Groupbydocs

doc/source/api.rst

@@ -1394,12 +1394,80 @@ Computations / Descriptive Stats
 .. autosummary::
    :toctree: generated/
 
+   GroupBy.count
+   GroupBy.cumcount
+   GroupBy.first
+   GroupBy.head
+   GroupBy.last
+   GroupBy.max
    GroupBy.mean
    GroupBy.median
+   GroupBy.min
+   GroupBy.nth 
+   GroupBy.ohlc
+   GroupBy.prod
+   GroupBy.size
    GroupBy.sem
    GroupBy.std
+   GroupBy.sum
    GroupBy.var
-   GroupBy.ohlc
+   GroupBy.tail
+
+The following methods are available in both ``SeriesGroupBy`` and
+``DataFrameGroupBy`` objects, but may differ slightly, usually in that
+the ``DataFrameGroupBy`` version usually permits the specification of an
+axis argument, and often an argument indicating whether to restrict
+application to columns of a specific data type.
+
+.. autosummary::
+   :toctree: generated/
+
+   DataFrameGroupBy.bfill
+   DataFrameGroupBy.cummax
+   DataFrameGroupBy.cummin
+   DataFrameGroupBy.cumprod
+   DataFrameGroupBy.cumsum
+   DataFrameGroupBy.describe
+   DataFrameGroupBy.all
+   DataFrameGroupBy.any
+   DataFrameGroupBy.corr
+   DataFrameGroupBy.cov
+   DataFrameGroupBy.diff
+   DataFrameGroupBy.ffill
+   DataFrameGroupBy.fillna
+   DataFrameGroupBy.hist
+   DataFrameGroupBy.idxmax
+   DataFrameGroupBy.idxmin
+   DataFrameGroupBy.irow
+   DataFrameGroupBy.mad
+   DataFrameGroupBy.pct_change
+   DataFrameGroupBy.plot
+   DataFrameGroupBy.quantile
+   DataFrameGroupBy.rank
+   DataFrameGroupBy.resample
+   DataFrameGroupBy.shift
+   DataFrameGroupBy.skew
+   DataFrameGroupBy.take
+   DataFrameGroupBy.tshift
+
+The following methods are available only for ``SeriesGroupBy`` objects.
+
+.. autosummary::
+   :toctree: generated/
+
+   SeriesGroupBy.nlargest
+   SeriesGroupBy.nsmallest
+   SeriesGroupBy.nunique
+   SeriesGroupBy.unique
+   SeriesGroupBy.value_counts
+
+The following methods are available only for ``DataFrameGroupBy`` objects.
+
+.. autosummary::
+   :toctree: generated/
+
+   DataFrameGroupBy.corrwith
+   DataFrameGroupBy.boxplot
 
 .. currentmodule:: pandas
 

pandas/core/groupby.py

@@ -18,7 +18,7 @@
 from pandas.core.internals import BlockManager, make_block
 from pandas.core.series import Series
 from pandas.core.panel import Panel
-from pandas.util.decorators import cache_readonly, Appender
+from pandas.util.decorators import cache_readonly, Appender, make_signature
 import pandas.core.algorithms as algos
 import pandas.core.common as com
 from pandas.core.common import(_possibly_downcast_to_dtype, isnull,
@@ -533,7 +533,7 @@ def wrapper(*args, **kwargs):
             # a little trickery for aggregation functions that need an axis
             # argument
             kwargs_with_axis = kwargs.copy()
-            if 'axis' not in kwargs_with_axis:
+            if 'axis' not in kwargs_with_axis or kwargs_with_axis['axis']==None:
                 kwargs_with_axis['axis'] = self.axis
 
             def curried_with_axis(x):
@@ -2133,9 +2133,73 @@ def _convert_grouper(axis, grouper):
     else:
         return grouper
 
+def _whitelist_method_generator(klass, whitelist) :
+    """
+    Yields all GroupBy member defs for DataFrame/Series names in _whitelist.
+
+    Parameters
+    ----------
+    klass - class where members are defined.  Should be Series or DataFrame
+
+    whitelist - list of names of klass methods to be constructed
+
+    Returns
+    -------
+    The generator yields a sequence of strings, each suitable for exec'ing,
+    that define implementations of the named methods for DataFrameGroupBy
+    or SeriesGroupBy.
+
+    Since we don't want to override methods explicitly defined in the
+    base class, any such name is skipped.
+    """
+
+    method_wrapper_template = \
+    """def %(name)s(%(sig)s) :
+    \"""
+    %(doc)s
+    \"""
+    f = %(self)s.__getattr__('%(name)s')
+    return f(%(args)s)"""
+    property_wrapper_template = \
+    """@property
+def %(name)s(self) :
+    \"""
+    %(doc)s
+    \"""
+    return self.__getattr__('%(name)s')"""
+    for name in whitelist :
+        # don't override anything that was explicitly defined
+        # in the base class
+        if hasattr(GroupBy,name) :
+            continue
+        # ugly, but we need the name string itself in the method.
+        f = getattr(klass,name)
+        doc = f.__doc__
+        doc = doc if type(doc)==str else ''
+        if type(f) == types.MethodType :
+            wrapper_template = method_wrapper_template
+            decl, args = make_signature(f)
+            # pass args by name to f because otherwise
+            # GroupBy._make_wrapper won't know whether
+            # we passed in an axis parameter.
+            args_by_name = ['{0}={0}'.format(arg) for arg in args[1:]]
+            params = {'name':name,
+                      'doc':doc,
+                      'sig':','.join(decl),
+                      'self':args[0],
+                      'args':','.join(args_by_name)}
+        else :
+            wrapper_template = property_wrapper_template
+            params = {'name':name, 'doc':doc}
+        yield wrapper_template % params
+
 class SeriesGroupBy(GroupBy):
+    #
+    # Make class defs of attributes on SeriesGroupBy whitelist
     _apply_whitelist = _series_apply_whitelist
-
+    for _def_str in _whitelist_method_generator(Series,_series_apply_whitelist) :
+        exec(_def_str)
+
     def aggregate(self, func_or_funcs, *args, **kwargs):
         """
         Apply aggregation function or functions to groups, yielding most likely
@@ -3045,6 +3109,10 @@ def filter(self, func, dropna=True, *args, **kwargs):
 
 class DataFrameGroupBy(NDFrameGroupBy):
     _apply_whitelist = _dataframe_apply_whitelist
+    #
+    # Make class defs of attributes on DataFrameGroupBy whitelist.
+    for _def_str in _whitelist_method_generator(DataFrame,_apply_whitelist) :
+        exec(_def_str)
 
     _block_agg_axis = 1
 

pandas/tests/test_groupby.py

@@ -19,7 +19,7 @@
                                  assert_index_equal, assertRaisesRegexp)
 from pandas.compat import(
     range, long, lrange, StringIO, lmap, lzip, map,
-    zip, builtins, OrderedDict
+    zip, builtins, OrderedDict, product as cart_product
 )
 from pandas import compat
 from pandas.core.panel import Panel
@@ -4327,7 +4327,43 @@ def test_groupby_whitelist(self):
             gb = obj.groupby(df.letters)
             self.assertEqual(whitelist, gb._apply_whitelist)
             for m in whitelist:
-                getattr(gb, m)
+                getattr(type(gb), m)
+
+    AGG_FUNCTIONS = ['sum', 'prod', 'min', 'max', 'median', 'mean', 'skew',
+                     'mad', 'std', 'var', 'sem']
+    AGG_FUNCTIONS_WITH_SKIPNA = ['skew', 'mad']
+
+    def test_regression_whitelist_methods(self) :
+
+        index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
+                                   ['one', 'two', 'three']],
+                           labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
+                                   [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
+                           names=['first', 'second'])
+        raw_frame = DataFrame(np.random.randn(10, 3), index=index,
+                               columns=Index(['A', 'B', 'C'], name='exp'))
+        raw_frame.ix[1, [1, 2]] = np.nan
+        raw_frame.ix[7, [0, 1]] = np.nan
+
+        for op, level, axis, skipna in cart_product(self.AGG_FUNCTIONS,
+                                                    lrange(2), lrange(2),
+                                                    [True,False]) :
+
+            if axis == 0 :
+                frame = raw_frame
+            else :
+                frame = raw_frame.T
+
+            if op in self.AGG_FUNCTIONS_WITH_SKIPNA :
+                grouped = frame.groupby(level=level,axis=axis)
+                result = getattr(grouped,op)(skipna=skipna)
+                expected = getattr(frame,op)(level=level,axis=axis,skipna=skipna)
+                assert_frame_equal(result, expected)
+            else :
+                grouped = frame.groupby(level=level,axis=axis)
+                result = getattr(grouped,op)()
+                expected = getattr(frame,op)(level=level,axis=axis)
+                assert_frame_equal(result, expected)
 
     def test_groupby_blacklist(self):
         from string import ascii_lowercase

pandas/util/decorators.py

@@ -258,3 +258,29 @@ def knownfailer(*args, **kwargs):
         return nose.tools.make_decorator(f)(knownfailer)
 
     return knownfail_decorator
+
+def make_signature(func) :
+    """
+    Returns a string repr of the arg list of a func call, with any defaults
+
+    Examples
+    --------
+
+    >>> def f(a,b,c=2) :
+    >>>     return a*b*c
+    >>> print(_make_signature(f))
+    a,b,c=2
+    """
+    from inspect import getargspec
+    spec = getargspec(func)
+    if spec.defaults == None :
+        n_wo_defaults = len(spec.args)
+        defaults = ('',) * n_wo_defaults
+    else :
+        n_wo_defaults = len(spec.args) - len(spec.defaults)
+        defaults = ('',) * n_wo_defaults + spec.defaults
+    args = []
+    for i, (var, default) in enumerate(zip(spec.args, defaults)) :
+        args.append(var if default=='' else var+'='+repr(default))
+    return args, spec.args
+