ENH: Support all Scipy window types in rolling(..., win_type)

doc/source/user_guide/computation.rst

@@ -451,6 +451,10 @@ The list of recognized types are the `scipy.signal window functions
 * ``slepian`` (needs width)
 * ``exponential`` (needs tau).
 
+.. versionadded:: 1.2.0
+
+All Scipy window types, concurrent with your installed version, are recognized ``win_types``.
+
 .. ipython:: python
 
    ser = pd.Series(np.random.randn(10), index=pd.date_range("1/1/2000", periods=10))

doc/source/whatsnew/v1.2.0.rst

@@ -226,6 +226,7 @@ Other enhancements
 - :meth:`DataFrame.to_parquet` now returns a ``bytes`` object when no ``path`` argument is passed (:issue:`37105`)
 - :class:`Rolling` now supports the ``closed`` argument for fixed windows (:issue:`34315`)
 - :class:`DatetimeIndex` and :class:`Series` with ``datetime64`` or ``datetime64tz`` dtypes now support ``std`` (:issue:`37436`)
+- :class:`Window` now supports all Scipy window types in ``win_type`` with flexible keyword argument support (:issue:`34556`)
 
 .. _whatsnew_120.api_breaking.python:
 

pandas/core/window/rolling.py

@@ -873,30 +873,14 @@ class Window(BaseWindow):
     To learn more about the offsets & frequency strings, please see `this link
     <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
 
-    The recognized win_types are:
-
-    * ``boxcar``
-    * ``triang``
-    * ``blackman``
-    * ``hamming``
-    * ``bartlett``
-    * ``parzen``
-    * ``bohman``
-    * ``blackmanharris``
-    * ``nuttall``
-    * ``barthann``
-    * ``kaiser`` (needs parameter: beta)
-    * ``gaussian`` (needs parameter: std)
-    * ``general_gaussian`` (needs parameters: power, width)
-    * ``slepian`` (needs parameter: width)
-    * ``exponential`` (needs parameter: tau), center is set to None.
-
-    If ``win_type=None`` all points are evenly weighted. To learn more about
-    different window types see `scipy.signal window functions
+    If ``win_type=None``, all points are evenly weighted; otherwise, ``win_type``
+    can accept a string of any `scipy.signal window function
     <https://docs.scipy.org/doc/scipy/reference/signal.windows.html#module-scipy.signal.windows>`__.
 
-    Certain window types require additional parameters to be passed. Please see
-    the third example below on how to add the additional parameters.
+    Certain Scipy window types require additional parameters to be passed
+    in the aggregation function. The additional parameters must match
+    the keywords specified in the Scipy window type method signature.
+    Please see the third example below on how to add the additional parameters.
 
     Examples
     --------
@@ -1000,71 +984,22 @@ def _constructor(self):
     def validate(self):
         super().validate()
 
-        window = self.window
-        if isinstance(window, BaseIndexer):
+        if isinstance(self.window, BaseIndexer):
             raise NotImplementedError(
                 "BaseIndexer subclasses not implemented with win_types."
             )
-        elif isinstance(window, (list, tuple, np.ndarray)):
-            pass
-        elif is_integer(window):
-            if window <= 0:
+        elif is_integer(self.window):
+            if self.window <= 0:
                 raise ValueError("window must be > 0 ")
-            import_optional_dependency(
-                "scipy", extra="Scipy is required to generate window weight."
+            sig = import_optional_dependency(
+                "scipy.signal", extra="Scipy is required to generate window weight."
             )
-            import scipy.signal as sig
-
             if not isinstance(self.win_type, str):
                 raise ValueError(f"Invalid win_type {self.win_type}")
             if getattr(sig, self.win_type, None) is None:
                 raise ValueError(f"Invalid win_type {self.win_type}")
         else:
-            raise ValueError(f"Invalid window {window}")
-
-    def _get_win_type(self, kwargs: Dict[str, Any]) -> Union[str, Tuple]:
-        """
-        Extract arguments for the window type, provide validation for it
-        and return the validated window type.
-
-        Parameters
-        ----------
-        kwargs : dict
-
-        Returns
-        -------
-        win_type : str, or tuple
-        """
-        # the below may pop from kwargs
-        def _validate_win_type(win_type, kwargs):
-            arg_map = {
-                "kaiser": ["beta"],
-                "gaussian": ["std"],
-                "general_gaussian": ["power", "width"],
-                "slepian": ["width"],
-                "exponential": ["tau"],
-            }
-
-            if win_type in arg_map:
-                win_args = _pop_args(win_type, arg_map[win_type], kwargs)
-                if win_type == "exponential":
-                    # exponential window requires the first arg (center)
-                    # to be set to None (necessary for symmetric window)
-                    win_args.insert(0, None)
-
-                return tuple([win_type] + win_args)
-
-            return win_type
-
-        def _pop_args(win_type, arg_names, kwargs):
-            all_args = []
-            for n in arg_names:
-                if n not in kwargs:
-                    raise ValueError(f"{win_type} window requires {n}")
-                all_args.append(kwargs.pop(n))
-            return all_args
-
-        return _validate_win_type(self.win_type, kwargs)
+            raise ValueError(f"Invalid window {self.window}")
 
     def _center_window(self, result: np.ndarray, offset: int) -> np.ndarray:
         """
@@ -1079,31 +1014,6 @@ def _center_window(self, result: np.ndarray, offset: int) -> np.ndarray:
             result = np.copy(result[tuple(lead_indexer)])
         return result
 
-    def _get_window_weights(
-        self, win_type: Optional[Union[str, Tuple]] = None
-    ) -> np.ndarray:
-        """
-        Get the window, weights.
-
-        Parameters
-        ----------
-        win_type : str, or tuple
-            type of window to create
-
-        Returns
-        -------
-        window : ndarray
-            the window, weights
-        """
-        window = self.window
-        if isinstance(window, (list, tuple, np.ndarray)):
-            return com.asarray_tuplesafe(window).astype(float)
-        elif is_integer(window):
-            import scipy.signal as sig
-
-            # GH #15662. `False` makes symmetric window, rather than periodic.
-            return sig.get_window(win_type, window, False).astype(float)
-
     def _apply(
         self,
         func: Callable[[np.ndarray, int, int], np.ndarray],
@@ -1124,14 +1034,17 @@ def _apply(
             whether to cache a numba compiled function. Only available for numba
             enabled methods (so far only apply)
         **kwargs
-            additional arguments for rolling function and window function
+            additional arguments for scipy windows if necessary
 
         Returns
         -------
         y : type of input
         """
-        win_type = self._get_win_type(kwargs)
-        window = self._get_window_weights(win_type=win_type)
+        signal = import_optional_dependency(
+            "scipy.signal", extra="Scipy is required to generate window weight."
+        )
+        assert self.win_type is not None  # for mypy
+        window = getattr(signal, self.win_type)(self.window, **kwargs)
         offset = (len(window) - 1) // 2 if self.center else 0
 
         def homogeneous_func(values: np.ndarray):

pandas/tests/window/moments/test_moments_rolling.py

@@ -431,7 +431,7 @@ def test_cmov_window_special(win_types_special):
     kwds = {
         "kaiser": {"beta": 1.0},
         "gaussian": {"std": 1.0},
-        "general_gaussian": {"power": 2.0, "width": 2.0},
+        "general_gaussian": {"p": 2.0, "sig": 2.0},
         "exponential": {"tau": 10},
     }
 
@@ -503,7 +503,7 @@ def test_cmov_window_special_linear_range(win_types_special):
     kwds = {
         "kaiser": {"beta": 1.0},
         "gaussian": {"std": 1.0},
-        "general_gaussian": {"power": 2.0, "width": 2.0},
+        "general_gaussian": {"p": 2.0, "sig": 2.0},
         "slepian": {"width": 0.5},
         "exponential": {"tau": 10},
     }

pandas/tests/window/test_window.py

@@ -6,7 +6,6 @@
 
 import pandas as pd
 from pandas import Series
-from pandas.core.window import Window
 
 
 @td.skip_if_no_scipy
@@ -50,7 +49,7 @@ def test_constructor_with_win_type(which, win_types):
 @pytest.mark.parametrize("method", ["sum", "mean"])
 def test_numpy_compat(method):
     # see gh-12811
-    w = Window(Series([2, 4, 6]), window=[0, 2])
+    w = Series([2, 4, 6]).rolling(window=2)
 
     msg = "numpy operations are not valid with window objects"
 
@@ -75,3 +74,11 @@ def test_agg_function_support(arg):
 
     with pytest.raises(AttributeError, match=msg):
         roll.agg({"A": arg})
+
+
+@td.skip_if_no_scipy
+def test_invalid_scipy_arg():
+    # This error is raised by scipy
+    msg = r"boxcar\(\) got an unexpected"
+    with pytest.raises(TypeError, match=msg):
+        Series(range(3)).rolling(1, win_type="boxcar").mean(foo="bar")