issues

Waiting on: - https://github.com/numba/numba/issues/8304 - https://github.com/numba/numba/issues/8841 - https://github.com/zarr-developers/numcodecs/issues/377 - https://github.com/Unidata/netcdf4-python/issues/1204 - https://github.com/h5py/h5py/issues/2146

• [ ] Closes #7316
• [ ] Tests added
• [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] New functions/methods are listed in api.rst

Feel free to push to this PR if you want.

• [x] Closes #7907
• [x] Closes #7908
• [x] User visible changes (including notable bug fixes) are documented in whats-new.rst

Using a different TypeVar strategy compared to #8281. The idea here is to typevar shape and dtype instead, just like numpy does.

Previously I tried to use the _data array as the TypeVar but that causes all kinds of issues since TypeVar is usually invariant and can't be updated to a new type. Since the dtype changes very frequently when doing array operations it quickly gets difficult to pass along the correct typing.

• This PR adds a from_array function. The intention is to use that function to create NamedArrays when you are passing around ArrayLikes. The init for NamedArray will now just assume the input data is correct. At runtime at least, mypy will catch any non-supported array types. There's some precedent to this:
• numpy.array_api.Array forces to use xp.asarray.
• Cubed assumes the inputs are correct. Has a xp.asarray and a from_array function.
• The ugly fastpath argument is therefore not needed.
• Adds a bunch of type hint classes, duckarray[ShapeType, DType] (corresponding to np.ndarray) or DuckArray[ScalarType] (corresponding to np.typing.NDArray) are the recommended ones.
• It's better to use these kinds of classes over creating is_duck_array functions with typeguards because isinstance also works on the else clause.

• This PR adds some array_api functions, the idea here is that NamedArray could also be array_api compliant.

• [x] Tests added

• [x] Closes #8291

References: https://github.com/tomwhite/cubed/blob/ea885193dd37d27917a24878b51bb086aaef5fb1/cubed/core/ops.py#L34 https://stackoverflow.com/questions/74633074/how-to-type-hint-a-generic-numpy-array https://numpy.org/doc/stable/reference/arrays.scalars.html#scalars https://github.com/numpy/numpy/blob/040ed2dc9847265c581a342301dd87d2b518a3c2/numpy/init.pyi#L1423 https://github.com/numpy/numpy/blob/040ed2dc9847265c581a342301dd87d2b518a3c2/numpy/_typing/_array_like.py#L32 https://stackoverflow.com/questions/69186176/determine-if-subclass-has-a-base-classs-method-implemented-in-python

Waiting on * https://github.com/ContinuumIO/anaconda-issues/issues/12669 * https://github.com/conda/conda/pull/10970 * https://github.com/conda-forge/python-feedstock/pull/511 * https://github.com/conda-forge/numba-feedstock/pull/86 * https://github.com/pydap/pydap/pull/210

• [ ] Closes #xxxx
• [ ] Tests added
• [ ] Passes pre-commit run --all-files
• [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] New functions/methods are listed in api.rst

Allow different datatypes (in particular booleans) to be interpolated instead of dropping them.

• [x] Requires #4740.
• [x] Closes #4761
• [x] Tests added
• [x] Passes pre-commit run --all-files

• [x] Requires #5314
• [x] Passes pre-commit run --all-files

Is your feature request related to a problem? Please describe. As a new user I find it difficult to get a new dataset initialized because the necessary parameters are not shown in the docstring. I have to google "xarray dataset" to get to http://xarray.pydata.org/en/stable/generated/xarray.Dataset.html to figure it out.

In the figure below xarray.Dataset does not show the necessary parameters in the help pane:

Compare to pandas.DataFrame that includes it:

Describe the solution you'd like Looking at https://github.com/pydata/xarray/blob/master/xarray/core/dataset.py#L428 the xr.Dataset.__init__.__doc__ does contain the necessary parameters so the suggestion is to simply move or copy that information up one level toxr.Dataset.__doc__

For reference pandas does not use a docstring for the init method: https://github.com/pandas-dev/pandas/blob/v1.1.0/pandas/core/frame.py#L339-L9257

The pandas docs also includes a few simple copy/pasteable examples on how to initialize. So a xarray example would be: ```python

import numpy as np import xarray as xr x = np.arange(4) y = 2*x ds = xr.Dataset({'y': (['x'], y)}, ... coords={'x': x}) print(ds) <xarray.Dataset> Dimensions: (x: 4) Coordinates: * x (x) int32 0 1 2 3 Data variables: y (x) int32 0 2 4 6 ``` Or take some examples from http://xarray.pydata.org/en/stable/quick-overview.html#datasets or http://xarray.pydata.org/en/stable/data-structures.html#dataset although I found those a little bit confusing as they were dependent on previous results or rather complex with many dimensions.

Environment: