various updates

Author: jreback

pandas/core/frame.py

@@ -4228,6 +4228,9 @@ def _gotitem(self, key, ndim, subset=None):
     --------
     pandas.DataFrame.apply
     pandas.DataFrame.transform
+    pandas.DataFrame.groupby.aggregate
+    pandas.DataFrame.resample.aggregate
+    pandas.DataFrame.rolling.aggregate
 
     """)
 
@@ -4306,7 +4309,7 @@ def apply(self, func, axis=0, broadcast=False, raw=False, reduce=None,
         See also
         --------
         DataFrame.applymap: For elementwise operations
-        DataFrame.agg: only perform aggregating type operations
+        DataFrame.aggregate: only perform aggregating type operations
         DataFrame.transform: only perform transformating type operations
 
         Returns

pandas/core/generic.py

@@ -2879,6 +2879,8 @@ def pipe(self, func, *args, **kwargs):
     (e.g., np.mean(arr_2d, axis=0)) as opposed to
     mimicking the default Numpy behavior (e.g., np.mean(arr_2d)).
 
+    agg is an alias for aggregate. Use it.
+
     Returns
     -------
     aggregated : %(klass)s
@@ -2925,6 +2927,11 @@ def pipe(self, func, *args, **kwargs):
     2000-01-09 -0.540524 -1.012676 -0.828968
     2000-01-10 -1.366388 -0.614710  0.005378
 
+    See also
+    --------
+    pandas.%(klass)s.aggregate
+    pandas.%(klass)s.apply
+
     """)
 
     # ----------------------------------------------------------------------

pandas/core/groupby.py

@@ -2780,9 +2780,9 @@ def _selection_name(self):
 
     See also
     --------
-    pandas.Series.apply
+    pandas.Series.groupby.apply
+    pandas.Series.groupby.transform
     pandas.Series.aggregate
-    pandas.Series.transform
 
     """)
 
@@ -3947,9 +3947,9 @@ class DataFrameGroupBy(NDFrameGroupBy):
 
     See also
     --------
-    pandas.DataFrame.apply
+    pandas.DataFrame.groupby.apply
+    pandas.DataFrame.groupby.transform
     pandas.DataFrame.aggregate
-    pandas.DataFrame.transform
 
     """)
 

pandas/core/resample.py

@@ -2,6 +2,7 @@
 import numpy as np
 import warnings
 import copy
+from textwrap import dedent
 
 import pandas as pd
 from pandas.core.base import AbstractMethodError, GroupByMixin
@@ -254,66 +255,56 @@ def plot(self, *args, **kwargs):
         # have the warnings shown here and just have this work
         return self._deprecated('plot').plot(*args, **kwargs)
 
+    _agg_doc = dedent("""
+
+    Examples
+    --------
+    >>> s = 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
+    2013-01-01 00:00:03    4
+    2013-01-01 00:00:04    5
+    Freq: S, dtype: int64
+
+    >>> r = s.resample('2s')
+    DatetimeIndexResampler [freq=<2 * Seconds>, axis=0, closed=left,
+                            label=left, convention=start, base=0]
+
+    >>> r.agg(np.sum)
+    2013-01-01 00:00:00    3
+    2013-01-01 00:00:02    7
+    2013-01-01 00:00:04    5
+    Freq: 2S, dtype: int64
+
+    >>> 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})
+                         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
+
+    See also
+    --------
+    pandas.DataFrame.groupby.aggregate
+    pandas.DataFrame.resample.transform
+    pandas.DataFrame.aggregate
+
+    """)
+
+    @Appender(_agg_doc)
+    @Appender(_shared_docs['aggregate'] % dict(
+        klass='DataFrame',
+        versionadded=''))
     def aggregate(self, arg, *args, **kwargs):
-        """
-        Apply aggregation function or functions to resampled groups, yielding
-        most likely Series but in some cases DataFrame depending on the output
-        of the aggregation function
-
-        Parameters
-        ----------
-        func_or_funcs : function or list / dict of functions
-            List/dict of functions will produce DataFrame with column names
-            determined by the function names themselves (list) or the keys in
-            the dict
-
-        Notes
-        -----
-        agg is an alias for aggregate. Use it.
-
-        Examples
-        --------
-        >>> s = 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
-        2013-01-01 00:00:03    4
-        2013-01-01 00:00:04    5
-        Freq: S, dtype: int64
-
-        >>> r = s.resample('2s')
-        DatetimeIndexResampler [freq=<2 * Seconds>, axis=0, closed=left,
-                                label=left, convention=start, base=0]
-
-        >>> r.agg(np.sum)
-        2013-01-01 00:00:00    3
-        2013-01-01 00:00:02    7
-        2013-01-01 00:00:04    5
-        Freq: 2S, dtype: int64
-
-        >>> 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})
-                             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
-
-        See also
-        --------
-        transform
-
-        Returns
-        -------
-        Series or DataFrame
-        """
 
         self._set_binner()
         result, how = self._aggregate(arg, *args, **kwargs)

pandas/core/window.py

@@ -645,8 +645,8 @@ def f(arg, *args, **kwargs):
 
     See also
     --------
-    pandas.Series.rolling
-    pandas.DataFrame.rolling
+    pandas.DataFrame.rolling.aggregate
+    pandas.DataFrame.aggregate
 
     """)
 
@@ -1414,8 +1414,9 @@ def _get_window(self, other=None):
 
     See also
     --------
-    pandas.Series.expanding
-    pandas.DataFrame.expanding
+    pandas.DataFrame.expanding.aggregate
+    pandas.DataFrame.rolling.aggregate
+    pandas.DataFrame.aggregate
 
     """)
 
@@ -1699,8 +1700,7 @@ def _constructor(self):
 
     See also
     --------
-    pandas.Series.ewm
-    pandas.DataFrame.ewm
+    pandas.DataFrame.rolling.aggregate
 
     """)