BUG: Preserve sparse dtype when reindexing #26183
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| Hello @alex-hutton! Thanks for updating this PR. We checked the lines you've touched for PEP 8 issues, and found: There are currently no PEP 8 issues detected in this Pull Request. Cheers! 🍻 Comment last updated at 2019-04-22 12:35:45 UTC |
alex-hutton force-pushed the preserve-sparse-dtype-26123 branch from 534aed6 to b8754b6 Compare - This is currently broken due to many tests relying on possibly broken behaviour. - BUG pandas-dev#26123.
alex-hutton force-pushed the preserve-sparse-dtype-26123 branch from b8754b6 to 6b9035c Compare alex-hutton changed the title 
gfyoung added Bug Regression 
jreback requested changes Apr 28, 2019
| new_data = new_data.copy() | ||
| | ||
| return self._constructor(new_data).__finalize__(self) | ||
| kwargs = {'dtype': self._data.dtype} if preserve_dtype else {} |
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what happens if you always just pass the dtype of the original in
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| closing as stale. if you'd to continue, pls merge master and ping. |
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git diff upstream/master -u -- "*.py" | flake8 --diffHi, I've been taking a look at this but I need to verify some of the surrounding functionality so that I prevent introducing more breakages.
In this test, https://github.com/alex-hutton/pandas/blob/preserve-sparse-dtype-26123/pandas/tests/sparse/test_combine_concat.py#L99-L110 , my interpretation is that its purpose is to verify that if two
SparseSeriesare concatenated, afill_valueset on either of thoseSparseSeriesshould be ignored in favour of the defaultfill_valueofNaN.Is my interpretation correct, and is this test correct? It seems odd that the the
fill_valueof theSparseSeriesshould be disregarded, but I am probably missing something.