ENH: add Series & DataFrame .agg/.aggregate to provide convienent

function application that mimics the groupby(..).agg/.aggregate
interface

.apply is now a synonym for .agg, and will accept dict/list-likes
for aggregations

CLN: rename .name attr -> ._selection_name from SeriesGroupby for compat (didn't exist on DataFrameGroupBy)
resolves conflicts w.r.t. setting .name on a groupby object

closes pandas-dev#1623
closes pandas-dev#14464

custom .describe
closes pandas-dev#14483
closes pandas-dev#15015
closes pandas-dev#7014

Author: jreback

doc/source/api.rst

@@ -314,6 +314,8 @@ Function application, GroupBy & Window
    :toctree: generated/
 
    Series.apply
+   Series.aggregate
+   Series.transform
    Series.map
    Series.groupby
    Series.rolling
@@ -831,6 +833,8 @@ Function application, GroupBy & Window
 
    DataFrame.apply
    DataFrame.applymap
+   DataFrame.aggregate
+   DataFrame.transform
    DataFrame.groupby
    DataFrame.rolling
    DataFrame.expanding

doc/source/basics.rst

@@ -702,7 +702,8 @@ on an entire ``DataFrame`` or ``Series``, row- or column-wise, or elementwise.
 
 1. `Tablewise Function Application`_: :meth:`~DataFrame.pipe`
 2. `Row or Column-wise Function Application`_: :meth:`~DataFrame.apply`
-3. Elementwise_ function application: :meth:`~DataFrame.applymap`
+3. `Aggregation API`_: :meth:`~DataFrame.agg` and :meth:`~DataFrame.transform`
+4. `Applying Elementwise Functions`_: :meth:`~DataFrame.applymap`
 
 .. _basics.pipe:
 
@@ -778,6 +779,13 @@ statistics methods, take an optional ``axis`` argument:
    df.apply(np.cumsum)
    df.apply(np.exp)
 
+``.apply()`` will also dispatch on a string method name.
+
+.. ipython:: python
+
+   df.apply('mean')
+   df.apply('mean', axis=1)
+
 Depending on the return type of the function passed to :meth:`~DataFrame.apply`,
 the result will either be of lower dimension or the same dimension.
 
@@ -827,16 +835,234 @@ set to True, the passed function will instead receive an ndarray object, which
 has positive performance implications if you do not need the indexing
 functionality.
 
-.. seealso::
+.. _basics.aggregate:
+
+Aggregation API
+~~~~~~~~~~~~~~~
+
+.. versionadded:: 0.20.0
+
+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>`.
+
+We will use a similar starting frame from above.
+
+.. ipython:: python
+
+   tsdf = pd.DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C'],
+                       index=pd.date_range('1/1/2000', periods=10))
+   tsdf.iloc[3:7] = np.nan
+   tsdf
+
+Using a single function is equivalent to ``.apply``; You can also pass named methods as strings.
+This will return a Series of the output.
+
+.. ipython:: python
+
+   tsdf.agg(np.sum)
+
+   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
+
+   tsdf.A.agg('sum')
+
+
+Aggregating multiple functions at once
+++++++++++++++++++++++++++++++++++++++
+
+You can pass arguments as a list. The results of each of the passed functions will be a row in the resultant DataFrame.
+These are naturally named from the aggregation function.
+
+.. ipython:: python
+
+   tsdf.agg(['sum'])
+
+Multiple functions yield multiple rows.
 
-   The section on :ref:`GroupBy <groupby>` demonstrates related, flexible
-   functionality for grouping by some criterion, applying, and combining the
-   results into a Series, DataFrame, etc.
+.. ipython:: python
+
+   tsdf.agg(['sum', 'mean'])
+
+On a Series, multiple functions return a Series, indexed by the function names.
+
+.. ipython:: python
+
+   tsdf.A.agg(['sum', 'mean'])
+
+
+Aggregating with a dict of functions
+++++++++++++++++++++++++++++++++++++
+
+Passing a dictionary of column name to function or list of functions, to ``DataFame.agg``
+allows you to customize which functions are applied to which columns.
+
+.. ipython:: python
+
+   tsdf.agg({'A': 'mean', 'B': 'sum'})
+
+Passing a list-like will generate a DataFrame output. You will get a matrix-like output
+of all of the aggregators; some may be missing values.
+
+.. ipython:: python
+
+   tsdf.agg({'A': ['mean', 'min'], 'B': 'sum'})
 
-.. _Elementwise:
+For a Series, you can pass a dict. You will get back a MultiIndex Series; The outer level will
+be the keys, the inner the name of the functions.
+
+.. ipython:: python
+
+   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 a MultiIndex Series. The outer level will be the keys, the inner
+the names of the functions.
+
+.. ipython:: python
+
+    tsdf.A.agg({'foo': ['sum', 'mean'], 'bar': ['min', 'max', lambda x: x.sum()+1]})
+
+.. _basics.aggregation.mixed_dtypes:
+
+Mixed Dtypes
+++++++++++++
+
+When presented with mixed dtypes that cannot aggregate, ``.agg`` will only take the valid
+aggregations. This is similiar to how groupby ``.agg`` works.
+
+.. ipython:: python
 
-Applying elementwise Python functions
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+   mdf = pd.DataFrame({'A': [1, 2, 3],
+                       'B': [1., 2., 3.],
+                       'C': ['foo', 'bar', 'baz'],
+                       'D': pd.date_range('20130101', periods=3)})
+   mdf.dtypes
+
+.. ipython:: python
+
+   mdf.agg(['min', 'sum'])
+
+.. _basics.aggregation.custom_describe:
+
+Custom describe
++++++++++++++++
+
+With ``.agg()`` is it possible to easily create a custom describe function, similar
+to the built in :ref:`describe function <basics.describe>`.
+
+.. ipython:: python
+
+   from functools import partial
+
+   q_25 = partial(pd.Series.quantile, q=0.25)
+   q_25.__name__ = '25%'
+   q_75 = partial(pd.Series.quantile, q=0.75)
+   q_75.__name__ = '75%'
+
+   tsdf.agg(['count', 'mean', 'std', 'min', q_25, 'median', q_75, 'max'])
+
+.. _basics.transform:
+
+Transform API
+~~~~~~~~~~~~~
+
+.. versionadded:: 0.20.0
+
+The ``transform`` method returns an object that is indexed the same (same size)
+as the original. This API allows you to provide *multiple* operations at the same
+time rather than one-by-one. Its api is quite similar to the ``.agg`` API.
+
+Use a similar frame to the above sections.
+
+.. ipython:: python
+
+   tsdf = pd.DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C'],
+                       index=pd.date_range('1/1/2000', periods=10))
+   tsdf.iloc[3:7] = np.nan
+   tsdf
+
+Transform the entire frame. Transform allows functions to input as a numpy function, string
+function name and user defined function.
+
+.. ipython:: python
+
+   tsdf.transform(np.abs)
+   tsdf.transform('abs')
+   tsdf.transform(lambda x: x.abs())
+
+Since this is a single function, this is equivalent to a ufunc application
+
+.. ipython:: python
+
+   np.abs(tsdf)
+
+Passing a single function to ``.transform()`` with a Series will yield a single Series in return.
+
+.. ipython:: python
+
+   tsdf.A.transform(np.abs)
+
+
+Transform with multiple functions
++++++++++++++++++++++++++++++++++
+
+Passing multiple functions will yield a column multi-indexed DataFrame.
+The first level will be the original frame column names; the second level
+will be the names of the transforming functions.
+
+.. ipython:: python
+
+   tsdf.transform([np.abs, lambda x: x+1])
+
+Passing multiple functions to a Series will yield a DataFrame. The
+resulting column names will be the transforming functions.
+
+.. ipython:: python
+
+   tsdf.A.transform([np.abs, lambda x: x+1])
+
+
+Transforming with a dict of functions
++++++++++++++++++++++++++++++++++++++
+
+
+Passing a dict of functions will will allow selective transforming per column.
+
+.. ipython:: python
+
+   tsdf.transform({'A': np.abs, 'B': lambda x: x+1})
+
+Passing a dict of lists will generate a multi-indexed DataFrame with these
+selective transforms.
+
+.. ipython:: python
+
+   tsdf.transform({'A': np.abs, 'B': [lambda x: x+1, 'sqrt']})
+
+On a Series, passing a dict allows renaming as in ``.agg()``
+
+.. ipython:: python
+
+   tsdf.A.transform({'foo': np.abs})
+   tsdf.A.transform({'foo': np.abs, 'bar': [lambda x: x+1, 'sqrt']})
+
+
+.. _basics.elementwise:
+
+Applying Elementwise Functions
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Since not all functions can be vectorized (accept NumPy arrays and return
 another array or value), the methods :meth:`~DataFrame.applymap` on DataFrame

doc/source/computation.rst

@@ -617,7 +617,9 @@ Aggregation
 -----------
 
 Once the ``Rolling``, ``Expanding`` or ``EWM`` objects have been created, several methods are available to
-perform multiple computations on the data. This is very similar to a ``.groupby(...).agg`` seen :ref:`here <groupby.aggregate>`.
+perform multiple computations on the data. These operations are similar to the :ref:`aggregating API <basics.aggregate>`,
+:ref:`groupby aggregates <groupby.aggregate>`, and :ref:`resample API <timeseries.aggregate>`.
+
 
 .. ipython:: python
 

doc/source/groupby.rst

@@ -439,7 +439,9 @@ Aggregation
 -----------
 
 Once the GroupBy object has been created, several methods are available to
-perform a computation on the grouped data.
+perform a computation on the grouped data. These operations are similar to the
+:ref:`aggregating API <basics.aggregate>`, :ref:`window functions <stats.aggregate>`,
+and :ref:`resample API <timeseries.aggregate>`.
 
 An obvious one is aggregation via the ``aggregate`` or equivalently ``agg`` method:
 

doc/source/timeseries.rst

@@ -1519,11 +1519,13 @@ We can instead only resample those groups where we have points as follows:
 
     ts.groupby(partial(round, freq='3T')).sum()
 
+.. _timeseries.aggregate:
+
 Aggregation
 ~~~~~~~~~~~
 
-Similar to :ref:`groupby aggregates <groupby.aggregate>` and the :ref:`window functions <stats.aggregate>`, a ``Resampler`` can be selectively
-resampled.
+Similar to the :ref:`aggregating API <basics.aggregate>`, :ref:`groupby aggregates <groupby.aggregate>`, and :ref:`window functions <stats.aggregate>`,
+a ``Resampler`` can be selectively resampled.
 
 Resampling a ``DataFrame``, the default will be to act on all columns with the same function.