DOC/CLN: Fix various docstring errors (#25295)

Author: Daniel Saxton

pandas/core/arrays/categorical.py

@@ -214,7 +214,7 @@ def contains(cat, key, container):
 
 class Categorical(ExtensionArray, PandasObject):
     """
-    Represent a categorical variable in classic R / S-plus fashion
+    Represent a categorical variable in classic R / S-plus fashion.
 
     `Categoricals` can only take on only a limited, and usually fixed, number
     of possible values (`categories`). In contrast to statistical categorical
@@ -235,7 +235,7 @@ class Categorical(ExtensionArray, PandasObject):
         The unique categories for this categorical. If not given, the
         categories are assumed to be the unique values of `values` (sorted, if
         possible, otherwise in the order in which they appear).
-    ordered : boolean, (default False)
+    ordered : bool, default False
         Whether or not this categorical is treated as a ordered categorical.
         If True, the resulting categorical will be ordered.
         An ordered categorical respects, when sorted, the order of its
@@ -253,7 +253,7 @@ class Categorical(ExtensionArray, PandasObject):
     codes : ndarray
         The codes (integer positions, which point to the categories) of this
         categorical, read only.
-    ordered : boolean
+    ordered : bool
         Whether or not this Categorical is ordered.
     dtype : CategoricalDtype
         The instance of ``CategoricalDtype`` storing the ``categories``
@@ -297,7 +297,7 @@ class Categorical(ExtensionArray, PandasObject):
     Ordered `Categoricals` can be sorted according to the custom order
     of the categories and can have a min and max value.
 
-    >>> c = pd.Categorical(['a','b','c','a','b','c'], ordered=True,
+    >>> c = pd.Categorical(['a', 'b', 'c', 'a', 'b', 'c'], ordered=True,
     ...                    categories=['c', 'b', 'a'])
     >>> c
     [a, b, c, a, b, c]
@@ -618,7 +618,7 @@ def from_codes(cls, codes, categories=None, ordered=None, dtype=None):
         ----------
         codes : array-like, integers
             An integer array, where each integer points to a category in
-            categories or dtype.categories, or else is -1 for NaN
+            categories or dtype.categories, or else is -1 for NaN.
         categories : index-like, optional
             The categories for the categorical. Items need to be unique.
             If the categories are not given here, then they must be provided
@@ -700,7 +700,7 @@ def _set_categories(self, categories, fastpath=False):
 
         Parameters
         ----------
-        fastpath : boolean (default: False)
+        fastpath : bool, default False
            Don't perform validation of the categories for uniqueness or nulls
 
         Examples
@@ -747,15 +747,15 @@ def _set_dtype(self, dtype):
 
     def set_ordered(self, value, inplace=False):
         """
-        Set the ordered attribute to the boolean value
+        Set the ordered attribute to the boolean value.
 
         Parameters
         ----------
-        value : boolean to set whether this categorical is ordered (True) or
-           not (False)
-        inplace : boolean (default: False)
-           Whether or not to set the ordered attribute inplace or return a copy
-           of this categorical with ordered set to the value
+        value : bool
+           Set whether this categorical is ordered (True) or not (False).
+        inplace : bool, default False
+           Whether or not to set the ordered attribute in-place or return
+           a copy of this categorical with ordered set to the value.
         """
         inplace = validate_bool_kwarg(inplace, 'inplace')
         new_dtype = CategoricalDtype(self.categories, ordered=value)
@@ -770,9 +770,9 @@ def as_ordered(self, inplace=False):
 
         Parameters
         ----------
-        inplace : boolean (default: False)
-           Whether or not to set the ordered attribute inplace or return a copy
-           of this categorical with ordered set to True
+        inplace : bool, default False
+           Whether or not to set the ordered attribute in-place or return
+           a copy of this categorical with ordered set to True.
         """
         inplace = validate_bool_kwarg(inplace, 'inplace')
         return self.set_ordered(True, inplace=inplace)
@@ -783,9 +783,9 @@ def as_unordered(self, inplace=False):
 
         Parameters
         ----------
-        inplace : boolean (default: False)
-           Whether or not to set the ordered attribute inplace or return a copy
-           of this categorical with ordered set to False
+        inplace : bool, default False
+           Whether or not to set the ordered attribute in-place or return
+           a copy of this categorical with ordered set to False.
         """
         inplace = validate_bool_kwarg(inplace, 'inplace')
         return self.set_ordered(False, inplace=inplace)
@@ -815,19 +815,19 @@ def set_categories(self, new_categories, ordered=None, rename=False,
         ----------
         new_categories : Index-like
            The categories in new order.
-        ordered : boolean, (default: False)
+        ordered : bool, default False
            Whether or not the categorical is treated as a ordered categorical.
            If not given, do not change the ordered information.
-        rename : boolean (default: False)
+        rename : bool, default False
            Whether or not the new_categories should be considered as a rename
            of the old categories or as reordered categories.
-        inplace : boolean (default: False)
-           Whether or not to reorder the categories inplace or return a copy of
-           this categorical with reordered categories.
+        inplace : bool, default False
+           Whether or not to reorder the categories in-place or return a copy
+           of this categorical with reordered categories.
 
         Returns
         -------
-        cat : Categorical with reordered categories or None if inplace.
+        Categorical with reordered categories or None if inplace.
 
         Raises
         ------
@@ -890,7 +890,7 @@ def rename_categories(self, new_categories, inplace=False):
               Currently, Series are considered list like. In a future version
               of pandas they'll be considered dict-like.
 
-        inplace : boolean (default: False)
+        inplace : bool, default False
            Whether or not to rename the categories inplace or return a copy of
            this categorical with renamed categories.
 
@@ -967,10 +967,10 @@ def reorder_categories(self, new_categories, ordered=None, inplace=False):
         ----------
         new_categories : Index-like
            The categories in new order.
-        ordered : boolean, optional
+        ordered : bool, optional
            Whether or not the categorical is treated as a ordered categorical.
            If not given, do not change the ordered information.
-        inplace : boolean (default: False)
+        inplace : bool, default False
            Whether or not to reorder the categories inplace or return a copy of
            this categorical with reordered categories.
 
@@ -1010,7 +1010,7 @@ def add_categories(self, new_categories, inplace=False):
         ----------
         new_categories : category or list-like of category
            The new categories to be included.
-        inplace : boolean (default: False)
+        inplace : bool, default False
            Whether or not to add the categories inplace or return a copy of
            this categorical with added categories.
 
@@ -1060,7 +1060,7 @@ def remove_categories(self, removals, inplace=False):
         ----------
         removals : category or list of categories
            The categories which should be removed.
-        inplace : boolean (default: False)
+        inplace : bool, default False
            Whether or not to remove the categories inplace or return a copy of
            this categorical with removed categories.
 
@@ -1108,7 +1108,7 @@ def remove_unused_categories(self, inplace=False):
 
         Parameters
         ----------
-        inplace : boolean (default: False)
+        inplace : bool, default False
            Whether or not to drop unused categories inplace or return a copy of
            this categorical with unused categories dropped.
 
@@ -1460,7 +1460,7 @@ def value_counts(self, dropna=True):
 
         Parameters
         ----------
-        dropna : boolean, default True
+        dropna : bool, default True
             Don't include counts of NaN.
 
         Returns
@@ -1581,9 +1581,9 @@ def sort_values(self, inplace=False, ascending=True, na_position='last'):
 
         Parameters
         ----------
-        inplace : boolean, default False
+        inplace : bool, default False
             Do operation in place.
-        ascending : boolean, default True
+        ascending : bool, default True
             Order ascending. Passing False orders descending. The
             ordering parameter provides the method by which the
             category values are organized.
@@ -2239,7 +2239,7 @@ def mode(self, dropna=True):
 
         Parameters
         ----------
-        dropna : boolean, default True
+        dropna : bool, default True
             Don't consider counts of NaN/NaT.
 
             .. versionadded:: 0.24.0
@@ -2332,7 +2332,7 @@ def equals(self, other):
 
         Returns
         -------
-        are_equal : boolean
+        bool
         """
         if self.is_dtype_equal(other):
             if self.categories.equals(other.categories):
@@ -2356,7 +2356,7 @@ def is_dtype_equal(self, other):
 
         Returns
         -------
-        are_equal : boolean
+        bool
         """
 
         try:

pandas/core/arrays/datetimes.py

@@ -799,14 +799,14 @@ def tz_convert(self, tz):
 
         Parameters
         ----------
-        tz : string, pytz.timezone, dateutil.tz.tzfile or None
+        tz : str, pytz.timezone, dateutil.tz.tzfile or None
             Time zone for time. Corresponding timestamps would be converted
             to this time zone of the Datetime Array/Index. A `tz` of None will
             convert to UTC and remove the timezone information.
 
         Returns
         -------
-        normalized : same type as self
+        Array or Index
 
         Raises
         ------
@@ -842,7 +842,7 @@ def tz_convert(self, tz):
         With the ``tz=None``, we can remove the timezone (after converting
         to UTC if necessary):
 
-        >>> dti = pd.date_range(start='2014-08-01 09:00',freq='H',
+        >>> dti = pd.date_range(start='2014-08-01 09:00', freq='H',
         ...                     periods=3, tz='Europe/Berlin')
 
         >>> dti
@@ -882,7 +882,7 @@ def tz_localize(self, tz, ambiguous='raise', nonexistent='raise',
 
         Parameters
         ----------
-        tz : string, pytz.timezone, dateutil.tz.tzfile or None
+        tz : str, pytz.timezone, dateutil.tz.tzfile or None
             Time zone to convert timestamps to. Passing ``None`` will
             remove the time zone information preserving local time.
         ambiguous : 'infer', 'NaT', bool array, default 'raise'
@@ -930,7 +930,7 @@ def tz_localize(self, tz, ambiguous='raise', nonexistent='raise',
 
         Returns
         -------
-        result : same type as self
+        Same type as self
             Array/Index converted to the specified time zone.
 
         Raises
@@ -970,43 +970,39 @@ def tz_localize(self, tz, ambiguous='raise', nonexistent='raise',
 
         Be careful with DST changes. When there is sequential data, pandas can
         infer the DST time:
-        >>> s = pd.to_datetime(pd.Series([
-        ... '2018-10-28 01:30:00',
-        ... '2018-10-28 02:00:00',
-        ... '2018-10-28 02:30:00',
-        ... '2018-10-28 02:00:00',
-        ... '2018-10-28 02:30:00',
-        ... '2018-10-28 03:00:00',
-        ... '2018-10-28 03:30:00']))
+        >>> s = pd.to_datetime(pd.Series(['2018-10-28 01:30:00',
+        ...                               '2018-10-28 02:00:00',
+        ...                               '2018-10-28 02:30:00',
+        ...                               '2018-10-28 02:00:00',
+        ...                               '2018-10-28 02:30:00',
+        ...                               '2018-10-28 03:00:00',
+        ...                               '2018-10-28 03:30:00']))
         >>> s.dt.tz_localize('CET', ambiguous='infer')
-        2018-10-28 01:30:00+02:00    0
-        2018-10-28 02:00:00+02:00    1
-        2018-10-28 02:30:00+02:00    2
-        2018-10-28 02:00:00+01:00    3
-        2018-10-28 02:30:00+01:00    4
-        2018-10-28 03:00:00+01:00    5
-        2018-10-28 03:30:00+01:00    6
-        dtype: int64
+        0   2018-10-28 01:30:00+02:00
+        1   2018-10-28 02:00:00+02:00
+        2   2018-10-28 02:30:00+02:00
+        3   2018-10-28 02:00:00+01:00
+        4   2018-10-28 02:30:00+01:00
+        5   2018-10-28 03:00:00+01:00
+        6   2018-10-28 03:30:00+01:00
+        dtype: datetime64[ns, CET]
 
         In some cases, inferring the DST is impossible. In such cases, you can
         pass an ndarray to the ambiguous parameter to set the DST explicitly
 
-        >>> s = pd.to_datetime(pd.Series([
-        ... '2018-10-28 01:20:00',
-        ... '2018-10-28 02:36:00',
-        ... '2018-10-28 03:46:00']))
+        >>> s = pd.to_datetime(pd.Series(['2018-10-28 01:20:00',
+        ...                               '2018-10-28 02:36:00',
+        ...                               '2018-10-28 03:46:00']))
         >>> s.dt.tz_localize('CET', ambiguous=np.array([True, True, False]))
-        0   2018-10-28 01:20:00+02:00
-        1   2018-10-28 02:36:00+02:00
-        2   2018-10-28 03:46:00+01:00
-        dtype: datetime64[ns, CET]
+        0   2015-03-29 03:00:00+02:00
+        1   2015-03-29 03:30:00+02:00
+        dtype: datetime64[ns, Europe/Warsaw]
 
         If the DST transition causes nonexistent times, you can shift these
         dates forward or backwards with a timedelta object or `'shift_forward'`
         or `'shift_backwards'`.
-        >>> s = pd.to_datetime(pd.Series([
-        ... '2015-03-29 02:30:00',
-        ... '2015-03-29 03:30:00']))
+        >>> s = pd.to_datetime(pd.Series(['2015-03-29 02:30:00',
+        ...                               '2015-03-29 03:30:00']))
         >>> s.dt.tz_localize('Europe/Warsaw', nonexistent='shift_forward')
         0   2015-03-29 03:00:00+02:00
         1   2015-03-29 03:30:00+02:00
@@ -1129,7 +1125,7 @@ def to_period(self, freq=None):
 
         Parameters
         ----------
-        freq : string or Offset, optional
+        freq : str or Offset, optional
             One of pandas' :ref:`offset strings <timeseries.offset_aliases>`
             or an Offset object. Will be inferred by default.
 
@@ -1150,7 +1146,7 @@ def to_period(self, freq=None):
 
         Examples
         --------
-        >>> df = pd.DataFrame({"y": [1,2,3]},
+        >>> df = pd.DataFrame({"y": [1, 2, 3]},
         ...                   index=pd.to_datetime(["2000-03-31 00:00:00",
         ...                                         "2000-05-31 00:00:00",
         ...                                         "2000-08-31 00:00:00"]))