Merge stable into master

doc/data-structures.rst

@@ -411,7 +411,7 @@ Any variables using that dimension are dropped:
 
 As an alternate to dictionary-like modifications, you can use
 :py:meth:`~xarray.Dataset.assign` and :py:meth:`~xarray.Dataset.assign_coords`.
-These methods return a new dataset with additional (or replaced) or values:
+These methods return a new dataset with additional (or replaced) values:
 
 .. ipython:: python
 
@@ -420,7 +420,7 @@ These methods return a new dataset with additional (or replaced) or values:
 There is also the :py:meth:`~xarray.Dataset.pipe` method that allows you to use
 a method call with an external function (e.g., ``ds.pipe(func)``) instead of
 simply calling it (e.g., ``func(ds)``). This allows you to write pipelines for
-transforming you data (using "method chaining") instead of writing hard to
+transforming your data (using "method chaining") instead of writing hard to
 follow nested function calls:
 
 .. ipython:: python

doc/terminology.rst

@@ -15,7 +15,7 @@ Terminology
 
 ----
 
-**Variable:** A `NetCDF-like variable <https://www.unidata.ucar.edu/software/netcdf/netcdf/Variables.html>`_ consisting of dimensions, data, and attributes which describe a single array. The main functional difference between variables and numpy arrays is that numerical operations on variables implement array broadcasting by dimension name. Each ``DataArray`` has an underlying variable that can be accessed via ``arr.variable``. However, a variable is not fully described outside of either a ``Dataset`` or a ``DataArray``.
+**Variable:** A `NetCDF-like variable <https://www.unidata.ucar.edu/software/netcdf/docs/netcdf_data_set_components.html#variables>`_ consisting of dimensions, data, and attributes which describe a single array. The main functional difference between variables and numpy arrays is that numerical operations on variables implement array broadcasting by dimension name. Each ``DataArray`` has an underlying variable that can be accessed via ``arr.variable``. However, a variable is not fully described outside of either a ``Dataset`` or a ``DataArray``.
 
 .. note::
 
@@ -39,4 +39,4 @@ Terminology
 
 ----
 
-**Index:** An *index* is a data structure optimized for efficient selecting and slicing of an associated array. Xarray creates indexes for dimension coordinates so that operations along dimensions are fast, while non-dimension coordinates are not indexed. Under the hood, indexes are implemented as :py:class:`pandas.Index` objects. The index associated with dimension name ``x`` can be retrieved by ``arr.indexes[x]``. By construction, ``len(arr.dims) == len(arr.indexes)``
+**Index:** An *index* is a data structure optimized for efficient selecting and slicing of an associated array. Xarray creates indexes for dimension coordinates so that operations along dimensions are fast, while non-dimension coordinates are not indexed. Under the hood, indexes are implemented as :py:class:`pandas.Index` objects. The index associated with dimension name ``x`` can be retrieved by ``arr.indexes[x]``. By construction, ``len(arr.dims) == len(arr.indexes)``