8000 PERF: optimize DataFrame.sparse.from_spmatrix performance by rth · Pull Request #32825 · pandas-dev/pandas · GitHub
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PERF: optimize DataFrame.sparse.from_spmatrix performance #32825

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Merged
merged 18 commits into from
Mar 22, 2020
Merged

PERF: optimize DataFrame.sparse.from_spmatrix performance #32825

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3ffe7f2
PERF: fix SparseArray._simple_new object initialization
jorisvandenbossche Mar 19, 2020
93bf825
Initial implementation
rth Mar 19, 2020
e21701b
Merge remote-tracking branch 'upstream/master' into opt-sparse-init
rth Mar 19, 2020
e095e7f
Improve docstring
rth Mar 19, 2020
11afe40
Add what's new
rth Mar 19, 2020
eda0732
Update doc/source/whatsnew/v1.1.0.rst
rth Mar 19, 2020
40f4cd6
Improve what's new
rth Mar 19, 2020
508fda5
Add random state
rth Mar 19, 2020
0053817
Add inline comment about sort_indices
rth Mar 19, 2020
42792a3
Use absolute import
rth Mar 19, 2020
c8f7abc
Update pandas/_libs/sparse.pyx
rth Mar 19, 2020
e541b0d
Rename variable
rth Mar 19, 2020
0a0bafc
Merge remote-tracking branch 'upstream/master' into opt-sparse-init
rth Mar 20, 2020
ce6619e
Use DataFrame._from_arrays(..., verify_integrity=False)
rth Mar 20, 2020
e063c8a
checkout upstream/master -- asv_bench/benchmarks/sparse.py
rth Mar 20, 2020
8efe3ad
Merge remote-tracking branch 'upstream/master' into opt-sparse-init
rth Mar 22, 2020
20c3685
Link to PR's by Joris in what's new
rth Mar 22, 2020
d750eb4
Use ensure_index when the columns/index is provided by the user
rth Mar 22, 2020
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Rename variable
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@rth
rth committed Mar 19, 2020
commit e541b0dbcfa57d66dc10466d853338eab8ad4ccb
6 changes: 3 additions & 3 deletions pandas/core/arrays/sparse/accessor.py
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Expand Up @@ -240,13 +240,13 @@ def from_spmatrix(cls, data, index=None, columns=None):
data.sort_indices()
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@rth rth Mar 19, 2020 •
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It might be already done in tocsc, but that's a scipy implementation detail, and it doesn't really cost much. We need to make sure indices are sorted, since we create IntIndex with check_integrity=False that used to check for this.

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Maybe add a comment about that inline?

indices = data.indices
indptr = data.indptr
data = data.data
dtype = SparseDtype(data.dtype, 0)
array_data = data.data
dtype = SparseDtype(array_data.dtype, 0)
arrays = []
for i in range(n_columns):
sl = slice(indptr[i], indptr[i + 1])
idx = IntIndex(n_rows, indices[sl], check_integrity=False)
arr = SparseArray._simple_new(data[sl], idx, dtype)
arr = SparseArray._simple_new(array_data[sl], idx, dtype)
arrays.append(arr)
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FWIW, also tried with a generator here to avoid pre-allocating all the arrays, but it doesn't really matter. Most of the remaining run time is in DataFrame._from_arrays.

return DataFrame._from_arrays(arrays, columns=columns, index=index)
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This line will be able to use the verify_integrity=False from #32858 (or if this PR goes in first, I can add it in that PR)


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