ENH: rolling_/expanding_apply take args, kwargs for func

DOC: Add rolling_apply/expanding_apply ENH to release notes.

BUG: Use dummy args to accomodate most general case.

Author: jseabold

doc/source/release.rst

@@ -65,6 +65,8 @@ Improvements to existing features
 
 - pd.read_clipboard will, if 'sep' is unspecified, try to detect data copied from a spreadsheet
   and parse accordingly. (:issue:`6223`)
+- pd.expanding_apply and pd.rolling_apply now take args and kwargs that are passed on to
+  the func (:issue:`6289`)
 
 .. _release.bug_fixes-0.14.0:
 

pandas/algos.pyx

@@ -1627,7 +1627,7 @@ def roll_quantile(ndarray[float64_t, cast=True] input, int win,
     return output
 
 def roll_generic(ndarray[float64_t, cast=True] input, int win,
-                 int minp, object func):
+                 int minp, object func, object args, object kwargs):
     cdef ndarray[double_t] output, counts, bufarr
     cdef Py_ssize_t i, n
     cdef float64_t *buf
@@ -1652,15 +1652,16 @@ def roll_generic(ndarray[float64_t, cast=True] input, int win,
     n = len(input)
     for i from 0 <= i < int_min(win, n):
         if counts[i] >= minp:
-            output[i] = func(input[int_max(i - win + 1, 0) : i + 1])
+            output[i] = func(input[int_max(i - win + 1, 0) : i + 1], *args,
+                             **kwargs)
         else:
             output[i] = NaN
 
     for i from win <= i < n:
         buf = buf + 1
         bufarr.data = <char*> buf
         if counts[i] >= minp:
-            output[i] = func(bufarr)
+            output[i] = func(bufarr, *args, **kwargs)
         else:
             output[i] = NaN
 

pandas/stats/moments.py

@@ -141,7 +141,7 @@ def rolling_count(arg, window, freq=None, center=False, time_rule=None):
     center : boolean, default False
         Whether the label should correspond with center of window
     time_rule : Legacy alias for freq
-    
+
     Returns
     -------
     rolling_count : type of caller
@@ -255,8 +255,8 @@ def rolling_corr_pairwise(df, window, min_periods=None):
     return Panel.from_dict(all_results).swapaxes('items', 'major')
 
 
-def _rolling_moment(arg, window, func, minp, axis=0, freq=None,
-                    center=False, time_rule=None, **kwargs):
+def _rolling_moment(arg, window, func, minp, axis=0, freq=None, center=False,
+                    time_rule=None, args=(), kwargs={}, **kwds):
     """
     Rolling statistical measure using supplied function. Designed to be
     used with passed-in Cython array-based functions.
@@ -274,13 +274,18 @@ def _rolling_moment(arg, window, func, minp, axis=0, freq=None,
     center : boolean, default False
         Whether the label should correspond with center of window
     time_rule : Legacy alias for freq
-    
+    args : tuple
+        Passed on to func
+    kwargs : dict
+        Passed on to func
+
     Returns
     -------
     y : type of input
     """
     arg = _conv_timerule(arg, freq, time_rule)
-    calc = lambda x: func(x, window, minp=minp, **kwargs)
+    calc = lambda x: func(x, window, minp=minp, args=args, kwargs=kwargs,
+                          **kwds)
     return_hook, values = _process_data_structure(arg)
     # actually calculate the moment. Faster way to do this?
     if values.ndim > 1:
@@ -509,7 +514,7 @@ def _rolling_func(func, desc, check_minp=_use_window):
     @wraps(func)
     def f(arg, window, min_periods=None, freq=None, center=False,
           time_rule=None, **kwargs):
-        def call_cython(arg, window, minp, **kwds):
+        def call_cython(arg, window, minp, args=(), kwargs={}, **kwds):
             minp = check_minp(minp, window)
             return func(arg, window, minp, **kwds)
         return _rolling_moment(arg, window, call_cython, min_periods,
@@ -551,21 +556,21 @@ def rolling_quantile(arg, window, quantile, min_periods=None, freq=None,
     center : boolean, default False
         Whether the label should correspond with center of window
     time_rule : Legacy alias for freq
-    
+
     Returns
     -------
     y : type of input argument
     """
 
-    def call_cython(arg, window, minp):
+    def call_cython(arg, window, minp, args=(), kwargs={}):
         minp = _use_window(minp, window)
         return algos.roll_quantile(arg, window, minp, quantile)
     return _rolling_moment(arg, window, call_cython, min_periods,
                            freq=freq, center=center, time_rule=time_rule)
 
 
 def rolling_apply(arg, window, func, min_periods=None, freq=None,
-                  center=False, time_rule=None):
+                  center=False, time_rule=None, args=(), kwargs={}):
     """Generic moving function application
 
     Parameters
@@ -581,16 +586,21 @@ def rolling_apply(arg, window, func, min_periods=None, freq=None,
     center : boolean, default False
         Whether the label should correspond with center of window
     time_rule : Legacy alias for freq
-    
+    args : tuple
+        Passed on to func
+    kwargs : dict
+        Passed on to func
+
     Returns
     -------
     y : type of input argument
     """
-    def call_cython(arg, window, minp):
+    def call_cython(arg, window, minp, args, kwargs):
         minp = _use_window(minp, window)
-        return algos.roll_generic(arg, window, minp, func)
+        return algos.roll_generic(arg, window, minp, func, args, kwargs)
     return _rolling_moment(arg, window, call_cython, min_periods,
-                           freq=freq, center=center, time_rule=time_rule)
+                           freq=freq, center=center, time_rule=time_rule,
+                           args=args, kwargs=kwargs)
 
 
 def rolling_window(arg, window=None, win_type=None, min_periods=None,
@@ -618,7 +628,7 @@ def rolling_window(arg, window=None, win_type=None, min_periods=None,
         If True computes weighted mean, else weighted sum
     time_rule : Legacy alias for freq
     axis : {0, 1}, default 0
-    
+
     Returns
     -------
     y : type of input argument
@@ -703,7 +713,7 @@ def f(arg, min_periods=1, freq=None, center=False, time_rule=None,
           **kwargs):
         window = len(arg)
 
-        def call_cython(arg, window, minp, **kwds):
+        def call_cython(arg, window, minp, args=(), kwargs={}, **kwds):
             minp = check_minp(minp, window)
             return func(arg, window, minp, **kwds)
         return _rolling_moment(arg, window, call_cython, min_periods,
@@ -744,7 +754,7 @@ def expanding_count(arg, freq=None, center=False, time_rule=None):
     center : boolean, default False
         Whether the label should correspond with center of window
     time_rule : Legacy alias for freq
-    
+
     Returns
     -------
     expanding_count : type of caller
@@ -768,7 +778,7 @@ def expanding_quantile(arg, quantile, min_periods=1, freq=None,
     center : boolean, default False
         Whether the label should correspond with center of window
     time_rule : Legacy alias for freq
-    
+
     Returns
     -------
     y : type of input argument
@@ -818,7 +828,7 @@ def expanding_corr_pairwise(df, min_periods=1):
 
 
 def expanding_apply(arg, func, min_periods=1, freq=None, center=False,
-                    time_rule=None):
+                    time_rule=None, args=(), kwargs={}):
     """Generic expanding function application
 
     Parameters
@@ -833,11 +843,16 @@ def expanding_apply(arg, func, min_periods=1, freq=None, center=False,
     center : boolean, default False
         Whether the label should correspond with center of window
     time_rule : Legacy alias for freq
-    
+    args : tuple
+        Passed on to func
+    kwargs : dict
+        Passed on to func
+
     Returns
     -------
     y : type of input argument
     """
     window = len(arg)
     return rolling_apply(arg, window, func, min_periods=min_periods, freq=freq,
-                         center=center, time_rule=time_rule)
+                         center=center, time_rule=time_rule, args=args,
+                         kwargs=kwargs)

pandas/stats/tests/test_moments.py

@@ -694,6 +694,21 @@ def expanding_mean(x, min_periods=1, freq=None):
                                        freq=freq)
         self._check_expanding(expanding_mean, np.mean)
 
+    def test_expanding_apply_args_kwargs(self):
+        def mean_w_arg(x, const):
+            return np.mean(x) + const
+
+        df = DataFrame(np.random.rand(20, 3))
+
+        expected = mom.expanding_apply(df, np.mean) + 20.
+
+        assert_frame_equal(mom.expanding_apply(df, mean_w_arg, args=(20,)),
+                            expected)
+        assert_frame_equal(mom.expanding_apply(df, mean_w_arg,
+                                               kwargs={'const' : 20}),
+                            expected)
+
+
     def test_expanding_corr(self):
         A = self.series.dropna()
         B = (A + randn(len(A)))[:-5]