Fix test failures / warnings for pandas 0.24 (#2720)

* Fix test failures / warnings for pandas 0.24

Fixes GH2717

* Brief doc note

* Comment on name order

Author: shoyer

doc/whats-new.rst

@@ -47,6 +47,8 @@ Enhancements
 Bug fixes
 ~~~~~~~~~
 
+- Silenced warnings that appear when using pandas 0.24.
+  By `Stephan Hoyer <https://github.com/shoyer>`_
 - Interpolating via resample now internally specifies ``bounds_error=False``
   as an argument to ``scipy.interpolate.interp1d``, allowing for interpolation
   from higher frequencies to lower frequencies.  Datapoints outside the bounds

xarray/core/coordinates.py

@@ -1,4 +1,5 @@
-from collections import Mapping, OrderedDict
+import collections.abc
+from collections import OrderedDict
 from contextlib import contextmanager
 
 import pandas as pd
@@ -14,7 +15,7 @@
 _THIS_ARRAY = ReprObject('<this-array>')
 
 
-class AbstractCoordinates(Mapping):
+class AbstractCoordinates(collections.abc.Mapping):
     def __getitem__(self, key):
         raise NotImplementedError
 

xarray/core/dataset.py

@@ -130,7 +130,7 @@ def merge_indexes(
         if isinstance(var_names, str):
             var_names = [var_names]
 
-        names, labels, levels = [], [], []  # type: (list, list, list)
+        names, codes, levels = [], [], []  # type: (list, list, list)
         current_index_variable = variables.get(dim)
 
         for n in var_names:
@@ -144,13 +144,18 @@ def merge_indexes(
         if current_index_variable is not None and append:
             current_index = current_index_variable.to_index()
             if isinstance(current_index, pd.MultiIndex):
+                try:
+                    current_codes = current_index.codes
+                except AttributeError:
+                    # fpr pandas<0.24
+                    current_codes = current_index.labels
                 names.extend(current_index.names)
-                labels.extend(current_index.labels)
+                codes.extend(current_codes)
                 levels.extend(current_index.levels)
             else:
                 names.append('%s_level_0' % dim)
                 cat = pd.Categorical(current_index.values, ordered=True)
-                labels.append(cat.codes)
+                codes.append(cat.codes)
                 levels.append(cat.categories)
 
         if not len(names) and len(var_names) == 1:
@@ -161,10 +166,10 @@ def merge_indexes(
                 names.append(n)
                 var = variables[n]
                 cat = pd.Categorical(var.values, ordered=True)
-                labels.append(cat.codes)
+                codes.append(cat.codes)
                 levels.append(cat.categories)
 
-            idx = pd.MultiIndex(labels=labels, levels=levels, names=names)
+            idx = pd.MultiIndex(levels, codes, names=names)
 
         vars_to_replace[dim] = IndexVariable(dim, idx)
         vars_to_remove.extend(var_names)

xarray/tests/test_coding_times.py

@@ -533,17 +533,22 @@ def test_infer_cftime_datetime_units(calendar, date_args, expected):
 
 @pytest.mark.parametrize(
     ['timedeltas', 'units', 'numbers'],
-    [('1D', 'days', np.int64(1)),
-     (['1D', '2D', '3D'], 'days', np.array([1, 2, 3], 'int64')),
-     ('1h', 'hours', np.int64(1)),
-     ('1ms', 'milliseconds', np.int64(1)),
-     ('1us', 'microseconds', np.int64(1)),
-     (['NaT', '0s', '1s'], None, [np.nan, 0, 1]),
-     (['30m', '60m'], 'hours', [0.5, 1.0]),
-     (np.timedelta64('NaT', 'ns'), 'days', np.nan),
-     (['NaT', 'NaT'], 'days', [np.nan, np.nan])])
+    [
+        ('1D', 'days', np.int64(1)),
+        (['1D', '2D', '3D'], 'days', np.array([1, 2, 3], 'int64')),
+        ('1h', 'hours', np.int64(1)),
+        ('1ms', 'milliseconds', np.int64(1)),
+        ('1us', 'microseconds', np.int64(1)),
+        (['NaT', '0s', '1s'], None, [np.nan, 0, 1]),
+        (['30m', '60m'], 'hours', [0.5, 1.0]),
+        ('NaT', 'days', np.nan),
+        (['NaT', 'NaT'], 'days', [np.nan, np.nan]),
+    ])
 def test_cf_timedelta(timedeltas, units, numbers):
-    timedeltas = pd.to_timedelta(timedeltas, box=False)
+    if timedeltas == 'NaT':
+        timedeltas = np.timedelta64('NaT', 'ns')
+    else:
+        timedeltas = pd.to_timedelta(timedeltas, box=False)
     numbers = np.array(numbers)
 
     expected = numbers

xarray/tests/test_dataarray.py

@@ -122,21 +122,14 @@ def test_struct_array_dims(self):
         """
         # GH837, GH861
         # checking array subraction when dims are the same
-        p_data = np.array([('John', 180), ('Stacy', 150), ('Dick', 200)],
+        # note: names need to be in sorted order to align consistently with
+        # pandas < 0.24 and >= 0.24.
+        p_data = np.array([('Abe', 180), ('Stacy', 150), ('Dick', 200)],
                           dtype=[('name', '|S256'), ('height', object)])
-
-        p_data_1 = np.array([('John', 180), ('Stacy', 150), ('Dick', 200)],
-                            dtype=[('name', '|S256'), ('height', object)])
-
-        p_data_2 = np.array([('John', 180), ('Dick', 200)],
-                            dtype=[('name', '|S256'), ('height', object)])
-
         weights_0 = DataArray([80, 56, 120], dims=['participant'],
                               coords={'participant': p_data})
-
         weights_1 = DataArray([81, 52, 115], dims=['participant'],
-                              coords={'participant': p_data_1})
-
+                              coords={'participant': p_data})
         actual = weights_1 - weights_0
 
         expected = DataArray([1, -4, -5], dims=['participant'],
@@ -145,31 +138,27 @@ def test_struct_array_dims(self):
         assert_identical(actual, expected)
 
         # checking array subraction when dims are not the same
-        p_data_1 = np.array([('John', 180), ('Stacy', 151), ('Dick', 200)],
-                            dtype=[('name', '|S256'), ('height', object)])
-
+        p_data_alt = np.array([('Abe', 180), ('Stacy', 151), ('Dick', 200)],
+                              dtype=[('name', '|S256'), ('height', object)])
         weights_1 = DataArray([81, 52, 115], dims=['participant'],
-                              coords={'participant': p_data_1})
-
+                              coords={'participant': p_data_alt})
         actual = weights_1 - weights_0
 
         expected = DataArray([1, -5], dims=['participant'],
-                             coords={'participant': p_data_2})
+                             coords={'participant': p_data[[0, 2]]})
 
         assert_identical(actual, expected)
 
         # checking array subraction when dims are not the same and one
         # is np.nan
-        p_data_1 = np.array([('John', 180), ('Stacy', np.nan), ('Dick', 200)],
-                            dtype=[('name', '|S256'), ('height', object)])
-
+        p_data_nan = np.array([('Abe', 180), ('Stacy', np.nan), ('Dick', 200)],
+                              dtype=[('name', '|S256'), ('height', object)])
         weights_1 = DataArray([81, 52, 115], dims=['participant'],
-                              coords={'participant': p_data_1})
-
+                              coords={'participant': p_data_nan})
         actual = weights_1 - weights_0
 
         expected = DataArray([1, -5], dims=['participant'],
-                             coords={'participant': p_data_2})
+                             coords={'participant': p_data[[0, 2]]})
 
         assert_identical(actual, expected)
 

xarray/tests/test_utils.py

@@ -74,7 +74,9 @@ def test_multiindex_from_product_levels():
     result = utils.multiindex_from_product_levels(
         [pd.Index(['b', 'a']), pd.Index([1, 3, 2])])
     np.testing.assert_array_equal(
-        result.labels, [[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]])
+        # compat for pandas < 0.24
+        result.codes if hasattr(result, 'codes') else result.labels,
+        [[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]])
     np.testing.assert_array_equal(result.levels[0], ['b', 'a'])
     np.testing.assert_array_equal(result.levels[1], [1, 3, 2])
 
@@ -86,7 +88,9 @@ def test_multiindex_from_product_levels_non_unique():
     result = utils.multiindex_from_product_levels(
         [pd.Index(['b', 'a']), pd.Index([1, 1, 2])])
     np.testing.assert_array_equal(
-        result.labels, [[0, 0, 0, 1, 1, 1], [0, 0, 1, 0, 0, 1]])
+        # compat for pandas < 0.24
+        result.codes if hasattr(result, 'codes') else result.labels,
+        [[0, 0, 0, 1, 1, 1], [0, 0, 1, 0, 0, 1]])
     np.testing.assert_array_equal(result.levels[0], ['b', 'a'])
     np.testing.assert_array_equal(result.levels[1], [1, 2])