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DOC Replace @doc decorator with inline docstring in core/arrays/sparse/array.py #63941

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Merged
mroeschke merged 5 commits into from
Jan 30, 2026
Merged

DOC Replace @doc decorator with inline docstring in core/arrays/sparse/array.py #63941

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d3f6795
Replaced @doc decorator in the duplicated method for SparseArray with…
evensong Jan 29, 2026
98921c5
Merge branch 'main' into sparse-array-replace-@doc
evensong Jan 29, 2026
ca59b11
Updated example to use SparseDtype(np.int64)
evensong Jan 29, 2026
3a1c58f
Merge branch 'main' into sparse-array-replace-@doc
evensong Jan 30, 2026
847e390
prepended 'pd' to SparseDtype to allow example to execute
evensong Jan 30, 2026
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31 changes: 29 additions & 2 deletions pandas/core/arrays/sparse/array.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,7 +32,6 @@
from pandas.compat.numpy import function as nv
from pandas.errors import PerformanceWarning
from pandas.util._decorators import (
doc,
set_module,
)
from pandas.util._exceptions import find_stack_level
Expand Down Expand Up @@ -885,10 +884,38 @@ def _first_fill_value_loc(self):
diff = np.r_[np.diff(indices), 2]
return indices[(diff > 1).argmax()] + 1

@doc(ExtensionArray.duplicated)
def duplicated(
self, keep: Literal["first", "last", False] = "first"
) -> npt.NDArray[np.bool_]:
"""
Return boolean ndarray denoting duplicate values.

Parameters
----------
keep : {'first', 'last', False}, default 'first'
- ``first`` : Mark duplicates as ``True`` except for the first occurrence.
- ``last`` : Mark duplicates as ``True`` except for the last occurrence.
- False : Mark all duplicates as ``True``.

Returns
-------
ndarray[bool]
With true in indices where elements are duplicated and false otherwise.

See Also
--------
DataFrame.duplicated : Return boolean Series denoting
duplicate rows.
Series.duplicated : Indicate duplicate Series values.
api.extensions.ExtensionArray.unique : Compute the ExtensionArray
of unique values.

Examples
--------
>>> pd.array([1, 1, 2, 3, 3], dtype=pd.SparseDtype(np.int64)).duplicated()
array([False, True, False, False, True])
"""

values = np.asarray(self)
mask = np.asarray(self.isna())
return algos.duplicated(values, keep=keep, mask=mask)
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