ENH: add Series.struct accessor for ArrowDtype[struct]

doc/source/reference/series.rst

@@ -525,6 +525,29 @@ Sparse-dtype specific methods and attributes are provided under the
    Series.sparse.from_coo
    Series.sparse.to_coo
 
+
+.. _api.series.struct:
+
+Struct accessor
+~~~~~~~~~~~~~~~
+
+Arrow struct-dtype specific methods and attributes are provided under the
+``Series.struct`` accessor.
+
+.. autosummary::
+   :toctree: api/
+   :template: autosummary/accessor_attribute.rst
+
+   Series.struct.dtypes
+
+.. autosummary::
+   :toctree: api/
+   :template: autosummary/accessor_method.rst
+
+   Series.struct.field
+   Series.struct.explode
+
+
 .. _api.series.flags:
 
 Flags

doc/source/whatsnew/v2.2.0.rst

@@ -36,6 +36,34 @@ There are two advantages of this engine:
 
 For more, see :ref:`io.calamine` in the user guide on IO tools.
 
+.. _whatsnew_220.enhancements.struct_accessor:
+
+Series.struct accessor to with PyArrow structured data
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The ``Series.struct`` accessor provides attributes and methods for processing
+data with ``struct[pyarrow]`` dtype Series. For example,
+:meth:`Series.struct.explode` converts PyArrow structured data to a pandas
+DataFrame. (:issue:`54938`)
+
+.. ipython:: python
+
+    import pyarrow as pa
+    series = pd.Series(
+        [
+            {"project": "pandas", "version": "2.2.0"},
+            {"project": "numpy", "version": "1.25.2"},
+            {"project": "pyarrow", "version": "13.0.0"},
+        ],
+        dtype=pd.ArrowDtype(
+            pa.struct([
+                ("project", pa.string()),
+                ("version", pa.string()),
+            ])
+        ),
+    )
+    series.struct.explode()
+
 .. _whatsnew_220.enhancements.enhancement2:
 
 enhancement2

pandas/core/arrays/arrow/__init__.py

@@ -1,3 +1,4 @@
+from pandas.core.arrays.arrow.accessors import StructAccessor
 from pandas.core.arrays.arrow.array import ArrowExtensionArray
 
-__all__ = ["ArrowExtensionArray"]
+__all__ = ["ArrowExtensionArray", "StructAccessor"]

pandas/core/arrays/arrow/accessors.py

@@ -0,0 +1,196 @@
+"""Accessors for arrow-backed data."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from pandas.compat import pa_version_under7p0
+
+if not pa_version_under7p0:
+    import pyarrow as pa
+    import pyarrow.compute as pc
+
+    from pandas.core.dtypes.dtypes import ArrowDtype
+
+if TYPE_CHECKING:
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+
+class StructAccessor:
+    """
+    Accessor object for structured data properties of the Series values.
+
+    Parameters
+    ----------
+    data : Series
+        Series containing Arrow struct data.
+    """
+
+    _validation_msg = (
+        "Can only use the '.struct' accessor with 'struct[pyarrow]' dtype, not {dtype}."
+    )
+
+    def __init__(self, data=None) -> None:
+        self._parent = data
+        self._validate(data)
+
+    def _validate(self, data):
+        dtype = data.dtype
+        if not isinstance(dtype, ArrowDtype):
+            # Raise AttributeError so that inspect can handle non-struct Series.
+            raise AttributeError(self._validation_msg.format(dtype=dtype))
+
+        if not pa.types.is_struct(dtype.pyarrow_dtype):
+            # Raise AttributeError so that inspect can handle non-struct Series.
+            raise AttributeError(self._validation_msg.format(dtype=dtype))
+
+    @property
+    def dtypes(self) -> Series:
+        """
+        Return the dtype object of each child field of the struct.
+
+        Returns
+        -------
+        pandas.Series
+            The data type of each child field.
+
+        Examples
+        --------
+        >>> import pyarrow as pa
+        >>> s = pd.Series(
+        ...     [
+        ...         {"version": 1, "project": "pandas"},
+        ...         {"version": 2, "project": "pandas"},
+        ...         {"version": 1, "project": "numpy"},
+        ...     ],
+        ...     dtype=pd.ArrowDtype(pa.struct(
+        ...         [("version", pa.int64()), ("project", pa.string())]
+        ...     ))
+        ... )
+        >>> s.struct.dtypes
+        version     int64[pyarrow]
+        project    string[pyarrow]
+        dtype: object
+        """
+        from pandas import (
+            Index,
+            Series,
+        )
+
+        pa_type = self._parent.dtype.pyarrow_dtype
+        types = [ArrowDtype(struct.type) for struct in pa_type]
+        names = [struct.name for struct in pa_type]
+        return Series(types, index=Index(names))
+
+    def field(self, name_or_index: str | int) -> Series:
+        """
+        Extract a child field of a struct as a Series.
+
+        Parameters
+        ----------
+        name_or_index : str | int
+            Name or index of the child field to extract.
+
+        Returns
+        -------
+        pandas.Series
+            The data corresponding to the selected child field.
+
+        See Also
+        --------
+        Series.struct.explode : Return all child fields as a DataFrame.
+
+        Examples
+        --------
+        >>> import pyarrow as pa
+        >>> s = pd.Series(
+        ...     [
+        ...         {"version": 1, "project": "pandas"},
+        ...         {"version": 2, "project": "pandas"},
+        ...         {"version": 1, "project": "numpy"},
+        ...     ],
+        ...     dtype=pd.ArrowDtype(pa.struct(
+        ...         [("version", pa.int64()), ("project", pa.string())]
+        ...     ))
+        ... )
+
+        Extract by field name.
+
+        >>> s.struct.field("project")
+        0    pandas
+        1    pandas
+        2     numpy
+        Name: project, dtype: string[pyarrow]
+
+        Extract by field index.
+
+        >>> s.struct.field(0)
+        0    1
+        1    2
+        2    1
+        Name: version, dtype: int64[pyarrow]
+        """
+        from pandas import Series
+
+        pa_arr = self._parent.array._pa_array
+        if isinstance(name_or_index, int):
+            index = name_or_index
+        elif isinstance(name_or_index, str):
+            index = pa_arr.type.get_field_index(name_or_index)
+        else:
+            raise ValueError(
+                "name_or_index must be an int or str, "
+                f"got {type(name_or_index).__name__}"
+            )
+
+        pa_field = pa_arr.type[index]
+        field_arr = pc.struct_field(pa_arr, [index])
+        return Series(
+            field_arr,
+            dtype=ArrowDtype(field_arr.type),
+            index=self._parent.index,
+            name=pa_field.name,
+        )
+
+    def explode(self) -> DataFrame:
+        """
+        Extract all child fields of a struct as a DataFrame.
+
+        Returns
+        -------
+        pandas.DataFrame
+            The data corresponding to all child fields.
+
+        See Also
+        --------
+        Series.struct.field : Return a single child field as a Series.
+
+        Examples
+        --------
+        >>> import pyarrow as pa
+        >>> s = pd.Series(
+        ...     [
+        ...         {"version": 1, "project": "pandas"},
+        ...         {"version": 2, "project": "pandas"},
+        ...         {"version": 1, "project": "numpy"},
+        ...     ],
+        ...     dtype=pd.ArrowDtype(pa.struct(
+        ...         [("version", pa.int64()), ("project", pa.string())]
+        ...     ))
+        ... )
+
+        >>> s.struct.explode()
+           version project
+        0        1  pandas
+        1        2  pandas
+        2        1   numpy
+        """
+        from pandas import concat
+
+        pa_type = self._parent.dtype.pyarrow_dtype
+        return concat(
+            [self.field(i) for i in range(pa_type.num_fields)], axis="columns"
+        )

pandas/core/series.py

@@ -101,6 +101,7 @@
 from pandas.core.accessor import CachedAccessor
 from pandas.core.apply import SeriesApply
 from pandas.core.arrays import ExtensionArray
+from pandas.core.arrays.arrow import StructAccessor
 from pandas.core.arrays.categorical import CategoricalAccessor
 from pandas.core.arrays.sparse import SparseAccessor
 from pandas.core.construction import (
@@ -5787,6 +5788,7 @@ def to_period(self, freq: str | None = None, copy: bool | None = None) -> Series
     cat = CachedAccessor("cat", CategoricalAccessor)
     plot = CachedAccessor("plot", pandas.plotting.PlotAccessor)
     sparse = CachedAccessor("sparse", SparseAccessor)
+    struct = CachedAccessor("struct", StructAccessor)
 
     # ----------------------------------------------------------------------
     # Add plotting methods to Series