issues

The following methods have an inplace argument: DataArray.reset_coords DataArray.set_index DataArray.reset_index DataArray.reorder_levels Dataset.set_coords Dataset.reset_coords Dataset.rename Dataset.swap_dims Dataset.set_index Dataset.reset_index Dataset.reorder_levels Dataset.update Dataset.merge

As proposed in https://github.com/pydata/xarray/issues/1755#issuecomment-348682403, let's deprecate all of these at the next major release (v0.11). They add unnecessary complexity to methods and promote confusing about xarray's data model.

Practically, we would change all of the default values to inplace=None and issue either a DeprecationWarning or FutureWarning (see PEP 565 for more details on that choice).

As an example I have a DataArray called "my_dataarray" that looks something like this:

<xarray.DataArray 'values' (Type: 3)> array([1, 2, 3]) Coordinates: * Type (Type) object 'Type 1' 'Type 2' 'Type 3'

'Type' is a dimension on my DataArray. Note that 'Type' is also a DataArray that looks like this:

OrderedDict([('Type', <xarray.IndexVariable 'Type' (Type: 3)> array(['Type 1', 'Type 2', 'Type 3'], dtype='object'))])

Let's say I run:

'Type 1' in my_dataarray.Type

The result is False, even though 'Type 1' is in the "Type" dimension.

To get the result I was expecting I need to run:

'Type 1' in my_dataarray.Type.values

Stepping through the code, the problematic line is here: https://github.com/pydata/xarray/blob/20ec32430fac63a8976699d9528b5fdc1cd4125d/xarray/core/dataarray.py#L487

The test used for __contains__(self, key) on the Type dimension is whether the key is in the _coords of Type.

This is probably the right thing to do when the DataArray is used for storing data, but probably not what we want if the DataArray is being used as a dimension - it should instead check if 'Type 1' is in the values of Type?

This has been a small-but-persistent issue for me for a while. I suspect that my perspective might be dependent on my current outlook, but socializing it here to test if it's secular...

Currently Dataset.keys() returns both variables and coordinates (but not its attrs keys):

python In [5]: ds=xr.Dataset({'a': (('x', 'y'), np.random.rand(10,2))}) In [12]: list(ds.keys()) Out[12]: ['a', 'x', 'y']

Is this conceptually correct? I would posit that a Dataset is a mapping of keys to variables, and the coordinates contain values that label that data.

So should Dataset.keys() instead return just the keys of the Variables?

We're often passing around a dataset as a Mapping of keys to values - but then when we run a function across each of the keys, we get something run on both the Variables' keys, and the Coordinate / label's keys.

In Pandas, DataFrame.keys() returns just the columns, so that conforms to what we need. While I think the xarray design is in general much better in these areas, this is one area that pandas seems to get correct - and because of the inconsistency between pandas & xarray, we're having to coerce our objects to pandas DataFrames before passing them off to functions that pull out their keys (this is also why we can't just look at ds.data_vars.keys() - because it breaks that duck-typing).

Does that make sense?

• [x] closes #2200, closes #1829, closes #2203
• [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)

This updates the following minimum versions:

• numpy: 1.11 (Mar 27, 2016) --> 1.12 (Jan 15, 2017)
• pandas: 0.18 (Mar 11, 2016) --> 0.19 (Oct 2, 2016)
• dask: 0.9 (May 10, 2016) --> 0.16

and drops our tests for python 3.4.

Python 3.7-final is due out in June (PEP 537). When do we want to deprecate 3.4 and when should we drop support all together. @maxim-lian brought this up in a PR he's working on: https://github.com/pydata/xarray/pull/1828#issuecomment-357562144.

For reference, we dropped Python 3.3 in #1175 (12/20/2016).