refactor

xarray/core/dataset.py

@@ -33,7 +33,7 @@
     Frozen, SortedKeysDict, either_dict_or_kwargs, decode_numpy_dict_values,
     ensure_us_time_resolution, hashable, maybe_wrap_array)
 from .variable import IndexVariable, Variable, as_variable, broadcast_variables
-from ..plot.plot import dataset_scatter
+from ..plot.plot import _Dataset_PlotMethods
 
 # list of attributes of pd.DatetimeIndex that are ndarrays of time info
 _DATETIMEINDEX_COMPONENTS = ['year', 'month', 'day', 'hour', 'minute',
@@ -3593,8 +3593,15 @@ def real(self):
     def imag(self):
         return self._unary_op(lambda x: x.imag, keep_attrs=True)(self)
 
-    def scatter(self,  x, y, **kwargs):
-        return dataset_scatter(ds=self, x=x, y=y, **kwargs)
+    @property
+    def plot(self):
+        """
+        Access plotting functions. Use it as a namespace to use
+        xarray.plot functions as Dataset methods
+        >>> ds.plot.scatter(...)  # equivalent to xarray.plot.scatter(ds,...)
+
+        """
+        return _Dataset_PlotMethods(self)
 
     def filter_by_attrs(self, **kwargs):
         """Returns a ``Dataset`` with variables that match specific conditions.

xarray/plot/facetgrid.py

@@ -322,7 +322,7 @@ def map_scatter(self, x=None, y=None, hue=None, discrete_legend=False,
         self : FacetGrid object
 
         """
-        from .plot import _infer_scatter_data, dataset_scatter
+        from .plot import _infer_scatter_data, scatter
 
         add_legend = kwargs.pop('add_legend', True)
         kwargs['add_legend'] = False
@@ -332,8 +332,8 @@ def map_scatter(self, x=None, y=None, hue=None, discrete_legend=False,
             # None is the sentinel value
             if d is not None:
                 subset = self.data.loc[d]
-                mappable = dataset_scatter(subset, x=x, y=y, hue=hue,
-                                           ax=ax, **kwargs)
+                mappable = scatter(subset, x=x, y=y, hue=hue,
+                                   ax=ax, **kwargs)
                 self._mappables.append(mappable)
 
         data = _infer_scatter_data(ds=self.data.loc[self.name_dicts.flat[0]],

xarray/plot/plot.py

@@ -997,10 +997,10 @@ def pcolormesh(x, y, z, ax, infer_intervals=None, **kwargs):
     return primitive
 
 
-def dataset_scatter(ds, x=None, y=None, hue=None, col=None, row=None,
-                    col_wrap=None, sharex=True, sharey=True, aspect=None,
-                    size=None, subplot_kws=None, add_legend=True,
-                    discrete_legend=None, **kwargs):
+def scatter(ds, x, y, hue=None, col=None, row=None,
+            col_wrap=None, sharex=True, sharey=True, aspect=None,
+            size=None, subplot_kws=None, add_legend=True,
+            discrete_legend=None, **kwargs):
 
     if hue and not _ensure_numeric(ds[hue].values):
         if discrete_legend is None:
@@ -1055,3 +1055,21 @@ def dataset_scatter(ds, x=None, y=None, hue=None, col=None, row=None,
             cbar.ax.set_ylabel(data.get('hue_label'))
 
     return primitive
+
+
+class _Dataset_PlotMethods(object):
+    """
+    Enables use of xarray.plot functions as attributes on a Dataset.
+    For example, Dataset.plot.scatter
+    """
+
+    def __init__(self, dataset):
+        self._ds = dataset
+
+    def __call__(self, *args, **kwargs):
+        raise ValueError('Dataset.plot cannot be called directly. Use'
+                         'an explicit plot method, e.g. ds.plot.scatter(...)')
+
+    @functools.wraps(scatter)
+    def scatter(self, *args, **kwargs):
+        return scatter(self._ds, *args, **kwargs)