Merge remote-tracking branch 'upstream/master' into fix/plot-broadcast

* upstream/master: format indexing.rst code with black (pydata#3511) add missing pint integration tests (pydata#3508) DOC: update bottleneck repo url (pydata#3507) add drop_sel, drop_vars, map to api.rst (pydata#3506) remove syntax warning (pydata#3505) Dataset.map, GroupBy.map, Resample.map (pydata#3459) tests for datasets with units (pydata#3447) fix pandas-dev tests (pydata#3491) unpin pseudonetcdf (pydata#3496) whatsnew corrections (pydata#3494) drop_vars; deprecate drop for variables (pydata#3475) uamiv test using only raw uamiv variables (pydata#3485) Optimize dask array equality checks. (pydata#3453) Propagate indexes in DataArray binary operations. (pydata#3481) python 3.8 tests (pydata#3477)
dcherian · Nov 13, 2019 · 4489394 · 4489394
2 parents 279ff1d + b74f80c
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diff --git a/azure-pipelines.yml b/azure-pipelines.yml
@@ -18,6 +18,8 @@ jobs:
         conda_env: py36
       py37:
         conda_env: py37
+      py38:
+        conda_env: py38
       py37-upstream-dev:
         conda_env: py37
         upstream_dev: true

diff --git a/ci/azure/install.yml b/ci/azure/install.yml
@@ -16,7 +16,7 @@ steps:
         --pre \
         --upgrade \
         matplotlib \
-        pandas=0.26.0.dev0+628.g03c1a3db2 \  # FIXME https://github.com/pydata/xarray/issues/3440
+        pandas \
         scipy
         # numpy \  # FIXME https://github.com/pydata/xarray/issues/3409
     pip install \

diff --git a/ci/requirements/py36.yml b/ci/requirements/py36.yml
@@ -29,7 +29,7 @@ dependencies:
   - pandas
   - pint
   - pip
-  - pseudonetcdf<3.1  # FIXME https://github.com/pydata/xarray/issues/3409
+  - pseudonetcdf
   - pydap
   - pynio
   - pytest

diff --git a/ci/requirements/py37-windows.yml b/ci/requirements/py37-windows.yml
@@ -29,7 +29,7 @@ dependencies:
   - pandas
   - pint
   - pip
-  - pseudonetcdf<3.1  # FIXME https://github.com/pydata/xarray/issues/3409
+  - pseudonetcdf
   - pydap
   # - pynio  # Not available on Windows
   - pytest

diff --git a/ci/requirements/py37.yml b/ci/requirements/py37.yml
@@ -29,7 +29,7 @@ dependencies:
   - pandas
   - pint
   - pip
-  - pseudonetcdf<3.1  # FIXME https://github.com/pydata/xarray/issues/3409
+  - pseudonetcdf
   - pydap
   - pynio
   - pytest

diff --git a/ci/requirements/py38.yml b/ci/requirements/py38.yml
@@ -0,0 +1,15 @@
+name: xarray-tests
+channels:
+  - conda-forge
+dependencies:
+  - python=3.8
+  - pip
+  - pip:
+      - coveralls
+      - dask
+      - distributed
+      - numpy
+      - pandas
+      - pytest
+      - pytest-cov
+      - pytest-env
diff --git a/doc/api.rst b/doc/api.rst
@@ -94,7 +94,7 @@ Dataset contents
    Dataset.rename_dims
    Dataset.swap_dims
    Dataset.expand_dims
-   Dataset.drop
+   Dataset.drop_vars
    Dataset.drop_dims
    Dataset.set_coords
    Dataset.reset_coords
@@ -118,6 +118,7 @@ Indexing
    Dataset.loc
    Dataset.isel
    Dataset.sel
+   Dataset.drop_sel
    Dataset.head
    Dataset.tail
    Dataset.thin
@@ -154,7 +155,7 @@ Computation
 .. autosummary::
    :toctree: generated/
 
-   Dataset.apply
+   Dataset.map
    Dataset.reduce
    Dataset.groupby
    Dataset.groupby_bins
@@ -263,7 +264,7 @@ DataArray contents
    DataArray.rename
    DataArray.swap_dims
    DataArray.expand_dims
-   DataArray.drop
+   DataArray.drop_vars
    DataArray.reset_coords
    DataArray.copy
 
@@ -283,6 +284,7 @@ Indexing
    DataArray.loc
    DataArray.isel
    DataArray.sel
+   DataArray.drop_sel
    DataArray.head
    DataArray.tail
    DataArray.thin
@@ -542,10 +544,10 @@ GroupBy objects
    :toctree: generated/
 
    core.groupby.DataArrayGroupBy
-   core.groupby.DataArrayGroupBy.apply
+   core.groupby.DataArrayGroupBy.map
    core.groupby.DataArrayGroupBy.reduce
    core.groupby.DatasetGroupBy
-   core.groupby.DatasetGroupBy.apply
+   core.groupby.DatasetGroupBy.map
    core.groupby.DatasetGroupBy.reduce
 
 Rolling objects
@@ -566,7 +568,7 @@ Resample objects
 ================
 
 Resample objects also implement the GroupBy interface
-(methods like ``apply()``, ``reduce()``, ``mean()``, ``sum()``, etc.).
+(methods like ``map()``, ``reduce()``, ``mean()``, ``sum()``, etc.).
 
 .. autosummary::
    :toctree: generated/

diff --git a/doc/computation.rst b/doc/computation.rst
@@ -183,7 +183,7 @@ a value when aggregating:
 
    Note that rolling window aggregations are faster and use less memory when bottleneck_ is installed. This only applies to numpy-backed xarray objects.
 
-.. _bottleneck: https://github.com/kwgoodman/bottleneck/
+.. _bottleneck: https://github.com/pydata/bottleneck/
 
 We can also manually iterate through ``Rolling`` objects:
 
@@ -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:

diff --git a/doc/dask.rst b/doc/dask.rst
@@ -292,7 +292,7 @@ For the best performance when using Dask's multi-threaded scheduler, wrap a
 function that already releases the global interpreter lock, which fortunately
 already includes most NumPy and Scipy functions. Here we show an example
 using NumPy operations and a fast function from
-`bottleneck <https://github.com/kwgoodman/bottleneck>`__, which
+`bottleneck <https://github.com/pydata/bottleneck>`__, which
 we use to calculate `Spearman's rank-correlation coefficient <https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient>`__:
 
 .. code-block:: python

diff --git a/doc/data-structures.rst b/doc/data-structures.rst
@@ -393,14 +393,14 @@ methods (like pandas) for transforming datasets into new objects.
 
 For removing variables, you can select and drop an explicit list of
 variables by indexing with a list of names or using the
-:py:meth:`~xarray.Dataset.drop` methods to return a new ``Dataset``. These
+:py:meth:`~xarray.Dataset.drop_vars` methods to return a new ``Dataset``. These
 operations keep around coordinates:
 
 .. ipython:: python
 
     ds[['temperature']]
     ds[['temperature', 'temperature_double']]
-    ds.drop('temperature')
+    ds.drop_vars('temperature')
 
 To remove a dimension, you can use :py:meth:`~xarray.Dataset.drop_dims` method.
 Any variables using that dimension are dropped:

diff --git a/doc/groupby.rst b/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`

diff --git a/doc/howdoi.rst b/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
-Original file line number
+Diff line change
@@ Expand Up / @@ -29,7 +29,7 @@ dependencies: @@
       - pandas
       - pint
       - pip
-      - pseudonetcdf<3.1  # FIXME https://github.com/pydata/xarray/issues/3409
+      - pseudonetcdf
       - pydap
       - pynio
       - pytest
@@ Expand Down @@