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