DOC: Fixing EX03 - Removing flake8 errors in docstrings (#55401)

fixing EX03 errors

Author: DeaMariaLeon

ci/code_checks.sh

@@ -63,16 +63,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
 
     MSG='Partially validate docstrings (EX03)' ;  echo $MSG
     $BASE_DIR/scripts/validate_docstrings.py --format=actions --errors=EX03 --ignore_functions \
-        pandas.Series.loc \
-        pandas.Series.iloc \
-        pandas.Series.pop \
-        pandas.Series.describe \
-        pandas.Series.skew \
-        pandas.Series.var \
-        pandas.Series.last \
-        pandas.Series.tz_convert \
-        pandas.Series.tz_localize \
-        pandas.Series.dt.month_name \
         pandas.Series.dt.day_name \
         pandas.Series.str.len \
         pandas.Series.cat.set_categories \

pandas/core/arrays/datetimes.py

@@ -1276,7 +1276,7 @@ def month_name(self, locale=None) -> npt.NDArray[np.object_]:
         >>> idx
         DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31'],
                       dtype='datetime64[ns]', freq='ME')
-        >>> idx.month_name(locale='pt_BR.utf8') # doctest: +SKIP
+        >>> idx.month_name(locale='pt_BR.utf8')  # doctest: +SKIP
         Index(['Janeiro', 'Fevereiro', 'Março'], dtype='object')
         """
         values = self._local_timestamps()

pandas/core/generic.py

@@ -9698,7 +9698,7 @@ def last(self, offset) -> Self:
 
         Get the rows for the last 3 days:
 
-        >>> ts.last('3D') # doctest: +SKIP
+        >>> ts.last('3D')  # doctest: +SKIP
                     A
         2018-04-13  3
         2018-04-15  4
@@ -11208,7 +11208,7 @@ def tz_convert(
         Pass None to convert to UTC and get a tz-naive index:
 
         >>> s = pd.Series([1],
-        ...     index=pd.DatetimeIndex(['2018-09-15 01:30:00+02:00']))
+        ...               index=pd.DatetimeIndex(['2018-09-15 01:30:00+02:00']))
         >>> s.tz_convert(None)
         2018-09-14 23:30:00    1
         dtype: int64
@@ -11326,7 +11326,7 @@ def tz_localize(
         Pass None to convert to tz-naive index and preserve local time:
 
         >>> s = pd.Series([1],
-        ...     index=pd.DatetimeIndex(['2018-09-15 01:30:00+02:00']))
+        ...               index=pd.DatetimeIndex(['2018-09-15 01:30:00+02:00']))
         >>> s.tz_localize(None)
         2018-09-15 01:30:00    1
         dtype: int64
@@ -11569,10 +11569,10 @@ def describe(
         Describing a ``DataFrame``. By default only numeric fields
         are returned.
 
-        >>> df = pd.DataFrame({'categorical': pd.Categorical(['d','e','f']),
+        >>> df = pd.DataFrame({'categorical': pd.Categorical(['d', 'e', 'f']),
         ...                    'numeric': [1, 2, 3],
         ...                    'object': ['a', 'b', 'c']
-        ...                   })
+        ...                    })
         >>> df.describe()
                numeric
         count      3.0
@@ -12688,9 +12688,9 @@ def last_valid_index(self) -> Hashable | None:
 Examples
 --------
 >>> df = pd.DataFrame({'person_id': [0, 1, 2, 3],
-...                   'age': [21, 25, 62, 43],
-...                   'height': [1.61, 1.87, 1.49, 2.01]}
-...                  ).set_index('person_id')
+...                    'age': [21, 25, 62, 43],
+...                    'height': [1.61, 1.87, 1.49, 2.01]}
+...                   ).set_index('person_id')
 >>> df
            age  height
 person_id
@@ -13516,7 +13516,7 @@ def make_doc(name: str, ndim: int) -> str:
             With a DataFrame
 
             >>> df = pd.DataFrame({'a': [1, 2, 3], 'b': [2, 3, 4], 'c': [1, 3, 5]},
-            ...                  index=['tiger', 'zebra', 'cow'])
+            ...                   index=['tiger', 'zebra', 'cow'])
             >>> df
                     a   b   c
             tiger   1   2   1
@@ -13540,7 +13540,7 @@ def make_doc(name: str, ndim: int) -> str:
             getting an error.
 
             >>> df = pd.DataFrame({'a': [1, 2, 3], 'b': ['T', 'Z', 'X']},
-            ...                  index=['tiger', 'zebra', 'cow'])
+            ...                   index=['tiger', 'zebra', 'cow'])
             >>> df.skew(numeric_only=True)
             a   0.0
             dtype: float64"""

pandas/core/indexing.py

@@ -187,7 +187,7 @@ def iloc(self) -> _iLocIndexer:
         --------
         >>> mydict = [{'a': 1, 'b': 2, 'c': 3, 'd': 4},
         ...           {'a': 100, 'b': 200, 'c': 300, 'd': 400},
-        ...           {'a': 1000, 'b': 2000, 'c': 3000, 'd': 4000 }]
+        ...           {'a': 1000, 'b': 2000, 'c': 3000, 'd': 4000}]
         >>> df = pd.DataFrame(mydict)
         >>> df
               a     b     c     d
@@ -328,16 +328,16 @@ def loc(self) -> _LocIndexer:
         DataFrame.at : Access a single value for a row/column label pair.
         DataFrame.iloc : Access group of rows and columns by integer position(s).
         DataFrame.xs : Returns a cross-section (row(s) or column(s)) from the
-            Series/DataFrame.
+                       Series/DataFrame.
         Series.loc : Access group of values using labels.
 
         Examples
         --------
         **Getting values**
 
         >>> df = pd.DataFrame([[1, 2], [4, 5], [7, 8]],
-        ...      index=['cobra', 'viper', 'sidewinder'],
-        ...      columns=['max_speed', 'shield'])
+        ...                   index=['cobra', 'viper', 'sidewinder'],
+        ...                   columns=['max_speed', 'shield'])
         >>> df
                     max_speed  shield
         cobra               1       2
@@ -380,8 +380,8 @@ def loc(self) -> _LocIndexer:
         Alignable boolean Series:
 
         >>> df.loc[pd.Series([False, True, False],
-        ...        index=['viper', 'sidewinder', 'cobra'])]
-                    max_speed  shield
+        ...                  index=['viper', 'sidewinder', 'cobra'])]
+                             max_speed  shield
         sidewinder          7       8
 
         Index (same behavior as ``df.reindex``)
@@ -407,7 +407,7 @@ def loc(self) -> _LocIndexer:
         Multiple conditional using ``&`` that returns a boolean Series
 
         >>> df.loc[(df['max_speed'] > 1) & (df['shield'] < 8)]
-               max_speed  shield
+                    max_speed  shield
         viper          4       5
 
         Multiple conditional using ``|`` that returns a boolean Series
@@ -496,7 +496,7 @@ def loc(self) -> _LocIndexer:
         Another example using integers for the index
 
         >>> df = pd.DataFrame([[1, 2], [4, 5], [7, 8]],
-        ...      index=[7, 8, 9], columns=['max_speed', 'shield'])
+        ...                   index=[7, 8, 9], columns=['max_speed', 'shield'])
         >>> df
            max_speed  shield
         7          1       2
@@ -517,13 +517,13 @@ def loc(self) -> _LocIndexer:
         A number of examples using a DataFrame with a MultiIndex
 
         >>> tuples = [
-        ...    ('cobra', 'mark i'), ('cobra', 'mark ii'),
-        ...    ('sidewinder', 'mark i'), ('sidewinder', 'mark ii'),
-        ...    ('viper', 'mark ii'), ('viper', 'mark iii')
+        ...     ('cobra', 'mark i'), ('cobra', 'mark ii'),
+        ...     ('sidewinder', 'mark i'), ('sidewinder', 'mark ii'),
+        ...     ('viper', 'mark ii'), ('viper', 'mark iii')
         ... ]
         >>> index = pd.MultiIndex.from_tuples(tuples)
         >>> values = [[12, 2], [0, 4], [10, 20],
-        ...         [1, 4], [7, 1], [16, 36]]
+        ...           [1, 4], [7, 1], [16, 36]]
         >>> df = pd.DataFrame(values, columns=['max_speed', 'shield'], index=index)
         >>> df
                              max_speed  shield

pandas/core/series.py

@@ -5186,7 +5186,7 @@ def pop(self, item: Hashable) -> Any:
 
         Examples
         --------
-        >>> ser = pd.Series([1,2,3])
+        >>> ser = pd.Series([1, 2, 3])
 
         >>> ser.pop(0)
         1