BUG: CategoricalIndex.format by topper-123 · Pull Request #35440 · pandas-dev/pandas (original) (raw)
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Thanks @topper-123 for the PR.
The regression was caused by #35118. Categorical types other than object were also affected. maybe need to parameterise test with other values for cols
>>> pd.__version__
'1.2.0.dev0+10.g3b1d4f1ee'
>>>
>>> data = [[4, 2], [3, 2], [4, 3]]
>>> cols = [1, None]
>>> res = pd.DataFrame(data, columns=cols)
>>> print(res)
1 NaN
0 4 2
1 3 2
2 4 3
>>>
>>> res = pd.DataFrame(data, columns=pd.CategoricalIndex(cols))
>>> print(res)
1 NaN
0 4 2
1 3 2
2 4 3
>>>
>>> pd.__version__
'1.0.5'
>>>
>>> data = [[4, 2], [3, 2], [4, 3]]
>>> cols = [1, None]
>>> res = pd.DataFrame(data, columns=cols)
>>> print(res)
1 NaN
0 4 2
1 3 2
2 4 3
>>>
>>> res = pd.DataFrame(data, columns=pd.CategoricalIndex(cols))
>>> print(res)
1.0 NaN
0 4 2
1 3 2
2 4 3
>>>
I've temporarily put the whatsnewentry in the v.1.1.0 release note, because there isn't a v.1.1.1 version yet.
doc\source\whatsnew\v1.1.1.rst now merged to master
| @@ -197,9 +197,6 @@ def _format_data(self, name=None): |
|---|
| # we are formatting thru the attributes |
| return None |
| def _format_with_header(self, header, na_rep="NaN") -> List[str]: |
| return header + [pprint_thing(x) for x in self._range] |
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The added tests revealed that this method in master made the output from RangeIndex.format different than for Int64Index.format:
pd.RangeIndex(0, 18, 2).format() ['0', '2', '4', '6', '8', '10', '12', '14', '16'] pd.Int64Index(range(0, 18, 2)).format() ['0 ', '2 ', '4 ', '6 ', '8 ', '10', '12', '14', '16']
Notice the extra space for one-digit scalars in the Int64Index case. The outputs from the two methods are identical after merging this PR.
meeseeksmachine pushed a commit to meeseeksmachine/pandas that referenced this pull request
simonjayhawkins pushed a commit that referenced this pull request
Co-authored-by: Terji Petersen contribute@tensortable.com