DOC: Fix remaining validation errors RT01 (pandas-dev#25356)

Author: ihsansecer

pandas/core/accessor.py

@@ -206,6 +206,11 @@ def decorator(accessor):
     Name under which the accessor should be registered. A warning is issued
     if this name conflicts with a preexisting attribute.
 
+Returns
+-------
+callable
+    A class decorator.
+
 See Also
 --------
 %(others)s

pandas/core/arrays/categorical.py

@@ -760,6 +760,11 @@ def as_ordered(self, inplace=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 True.
+
+        Returns
+        -------
+        Categorical
+            Ordered Categorical.
         """
         inplace = validate_bool_kwarg(inplace, 'inplace')
         return self.set_ordered(True, inplace=inplace)
@@ -768,6 +773,11 @@ def as_unordered(self, inplace=False):
         """
         Set the Categorical to be unordered.
 
+        Returns
+        -------
+        Categorical
+            Unordered Categorical.
+
         Parameters
         ----------
         inplace : bool, default False

pandas/core/arrays/interval.py

@@ -239,6 +239,10 @@ def _from_factorized(cls, values, original):
 
         .. versionadded:: 0.23.0
 
+    Returns
+    -------
+    %(klass)s
+
     See Also
     --------
     interval_range : Function to create a fixed frequency IntervalIndex.
@@ -383,6 +387,10 @@ def from_arrays(cls, left, right, closed='right', copy=False, dtype=None):
 
         ..versionadded:: 0.23.0
 
+    Returns
+    -------
+    %(klass)s
+
     See Also
     --------
     interval_range : Function to create a fixed frequency IntervalIndex.

pandas/core/groupby/generic.py

@@ -999,6 +999,11 @@ def true_and_notna(x, *args, **kwargs):
     def nunique(self, dropna=True):
         """
         Return number of unique elements in the group.
+
+        Returns
+        -------
+        Series
+            Numver of unique values within each group.
         """
         ids, _, _ = self.grouper.group_info
 
@@ -1181,7 +1186,14 @@ def value_counts(self, normalize=False, sort=True, ascending=False,
         return Series(out, index=mi, name=self._selection_name)
 
     def count(self):
-        """ Compute count of group, excluding missing values """
+        """
+        Compute count of group, excluding missing values.
+        
+        Returns
+        -------
+        Series
+            Count of values within each group.
+        """
         ids, _, ngroups = self.grouper.group_info
         val = self.obj.get_values()
 
@@ -1479,7 +1491,14 @@ def _fill(self, direction, limit=None):
         return concat((self._wrap_transformed_output(output), res), axis=1)
 
     def count(self):
-        """ Compute count of group, excluding missing values """
+        """
+        Compute count of group, excluding missing values
+        
+        Returns
+        -------
+        DataFrame
+            Count of values within each group.
+        """
         from pandas.core.dtypes.missing import _isna_ndarraylike as _isna
 
         data, _ = self._get_data_to_aggregate()

pandas/core/groupby/groupby.py

@@ -1071,6 +1071,10 @@ def any(self, skipna=True):
         ----------
         skipna : bool, default True
             Flag to ignore nan values during truth testing
+
+        Returns
+        -------
+        boolean
         """
         return self._bool_agg('any', skipna)
 
@@ -1084,6 +1088,10 @@ def all(self, skipna=True):
         ----------
         skipna : bool, default True
             Flag to ignore nan values during truth testing
+
+        Returns
+        -------
+        boolean
         """
         return self._bool_agg('all', skipna)
 
@@ -1092,6 +1100,11 @@ def all(self, skipna=True):
     def count(self):
         """
         Compute count of group, excluding missing values.
+
+        Returns
+        -------
+        Series or DataFrame
+            Count of values within each group.
         """
 
         # defined here for API doc
@@ -1161,6 +1174,11 @@ def median(self, **kwargs):
         Compute median of groups, excluding missing values.
 
         For multiple groupings, the result index will be a MultiIndex
+
+        Returns
+        -------
+        Series or DataFrame
+            Median of values within each group.
         """
         try:
             return self._cython_agg_general('median', **kwargs)
@@ -1187,6 +1205,11 @@ def std(self, ddof=1, *args, **kwargs):
         ----------
         ddof : integer, default 1
             degrees of freedom
+     
+        Returns
+        -------
+        Series or DataFrame
+            Standard deviation of values within each group.
         """
 
         # TODO: implement at Cython level?
@@ -1205,6 +1228,11 @@ def var(self, ddof=1, *args, **kwargs):
         ----------
         ddof : integer, default 1
             degrees of freedom
+
+        Returns
+        -------
+        Series or DataFrame
+            Variance of values within each group.
         """
         nv.validate_groupby_func('var', args, kwargs)
         if ddof == 1:
@@ -1231,6 +1259,11 @@ def sem(self, ddof=1):
         ----------
         ddof : integer, default 1
             degrees of freedom
+
+        Returns
+        -------
+        Series or DataFrame
+            Standard error of the mean of values within each group.
         """
 
         return self.std(ddof=ddof) / np.sqrt(self.count())
@@ -1240,6 +1273,11 @@ def sem(self, ddof=1):
     def size(self):
         """
         Compute group sizes.
+
+        Returns
+        -------
+        Series
+            Number of rows in each group.
         """
         result = self.grouper.size()
 
@@ -1257,7 +1295,14 @@ def groupby_function(name, alias, npfunc,
                              numeric_only=True, _convert=False,
                              min_count=-1):
 
-            _local_template = "Compute %(f)s of group values"
+            _local_template = """
+            Compute %(f)s of group values
+            
+            Returns
+            -------
+            Series or DataFrame
+                %(f)s of values within each group.
+            """
 
             @Substitution(name='groupby', f=name)
             @Appender(_common_see_also)
@@ -1330,6 +1375,11 @@ def ohlc(self):
         Compute sum of values, excluding missing values.
 
         For multiple groupings, the result index will be a MultiIndex
+
+        Returns
+        -------
+        DataFrame
+            Open, high, low and close values within each group.
         """
 
         return self._apply_to_column_groupbys(
@@ -1514,6 +1564,11 @@ def pad(self, limit=None):
         limit : integer, optional
             limit of how many values to fill
 
+        Returns
+        -------
+        Series or DataFrame
+            Object with missing values filled.
+
         See Also
         --------
         Series.pad
@@ -1534,6 +1589,11 @@ def backfill(self, limit=None):
         limit : integer, optional
             limit of how many values to fill
 
+        Returns
+        -------
+        Series or DataFrame
+            Object with missing values filled.
+
         See Also
         --------
         Series.backfill
@@ -1563,6 +1623,11 @@ def nth(self, n, dropna=None):
         dropna : None or str, optional
             apply the specified dropna operation before counting which row is
             the nth row. Needs to be None, 'any' or 'all'
+
+        Returns
+        -------
+        Series or DataFrame
+            Nth value within each group.
         %(see_also)s
         Examples
         --------
@@ -1793,6 +1858,11 @@ def ngroup(self, ascending=True):
         ascending : bool, default True
             If False, number in reverse, from number of group - 1 to 0.
 
+        Returns
+        -------
+        Series
+            Unique numbers for each group.
+
         See Also
         --------
         .cumcount : Number the rows in each group.
@@ -1856,6 +1926,11 @@ def cumcount(self, ascending=True):
         ascending : bool, default True
             If False, number in reverse, from length of group - 1 to 0.
 
+        Returns
+        -------
+        Series
+            Sequence number of each element within each group.
+
         See Also
         --------
         .ngroup : Number the groups themselves.
@@ -1938,6 +2013,10 @@ def rank(self, method='average', ascending=True, na_option='keep',
     def cumprod(self, axis=0, *args, **kwargs):
         """
         Cumulative product for each group.
+
+        Returns
+        -------
+        Series or DataFrame
         """
         nv.validate_groupby_func('cumprod', args, kwargs,
                                  ['numeric_only', 'skipna'])
@@ -1951,6 +2030,10 @@ def cumprod(self, axis=0, *args, **kwargs):
     def cumsum(self, axis=0, *args, **kwargs):
         """
         Cumulative sum for each group.
+
+        Returns
+        -------
+        Series or DataFrame
         """
         nv.validate_groupby_func('cumsum', args, kwargs,
                                  ['numeric_only', 'skipna'])
@@ -1964,6 +2047,10 @@ def cumsum(self, axis=0, *args, **kwargs):
     def cummin(self, axis=0, **kwargs):
         """
         Cumulative min for each group.
+
+        Returns
+        -------
+        Series or DataFrame
         """
         if axis != 0:
             return self.apply(lambda x: np.minimum.accumulate(x, axis))
@@ -1975,6 +2062,10 @@ def cummin(self, axis=0, **kwargs):
     def cummax(self, axis=0, **kwargs):
         """
         Cumulative max for each group.
+
+        Returns
+        -------
+        Series or DataFrame
         """
         if axis != 0:
             return self.apply(lambda x: np.maximum.accumulate(x, axis))
@@ -2102,6 +2193,11 @@ def shift(self, periods=1, freq=None, axis=0, fill_value=None):
         fill_value : optional
 
             .. versionadded:: 0.24.0
+
+        Returns
+        -------    
+        Series or DataFrame
+            Object shifted within each group.
         """
 
         if freq is not None or axis != 0 or not isna(fill_value):
@@ -2120,6 +2216,11 @@ def pct_change(self, periods=1, fill_method='pad', limit=None, freq=None,
                    axis=0):
         """
         Calculate pct_change of each value to previous entry in group.
+
+        Returns
+        -------    
+        Series or DataFrame
+            Percentage changes within each group.
         """
         if freq is not None or axis != 0:
             return self.apply(lambda x: x.pct_change(periods=periods,
@@ -2140,6 +2241,10 @@ def head(self, n=5):
 
         Essentially equivalent to ``.apply(lambda x: x.head(n))``,
         except ignores as_index flag.
+
+        Returns
+        -------    
+        Series or DataFrame
         %(see_also)s
         Examples
         --------
@@ -2167,6 +2272,10 @@ def tail(self, n=5):
 
         Essentially equivalent to ``.apply(lambda x: x.tail(n))``,
         except ignores as_index flag.
+
+        Returns
+        -------    
+        Series or DataFrame
         %(see_also)s
         Examples
         --------