Comments (2)
I would recommend COO
over GCXS
as it is better tested, but in essence, yes; that's what you'd do.
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Closing as the original question was answered. Please open a new issue or comment/re-open if needed.
from sparse.
Related Issues (20)
- Support `arr.size >= 2 ** 64` as long as each dimension is `< 2 ** 64` HOT 1
- Constructing GCXS from non-canonical `scipy.sparse.csr_matrix` results in wrong results HOT 5
- Support for Fortran order in COO.flatten() HOT 1
- Consider MatRepr for `__repr__` and `_repr_html_` HOT 9
- `GCXS` matmul => slice leads to incorrect results HOT 4
- `TypeError` when accessing `.real` and `.imag` attribute on sparse array of string type
- Installing directly from GitHub fails on Python 3.12 HOT 5
- Some functions seem missing from the top level import HOT 2
- [conda] Sparse version string is not recognized by pip list/check HOT 1
- Segmentation fault on arm64 HOT 12
- Remove runtime dependency on SciPy HOT 1
- Xarray/Numpy/Dask Indexing using a Sparse Array HOT 6
- Faster matmul for case sparse.coo * np.ndarray HOT 1
- Memory usage compared to scipy.sparse.coo_matrix HOT 1
- `to_coo` raises DeprecationWarning HOT 1
- NumPy 2.0 support HOT 3
- ``RecursionError`` using `numpy.real`/`numpy.imag` on ``sparse.COO`` array HOT 4
- Include notebooks in documentation HOT 1
- Reduce benchmark CI load
- Quansight Labs 2024 Internship
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from sparse.