Merge branch 'main' into 58536-enforce-docstring-validation-offsets

Author: Siniade

ci/code_checks.sh

@@ -158,15 +158,10 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Series.sparse.sp_values SA01" \
         -i "pandas.Series.sparse.to_coo PR07,RT03,SA01" \
         -i "pandas.Series.std PR01,RT03,SA01" \
-        -i "pandas.Series.str.lstrip RT03" \
         -i "pandas.Series.str.match RT03" \
         -i "pandas.Series.str.normalize RT03,SA01" \
-        -i "pandas.Series.str.partition RT03" \
         -i "pandas.Series.str.repeat SA01" \
         -i "pandas.Series.str.replace SA01" \
-        -i "pandas.Series.str.rpartition RT03" \
-        -i "pandas.Series.str.rstrip RT03" \
-        -i "pandas.Series.str.strip RT03" \
         -i "pandas.Series.str.wrap RT03,SA01" \
         -i "pandas.Series.str.zfill RT03" \
         -i "pandas.Series.struct.dtypes SA01" \

doc/source/whatsnew/v3.0.0.rst

@@ -53,6 +53,7 @@ Other enhancements
 - :meth:`pandas.concat` will raise a ``ValueError`` when ``ignore_index=True`` and ``keys`` is not ``None`` (:issue:`59274`)
 - Multiplying two :class:`DateOffset` objects will now raise a ``TypeError`` instead of a ``RecursionError`` (:issue:`59442`)
 - Restore support for reading Stata 104-format and enable reading 103-format dta files (:issue:`58554`)
+- Support passing a :class:`Iterable[Hashable]` input to :meth:`DataFrame.drop_duplicates` (:issue:`59237`)
 - Support reading Stata 102-format (Stata 1) dta files (:issue:`58978`)
 - Support reading Stata 110-format (Stata 7) dta files (:issue:`47176`)
 

pandas/core/frame.py

@@ -6406,7 +6406,7 @@ def dropna(
 
         thresh : int, optional
             Require that many non-NA values. Cannot be combined with how.
-        subset : column label or sequence of labels, optional
+        subset : column label or iterable of labels, optional
             Labels along other axis to consider, e.g. if you are dropping rows
             these would be a list of columns to include.
         inplace : bool, default False
@@ -6536,7 +6536,7 @@ def dropna(
     @overload
     def drop_duplicates(
         self,
-        subset: Hashable | Sequence[Hashable] | None = ...,
+        subset: Hashable | Iterable[Hashable] | None = ...,
         *,
         keep: DropKeep = ...,
         inplace: Literal[True],
@@ -6546,7 +6546,7 @@ def drop_duplicates(
     @overload
     def drop_duplicates(
         self,
-        subset: Hashable | Sequence[Hashable] | None = ...,
+        subset: Hashable | Iterable[Hashable] | None = ...,
         *,
         keep: DropKeep = ...,
         inplace: Literal[False] = ...,
@@ -6556,7 +6556,7 @@ def drop_duplicates(
     @overload
     def drop_duplicates(
         self,
-        subset: Hashable | Sequence[Hashable] | None = ...,
+        subset: Hashable | Iterable[Hashable] | None = ...,
         *,
         keep: DropKeep = ...,
         inplace: bool = ...,
@@ -6565,7 +6565,7 @@ def drop_duplicates(
 
     def drop_duplicates(
         self,
-        subset: Hashable | Sequence[Hashable] | None = None,
+        subset: Hashable | Iterable[Hashable] | None = None,
         *,
         keep: DropKeep = "first",
         inplace: bool = False,
@@ -6579,7 +6579,7 @@ def drop_duplicates(
 
         Parameters
         ----------
-        subset : column label or sequence of labels, optional
+        subset : column label or iterable of labels, optional
             Only consider certain columns for identifying duplicates, by
             default use all of the columns.
         keep : {'first', 'last', ``False``}, default 'first'
@@ -6669,7 +6669,7 @@ def drop_duplicates(
 
     def duplicated(
         self,
-        subset: Hashable | Sequence[Hashable] | None = None,
+        subset: Hashable | Iterable[Hashable] | None = None,
         keep: DropKeep = "first",
     ) -> Series:
         """
@@ -6679,7 +6679,7 @@ def duplicated(
 
         Parameters
         ----------
-        subset : column label or sequence of labels, optional
+        subset : column label or iterable of labels, optional
             Only consider certain columns for identifying duplicates, by
             default use all of the columns.
         keep : {'first', 'last', False}, default 'first'
@@ -6771,10 +6771,7 @@ def f(vals) -> tuple[np.ndarray, int]:
             return labels.astype("i8"), len(shape)
 
         if subset is None:
-            # https://github.com/pandas-dev/pandas/issues/28770
-            # Incompatible types in assignment (expression has type "Index", variable
-            # has type "Sequence[Any]")
-            subset = self.columns  # type: ignore[assignment]
+            subset = self.columns
         elif (
             not np.iterable(subset)
             or isinstance(subset, str)
@@ -6795,7 +6792,7 @@ def f(vals) -> tuple[np.ndarray, int]:
 
         if len(subset) == 1 and self.columns.is_unique:
             # GH#45236 This is faster than get_group_index below
-            result = self[subset[0]].duplicated(keep)
+            result = self[next(iter(subset))].duplicated(keep)
             result.name = None
         else:
             vals = (col.values for name, col in self.items() if name in subset)

pandas/core/strings/accessor.py

@@ -969,6 +969,8 @@ def rsplit(self, pat=None, *, n=-1, expand: bool = False):
     Returns
     -------
     DataFrame/MultiIndex or Series/Index of objects
+        Returns appropriate type based on `expand` parameter with strings
+        split based on the `sep` parameter.
 
     See Also
     --------
@@ -2127,6 +2129,7 @@ def encode(self, encoding, errors: str = "strict"):
     Returns
     -------
     Series or Index of object
+        Series or Index with the strings being stripped from the %(side)s.
 
     See Also
     --------

pandas/tests/frame/methods/test_drop_duplicates.py

@@ -476,3 +476,41 @@ def test_drop_duplicates_non_boolean_ignore_index(arg):
     msg = '^For argument "ignore_index" expected type bool, received type .*.$'
     with pytest.raises(ValueError, match=msg):
         df.drop_duplicates(ignore_index=arg)
+
+
+def test_drop_duplicates_set():
+    # GH#59237
+    df = DataFrame(
+        {
+            "AAA": ["foo", "bar", "foo", "bar", "foo", "bar", "bar", "foo"],
+            "B": ["one", "one", "two", "two", "two", "two", "one", "two"],
+            "C": [1, 1, 2, 2, 2, 2, 1, 2],
+            "D": range(8),
+        }
+    )
+    # single column
+    result = df.drop_duplicates({"AAA"})
+    expected = df[:2]
+    tm.assert_frame_equal(result, expected)
+
+    result = df.drop_duplicates({"AAA"}, keep="last")
+    expected = df.loc[[6, 7]]
+    tm.assert_frame_equal(result, expected)
+
+    result = df.drop_duplicates({"AAA"}, keep=False)
+    expected = df.loc[[]]
+    tm.assert_frame_equal(result, expected)
+    assert len(result) == 0
+
+    # multi column
+    expected = df.loc[[0, 1, 2, 3]]
+    result = df.drop_duplicates({"AAA", "B"})
+    tm.assert_frame_equal(result, expected)
+
+    result = df.drop_duplicates({"AAA", "B"}, keep="last")
+    expected = df.loc[[0, 5, 6, 7]]
+    tm.assert_frame_equal(result, expected)
+
+    result = df.drop_duplicates({"AAA", "B"}, keep=False)
+    expected = df.loc[[0]]
+    tm.assert_frame_equal(result, expected)