update docs

add alt api for series agg with dict

Author: jreback

doc/source/basics.rst

@@ -840,7 +840,7 @@ Aggregation API
 
 .. versionadded:: 0.20.0
 
-The aggregation APi allows one to express possibly multiple aggregation operations in a single concise way.
+The aggregation API allows one to express possibly multiple aggregation operations in a single concise way.
 This API is similar across pandas objects, :ref:`groupby aggregates <groupby.aggregate>`,
 :ref:`window functions <stats.aggregate>`, and the :ref:`resample API <timeseries.aggregate>`.
 
@@ -862,6 +862,9 @@ This will return a Series of the output.
 
    tsdf.agg('sum')
 
+   # these are equivalent to a ``.sum()`` because we are aggregating on a single function
+   tsdf.sum()
+
 On a Series this will result in a scalar value
 
 .. ipython:: python
@@ -915,6 +918,12 @@ For a Series, you can pass a dict; the keys will set the name of the column
 
    tsdf.A.agg({'foo' : ['sum', 'mean']})
 
+Alternatively, using multiple dictionaries, you can have renamed elements with the aggregation
+
+.. ipython:: python
+
+    tsdf.A.agg({'foo' : 'sum', 'bar':'mean'})
+
 Multiple keys will yield multiple columns.
 
 .. ipython:: python
@@ -969,12 +978,11 @@ function name and user defined function.
    tsdf.transform('abs')
    tsdf.transform(lambda x: x.abs())
 
-``.transform()`` with a single function is equivalent to applying a function across the
-columns.
+Since this is a single function, this is equivalent to a ufunc application
 
 .. ipython:: python
 
-   tsdf.apply(np.abs, axis=1)
+   np.abs(tsdf)
 
 Passing a single function to ``.transform()`` with a Series will yield a single Series in return.
 

pandas/core/generic.py

@@ -2782,17 +2782,16 @@ def pipe(self, func, *args, **kwargs):
 
     Parameters
     ----------
-    arg : function or dict
+    func : callable, string, dictionary, or list of string/callables
         Function to use for aggregating the data. If a function, must either
         work when passed a DataFrame or when passed to DataFrame.apply. If
         passed a dict, the keys must be DataFrame column names.
 
         Accepted Combinations are:
-          - string cythonized function name
-          - function
-          - list of functions
-          - dict of columns -> functions
-          - nested dict of names -> dicts of functions
+        - string function name
+        - function
+        - list of functions
+        - dict of column names -> functions (or list of functions)
 
     Notes
     -----
@@ -2817,9 +2816,15 @@ def pipe(self, func, *args, **kwargs):
 
     Parameters
     ----------
-    func : function, callable or string
+    func : callable, string, dictionary, or list of string/callables
         To apply to column
 
+        Accepted Combinations are:
+        - string function name
+        - function
+        - list of functions
+        - dict of column names -> functions (or list of functions)
+
     Examples
     --------
     >>> df.transform(lambda x: (x - x.mean()) / x.std())

pandas/tests/frame/test_apply.py

@@ -482,6 +482,9 @@ def test_agg_transform(self):
             result = self.frame.apply(np.sqrt)
             assert_frame_equal(result, expected)
 
+            result = self.frame.transform(np.sqrt)
+            assert_frame_equal(result, expected)
+
             # list-like
             result = self.frame.apply([np.sqrt])
             expected = f_sqrt.copy()

pandas/tests/series/test_apply.py

@@ -234,7 +234,11 @@ def test_demo(self):
         expected = DataFrame({'foo': [0, 5]}, index=['min', 'max'])
         tm.assert_frame_equal(result, expected)
 
+    def test_multiple_aggregators_with_dict_api(self):
+
+        s = Series(range(6), dtype='int64')
         result = s.agg({'foo': ['min', 'max'], 'bar': ['sum', 'mean']})
+
         expected = DataFrame({'foo': [5.0, np.nan, 0.0, np.nan],
                               'bar': [np.nan, 2.5, np.nan, 15.0]},
                              columns=['foo', 'bar'],