make grouping column an agg

pandas/core/groupby.py

@@ -673,9 +673,17 @@ def var(self, ddof=1):
     def size(self):
         """
         Compute group sizes
+
         """
         return self.grouper.size()
 
+    def count(self):
+        """
+        Number of non-null items in each group.
+
+        """
+        return self._python_agg_general(lambda x: notnull(x).sum())
+
     sum = _groupby_function('sum', 'add', np.sum)
     prod = _groupby_function('prod', 'prod', np.prod)
     min = _groupby_function('min', 'min', np.min, numeric_only=False)
@@ -687,12 +695,10 @@ def size(self):
 
     def ohlc(self):
         """
-        Compute sum of values, excluding missing values
-
-        For multiple groupings, the result index will be a MultiIndex
+        Deprecated, use .resample(how="ohlc") instead.
 
         """
-        return self._cython_agg_general('ohlc')
+        raise AttributeError('ohlc is deprecated, use resample(how="ohlc").')
 
     def nth(self, n, dropna=None):
         """
@@ -939,6 +945,7 @@ def _cython_agg_general(self, how, numeric_only=True):
                 result, names = self.grouper.aggregate(obj.values, how)
             except AssertionError as e:
                 raise GroupByError(str(e))
+            # infer old dytpe
             output[name] = self._try_cast(result, obj)
 
         if len(output) == 0:
@@ -947,6 +954,8 @@ def _cython_agg_general(self, how, numeric_only=True):
         return self._wrap_aggregated_output(output, names)
 
     def _python_agg_general(self, func, *args, **kwargs):
+        _dtype = kwargs.pop("_dtype", None)
+
         func = _intercept_function(func)
         f = lambda x: func(x, *args, **kwargs)
 
@@ -955,7 +964,14 @@ def _python_agg_general(self, func, *args, **kwargs):
         for name, obj in self._iterate_slices():
             try:
                 result, counts = self.grouper.agg_series(obj, f)
-                output[name] = self._try_cast(result, obj)
+
+                if _dtype is None:  # infer old dytpe
+                    output[name] = self._try_cast(result, obj)
+                elif _dtype is False:
+                    output[name] = result
+                else:
+                    output[name] = _possibly_downcast_to_dtype(result, _dtype)
+
             except TypeError:
                 continue
 
@@ -2889,16 +2905,6 @@ def _apply_to_column_groupbys(self, func):
              in self._iterate_column_groupbys()),
             keys=self._selected_obj.columns, axis=1)
 
-    def ohlc(self):
-        """
-        Compute sum of values, excluding missing values
-
-        For multiple groupings, the result index will be a MultiIndex
-        """
-        return self._apply_to_column_groupbys(
-            lambda x: x._cython_agg_general('ohlc'))
-
-
 from pandas.tools.plotting import boxplot_frame_groupby
 DataFrameGroupBy.boxplot = boxplot_frame_groupby
 

pandas/tests/test_groupby.py

@@ -1970,6 +1970,18 @@ def test_size(self):
         for key, group in grouped:
             self.assertEquals(result[key], len(group))
 
+    def test_count(self):
+        df = pd.DataFrame([[1, 2], [1, nan], [3, nan]], columns=['A', 'B'])
+        count_as = df.groupby('A').count()
+        count_not_as = df.groupby('A', as_index=False).count()
+
+        res = pd.DataFrame([[1, 1], [3, 0]], columns=['A', 'B'])
+        assert_frame_equal(count_not_as, res)
+        assert_frame_equal(count_as, res.set_index('A'))
+
+        count_B = df.groupby('A')['B'].count()
+        assert_series_equal(count_B, res['B'])
+
     def test_grouping_ndarray(self):
         grouped = self.df.groupby(self.df['A'].values)