merge main

Author: kastkeepitjumpinlikekangaroos

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

doc/source/whatsnew/v3.0.0.rst

@@ -702,6 +702,7 @@ I/O
 - Bug in :meth:`read_stata` raising ``KeyError`` when input file is stored in big-endian format and contains strL data. (:issue:`58638`)
 - Bug in :meth:`read_stata` where extreme value integers were incorrectly interpreted as missing for format versions 111 and prior (:issue:`58130`)
 - Bug in :meth:`read_stata` where the missing code for double was not recognised for format versions 105 and prior (:issue:`58149`)
+- Bug in :meth:`set_option` where setting the pandas option ``display.html.use_mathjax`` to ``False`` has no effect (:issue:`59884`)
 - Bug in :meth:`to_excel` where :class:`MultiIndex` columns would be merged to a single row when ``merge_cells=False`` is passed (:issue:`60274`)
 
 Period

pandas/__init__.py

@@ -191,6 +191,7 @@
 
 
 # module level doc-string
+__version__ = "3.0.0"
 __doc__ = """
 pandas - a powerful data analysis and manipulation library for Python
 =====================================================================

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/cast.py

@@ -1162,6 +1162,7 @@ def convert_dtypes(
 
 def maybe_infer_to_datetimelike(
     value: npt.NDArray[np.object_],
+    convert_to_nullable_dtype: bool = False,
 ) -> np.ndarray | DatetimeArray | TimedeltaArray | PeriodArray | IntervalArray:
     """
     we might have a array (or single object) that is datetime like,
@@ -1199,6 +1200,7 @@ def maybe_infer_to_datetimelike(
         #  numpy would have done it for us.
         convert_numeric=False,
         convert_non_numeric=True,
+        convert_to_nullable_dtype=convert_to_nullable_dtype,
         dtype_if_all_nat=np.dtype("M8[s]"),
     )
 

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/frame.py

@@ -2115,8 +2115,8 @@ def from_records(
         """
         Convert structured or record ndarray to DataFrame.
 
-        Creates a DataFrame object from a structured ndarray, sequence of
-        tuples or dicts, or DataFrame.
+        Creates a DataFrame object from a structured ndarray, or sequence of
+        tuples or dicts.
 
         Parameters
         ----------

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(