Remove some backcompat code

xarray/core/dataarray.py

@@ -2969,7 +2969,7 @@ def quantile(
 
         See Also
         --------
-        numpy.nanpercentile, pandas.Series.quantile, Dataset.quantile
+        numpy.nanquantile, pandas.Series.quantile, Dataset.quantile
 
         Examples
         --------

xarray/core/dataset.py

@@ -4466,11 +4466,7 @@ def _set_sparse_data_from_dataframe(
 
         idx = dataframe.index
         if isinstance(idx, pd.MultiIndex):
-            try:
-                codes = idx.codes
-            except AttributeError:
-                # deprecated since pandas 0.24
-                codes = idx.labels
+            codes = idx.codes
             coords = np.stack([np.asarray(code) for code in codes], axis=0)
             is_sorted = idx.is_lexsorted
         else:
@@ -5171,7 +5167,7 @@ def quantile(
 
         See Also
         --------
-        numpy.nanpercentile, pandas.Series.quantile, DataArray.quantile
+        numpy.nanquantile, pandas.Series.quantile, DataArray.quantile
 
         Examples
         --------

xarray/core/groupby.py

@@ -595,7 +595,7 @@ def quantile(self, q, dim=None, interpolation="linear", keep_attrs=None):
 
         See Also
         --------
-        numpy.nanpercentile, pandas.Series.quantile, Dataset.quantile,
+        numpy.nanquantile, pandas.Series.quantile, Dataset.quantile,
         DataArray.quantile
 
         Examples

xarray/core/nputils.py

@@ -14,12 +14,7 @@
 
 
 def _validate_axis(data, axis):
-    ndim = data.ndim
-    if not -ndim <= axis < ndim:
-        raise IndexError(f"axis {axis!r} out of bounds [-{ndim}, {ndim})")
-    if axis < 0:
-        axis += ndim
-    return axis
+    return np.core.multiarray.normalize_axis_index(axis, data.ndim)
 
 
 def _select_along_axis(values, idx, axis):

xarray/core/variable.py

@@ -1722,7 +1722,7 @@ def quantile(self, q, dim=None, interpolation="linear", keep_attrs=None):
 
         See Also
         --------
-        numpy.nanpercentile, pandas.Series.quantile, Dataset.quantile,
+        numpy.nanquantile, pandas.Series.quantile, Dataset.quantile,
         DataArray.quantile
         """
 
@@ -1734,10 +1734,6 @@ def quantile(self, q, dim=None, interpolation="linear", keep_attrs=None):
         scalar = utils.is_scalar(q)
         q = np.atleast_1d(np.asarray(q, dtype=np.float64))
 
-        # TODO: remove once numpy >= 1.15.0 is the minimum requirement
-        if np.count_nonzero(q < 0.0) or np.count_nonzero(q > 1.0):
-            raise ValueError("Quantiles must be in the range [0, 1]")
-
         if dim is None:
             dim = self.dims
 
@@ -1746,9 +1742,7 @@ def quantile(self, q, dim=None, interpolation="linear", keep_attrs=None):
 
         def _wrapper(npa, **kwargs):
             # move quantile axis to end. required for apply_ufunc
-
-            # TODO: use np.nanquantile once numpy >= 1.15.0 is the minimum requirement
-            return np.moveaxis(np.nanpercentile(npa, **kwargs), 0, -1)
+            return np.moveaxis(np.nanquantile(npa, **kwargs), 0, -1)
 
         axis = np.arange(-1, -1 * len(dim) - 1, -1)
         result = apply_ufunc(
@@ -1760,7 +1754,7 @@ def _wrapper(npa, **kwargs):
             output_dtypes=[np.float64],
             output_sizes={"quantile": len(q)},
             dask="parallelized",
-            kwargs={"q": q * 100, "axis": axis, "interpolation": interpolation},
+            kwargs={"q": q, "axis": axis, "interpolation": interpolation},
         )
 
         # for backward compatibility

xarray/plot/utils.py

@@ -547,10 +547,6 @@ def _add_colorbar(primitive, ax, cbar_ax, cbar_kwargs, cmap_params):
 
 def _rescale_imshow_rgb(darray, vmin, vmax, robust):
     assert robust or vmin is not None or vmax is not None
-    # TODO: remove when min numpy version is bumped to 1.13
-    # There's a cyclic dependency via DataArray, so we can't import from
-    # xarray.ufuncs in global scope.
-    from xarray.ufuncs import maximum, minimum
 
     # Calculate vmin and vmax automatically for `robust=True`
     if robust:
@@ -579,9 +575,7 @@ def _rescale_imshow_rgb(darray, vmin, vmax, robust):
     # After scaling, downcast to 32-bit float.  This substantially reduces
     # memory usage after we hand `darray` off to matplotlib.
     darray = ((darray.astype("f8") - vmin) / (vmax - vmin)).astype("f4")
-    with warnings.catch_warnings():
-        warnings.filterwarnings("ignore", "xarray.ufuncs", PendingDeprecationWarning)
-        return minimum(maximum(darray, 0), 1)
+    return np.minimum(np.maximum(darray, 0), 1)
 
 
 def _update_axes(

xarray/tests/test_dataarray.py

@@ -79,10 +79,6 @@ def test_repr_multiindex(self):
         )
         assert expected == repr(self.mda)
 
-    @pytest.mark.skipif(
-        LooseVersion(np.__version__) < "1.15",
-        reason="old versions of numpy have different printing behavior",
-    )
     def test_repr_multiindex_long(self):
         mindex_long = pd.MultiIndex.from_product(
             [["a", "b", "c", "d"], [1, 2, 3, 4, 5, 6, 7, 8]],