issue_comments

It might make sense to use a list instead of a set here. On Thu, Aug 23, 2018 at 8:37 PM Keisuke Fujii notifications@github.com wrote:

@fujiisoup commented on this pull request.

Thanks. A few comments.

In xarray/core/dataset.py https://github.com/pydata/xarray/pull/2375#discussion_r212513643:

+ + missing_dims = [dim for dim in dims if dim not in self.dims] + if missing_dims: + raise ValueError('Dataset does not contain the dimensions: %s' + % missing_dims) + + non_multi_dims = [dim for dim in dims + if not isinstance(self.get_index(dim), pd.MultiIndex)] + if non_multi_dims and dim_from_kwarg: + raise ValueError('cannot unstack dimensions that do not ' + 'have a MultiIndex: %s' % non_multi_dims) + + dims = dims - set(non_multi_dims) + if len(dims) == 0: + raise ValueError('cannot unstack an object that does not have ' + 'MultiIndex dimensions')

I think that we can allow to unstack an object without MultiIndex, which just returns as is. It would be useful if users want to remove any MultiIndexes from an object.

---

In xarray/core/dataset.py https://github.com/pydata/xarray/pull/2375#discussion_r212513859:

• ---

• unstacked : Dataset
• Dataset with unstacked data. +
• See also

• ---

• Dataset.stack
• """
• dim_from_kwarg = dim is not None +
• if isinstance(dim, basestring):
• dims = set([dim])
• elif dim is None:
• dims = set(self.dims)
• else:
• dims = set(dim)

Maybe we can use OrderedSet instead of set so that the resultant dimension order is fixed.

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Dataset.transpose accepts *args based on the design of numpy.ndarray.transpose, but that API is probably a mistake (both in NumPy and xarray). Everything else uses an axis/dim argument that can take a scalar or sequence value. On Thu, Aug 23, 2018 at 9:56 AM Julia Signell notifications@github.com wrote:

I can change it. I guess I was looking at Dataset.transpose: https://github.com/pydata/xarray/blob/master/xarray/core/dataset.py#L2498

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I chose to use *dims rather than a list of dims so that this change will have a very small impact on people. Most people probably do something like unstack('z') right now, and that will still work.

Usually we prefer to stick to a single argument, but use isinstance checks to support both single dimensions and lists of dimensions, e.g., see how dim is parsed in Dataset.reduce: https://github.com/pydata/xarray/blob/master/xarray/core/dataset.py#L2774-L2779

I think unstack() unstacking all dimensions by default would make sense.

Should we be using xr.full_like in this way?

I'm not really opposed to full_like working this way, but it does look a little strange to my eye. The "full" part of the name doesn't really make sense to me. I would usually suggest using the DataArray constructor here, e.g., xr.DataArray(output_values, flat_input.coords, flat_input.dims, flat_inputs.attrs).

Maybe we can figure a better way to spell "label these arrays like this template xarray object" that doesn't require referencing flat_input multiple times. Maybe xarray.label_like(array, source) or source.with_data(array)?

Would something like xr.unstack_like be desirable?

I'm not sure that a dedicated function unstack_like would make sense for xarray. This is the sort of helper function that you can write yourself in a couple of lines.