make Table.to_dataframe create real sparse frames
#809
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This is great, thank you! In prior versions of pandas, we couldn't pass
scipy sparse objects. Do you know by chance if this effects our present
minimum version requirement?
…On Thu, Mar 7, 2019, 1:01 PM Christian Diener ***@***.***> wrote:
So this is a proposed fix to #808
<#808>. It basically
specifies the fill value for sparse data explicitly. I also added a test to
check for that in the future.
Before that the the matrix data was first converted to individual
pandas.SparseSeries and passed to pandas.SparseDataFrame. This is now
done directly without the intermediate allocations since SparseDataFrame
accepts any scipy sparse matrix. If Table.matrix_data can be something
else than a numpy matrix or scipy sparse matrix this might not work but for
now all tests seem to pass. Happy to change that back to the old behavior
if that was on purpose.
As a side effect to_dataframe is now much faster on large sparse data
sets. Takes about 20s on my machine for the American Gut biom (was more
than 30m before).
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#809
Commit Summary
- specify fill value
File Changes
- *M* biom/table.py
<https://github.com/biocore/biom-format/pull/809/files#diff-0> (9)
- *M* biom/tests/test_table.py
<https://github.com/biocore/biom-format/pull/809/files#diff-1> (9)
Patch Links:
- https://github.com/biocore/biom-format/pull/809.patch
- https://github.com/biocore/biom-format/pull/809.diff
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wasade
requested changes
Mar 7, 2019
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Was introduced in pandas 0.20.0, so should be fine with the current version requirement (>=0.20.0). |
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Looks great! |
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Thanks @cdiener and @ElDeveloper! |
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So this is a proposed fix to #808. It basically specifies the fill value for sparse data explicitly. I also added a test to check for that in the future.
Before that the the matrix data was first converted to individual
pandas.SparseSeriesand passed topandas.SparseDataFrame. This is now done directly without the intermediate allocations sinceSparseDataFrameaccepts any scipy sparse matrix. IfTable.matrix_datacan be something else than a numpy matrix or scipy sparse matrix this might not work but for now all tests seem to pass. Happy to change that back to the old behavior if that was on purpose.As a side effect
to_dataframeis now much faster on large sparse data sets. Takes about 20s on my machine for the American Gut biom (was more than 30m before).