842 dump and load scalars of sessions#843
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larray/inout/hdf.py
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| res = LGroup(key=key, name=name, axis=axis) | ||
| except KeyError: | ||
| scalar_key = key | ||
| attrs = store.root._v_attrs |
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From the description of the "obj" argument of the tables.Array class (at http://www.pytables.org/usersguide/libref/homogenous_storage.html#tables.Array), it seems like Python scalars might be natively supported by pytables...
| axis = read_hdf(filepath_or_buffer, attrs['axis_key']) | ||
| res = LGroup(key=key, name=name, axis=axis) | ||
| elif _type in {cls.__name__ for cls in _supported_scalars_types}: | ||
| res = pd_obj.values |
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I wonder if you should not finish #761 before this PR, because supporting scalars via Pandas is not actually needed. We will need to support them in the "direct"/#761 path anyway and have to support Arrays, Axis and Groups on both (for backward compatibility) be we can skip scalars via Pandas (that would mean a few lines of code fewer to maintain).
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Now it is there, let's finish this. I don't know when I will go back to work on PR #761 (partly because I don't like to work with pytables and prefer h5py but my intuition tells me we need pytables for our case).
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I thought h5py had the same features than pytables with a different API, except for querying (and optionally indexing) tables. And since we wouldn't need either feature, I would think using h5py would not be a problem. That said I never used h5py so I don't know for sure.
In either case, I wouldn't use h5py anyway because pandas relies on pytables and since we will have to keep the current code (using pytables indirectly) for backward compat for a while, depending on both pytables and h5py would be weird.
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OK with the last commit? |
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sure |
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So, OK to merge? |
…ession object (hdf5 + pickle formats)
…ore updating conda
alixdamman
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Thanks for the review. |
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