Allow passing of positional arguments in apply for Groupby objects

doc/whats-new.rst

@@ -66,6 +66,10 @@ Enhancements
 - :py:meth:`DataArray.resample` and :py:meth:`Dataset.resample` now supports the
   ``loffset`` kwarg just like Pandas.
   By `Deepak Cherian <https://github.com/dcherian>`_
+- The `apply` methods for `DatasetGroupBy`, `DataArrayGroupBy`,
+  `DatasetResample` and `DataArrayResample` can now pass positional arguments to
+  the applied function.
+  By `Matti Eskelinen <https://github.com/maaleske>`_.
 - 0d slices of ndarrays are now obtained directly through indexing, rather than
   extracting and wrapping a scalar, avoiding unnecessary copying. By `Daniel
   Wennberg <https://github.com/danielwe>`_.
@@ -260,13 +264,17 @@ Announcements of note:
   for more details.
 - We have a new :doc:`roadmap` that outlines our future development plans.
 
+- `Dataset.apply` now properly documents the way `func` is called.
+  By `Matti Eskelinen <https://github.com/maaleske>`_.
+
 Enhancements
 ~~~~~~~~~~~~
 
 - :py:meth:`~xarray.DataArray.differentiate` and
   :py:meth:`~xarray.Dataset.differentiate` are newly added.
   (:issue:`1332`)
   By `Keisuke Fujii <https://github.com/fujiisoup>`_.
+
 - Default colormap for sequential and divergent data can now be set via
   :py:func:`~xarray.set_options()`
   (:issue:`2394`)

xarray/core/dataset.py

@@ -2953,8 +2953,8 @@ def apply(self, func, keep_attrs=None, args=(), **kwargs):
         Parameters
         ----------
         func : function
-            Function which can be called in the form `f(x, **kwargs)` to
-            transform each DataArray `x` in this dataset into another
+            Function which can be called in the form `func(x, *args, **kwargs)`
+            to transform each DataArray `x` in this dataset into another
             DataArray.
         keep_attrs : bool, optional
             If True, the dataset's attributes (`attrs`) will be copied from

xarray/core/groupby.py

@@ -503,7 +503,7 @@ def lookup_order(dimension):
         new_order = sorted(stacked.dims, key=lookup_order)
         return stacked.transpose(*new_order)
 
-    def apply(self, func, shortcut=False, **kwargs):
+    def apply(self, func, shortcut=False, args=(), **kwargs):
         """Apply a function over each array in the group and concatenate them
         together into a new array.
 
@@ -532,6 +532,8 @@ def apply(self, func, shortcut=False, **kwargs):
             If these conditions are satisfied `shortcut` provides significant
             speedup. This should be the case for many common groupby operations
             (e.g., applying numpy ufuncs).
+        args : tuple, optional
+            Positional arguments passed to `func`.
         **kwargs
             Used to call `func(ar, **kwargs)` for each array `ar`.
 
@@ -544,7 +546,7 @@ def apply(self, func, shortcut=False, **kwargs):
             grouped = self._iter_grouped_shortcut()
         else:
             grouped = self._iter_grouped()
-        applied = (maybe_wrap_array(arr, func(arr, **kwargs))
+        applied = (maybe_wrap_array(arr, func(arr, *args, **kwargs))
                    for arr in grouped)
         return self._combine(applied, shortcut=shortcut)
 
@@ -642,7 +644,7 @@ def wrapped_func(self, dim=DEFAULT_DIMS, axis=None,
 
 
 class DatasetGroupBy(GroupBy, ImplementsDatasetReduce):
-    def apply(self, func, **kwargs):
+    def apply(self, func, args=(), **kwargs):
         """Apply a function over each Dataset in the group and concatenate them
         together into a new Dataset.
 
@@ -661,6 +663,8 @@ def apply(self, func, **kwargs):
         ----------
         func : function
             Callable to apply to each sub-dataset.
+        args : tuple, optional
+            Positional arguments to pass to `func`.
         **kwargs
             Used to call `func(ds, **kwargs)` for each sub-dataset `ar`.
 
@@ -670,7 +674,7 @@ def apply(self, func, **kwargs):
             The result of splitting, applying and combining this dataset.
         """
         kwargs.pop('shortcut', None)  # ignore shortcut if set (for now)
-        applied = (func(ds, **kwargs) for ds in self._iter_grouped())
+        applied = (func(ds, *args, **kwargs) for ds in self._iter_grouped())
         return self._combine(applied)
 
     def _combine(self, applied):

xarray/core/resample.py

@@ -129,7 +129,7 @@ def __init__(self, *args, **kwargs):
                              "('{}')! ".format(self._resample_dim, self._dim))
         super(DataArrayResample, self).__init__(*args, **kwargs)
 
-    def apply(self, func, shortcut=False, **kwargs):
+    def apply(self, func, shortcut=False, args=(), **kwargs):
         """Apply a function over each array in the group and concatenate them
         together into a new array.
 
@@ -158,6 +158,8 @@ def apply(self, func, shortcut=False, **kwargs):
             If these conditions are satisfied `shortcut` provides significant
             speedup. This should be the case for many common groupby operations
             (e.g., applying numpy ufuncs).
+        args : tuple, optional
+            Positional arguments passed on to `func`.
         **kwargs
             Used to call `func(ar, **kwargs)` for each array `ar`.
 
@@ -167,7 +169,7 @@ def apply(self, func, shortcut=False, **kwargs):
             The result of splitting, applying and combining this array.
         """
         combined = super(DataArrayResample, self).apply(
-            func, shortcut=shortcut, **kwargs)
+            func, shortcut=shortcut, args=args, **kwargs)
 
         # If the aggregation function didn't drop the original resampling
         # dimension, then we need to do so before we can rename the proxy
@@ -240,7 +242,7 @@ def __init__(self, *args, **kwargs):
                              "('{}')! ".format(self._resample_dim, self._dim))
         super(DatasetResample, self).__init__(*args, **kwargs)
 
-    def apply(self, func, **kwargs):
+    def apply(self, func, args=(), **kwargs):
         """Apply a function over each Dataset in the groups generated for
         resampling  and concatenate them together into a new Dataset.
 
@@ -259,6 +261,8 @@ def apply(self, func, **kwargs):
         ----------
         func : function
             Callable to apply to each sub-dataset.
+        args : tuple, optional
+            Positional arguments passed on to `func`.
         **kwargs
             Used to call `func(ds, **kwargs)` for each sub-dataset `ar`.
 
@@ -268,7 +272,7 @@ def apply(self, func, **kwargs):
             The result of splitting, applying and combining this dataset.
         """
         kwargs.pop('shortcut', None)  # ignore shortcut if set (for now)
-        applied = (func(ds, **kwargs) for ds in self._iter_grouped())
+        applied = (func(ds, *args, **kwargs) for ds in self._iter_grouped())
         combined = self._combine(applied)
 
         return combined.rename({self._resample_dim: self._dim})

xarray/tests/test_dataarray.py

@@ -2295,6 +2295,17 @@ def test_resample(self):
         with raises_regex(ValueError, 'index must be monotonic'):
             array[[2, 0, 1]].resample(time='1D')
 
+    def test_da_resample_func_args(self):
+
+        def func(arg1, arg2, arg3=0.):
+            return arg1.mean('time') + arg2 + arg3
+
+        times = pd.date_range('2000', periods=3, freq='D')
+        da = xr.DataArray([1., 1., 1.], coords=[times], dims=['time'])
+        expected = xr.DataArray([3., 3., 3.], coords=[times], dims=['time'])
+        actual = da.resample(time='D').apply(func, args=(1.,), arg3=1.)
+        assert_identical(actual, expected)
+
     @requires_cftime
     def test_resample_cftimeindex(self):
         cftime = _import_cftime()

xarray/tests/test_dataset.py

@@ -2886,6 +2886,19 @@ def test_resample_old_api(self):
         with raises_regex(TypeError, r'resample\(\) no longer supports'):
             ds.resample('1D', dim='time')
 
+    def test_ds_resample_apply_func_args(self):
+
+        def func(arg1, arg2, arg3=0.):
+            return arg1.mean('time') + arg2 + arg3
+
+        times = pd.date_range('2000', freq='D', periods=3)
+        ds = xr.Dataset({'foo': ('time', [1., 1., 1.]),
+                         'time': times})
+        expected = xr.Dataset({'foo': ('time', [3., 3., 3.]),
+                               'time': times})
+        actual = ds.resample(time='D').apply(func, args=(1.,), arg3=1.)
+        assert_identical(expected, actual)
+
     def test_to_array(self):
         ds = Dataset(OrderedDict([('a', 1), ('b', ('x', [1, 2, 3]))]),
                      coords={'c': 42}, attrs={'Conventions': 'None'})

xarray/tests/test_groupby.py

@@ -85,4 +85,26 @@ def test_groupby_input_mutation():
     assert_identical(array, array_copy)  # should not modify inputs
 
 
+def test_da_groupby_apply_func_args():
+
+    def func(arg1, arg2, arg3=0):
+        return arg1 + arg2 + arg3
+
+    array = xr.DataArray([1, 1, 1], [('x', [1, 2, 3])])
+    expected = xr.DataArray([3, 3, 3], [('x', [1, 2, 3])])
+    actual = array.groupby('x').apply(func, args=(1,), arg3=1)
+    assert_identical(expected, actual)
+
+
+def test_ds_groupby_apply_func_args():
+
+    def func(arg1, arg2, arg3=0):
+        return arg1 + arg2 + arg3
+
+    dataset = xr.Dataset({'foo': ('x', [1, 1, 1])}, {'x': [1, 2, 3]})
+    expected = xr.Dataset({'foo': ('x', [3, 3, 3])}, {'x': [1, 2, 3]})
+    actual = dataset.groupby('x').apply(func, args=(1,), arg3=1)
+    assert_identical(expected, actual)
+
+
 # TODO: move other groupby tests from test_dataset and test_dataarray over here