DOC: Fixing EX01 - Added examples (#53526)

Added examples

Author: DeaMariaLeon

ci/code_checks.sh

@@ -82,15 +82,7 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
     $BASE_DIR/scripts/validate_docstrings.py --format=actions --errors=EX01 --ignore_functions \
         pandas.Series.backfill \
         pandas.Series.pad \
-        pandas.Series.sparse \
         pandas.DataFrame.sparse \
-        pandas.Series.cat.codes \
-        pandas.Series.cat.reorder_categories \
-        pandas.Series.cat.set_categories \
-        pandas.Series.cat.as_ordered \
-        pandas.Series.cat.as_unordered \
-        pandas.Series.sparse.fill_value \
-        pandas.Flags \
         pandas.Series.attrs \
         pandas.Series.plot \
         pandas.Series.hist \
@@ -242,7 +234,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         pandas.IntervalIndex.to_tuples \
         pandas.MultiIndex.dtypes \
         pandas.MultiIndex.drop \
-        pandas.DatetimeIndex.indexer_between_time \
         pandas.DatetimeIndex.snap \
         pandas.DatetimeIndex.as_unit \
         pandas.DatetimeIndex.to_pydatetime \

pandas/core/arrays/categorical.py

@@ -875,6 +875,15 @@ def as_ordered(self) -> Self:
         -------
         Categorical
             Ordered Categorical.
+
+        Examples
+        --------
+        >>> ser = pd.Series(["a", "b", "c", "a"], dtype="category")
+        >>> ser.cat.ordered
+        False
+        >>> ser = ser.cat.as_ordered()
+        >>> ser.cat.ordered
+        True
         """
         return self.set_ordered(True)
 
@@ -886,6 +895,15 @@ def as_unordered(self) -> Self:
         -------
         Categorical
             Unordered Categorical.
+
+        Examples
+        --------
+        >>> raw_cate = pd.Categorical(["a", "b", "c"],
+        ...                           categories=["a", "b", "c"], ordered=True)
+        >>> ser = pd.Series(raw_cate)
+        >>> ser = ser.cat.as_unordered()
+        >>> ser.cat.ordered
+        False
         """
         return self.set_ordered(False)
 
@@ -936,6 +954,26 @@ def set_categories(self, new_categories, ordered=None, rename: bool = False):
         add_categories : Add new categories.
         remove_categories : Remove the specified categories.
         remove_unused_categories : Remove categories which are not used.
+
+        Examples
+        --------
+        >>> raw_cate = pd.Categorical(["a", "b", "c", "A"],
+        ...                           categories=["a", "b", "c"], ordered=True)
+        >>> ser = pd.Series(raw_cate)
+        >>> ser
+        0   a
+        1   b
+        2   c
+        3   NaN
+        dtype: category
+        Categories (3, object): ['a' < 'b' < 'c']
+        >>> ser.cat.set_categories(["A", "B", "C"], rename=True)
+        0   A
+        1   B
+        2   C
+        3   NaN
+        dtype: category
+        Categories (3, object): ['A' < 'B' < 'C']
         """
 
         if ordered is None:
@@ -1062,6 +1100,26 @@ def reorder_categories(self, new_categories, ordered=None) -> Self:
         remove_categories : Remove the specified categories.
         remove_unused_categories : Remove categories which are not used.
         set_categories : Set the categories to the specified ones.
+
+        Examples
+        --------
+        >>> ser = pd.Series(["a", "b", "c", "a"], dtype="category")
+        >>> ser = ser.cat.reorder_categories(['c', 'b', 'a'], ordered=True)
+        >>> ser
+        0   a
+        1   b
+        2   c
+        3   a
+        dtype: category
+        Categories (3, object): ['c' < 'b' < 'a']
+        >>> ser = ser.sort_values()
+        >>> ser
+        2   c
+        1   b
+        0   a
+        3   a
+        dtype: category
+        Categories (3, object): ['c' < 'b' < 'a']
         """
         if (
             len(self.categories) != len(new_categories)
@@ -2663,6 +2721,17 @@ def _delegate_property_set(self, name: str, new_values):  # type: ignore[overrid
     def codes(self) -> Series:
         """
         Return Series of codes as well as the index.
+
+        Examples
+        --------
+        >>> raw_cate = pd.Categorical(["a", "b", "c", "a"], categories=["a", "b"])
+        >>> ser = pd.Series(raw_cate)
+        >>> ser.cat.codes
+        0   0
+        1   1
+        2  -1
+        3   0
+        dtype: int8
         """
         from pandas import Series
 

pandas/core/arrays/sparse/accessor.py

@@ -40,6 +40,14 @@ def _validate(self, data):
 class SparseAccessor(BaseAccessor, PandasDelegate):
     """
     Accessor for SparseSparse from other sparse matrix data types.
+
+    Examples
+    --------
+    >>> ser = pd.Series([0, 0, 2, 2, 2], dtype="Sparse[int]")
+    >>> ser.sparse.density
+    0.6
+    >>> ser.sparse.sp_values
+    array([2, 2, 2])
     """
 
     def _validate(self, data):

pandas/core/arrays/sparse/array.py

@@ -629,6 +629,16 @@ def fill_value(self):
         Elements in `data` that are `fill_value` are not stored.
 
         For memory savings, this should be the most common value in the array.
+
+        Examples
+        --------
+        >>> ser = pd.Series([0, 0, 2, 2, 2], dtype="Sparse[int]")
+        >>> ser.sparse.fill_value
+        0
+        >>> spa_dtype = pd.SparseDtype(dtype=np.int32, fill_value=2)
+        >>> ser = pd.Series([0, 0, 2, 2, 2], dtype=spa_dtype)
+        >>> ser.sparse.fill_value
+        2
         """
         return self.dtype.fill_value
 

pandas/core/flags.py

@@ -32,9 +32,9 @@ class Flags:
            it is expected that every method taking or returning one or more
            DataFrame or Series objects will propagate ``allows_duplicate_labels``.
 
-    Notes
-    -----
-    Attributes can be set in two ways
+    Examples
+    --------
+    Attributes can be set in two ways:
 
     >>> df = pd.DataFrame()
     >>> df.flags

pandas/core/indexes/datetimes.py

@@ -764,6 +764,16 @@ def indexer_between_time(
         --------
         indexer_at_time : Get index locations of values at particular time of day.
         DataFrame.between_time : Select values between particular times of day.
+
+        Examples
+        --------
+        >>> idx = pd.date_range("2023-01-01", periods=4, freq="H")
+        >>> idx
+        DatetimeIndex(['2023-01-01 00:00:00', '2023-01-01 01:00:00',
+                           '2023-01-01 02:00:00', '2023-01-01 03:00:00'],
+                          dtype='datetime64[ns]', freq='H')
+        >>> idx.indexer_between_time("00:00", "2:00", include_end=False)
+        array([0, 1])
         """
         start_time = to_time(start_time)
         end_time = to_time(end_time)