Dataset.map

doc/computation.rst

@@ -462,13 +462,13 @@ Datasets support most of the same methods found on data arrays:
     abs(ds)
 
 Datasets also support NumPy ufuncs (requires NumPy v1.13 or newer), or
-alternatively you can use :py:meth:`~xarray.Dataset.apply` to apply a function
+alternatively you can use :py:meth:`~xarray.Dataset.map` to map a function
 to each variable in a dataset:
 
 .. ipython:: python
 
     np.sin(ds)
-    ds.apply(np.sin)
+    ds.map(np.sin)
 
 Datasets also use looping over variables for *broadcasting* in binary
 arithmetic. You can do arithmetic between any ``DataArray`` and a dataset:

doc/groupby.rst

@@ -35,10 +35,11 @@ Let's create a simple example dataset:
 
 .. ipython:: python
 
-    ds = xr.Dataset({'foo': (('x', 'y'), np.random.rand(4, 3))},
-                    coords={'x': [10, 20, 30, 40],
-                            'letters': ('x', list('abba'))})
-    arr = ds['foo']
+    ds = xr.Dataset(
+        {"foo": (("x", "y"), np.random.rand(4, 3))},
+        coords={"x": [10, 20, 30, 40], "letters": ("x", list("abba"))},
+    )
+    arr = ds["foo"]
     ds
 
 If we groupby the name of a variable or coordinate in a dataset (we can also
@@ -93,15 +94,15 @@ Apply
 ~~~~~
 
 To apply a function to each group, you can use the flexible
-:py:meth:`~xarray.DatasetGroupBy.apply` method. The resulting objects are automatically
+:py:meth:`~xarray.DatasetGroupBy.map` method. The resulting objects are automatically
 concatenated back together along the group axis:
 
 .. ipython:: python
 
     def standardize(x):
         return (x - x.mean()) / x.std()
 
-    arr.groupby('letters').apply(standardize)
+    arr.groupby('letters').map(standardize)
 
 GroupBy objects also have a :py:meth:`~xarray.DatasetGroupBy.reduce` method and
 methods like :py:meth:`~xarray.DatasetGroupBy.mean` as shortcuts for applying an
@@ -202,7 +203,7 @@ __ http://cfconventions.org/cf-conventions/v1.6.0/cf-conventions.html#_two_dimen
         dims=['ny','nx'])
     da
     da.groupby('lon').sum(...)
-    da.groupby('lon').apply(lambda x: x - x.mean(), shortcut=False)
+    da.groupby('lon').map(lambda x: x - x.mean(), shortcut=False)
 
 Because multidimensional groups have the ability to generate a very large
 number of bins, coarse-binning via :py:meth:`~xarray.Dataset.groupby_bins`

doc/howdoi.rst

@@ -44,7 +44,7 @@ How do I ...
    * - convert a possibly irregularly sampled timeseries to a regularly sampled timeseries
      - :py:meth:`DataArray.resample`, :py:meth:`Dataset.resample` (see :ref:`resampling` for more)
    * - apply a function on all data variables in a Dataset
-     - :py:meth:`Dataset.apply`
+     - :py:meth:`Dataset.map`
    * - write xarray objects with complex values to a netCDF file
      - :py:func:`Dataset.to_netcdf`, :py:func:`DataArray.to_netcdf` specifying ``engine="h5netcdf", invalid_netcdf=True``
    * - make xarray objects look like other xarray objects

doc/quick-overview.rst

@@ -142,7 +142,7 @@ xarray supports grouped operations using a very similar API to pandas (see :ref:
     labels = xr.DataArray(['E', 'F', 'E'], [data.coords['y']], name='labels')
     labels
     data.groupby(labels).mean('y')
-    data.groupby(labels).apply(lambda x: x - x.min())
+    data.groupby(labels).map(lambda x: x - x.min())
 
 Plotting
 --------

doc/whats-new.rst

@@ -44,6 +44,13 @@ New Features
   option for dropping either labels or variables, but using the more specific methods is encouraged.
   (:pull:`3475`)
   By `Maximilian Roos <https://github.com/max-sixty>`_
+- :py:meth:`Dataset.map` & :py:meth:`GroupBy.map` & :py:meth:`Resample.map` have been added for 
+  mapping / applying a function over each item in the collection, reflecting the widely used
+  and least surprising name for this operation.
+  The existing ``apply`` methods remain for backward compatibility, though using the ``map``
+  methods is encouraged.
+  (:pull:`3459`)
+  By `Maximilian Roos <https://github.com/max-sixty>`_
 - :py:meth:`Dataset.transpose` and :py:meth:`DataArray.transpose` now support an ellipsis (`...`)
   to represent all 'other' dimensions. For example, to move one dimension to the front,
   use `.transpose('x', ...)`. (:pull:`3421`)

xarray/core/dataset.py

@@ -4108,14 +4108,14 @@ def reduce(
             variables, coord_names=coord_names, attrs=attrs, indexes=indexes
         )
 
-    def apply(
+    def map(
         self,
         func: Callable,
         keep_attrs: bool = None,
         args: Iterable[Any] = (),
         **kwargs: Any,
     ) -> "Dataset":
-        """Apply a function over the data variables in this dataset.
+        """Apply a function to each variable in this dataset
 
         Parameters
         ----------
@@ -4135,7 +4135,7 @@ def apply(
         Returns
         -------
         applied : Dataset
-            Resulting dataset from applying ``func`` over each data variable.
+            Resulting dataset from applying ``func`` to each data variable.
 
         Examples
         --------
@@ -4148,7 +4148,7 @@ def apply(
         Data variables:
             foo      (dim_0, dim_1) float64 -0.3751 -1.951 -1.945 0.2948 0.711 -0.3948
             bar      (x) int64 -1 2
-        >>> ds.apply(np.fabs)
+        >>> ds.map(np.fabs)
         <xarray.Dataset>
         Dimensions:  (dim_0: 2, dim_1: 3, x: 2)
         Dimensions without coordinates: dim_0, dim_1, x
@@ -4165,6 +4165,20 @@ def apply(
         attrs = self.attrs if keep_attrs else None
         return type(self)(variables, attrs=attrs)
 
+    def apply(
+        self,
+        func: Callable,
+        keep_attrs: bool = None,
+        args: Iterable[Any] = (),
+        **kwargs: Any,
+    ) -> "Dataset":
+        warnings.warn(
+            "Dataset.apply may be deprecated in the future. Using Dataset.map is encouraged",
+            PendingDeprecationWarning,
+            stacklevel=2,
+        )
+        return self.map(func, keep_attrs, args, **kwargs)
+
     def assign(
         self, variables: Mapping[Hashable, Any] = None, **variables_kwargs: Hashable
     ) -> "Dataset":

xarray/core/groupby.py

@@ -608,7 +608,7 @@ def assign_coords(self, coords=None, **coords_kwargs):
         Dataset.swap_dims
         """
         coords_kwargs = either_dict_or_kwargs(coords, coords_kwargs, "assign_coords")
-        return self.apply(lambda ds: ds.assign_coords(**coords_kwargs))
+        return self.map(lambda ds: ds.assign_coords(**coords_kwargs))
 
 
 def _maybe_reorder(xarray_obj, dim, positions):
@@ -655,8 +655,8 @@ def lookup_order(dimension):
         new_order = sorted(stacked.dims, key=lookup_order)
         return stacked.transpose(*new_order, transpose_coords=self._restore_coord_dims)
 
-    def apply(self, func, shortcut=False, args=(), **kwargs):
-        """Apply a function over each array in the group and concatenate them
+    def map(self, func, shortcut=False, args=(), **kwargs):
+        """Apply a function to each array in the group and concatenate them
         together into a new array.
 
         `func` is called like `func(ar, *args, **kwargs)` for each array `ar`
@@ -702,6 +702,14 @@ def apply(self, func, shortcut=False, args=(), **kwargs):
         applied = (maybe_wrap_array(arr, func(arr, *args, **kwargs)) for arr in grouped)
         return self._combine(applied, shortcut=shortcut)
 
+    def apply(self, func, shortcut=False, args=(), **kwargs):
+        warnings.warn(
+            "GroupBy.apply may be deprecated in the future. Using GroupBy.map is encouraged",
+            PendingDeprecationWarning,
+            stacklevel=2,
+        )
+        return self.map(func, shortcut=shortcut, args=args, **kwargs)
+
     def _combine(self, applied, restore_coord_dims=False, shortcut=False):
         """Recombine the applied objects like the original."""
         applied_example, applied = peek_at(applied)
@@ -765,7 +773,7 @@ def quantile(self, q, dim=None, interpolation="linear", keep_attrs=None):
         if dim is None:
             dim = self._group_dim
 
-        out = self.apply(
+        out = self.map(
             self._obj.__class__.quantile,
             shortcut=False,
             q=q,
@@ -820,16 +828,16 @@ def reduce_array(ar):
 
         check_reduce_dims(dim, self.dims)
 
-        return self.apply(reduce_array, shortcut=shortcut)
+        return self.map(reduce_array, shortcut=shortcut)
 
 
 ops.inject_reduce_methods(DataArrayGroupBy)
 ops.inject_binary_ops(DataArrayGroupBy)
 
 
 class DatasetGroupBy(GroupBy, ImplementsDatasetReduce):
-    def apply(self, func, args=(), shortcut=None, **kwargs):
-        """Apply a function over each Dataset in the group and concatenate them
+    def map(self, func, args=(), shortcut=None, **kwargs):
+        """Apply a function to each Dataset in the group and concatenate them
         together into a new Dataset.
 
         `func` is called like `func(ds, *args, **kwargs)` for each dataset `ds`
@@ -862,6 +870,14 @@ def apply(self, func, args=(), shortcut=None, **kwargs):
         applied = (func(ds, *args, **kwargs) for ds in self._iter_grouped())
         return self._combine(applied)
 
+    def apply(self, func, args=(), shortcut=None, **kwargs):
+        warnings.warn(
+            "GroupBy.apply may be deprecated in the future. Using GroupBy.map is encouraged",
+            PendingDeprecationWarning,
+            stacklevel=2,
+        )
+        return self.map(func, shortcut=shortcut, args=args, **kwargs)
+
     def _combine(self, applied):
         """Recombine the applied objects like the original."""
         applied_example, applied = peek_at(applied)
@@ -914,7 +930,7 @@ def reduce_dataset(ds):
 
         check_reduce_dims(dim, self.dims)
 
-        return self.apply(reduce_dataset)
+        return self.map(reduce_dataset)
 
     def assign(self, **kwargs):
         """Assign data variables by group.
@@ -923,7 +939,7 @@ def assign(self, **kwargs):
         --------
         Dataset.assign
         """
-        return self.apply(lambda ds: ds.assign(**kwargs))
+        return self.map(lambda ds: ds.assign(**kwargs))
 
 
 ops.inject_reduce_methods(DatasetGroupBy)

xarray/core/resample.py

@@ -1,3 +1,5 @@
+import warnings
+
 from . import ops
 from .groupby import DataArrayGroupBy, DatasetGroupBy
 
@@ -173,8 +175,8 @@ def __init__(self, *args, dim=None, resample_dim=None, **kwargs):
 
         super().__init__(*args, **kwargs)
 
-    def apply(self, func, shortcut=False, args=(), **kwargs):
-        """Apply a function over each array in the group and concatenate them
+    def map(self, func, shortcut=False, args=(), **kwargs):
+        """Apply a function to each array in the group and concatenate them
         together into a new array.
 
         `func` is called like `func(ar, *args, **kwargs)` for each array `ar`
@@ -212,7 +214,9 @@ def apply(self, func, shortcut=False, args=(), **kwargs):
         applied : DataArray or DataArray
             The result of splitting, applying and combining this array.
         """
-        combined = super().apply(func, shortcut=shortcut, args=args, **kwargs)
+        # TODO: the argument order for Resample doesn't match that for its parent,
+        # GroupBy
+        combined = super().map(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
@@ -225,6 +229,14 @@ def apply(self, func, shortcut=False, args=(), **kwargs):
 
         return combined
 
+    def apply(self, func, args=(), shortcut=None, **kwargs):
+        warnings.warn(
+            "Resample.apply may be deprecated in the future. Using Resample.map is encouraged",
+            PendingDeprecationWarning,
+            stacklevel=2,
+        )
+        return self.map(func=func, shortcut=shortcut, args=args, **kwargs)
+
 
 ops.inject_reduce_methods(DataArrayResample)
 ops.inject_binary_ops(DataArrayResample)
@@ -247,7 +259,7 @@ def __init__(self, *args, dim=None, resample_dim=None, **kwargs):
 
         super().__init__(*args, **kwargs)
 
-    def apply(self, func, args=(), shortcut=None, **kwargs):
+    def map(self, func, args=(), shortcut=None, **kwargs):
         """Apply a function over each Dataset in the groups generated for
         resampling  and concatenate them together into a new Dataset.
 
@@ -282,6 +294,14 @@ def apply(self, func, args=(), shortcut=None, **kwargs):
 
         return combined.rename({self._resample_dim: self._dim})
 
+    def apply(self, func, args=(), shortcut=None, **kwargs):
+        warnings.warn(
+            "Resample.apply may be deprecated in the future. Using Resample.map is encouraged",
+            PendingDeprecationWarning,
+            stacklevel=2,
+        )
+        return self.map(func=func, shortcut=shortcut, args=args, **kwargs)
+
     def reduce(self, func, dim=None, keep_attrs=None, **kwargs):
         """Reduce the items in this group by applying `func` along the
         pre-defined resampling dimension.