REF (string dtype): rename using_pyarrow_string_dtype to using_string_dtype

Author: jorisvandenbossche

pandas/_config/__init__.py

@@ -30,6 +30,6 @@
 from pandas._config.display import detect_console_encoding
 
 
-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
 
@@ -2699,7 +2699,7 @@ 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

@@ -16,7 +16,7 @@
 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 (
@@ -571,11 +571,7 @@ 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")
@@ -609,7 +605,7 @@ def sanitize_array(
             subarr = data
             if data.dtype == object and infer_object:
                 subarr = maybe_infer_to_datetimelike(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,
@@ -798,7 +798,7 @@ 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

@@ -14,7 +14,7 @@
 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
 
@@ -301,7 +301,7 @@ 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

@@ -8,7 +8,7 @@
 )
 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
@@ -131,7 +131,7 @@ 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():
             with warnings.catch_warnings():
                 warnings.filterwarnings(
                     "ignore",
@@ -155,7 +155,7 @@ 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
@@ -136,7 +136,7 @@ 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

@@ -15,7 +15,7 @@
     filterwarnings,
 )
 
-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
@@ -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()
 
         path_or_handle, handles, filesystem = _get_path_or_handle(

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
@@ -301,7 +301,7 @@ 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

@@ -30,7 +30,7 @@
 from pandas._config import (
     config,
     get_option,
-    using_pyarrow_string_dtype,
+    using_string_dtype,
 )
 
 from pandas._libs import (
@@ -3294,7 +3294,7 @@ 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
 
@@ -3363,7 +3363,7 @@ 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)
 
@@ -4735,9 +4735,9 @@ 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,
             ):