scverse / scverse/fast-array-utils
Which datatypes does `to_dense` accept?
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- Python
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Description
Thank you for this package!
I was wondering if you could extend either the functionality or the documentation of supported input datatypes of to_dense
For example the following common use-case works:
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: from fast_array_utils.conv import to_dense
In [4]: to_dense(pd.DataFrame(np.random.randn(100,5)))
but it is not documented.
Furthermore, I was wondering why you split the functionality into: fast_array_utils.conv.scipy.to_dense and fast_array_utils.conv.to_dense
For me the advantage of this function is precisely not having to think about the which kind of array we are putting in.
Thank you!
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Research direction
Start by reading the public fast_array_utils.conv.to_dense and fast_array_utils.conv.scipy.to_dense entry points mentioned in the issue. Document which input datatypes, including the pandas DataFrame example, are supported and explain why the two entry points are separate. Done means the supported inputs and their relationship are clear to users.
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Assessment
- Tech stack
- numpy, pandas, python
- Domain
- data
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- Half a day
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 38/100