Merge branch 'main' into 60237

Author: road-1

ci/code_checks.sh

@@ -84,7 +84,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Timestamp.resolution PR02" \
         -i "pandas.Timestamp.tzinfo GL08" \
         -i "pandas.api.types.is_re_compilable PR07,SA01" \
-        -i "pandas.api.types.pandas_dtype PR07,RT03,SA01" \
         -i "pandas.arrays.ArrowExtensionArray PR07,SA01" \
         -i "pandas.arrays.IntegerArray SA01" \
         -i "pandas.arrays.IntervalArray.length SA01" \

doc/source/whatsnew/v2.3.0.rst

@@ -118,7 +118,7 @@ Interval
 
 Indexing
 ^^^^^^^^
--
+- Fixed bug in :meth:`Index.get_indexer` round-tripping through string dtype when ``infer_string`` is enabled (:issue:`55834`)
 -
 
 Missing

pandas/core/array_algos/replace.py

@@ -151,4 +151,6 @@ def re_replacer(s):
     if mask is None:
         values[:] = f(values)
     else:
+        if values.ndim != mask.ndim:
+            mask = np.broadcast_to(mask, values.shape)
         values[mask] = f(values[mask])

pandas/core/arrays/arrow/array.py

@@ -1644,7 +1644,11 @@ def _accumulate(
             else:
                 data_to_accum = data_to_accum.cast(pa.int64())
 
-        result = pyarrow_meth(data_to_accum, skip_nulls=skipna, **kwargs)
+        try:
+            result = pyarrow_meth(data_to_accum, skip_nulls=skipna, **kwargs)
+        except pa.ArrowNotImplementedError as err:
+            msg = f"operation '{name}' not supported for dtype '{self.dtype}'"
+            raise TypeError(msg) from err
 
         if convert_to_int:
             result = result.cast(pa_dtype)

pandas/core/dtypes/common.py

@@ -1785,16 +1785,22 @@ def pandas_dtype(dtype) -> DtypeObj:
 
     Parameters
     ----------
-    dtype : object to be converted
+    dtype : object
+        The object to be converted into a dtype.
 
     Returns
     -------
     np.dtype or a pandas dtype
+        The converted dtype, which can be either a numpy dtype or a pandas dtype.
 
     Raises
     ------
     TypeError if not a dtype
 
+    See Also
+    --------
+    api.types.is_dtype : Return true if the condition is satisfied for the arr_or_dtype.
+
     Examples
     --------
     >>> pd.api.types.pandas_dtype(int)

pandas/core/indexes/base.py

@@ -6556,7 +6556,16 @@ def _maybe_cast_listlike_indexer(self, target) -> Index:
         """
         Analogue to maybe_cast_indexer for get_indexer instead of get_loc.
         """
-        return ensure_index(target)
+        target_index = ensure_index(target)
+        if (
+            not hasattr(target, "dtype")
+            and self.dtype == object
+            and target_index.dtype == "string"
+        ):
+            # If we started with a list-like, avoid inference to string dtype if self
+            # is object dtype (coercing to string dtype will alter the missing values)
+            target_index = Index(target, dtype=self.dtype)
+        return target_index
 
     @final
     def _validate_indexer(

pandas/core/interchange/from_dataframe.py

@@ -9,6 +9,8 @@
 
 import numpy as np
 
+from pandas._config import using_string_dtype
+
 from pandas.compat._optional import import_optional_dependency
 
 import pandas as pd
@@ -147,8 +149,6 @@ def protocol_df_chunk_to_pandas(df: DataFrameXchg) -> pd.DataFrame:
     -------
     pd.DataFrame
     """
-    # We need a dict of columns here, with each column being a NumPy array (at
-    # least for now, deal with non-NumPy dtypes later).
     columns: dict[str, Any] = {}
     buffers = []  # hold on to buffers, keeps memory alive
     for name in df.column_names():
@@ -347,8 +347,12 @@ def string_column_to_ndarray(col: Column) -> tuple[np.ndarray, Any]:
         # Add to our list of strings
         str_list[i] = string
 
-    # Convert the string list to a NumPy array
-    return np.asarray(str_list, dtype="object"), buffers
+    if using_string_dtype():
+        res = pd.Series(str_list, dtype="str")
+    else:
+        res = np.asarray(str_list, dtype="object")  # type: ignore[assignment]
+
+    return res, buffers  # type: ignore[return-value]
 
 
 def parse_datetime_format_str(format_str, data) -> pd.Series | np.ndarray:

pandas/core/internals/blocks.py

@@ -1688,6 +1688,13 @@ def where(self, other, cond) -> list[Block]:
                 if isinstance(self.dtype, (IntervalDtype, StringDtype)):
                     # TestSetitemFloatIntervalWithIntIntervalValues
                     blk = self.coerce_to_target_dtype(orig_other, raise_on_upcast=False)
+                    if (
+                        self.ndim == 2
+                        and isinstance(orig_cond, np.ndarray)
+                        and orig_cond.ndim == 1
+                        and not is_1d_only_ea_dtype(blk.dtype)
+                    ):
+                        orig_cond = orig_cond[:, None]
                     return blk.where(orig_other, orig_cond)
 
                 elif isinstance(self, NDArrayBackedExtensionBlock):

pandas/core/reshape/concat.py

@@ -17,6 +17,7 @@
 import numpy as np
 
 from pandas._libs import lib
+from pandas.util._decorators import set_module
 from pandas.util._exceptions import find_stack_level
 
 from pandas.core.dtypes.common import (
@@ -149,6 +150,7 @@ def concat(
 ) -> DataFrame | Series: ...
 
 
+@set_module("pandas")
 def concat(
     objs: Iterable[Series | DataFrame] | Mapping[HashableT, Series | DataFrame],
     *,

pandas/io/_util.py

@@ -1,9 +1,15 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING
+from typing import (
+    TYPE_CHECKING,
+    Literal,
+)
 
 import numpy as np
 
+from pandas._config import using_string_dtype
+
+from pandas._libs import lib
 from pandas.compat import pa_version_under18p0
 from pandas.compat._optional import import_optional_dependency
 
@@ -12,6 +18,10 @@
 if TYPE_CHECKING:
     from collections.abc import Callable
 
+    import pyarrow
+
+    from pandas._typing import DtypeBackend
+
 
 def _arrow_dtype_mapping() -> dict:
     pa = import_optional_dependency("pyarrow")
@@ -33,7 +43,7 @@ def _arrow_dtype_mapping() -> dict:
     }
 
 
-def arrow_string_types_mapper() -> Callable:
+def _arrow_string_types_mapper() -> Callable:
     pa = import_optional_dependency("pyarrow")
 
     mapping = {
@@ -44,3 +54,31 @@ def arrow_string_types_mapper() -> Callable:
         mapping[pa.string_view()] = pd.StringDtype(na_value=np.nan)
 
     return mapping.get
+
+
+def arrow_table_to_pandas(
+    table: pyarrow.Table,
+    dtype_backend: DtypeBackend | Literal["numpy"] | lib.NoDefault = lib.no_default,
+    null_to_int64: bool = False,
+) -> pd.DataFrame:
+    pa = import_optional_dependency("pyarrow")
+
+    types_mapper: type[pd.ArrowDtype] | None | Callable
+    if dtype_backend == "numpy_nullable":
+        mapping = _arrow_dtype_mapping()
+        if null_to_int64:
+            # Modify the default mapping to also map null to Int64
+            # (to match other engines - only for CSV parser)
+            mapping[pa.null()] = pd.Int64Dtype()
+        types_mapper = mapping.get
+    elif dtype_backend == "pyarrow":
+        types_mapper = pd.ArrowDtype
+    elif using_string_dtype():
+        types_mapper = _arrow_string_types_mapper()
+    elif dtype_backend is lib.no_default or dtype_backend == "numpy":
+        types_mapper = None
+    else:
+        raise NotImplementedError
+
+    df = table.to_pandas(types_mapper=types_mapper)
+    return df