issues

closes #2084
a) plot.line can now accept also 'row' and 'col' keywords.
b) If 'hue' is passed as a keyword to DataArray.plot() it generates
   a line plot FacetGrid.
c) Line plots are automatically generated if the number of dimensions
   after faceting along row and/or col is one.

• [x] Closes #xxxx (remove if there is no corresponding issue, which should only be the case for minor changes)
• [x] Tests added (for all bug fixes or enhancements)
• [x] Tests passed (for all non-documentation changes)
• [x] Fully documented, including whats-new.rst for all changes and api.rst for new API (remove if this change should not be visible to users, e.g., if it is an internal clean-up, or if this is part of a larger project that will be documented later)

• [ ] Tests added
• [ ] Fully documented, including whats-new.rst for all changes and api.rst for new API

A very simple way to iterate over DataArrays, imitating numpy's flat iterator.

For a DataArray da, I implemented a flat iterator that works exactly like da.values.flat, but returns a singelton DataArray that has all the coordinates and meta data and whatnot.

I wrote it because I needed it for something and thought it would be useful for others too. I'd love to hear your opinions - if you think it's useful I'll write up some unittests.

Fix typo in error message in plot.py

• [ ] Closes #xxxx
• [ ] Tests added
• [ ] Passes black . && mypy . && flake8
• [ ] Fully documented, including whats-new.rst for all changes and api.rst for new API

• [x] Closes #470
• [x] Tests added (for all bug fixes or enhancements)
• [x] Tests passed (for all non-documentation changes)
• [x] Fully documented, including whats-new.rst for all changes and api.rst for new API
• [x] Add support for size?
• [x] Revert hue=datetime support bits

Say you have two variables in a Dataset and you want to make a scatter plot of one vs the other, possibly using different hues and/or faceting. This is useful if you want to inspect the data to see whether two variables have some underlying relationships between them that you might have missed. It's something that I found myself manually writing the code for quite a few times, so I thought it would be better to have it as a feature. I'm not sure if this is actually useful for other people, but I have the feeling that it probably is.

First, set up dataset with two variables:

```python import xarray as xr import numpy as np import matplotlib from matplotlib import pyplot as plt

A = xr.DataArray(np.zeros([3, 11, 4, 4]), dims=[ 'x', 'y', 'z', 'w'], coords=[np.arange(3), np.linspace(0,1,11), np.arange(4), 0.1np.random.randn(4)]) B = 0.1A.x2+A.y2.5+0.1A.zA.w A = -0.1*A.x+A.y/(5+A.z)+A.w ds = xr.Dataset({'A':A, 'B':B}) ds['w'] = ['one', 'two', 'three', 'five'] Now, we can plot all values of `A` vs all values of `B`:python plt.plot(A.values.flat,B.values.flat,'.') ```

What a mess. Wouldn't it be nice if you could color each point according to the value of some coordinate, say w? python ds.scatter(x='A',y='B', hue='w') Huh! There seems to be some underlying structure there. Can we also facet over a different coordinate? python ds.scatter(x='A',y='B',col='x', hue='w') or two coordinates? python ds.scatter(x='A',y='B',col='x', row='z', hue='w')

The logic is that dimensions that are not faceted/hue are just stacked using xr.stack and plotted. Only variables that have exactly the same dimensions are allowed.

Regarding implementation -- I am certainly not sure about the API and I probably haven't thought about edge cases with missing data or nans or whatnot, so any input would be welcome. Also, there might be a simpler implementation by first using to_array and then using existing line plot functions, but I couldn't find it.

Small typo.

just a small typo

• [x] Closes #2457 #2748
• [x] Tests added
• [x] Fully documented, including whats-new.rst for all changes and api.rst for new API

I took a simple-minded strategy to fix: just mask and then rename the result to self.name. This is a slight overkill since the renaming will be done on every masking, not just on the edge cases when it's needed, but this is really a very small computational cost and it's much easier and bulletproof.

• [x] Closes #2248 (remove if there is no corresponding issue, which should only be the case for minor changes)
• [x] Tests added (for all bug fixes or enhancements)
• [x] Tests passed (for all non-documentation changes)

Fixed a syntax inconsistency, allowing to pass unnamed arguments to faceted line plots.

Example code: ```python import xarray as xr import numpy as np

np.random.seed(0) da = xr.DataArray(np.random.randn(3, 3, 3, 3), dims=['x', 'y', 'z', 'w'], coords=[range(3), [3.5, 4.9, 6.7], ['a','b','c'], ['foo', 'bar', 'foobar']], name='arr_name') da.plot.line('o--',row='y', col='w',hue='z') ```

Closes #2295 (remove if there is no corresponding issue, which should only be the case for minor changes)

• [x] Closes #2248 (remove if there is no corresponding issue, which should only be the case for minor changes)
• [x] Tests added (for all bug fixes or enhancements)
• [x] Tests passed (for all non-documentation changes)

Fixed a syntax inconsistency, allowing to pass unnamed arguments to faceted line plots.

```python Example code: import xarray as xr import numpy as np

np.random.seed(0) da = xr.DataArray(np.random.randn(3, 3, 3, 3), dims=['x', 'y', 'z', 'w'], coords=[range(3), [3.5, 4.9, 6.7], ['a','b','c'], ['foo', 'bar', 'foobar']], name='arr_name') da.plot.line('o--',row='y', col='w',hue='z') ```