TST mark test as xfail due to bug fix in pandas-dev #26344
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So this means we won't be really supporting |
This does not change. Here, it is just that the number of column detected as numerical or categorical will changed depending if |
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I think we can adjust the implementation to infer better dtypes. I opened #26386 as an alternative to this PR. |
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Closing since #26386 has been merged |
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Partially address #26154
Solving the issue pointed out here: #26154 (comment)
In short,
pandaswill better infer type duringDataFrameconcatenation with missing values. Previously, due to the way we read by chunk in theliac-arffparser, we could end up withNoneandnp.nanin the same column. The new version of pandas will identify both values are missing values.Since the new behaviour is what one would expect but we cannot make a backport, a way is to mark the test as
xfail.