MCVE Code Sample

```python import numpy as np import xarray as xr import pandas as pd import time

size = 1000000 data = np.random.random(size) times = pd.date_range('2019-01-01', periods=size, freq='ms') da = xr.DataArray(data, dims=['time'], coords={'time': times})

start = time.time()

da.resample(time='s').mean()

print('Elapsed time: ' + str(time.time() - start)) print('xarray version: ' + str(xr.version)) ```

• Output for xarray==0.11.3

Elapsed time: 0.2671010494232178 xarray version: 0.11.3

• Output for xarray==0.12.0

Elapsed time: 6.652455568313599 xarray version: 0.12.0

Expected Output

I expect that for the default parameters, resample should take a similar amount of time as previous versions. Or at least the documentation should specify what parameters must be passed into resample or mean to achieve the same time as in version 0.11.3

Problem Description

resampling a dataarray or dataset and then calling mean takes significantly longer in the latest versions of xarray 0.12. The changelog specifies multiple changes to the resample method and new parameters, but its not clear what one would specify to achieve execution time improvements.

Output of xr.show_versions()

label_from_attrs used textwrap.wrap with a default wrap width of 30, this commit changes label_from_attrs to instead take an argument wrap_width that specifies the wrap width.

context: I find the label_from_attrs function useful for plots that I make using pyplot, but needed to change the text wrap with.

I am new to contributing to projects like this so bear with me. I only had the mypy and flake8 linters and figured that was good enough for a minor change like this. And I didn't see label_from_attrs in the api document so just added a line to whats-new.rst

• [x] Passes mypy . && flake8`
• [x] Fully documented, including whats-new.rst

What happened?

This issue originates from this discussion where I was trying to figure out the best way to replace coordinate values in a MultiIndex level. I found that removing the level with reset_index and replacing the coordinate level with assign_coords works except when removing level leaves you with a MultIndex with only one level. In this case a ValueError is thrown.

What did you expect to happen?

I expect that removing and replacing a coordinate level would work the same independent of the number of levels in the MultiIndex.

Minimal Complete Verifiable Example

```Python import numpy as np import pandas as pd import xarray as xr

Replace the coordinates in level 'one'. This works as expected.

midx = pd.MultiIndex.from_product([[0,1,2], [3, 4], [5,6]], names=("one", "two","three")) mda = xr.DataArray(np.random.rand(12, 3), [("x", midx), ("y", range(3))])

new_coords = mda.coords['one'].values*2 mda.reset_index('one', drop=True).assign_coords(one= ('x',new_coords)).set_index(x='one',append=True) #Works

Drop the two level before had such that the intermediate state has a multindex

with only the 'three' level, this throws a ValueError

mda.reset_index('two',drop=True).reset_index('one', drop=True).assign_coords(one= ('x',new_coords)) #ValueError

We can intialize a data array with only two levels and only drop the 'one'

level, which gives the same ValueError. This shows that the problem is not

due to something with dropping the 'two' level above, but something inherent

to dropping to a state with only one multinddex level

midx = pd.MultiIndex.from_product([[0,1,2], [3, 4]], names=("one", "two")) mda = xr.DataArray(np.random.rand(6, 2), [("x", midx), ("y", range(2))])

new_coords = mda.coords['one'].values*2 mda.reset_index('one', drop=True).assign_coords(one= ('x',new_coords)).set_index(x='one',append=True) #ValueError ```

MVCE confirmation

• [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
• [X] Complete example — the example is self-contained, including all data and the text of any traceback.
• [X] Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
• [X] New issue — a search of GitHub Issues suggests this is not a duplicate.

Relevant log output

```Python

First example, starting from 3 level multiindex and dropping two levels

ValueError Traceback (most recent call last) c:\Users\aspit\Git\Learn\xarray\replace_coord_issue.py in <module> 15 # Drop the two level before had such that the intermediate state has a multindex 16 # with only the 'three' level, this throws a ValueError ---> 17 mda.reset_index('two',drop=True).reset_index('one', drop=True).assign_coords(one= ('x',new_coords))

c:\Users\aspit\anaconda3\envs\dataanalysis\lib\site-packages\xarray\core\common.py in assign_coords(self, coords, **coords_kwargs) 590 data = self.copy(deep=False) 591 results: dict[Hashable, Any] = self._calc_assign_results(coords_combined) --> 592 data.coords.update(results) 593 return data 594

c:\Users\aspit\anaconda3\envs\dataanalysis\lib\site-packages\xarray\core\coordinates.py in update(self, other) 160 other_vars = getattr(other, "variables", other) 161 self._maybe_drop_multiindex_coords(set(other_vars)) --> 162 coords, indexes = merge_coords( 163 [self.variables, other_vars], priority_arg=1, indexes=self.xindexes 164 )

c:\Users\aspit\anaconda3\envs\dataanalysis\lib\site-packages\xarray\core\merge.py in merge_coords(objects, compat, join, priority_arg, indexes, fill_value) 564 collected = collect_variables_and_indexes(aligned) 565 prioritized = _get_priority_vars_and_indexes(aligned, priority_arg, compat=compat) --> 566 variables, out_indexes = merge_collected(collected, prioritized, compat=compat) 567 return variables, out_indexes 568

c:\Users\aspit\anaconda3\envs\dataanalysis\lib\site-packages\xarray\core\merge.py in merge_collected(grouped, prioritized, compat, combine_attrs, equals) 252 253 _assert_compat_valid(compat) --> 254 _assert_prioritized_valid(grouped, prioritized) 255 256 merged_vars: dict[Hashable, Variable] = {}

c:\Users\aspit\anaconda3\envs\dataanalysis\lib\site-packages\xarray\core\merge.py in _assert_prioritized_valid(grouped, prioritized) 199 common_names_str = ", ".join(f"{k!r}" for k in common_names) 200 index_names_str = ", ".join(f"{k!r}" for k in index_coord_names) --> 201 raise ValueError( 202 f"cannot set or update variable(s) {common_names_str}, which would corrupt " 203 f"the following index built from coordinates {index_names_str}:\n"

ValueError: cannot set or update variable(s) 'one', which would corrupt the following index built from coordinates 'x', 'one', 'three': <xarray.core.indexes.PandasMultiIndex object at 0x00000225AA4B5200>

Second Example Starting from two level multindex and dropping one level

ValueError Traceback (most recent call last) c:\Users\aspit\Git\Learn\xarray\replace_coord_issue.py in <module> 11 12 new_coords = mda.coords['one'].values*2 ---> 13 mda.reset_index('one', drop=True).assign_coords(one= ('x',new_coords)).set_index(x='one',append=True)

c:\Users\aspit\anaconda3\envs\dataanalysis\lib\site-packages\xarray\core\common.py in assign_coords(self, coords, **coords_kwargs) 590 data = self.copy(deep=False) 591 results: dict[Hashable, Any] = self._calc_assign_results(coords_combined) --> 592 data.coords.update(results) 593 return data 594

c:\Users\aspit\anaconda3\envs\dataanalysis\lib\site-packages\xarray\core\coordinates.py in update(self, other) 160 other_vars = getattr(other, "variables", other) 161 self._maybe_drop_multiindex_coords(set(other_vars)) --> 162 coords, indexes = merge_coords( 163 [self.variables, other_vars], priority_arg=1, indexes=self.xindexes 164 )

c:\Users\aspit\anaconda3\envs\dataanalysis\lib\site-packages\xarray\core\merge.py in merge_coords(objects, compat, join, priority_arg, indexes, fill_value) 564 collected = collect_variables_and_indexes(aligned) 565 prioritized = _get_priority_vars_and_indexes(aligned, priority_arg, compat=compat) --> 566 variables, out_indexes = merge_collected(collected, prioritized, compat=compat) 567 return variables, out_indexes 568

c:\Users\aspit\anaconda3\envs\dataanalysis\lib\site-packages\xarray\core\merge.py in merge_collected(grouped, prioritized, compat, combine_attrs, equals) 252 253 _assert_compat_valid(compat) --> 254 _assert_prioritized_valid(grouped, prioritized) 255 256 merged_vars: dict[Hashable, Variable] = {}

c:\Users\aspit\anaconda3\envs\dataanalysis\lib\site-packages\xarray\core\merge.py in _assert_prioritized_valid(grouped, prioritized) 199 common_names_str = ", ".join(f"{k!r}" for k in common_names) 200 index_names_str = ", ".join(f"{k!r}" for k in index_coord_names) --> 201 raise ValueError( 202 f"cannot set or update variable(s) {common_names_str}, which would corrupt " 203 f"the following index built from coordinates {index_names_str}:\n"

ValueError: cannot set or update variable(s) 'one', which would corrupt the following index built from coordinates 'x', 'one', 'two': <xarray.core.indexes.PandasMultiIndex object at 0x00000225AA53C9E0> ```

Anything else we need to know?

No response

Environment

``` import numpy as np import pandas as pd import xarray as xr

data = np.random.rand(50,5)

x_idx = np.linspace(0, 50)

mi_idx1 = ['a','b','c','d','e'] mi_idx2 = [1,2,3,4,5]

mi = pd.MultiIndex.from_arrays([mi_idx1,mi_idx2], names=['mi_idx1', 'mi_idx2'])

coords = { 'x': x_idx, 'mi': mi }

da = xr.DataArray(data, coords=coords, dims = ['x', 'mi'])

da.plot(hue='mi') ```

It appears since version 0.16.0, that plotting with a multindex coordinate as a hue dimension no longer works. In version 0.15.1 I get a plot like this:

However, when upgrading to 0.16.0 or 0.16.1 I get the following traceback:

```

ValueError Traceback (most recent call last) <ipython-input-1-2de8705a6ec5> in <module> 19 da = xr.DataArray(data, coords=coords, dims = ['x', 'mi']) 20 ---> 21 da.plot(hue='mi')

~\anaconda3\envs\datanalysis\lib\site-packages\xarray\plot\plot.py in call(self, kwargs) 444 445 def call(self, kwargs): --> 446 return plot(self._da, **kwargs) 447 448 # we can't use functools.wraps here since that also modifies the name / qualname

~\anaconda3\envs\datanalysis\lib\site-packages\xarray\plot\plot.py in plot(darray, row, col, col_wrap, ax, hue, rtol, subplot_kws, kwargs) 198 kwargs["ax"] = ax 199 --> 200 return plotfunc(darray, kwargs) 201 202

~\anaconda3\envs\datanalysis\lib\site-packages\xarray\plot\plot.py in line(darray, row, col, figsize, aspect, size, ax, hue, x, y, xincrease, yincrease, xscale, yscale, xticks, yticks, xlim, ylim, add_legend, _labels, args, *kwargs) 293 294 ax = get_axis(figsize, size, aspect, ax) --> 295 xplt, yplt, hueplt, xlabel, ylabel, hue_label = _infer_line_data(darray, x, y, hue) 296 297 # Remove pd.Intervals if contained in xplt.values and/or yplt.values.

~\anaconda3\envs\datanalysis\lib\site-packages\xarray\plot\plot.py in _infer_line_data(darray, x, y, hue) 66 67 if y is None: ---> 68 xname, huename = _infer_xy_labels(darray=darray, x=x, y=hue) 69 xplt = darray[xname] 70 if xplt.ndim > 1:

~\anaconda3\envs\datanalysis\lib\site-packages\xarray\plot\utils.py in _infer_xy_labels(darray, x, y, imshow, rgb) 378 y, x = darray.dims 379 elif x is None: --> 380 _assert_valid_xy(darray, y, "y") 381 x = darray.dims[0] if y == darray.dims[1] else darray.dims[1] 382 elif y is None:

~\anaconda3\envs\datanalysis\lib\site-packages\xarray\plot\utils.py in _assert_valid_xy(darray, xy, name) 410 if xy not in valid_xy: 411 valid_xy_str = "', '".join(sorted(valid_xy)) --> 412 raise ValueError(f"{name} must be one of None, '{valid_xy_str}'") 413 414

ValueError: y must be one of None, 'mi_idx1', 'mi_idx2', 'x'

```

Setting x='x' does not fix the problem