr):
                     # Avoid inference when string option is set
                     subarr = data
-            elif data.dtype.kind == "U" and using_pyarrow_string_dtype():
+            elif data.dtype.kind == "U" and using_string_dtype():
                 from pandas.core.arrays.string_ import StringDtype
 
                 dtype = StringDtype(storage="pyarrow_numpy")

pandas/core/dtypes/cast.py

@@ -18,7 +18,7 @@
 
 import numpy as np
 
-from pandas._config import using_pyarrow_string_dtype
+from pandas._config import using_string_dtype
 
 from pandas._libs import (
     Interval,
@@ -799,7 +799,7 @@ def infer_dtype_from_scalar(val) -> tuple[DtypeObj, Any]:
         # coming out as np.str_!
 
         dtype = _dtype_obj
-        if using_pyarrow_string_dtype():
+        if using_string_dtype():
             from pandas.core.arrays.string_ import StringDtype
 
             dtype = StringDtype(storage="pyarrow_numpy")

pandas/core/internals/construction.py

@@ -13,7 +13,7 @@
 import numpy as np
 from numpy import ma
 
-from pandas._config import using_pyarrow_string_dtype
+from pandas._config import using_string_dtype
 
 from pandas._libs import lib
 
@@ -375,7 +375,7 @@ def ndarray_to_mgr(
             bp = BlockPlacement(slice(len(columns)))
             nb = new_block_2d(values, placement=bp, refs=refs)
             block_values = [nb]
-    elif dtype is None and values.dtype.kind == "U" and using_pyarrow_string_dtype():
+    elif dtype is None and values.dtype.kind == "U" and using_string_dtype():
         dtype = StringDtype(storage="pyarrow_numpy")
 
         obj_columns = list(values)

pandas/io/feather_format.py

@@ -6,7 +6,7 @@
     Any,
 )
 
-from pandas._config import using_pyarrow_string_dtype
+from pandas._config import using_string_dtype
 
 from pandas._libs import lib
 from pandas.compat._optional import import_optional_dependency
@@ -120,7 +120,7 @@ def read_feather(
     with get_handle(
         path, "rb", storage_options=storage_options, is_text=False
     ) as handles:
-        if dtype_backend is lib.no_default and not using_pyarrow_string_dtype():
+        if dtype_backend is lib.no_default and not using_string_dtype():
             return feather.read_feather(
                 handles.handle, columns=columns, use_threads=bool(use_threads)
             )
@@ -137,7 +137,7 @@ def read_feather(
         elif dtype_backend == "pyarrow":
             return pa_table.to_pandas(types_mapper=pd.ArrowDtype)
 
-        elif using_pyarrow_string_dtype():
+        elif using_string_dtype():
             return pa_table.to_pandas(types_mapper=arrow_string_types_mapper())
         else:
             raise NotImplementedError

pandas/io/orc.py

@@ -9,7 +9,7 @@
     Literal,
 )
 
-from pandas._config import using_pyarrow_string_dtype
+from pandas._config import using_string_dtype
 
 from pandas._libs import lib
 from pandas.compat._optional import import_optional_dependency
@@ -127,7 +127,7 @@ def read_orc(
             df = pa_table.to_pandas(types_mapper=mapping.get)
         return df
     else:
-        if using_pyarrow_string_dtype():
+        if using_string_dtype():
             types_mapper = arrow_string_types_mapper()
         else:
             types_mapper = None

pandas/io/parquet.py

@@ -12,7 +12,7 @@
 import warnings
 from warnings import catch_warnings
 
-from pandas._config import using_pyarrow_string_dtype
+from pandas._config import using_string_dtype
 from pandas._config.config import _get_option
 
 from pandas._libs import lib
@@ -257,7 +257,7 @@ def read(
             to_pandas_kwargs["types_mapper"] = mapping.get
         elif dtype_backend == "pyarrow":
             to_pandas_kwargs["types_mapper"] = pd.ArrowDtype  # type: ignore[assignment]
-        elif using_pyarrow_string_dtype():
+        elif using_string_dtype():
             to_pandas_kwargs["types_mapper"] = arrow_string_types_mapper()
 
         manager = _get_option("mode.data_manager", silent=True)

pandas/io/parsers/arrow_parser_wrapper.py

@@ -3,7 +3,7 @@
 from typing import TYPE_CHECKING
 import warnings
 
-from pandas._config import using_pyarrow_string_dtype
+from pandas._config import using_string_dtype
 
 from pandas._libs import lib
 from pandas.compat._optional import import_optional_dependency
@@ -295,7 +295,7 @@ def read(self) -> DataFrame:
             dtype_mapping = _arrow_dtype_mapping()
             dtype_mapping[pa.null()] = pd.Int64Dtype()
             frame = table.to_pandas(types_mapper=dtype_mapping.get)
-        elif using_pyarrow_string_dtype():
+        elif using_string_dtype():
             frame = table.to_pandas(types_mapper=arrow_string_types_mapper())
 
         else:

pandas/io/pytables.py

@@ -31,7 +31,7 @@
     config,
     get_option,
     using_copy_on_write,
-    using_pyarrow_string_dtype,
+    using_string_dtype,
 )
 
 from pandas._libs import (
@@ -3224,7 +3224,7 @@ def read(
         index = self.read_index("index", start=start, stop=stop)
         values = self.read_array("values", start=start, stop=stop)
         result = Series(values, index=index, name=self.name, copy=False)
-        if using_pyarrow_string_dtype() and is_string_array(values, skipna=True):
+        if using_string_dtype() and is_string_array(values, skipna=True):
             result = result.astype("string[pyarrow_numpy]")
         return result
 
@@ -3293,7 +3293,7 @@ def read(
 
             columns = items[items.get_indexer(blk_items)]
             df = DataFrame(values.T, columns=columns, index=axes[1], copy=False)
-            if using_pyarrow_string_dtype() and is_string_array(values, skipna=True):
+            if using_string_dtype() and is_string_array(values, skipna=True):
                 df = df.astype("string[pyarrow_numpy]")
             dfs.append(df)
 
@@ -4679,9 +4679,9 @@ def read(
             else:
                 # Categorical
                 df = DataFrame._from_arrays([values], columns=cols_, index=index_)
-            if not (using_pyarrow_string_dtype() and values.dtype.kind == "O"):
+            if not (using_string_dtype() and values.dtype.kind == "O"):
                 assert (df.dtypes == values.dtype).all(), (df.dtypes, values.dtype)
-            if using_pyarrow_string_dtype() and is_string_array(
+            if using_string_dtype() and is_string_array(
                 values,  # type: ignore[arg-type]
                 skipna=True,
             ):