DOC: Fixes to docstrings, mainly PR09 errors

ci/code_checks.sh

@@ -286,7 +286,7 @@ fi
 ### DOCSTRINGS ###
 if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
 
-    MSG='Validate docstrings (GL03, GL04, GL05, GL06, GL07, GL09, GL10, SS04, SS05, PR03, PR04, PR05, PR10, EX04, RT01, RT04, RT05, SA01, SA02, SA03, SA05)' ; echo $MSG
+    MSG='Validate docstrings (GL03, GL04, GL05, GL06, GL07, GL09, GL10, SS04, SS05, PR03, PR04, PR05, PR09, PR10, EX04, RT01, RT04, RT05, SA01, SA02, SA03, SA05)' ; echo $MSG
     $BASE_DIR/scripts/validate_docstrings.py --format=azure --errors=GL03,GL04,GL05,GL06,GL07,GL09,GL10,SS04,SS05,PR03,PR04,PR05,PR10,EX04,RT01,RT04,RT05,SA01,SA02,SA03,SA05
     RET=$(($RET + $?)) ; echo $MSG "DONE"
 

pandas/core/arrays/categorical.py

@@ -232,7 +232,7 @@ class Categorical(ExtensionArray, PandasObject):
         `categories` attribute (which in turn is the `categories` argument, if
         provided).
     dtype : CategoricalDtype
-        An instance of ``CategoricalDtype`` to use for this categorical
+        An instance of ``CategoricalDtype`` to use for this categorical.
 
         .. versionadded:: 0.21.0
 

pandas/core/arrays/datetimes.py

@@ -230,12 +230,12 @@ class DatetimeArray(dtl.DatetimeLikeArrayMixin, dtl.TimelikeOps, dtl.DatelikeOps
         The datetime data.
 
         For DatetimeArray `values` (or a Series or Index boxing one),
-        `dtype` and `freq` will be extracted from `values`, with
-        precedence given to
+        `dtype` and `freq` will be extracted from `values`.
 
     dtype : numpy.dtype or DatetimeTZDtype
         Note that the only NumPy dtype allowed is 'datetime64[ns]'.
     freq : str or Offset, optional
+        The frequency.
     copy : bool, default False
         Whether to copy the underlying array of values.
 

pandas/core/arrays/period.py

@@ -440,8 +440,9 @@ def to_timestamp(self, freq=None, how="start"):
         ----------
         freq : str or DateOffset, optional
             Target frequency. The default is 'D' for week or longer,
-            'S' otherwise
+            'S' otherwise.
         how : {'s', 'e', 'start', 'end'}
+            Whether to use the start or end of the time period being converted.
 
         Returns
         -------
@@ -530,17 +531,20 @@ def asfreq(self, freq=None, how="E"):
         Parameters
         ----------
         freq : str
-            a frequency
+            A frequency.
         how : str {'E', 'S'}
-            'E', 'END', or 'FINISH' for end,
-            'S', 'START', or 'BEGIN' for start.
             Whether the elements should be aligned to the end
-            or start within pa period. January 31st ('END') vs.
-            January 1st ('START') for example.
+            or start within pa period.
+
+            * 'E', 'END', or 'FINISH' for end,
+            * 'S', 'START', or 'BEGIN' for start.
+
+            January 31st ('END') vs. January 1st ('START') for example.
 
         Returns
         -------
-        new : Period Array/Index with the new frequency
+        Period Array/Index
+            Constructed with the new frequency.
 
         Examples
         --------

pandas/core/frame.py

@@ -132,11 +132,12 @@
             Name or list of names to sort by.
 
             - if `axis` is 0 or `'index'` then `by` may contain index
-              levels and/or column labels
+              levels and/or column labels.
             - if `axis` is 1 or `'columns'` then `by` may contain column
-              levels and/or index labels
+              levels and/or index labels.
 
             .. versionchanged:: 0.23.0
+
                Allow specifying index or column level names.""",
     versionadded_to_excel="",
     optional_labels="""labels : array-like, optional
@@ -2112,9 +2113,10 @@ def to_html(
             A ``border=border`` attribute is included in the opening
             `<table>` tag. Default ``pd.options.display.html.border``.
         encoding : str, default "utf-8"
-            Set character encoding
+            Set character encoding.
 
             .. versionadded:: 1.0
+
         table_id : str, optional
             A css id is included in the opening `<table>` tag if specified.
 
@@ -7146,11 +7148,11 @@ def corr(self, method="pearson", min_periods=1):
             * kendall : Kendall Tau correlation coefficient
             * spearman : Spearman rank correlation
             * callable: callable with input two 1d ndarrays
-                and returning a float. Note that the returned matrix from corr
-                will have 1 along the diagonals and will be symmetric
-                regardless of the callable's behavior.
+              and returning a float. Note that the returned matrix from corr
+              will have 1 along the diagonals and will be symmetric
+              regardless of the callable's behavior.
 
-                .. versionadded:: 0.24.0
+              .. versionadded:: 0.24.0
 
         min_periods : int, optional
             Minimum number of observations required per pair of columns
@@ -7716,7 +7718,7 @@ def idxmin(self, axis=0, skipna=True):
         Parameters
         ----------
         axis : {0 or 'index', 1 or 'columns'}, default 0
-            The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise
+            The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
         skipna : bool, default True
             Exclude NA/null values. If an entire row/column is NA, the result
             will be NA.
@@ -7754,7 +7756,7 @@ def idxmax(self, axis=0, skipna=True):
         Parameters
         ----------
         axis : {0 or 'index', 1 or 'columns'}, default 0
-            The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise
+            The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
         skipna : bool, default True
             Exclude NA/null values. If an entire row/column is NA, the result
             will be NA.

pandas/core/groupby/groupby.py

@@ -2098,17 +2098,17 @@ def rank(
         Parameters
         ----------
         method : {'average', 'min', 'max', 'first', 'dense'}, default 'average'
-            * average: average rank of group
-            * min: lowest rank in group
-            * max: highest rank in group
-            * first: ranks assigned in order they appear in the array
-            * dense: like 'min', but rank always increases by 1 between groups
+            * average: average rank of group.
+            * min: lowest rank in group.
+            * max: highest rank in group.
+            * first: ranks assigned in order they appear in the array.
+            * dense: like 'min', but rank always increases by 1 between groups.
         ascending : bool, default True
             False for ranks by high (1) to low (N).
         na_option : {'keep', 'top', 'bottom'}, default 'keep'
-            * keep: leave NA values where they are
-            * top: smallest rank if ascending
-            * bottom: smallest rank if descending
+            * keep: leave NA values where they are.
+            * top: smallest rank if ascending.
+            * bottom: smallest rank if descending.
         pct : bool, default False
             Compute percentage rank of data within each group.
         axis : int, default 0

pandas/core/groupby/grouper.py

@@ -34,8 +34,7 @@
 
 class Grouper:
     """
-    A Grouper allows the user to specify a groupby instruction for a target
-    object.
+    A Grouper allows the user to specify a groupby instruction for an object.
 
     This specification will select a column via the key parameter, or if the
     level and/or axis parameters are given, a level of the index of the target
@@ -47,17 +46,18 @@ class Grouper:
     Parameters
     ----------
     key : str, defaults to None
-        groupby key, which selects the grouping column of the target
+        Groupby key, which selects the grouping column of the target.
     level : name/number, defaults to None
-        the level for the target index
+        The level for the target index.
     freq : str / frequency object, defaults to None
         This will groupby the specified frequency if the target selection
         (via key or level) is a datetime-like object. For full specification
         of available frequencies, please see `here
         <http://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`_.
-    axis : number/name of the axis, defaults to 0
+    axis : str, int, defaults to 0
+        Number/name of the axis.
     sort : bool, default to False
-        whether to sort the resulting labels
+        Whether to sort the resulting labels.
     closed : {'left' or 'right'}
         Closed end of interval. Only when `freq` parameter is passed.
     label : {'left' or 'right'}

pandas/core/indexes/period.py

@@ -77,18 +77,17 @@ class PeriodDelegateMixin(DatetimelikeDelegateMixin):
 )
 class PeriodIndex(DatetimeIndexOpsMixin, Int64Index, PeriodDelegateMixin):
     """
-    Immutable ndarray holding ordinal values indicating regular periods in
-    time such as particular years, quarters, months, etc.
+    Immutable ndarray holding ordinal values indicating regular periods in time.
 
     Index keys are boxed to Period objects which carries the metadata (eg,
     frequency information).
 
     Parameters
     ----------
     data : array-like (1d int np.ndarray or PeriodArray), optional
-        Optional period-like data to construct index with
+        Optional period-like data to construct index with.
     copy : bool
-        Make a copy of input ndarray
+        Make a copy of input ndarray.
     freq : str or period object, optional
         One of pandas period strings or corresponding objects
     year : int, array, or Series, default None
@@ -99,7 +98,7 @@ class PeriodIndex(DatetimeIndexOpsMixin, Int64Index, PeriodDelegateMixin):
     minute : int, array, or Series, default None
     second : int, array, or Series, default None
     tz : object, default None
-        Timezone for converting datetime64 data to Periods
+        Timezone for converting datetime64 data to Periods.
     dtype : str or PeriodDtype, default None
 
     Attributes

pandas/core/window/ewm.py

@@ -314,7 +314,7 @@ def cov(self, other=None, pairwise=None, bias=False, **kwargs):
             inputs. In the case of missing elements, only complete pairwise
             observations will be used.
         bias : bool, default False
-            Use a standard estimation bias correction
+            Use a standard estimation bias correction.
         **kwargs
            Keyword arguments to be passed into func.
         """

pandas/io/excel/_base.py

@@ -528,8 +528,10 @@ def parse(
 
 class ExcelWriter(metaclass=abc.ABCMeta):
     """
-    Class for writing DataFrame objects into excel sheets, default is to use
-    xlwt for xls, openpyxl for xlsx.  See DataFrame.to_excel for typical usage.
+    Class for writing DataFrame objects into excel sheets.
+
+    Default is to use xlwt for xls, openpyxl for xlsx.
+    See DataFrame.to_excel for typical usage.
 
     Parameters
     ----------
@@ -543,7 +545,7 @@ class ExcelWriter(metaclass=abc.ABCMeta):
         Format string for dates written into Excel files (e.g. 'YYYY-MM-DD').
     datetime_format : str, default None
         Format string for datetime objects written into Excel files.
-        (e.g. 'YYYY-MM-DD HH:MM:SS')
+        (e.g. 'YYYY-MM-DD HH:MM:SS').
     mode : {'w', 'a'}, default 'w'
         File mode to use (write or append).