Reordered incorrect orderings on docstrings. Moved Notes section out of shared doc and into individual occurences to avoid incorrect order of Notes and See Also sections.

Author: benjaminr

pandas/core/frame.py

@@ -6051,12 +6051,17 @@ def _gotitem(self,
     --------
     DataFrame.apply : Perform any type of operations.
     DataFrame.transform : Perform transformation type operations.
-    pandas.core.groupby.GroupBy : Perform operations over groups.
-    pandas.core.resample.Resampler : Perform operations over resampled bins.
-    pandas.core.window.Rolling : Perform operations over rolling window.
-    pandas.core.window.Expanding : Perform operations over expanding window.
-    pandas.core.window.EWM : Perform operation over exponential weighted
-        window.
+    core.groupby.GroupBy : Perform operations over groups.
+    core.resample.Resampler : Perform operations over resampled bins.
+    core.window.Rolling : Perform operations over rolling window.
+    core.window.Expanding : Perform operations over expanding window.
+    core.window.EWM : Perform operation over exponential weighted window.
+    
+    Notes
+    -----
+    `agg` is an alias for `aggregate`. Use the alias.
+
+    A passed user-defined-function will be passed a Series for evaluation.
 
     Examples
     --------
@@ -6075,7 +6080,7 @@ def _gotitem(self,
 
     Different aggregations per column.
 
-    >>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']})
+    >>> df.agg({'A': ['sum', 'min'], 'B': ['min', 'max']})
             A    B
     max   NaN  8.0
     min   1.0  2.0
@@ -6091,10 +6096,10 @@ def _gotitem(self,
     dtype: float64
     """)
 
+    @Appender(_agg_doc)
     @Appender(_shared_docs['aggregate'] % dict(
         versionadded='.. versionadded:: 0.20.0',
         **_shared_doc_kwargs))
-    @Appender(_agg_doc)
     def aggregate(self, func, axis=0, *args, **kwargs):
         axis = self._get_axis_number(axis)
 

pandas/core/generic.py

@@ -4925,12 +4925,6 @@ def pipe(self, func, *args, **kwargs):
         if DataFrame.agg is called with several functions, returns a DataFrame
         if Series.agg is called with single function, returns a scalar
         if Series.agg is called with several functions, returns a Series
-
-    Notes
-    -----
-    `agg` is an alias for `aggregate`. Use the alias.
-
-    A passed user-defined-function will be passed a Series for evaluation.
     """)
 
     _shared_docs['transform'] = ("""

pandas/core/groupby/generic.py

@@ -707,9 +707,20 @@ def _selection_name(self):
             return self._selection
 
     _agg_doc = dedent("""
-    Examples
+    See Also
     --------
+    pandas.Series.groupby.apply
+    pandas.Series.groupby.transform
+    pandas.Series.aggregate
+
+    Notes
+    -----
+    `agg` is an alias for `aggregate`. Use the alias.
 
+    A passed user-defined-function will be passed a Series for evaluation.
+
+    Examples
+    --------
     >>> s = pd.Series([1, 2, 3, 4])
 
     >>> s
@@ -733,13 +744,6 @@ def _selection_name(self):
        min  max
     1    1    2
     2    3    4
-
-    See Also
-    --------
-    pandas.Series.groupby.apply
-    pandas.Series.groupby.transform
-    pandas.Series.aggregate
-
     """)
 
     @Appender(_apply_docs['template']
@@ -1249,9 +1253,15 @@ class DataFrameGroupBy(NDFrameGroupBy):
     _agg_doc = dedent("""
     See Also
     --------
-    pandas.DataFrame.groupby.apply
-    pandas.DataFrame.groupby.transform
-    pandas.DataFrame.aggregate
+    DataFrame.groupby.apply
+    DataFrame.groupby.transform
+    DataFrame.aggregate
+    
+    Notes
+    -----
+    `agg` is an alias for `aggregate`. Use the alias.
+
+    A passed user-defined-function will be passed a Series for evaluation.
 
     Examples
     --------

pandas/core/groupby/groupby.py

@@ -44,9 +44,9 @@ class providing the base-class of operations.
 _common_see_also = """
         See Also
         --------
-        pandas.Series.%(name)s
-        pandas.DataFrame.%(name)s
-        pandas.Panel.%(name)s
+        Series.%(name)s
+        DataFrame.%(name)s
+        Panel.%(name)s
 """
 
 _apply_docs = dict(
@@ -2048,9 +2048,7 @@ def head(self, n=5):
 
         Examples
         --------
-
-        >>> df = pd.DataFrame([[1, 2], [1, 4], [5, 6]],
-                              columns=['A', 'B'])
+        >>> df = pd.DataFrame([[1, 2], [1, 4], [5, 6]], columns=['A', 'B'])
         >>> df.groupby('A', as_index=False).head(1)
            A  B
         0  1  2
@@ -2078,8 +2076,7 @@ def tail(self, n=5):
         Examples
         --------
 
-        >>> df = pd.DataFrame([['a', 1], ['a', 2], ['b', 1], ['b', 2]],
-                              columns=['A', 'B'])
+        >>> df = pd.DataFrame([['a', 1], ['a', 2], ['b', 1], ['b', 2]], columns=['A', 'B'])
         >>> df.groupby('A').tail(1)
            A  B
         1  a  2

pandas/core/resample.py

@@ -214,14 +214,19 @@ def pipe(self, func, *args, **kwargs):
     _agg_doc = dedent("""
     See Also
     --------
-    pandas.DataFrame.groupby.aggregate
-    pandas.DataFrame.resample.transform
-    pandas.DataFrame.aggregate
+    DataFrame.groupby.aggregate
+    DataFrame.resample.transform
+    DataFrame.aggregate
+    
+    Notes
+    -----
+    `agg` is an alias for `aggregate`. Use the alias.
+
+    A passed user-defined-function will be passed a Series for evaluation.
 
     Examples
     --------
-    >>> s = pd.Series([1,2,3,4,5],
-                      index=pd.date_range('20130101', periods=5,freq='s'))
+    >>> s = pd.Series([1, 2, 3, 4, 5], index=pd.date_range('20130101', periods=5, freq='s'))
     2013-01-01 00:00:00    1
     2013-01-01 00:00:01    2
     2013-01-01 00:00:02    3
@@ -239,25 +244,24 @@ def pipe(self, func, *args, **kwargs):
     2013-01-01 00:00:04    5
     Freq: 2S, dtype: int64
 
-    >>> r.agg(['sum','mean','max'])
+    >>> r.agg(['sum', 'mean', 'max'])
                          sum  mean  max
     2013-01-01 00:00:00    3   1.5    2
     2013-01-01 00:00:02    7   3.5    4
     2013-01-01 00:00:04    5   5.0    5
 
-    >>> r.agg({'result' : lambda x: x.mean() / x.std(),
-               'total' : np.sum})
+    >>> r.agg({'result': lambda x: x.mean() / x.std(), 'total': np.sum})
                          total    result
     2013-01-01 00:00:00      3  2.121320
     2013-01-01 00:00:02      7  4.949747
     2013-01-01 00:00:04      5       NaN
     """)
 
+    @Appender(_agg_doc)
     @Appender(_shared_docs['aggregate'] % dict(
         klass='DataFrame',
         versionadded='',
         axis=''))
-    @Appender(_agg_doc)
     def aggregate(self, func, *args, **kwargs):
 
         self._set_binner()

pandas/core/series.py

@@ -3281,6 +3281,12 @@ def _gotitem(self, key, ndim, subset=None):
     Series.apply : Invoke function on a Series.
     Series.transform : Transform function producing
         a Series with like indexes.
+    
+    Notes
+    -----
+    `agg` is an alias for `aggregate`. Use the alias.
+
+    A passed user-defined-function will be passed a Series for evaluation.
 
     Examples
     --------
@@ -3302,10 +3308,10 @@ def _gotitem(self, key, ndim, subset=None):
     dtype: int64
     """)
 
+    @Appender(_agg_doc)
     @Appender(generic._shared_docs['aggregate'] % dict(
         versionadded='.. versionadded:: 0.20.0',
         **_shared_doc_kwargs))
-    @Appender(_agg_doc)
     def aggregate(self, func, axis=0, *args, **kwargs):
         # Validate the axis parameter
         self._get_axis_number(axis)

pandas/core/window.py

@@ -694,9 +694,19 @@ def f(arg, *args, **kwargs):
         return self._wrap_results(results, blocks, obj)
 
     _agg_doc = dedent("""
-    Examples
+    See Also
     --------
+    pandas.DataFrame.rolling.aggregate
+    pandas.DataFrame.aggregate
+    
+    Notes
+    -----
+    `agg` is an alias for `aggregate`. Use the alias.
 
+    A passed user-defined-function will be passed a Series for evaluation.
+    
+    Examples
+    --------
     >>> df = pd.DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C'])
     >>> df
               A         B         C
@@ -723,12 +733,6 @@ def f(arg, *args, **kwargs):
     7  0.906020  1.283573  0.085482
     8 -0.096361  0.818139  0.472290
     9  0.070889  0.134399 -0.031308
-
-    See Also
-    --------
-    pandas.DataFrame.rolling.aggregate
-    pandas.DataFrame.aggregate
-
     """)
 
     @Appender(_agg_doc)
@@ -1598,12 +1602,17 @@ def _validate_freq(self):
     _agg_doc = dedent("""
     See Also
     --------
-    pandas.Series.rolling
-    pandas.DataFrame.rolling
+    Series.rolling
+    DataFrame.rolling
+    
+    Notes
+    -----
+    `agg` is an alias for `aggregate`. Use the alias.
+
+    A passed user-defined-function will be passed a Series for evaluation.
 
     Examples
     --------
-
     >>> df = pd.DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C'])
     >>> df
               A         B         C
@@ -1631,7 +1640,7 @@ def _validate_freq(self):
     8 -0.289082  2.454418  1.416871
     9  0.212668  0.403198 -0.093924
 
-    >>> df.rolling(3).agg({'A':'sum', 'B':'min'})
+    >>> df.rolling(3).agg({'A': 'sum', 'B': 'min'})
               A         B
     0       NaN       NaN
     1       NaN       NaN
@@ -1645,11 +1654,11 @@ def _validate_freq(self):
     9  0.212668 -1.647453
     """)
 
+    @Appender(_agg_doc)
     @Appender(_shared_docs['aggregate'] % dict(
         versionadded='',
         klass='Series/DataFrame',
         axis=''))
-    @Appender(_agg_doc)
     def aggregate(self, arg, *args, **kwargs):
         return super(Rolling, self).aggregate(arg, *args, **kwargs)
 
@@ -1886,13 +1895,18 @@ def _get_window(self, other=None):
     _agg_doc = dedent("""
     See Also
     --------
-    pandas.DataFrame.expanding.aggregate
-    pandas.DataFrame.rolling.aggregate
-    pandas.DataFrame.aggregate
+    DataFrame.expanding.aggregate
+    DataFrame.rolling.aggregate
+    DataFrame.aggregate
+    
+    Notes
+    -----
+    `agg` is an alias for `aggregate`. Use the alias.
+
+    A passed user-defined-function will be passed a Series for evaluation.
 
     Examples
     --------
-
     >>> df = pd.DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C'])
     >>> df
               A         B         C
@@ -1921,11 +1935,11 @@ def _get_window(self, other=None):
     9 -0.286980  0.618493 -0.694496
     """)
 
+    @Appender(_agg_doc)
     @Appender(_shared_docs['aggregate'] % dict(
         versionadded='',
         klass='Series/DataFrame',
         axis=''))
-    @Appender(_agg_doc)
     def aggregate(self, arg, *args, **kwargs):
         return super(Expanding, self).aggregate(arg, *args, **kwargs)
 
@@ -2185,9 +2199,18 @@ def _constructor(self):
         return EWM
 
     _agg_doc = dedent("""
-    Examples
+    See Also
     --------
+    pandas.DataFrame.rolling.aggregate
 
+    Notes
+    -----
+    `agg` is an alias for `aggregate`. Use the alias.
+
+    A passed user-defined-function will be passed a Series for evaluation.
+    
+    Examples
+    --------
     >>> df = pd.DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C'])
     >>> df
               A         B         C
@@ -2214,10 +2237,6 @@ def _constructor(self):
     7  0.680292  0.132049  0.548693
     8  0.067236  0.948257  0.163353
     9 -0.286980  0.618493 -0.694496
-
-    See Also
-    --------
-    pandas.DataFrame.rolling.aggregate
     """)
 
     @Appender(_agg_doc)