BUG: iloc raising for ea series #50171
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phofl
added
Indexing
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See also my more general comment about using IntegerArrays in .iloc in #49521.
| df = DataFrame([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]) | ||
| result = df.iloc[Series([1], dtype="Int64"), Series([0, 1], dtype="Int64")] | ||
| expected = DataFrame([[5, 6]], index=[1]) | ||
| tm.assert_frame_equal(result, expected) |
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I'm pretty suire this would fail if a pd.NA was in the indexers?
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Yep it does, which is fine. Will add a test
| @@ -1481,7 +1481,10 @@ def _validate_key(self, key, axis: AxisInt): | |||
| # so don't treat a tuple as a valid indexer | |||
| raise IndexingError("Too many indexers") | |||
| elif is_list_like_indexer(key): | |||
| arr = np.array(key) | |||
| if isinstance(key, ABCSeries): | |||
| arr = key._values | |||
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How about integer arrays with pd.NA?
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The errors are different for Series and IntegerArray:
>>> import pandas as pd
>>> df = pd.DataFrame([[0,1,2,3,4],[5,6,7,8,9]])
>>> iarr = pd.array([0,1,2, pd.NA], dtype = pd.Int64Dtype())
>>> df.iloc[:, iarr]
IndexError: .iloc requires numeric indexers, got [0 1 2 <NA>]
df.iloc[:, pd.Series(iarr)]
ValueError: cannot convert to 'int64'-dtype NumPy array with missing values. Specify an appropriate 'na_value' for this dtype.I think the ValueError should be raised in both cases?
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I guess you tried the series case on main? I am getting consistent errors on my pr.
In General I would prefer a better suited error, pointing to NA in iloc. But will probably do in a follow up. We will have to check this later, because not all cases get here and this does not cover the series.iloc case
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Sorry wrong snippet. It's the non-NA array:
>>> import pandas as pd
>>> df = pd.DataFrame([[0,1,2,3,4],[5,6,7,8,9]])
>>> iarr = pd.array([0,1,2], dtype = pd.Int64Dtype())
>>> df.iloc[:, iarr]
IndexError: .iloc requires numeric indexers, got [0 1]There was a problem hiding this comment.
Ah got you, fixed that as well
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Thanks @phofl |
doc/source/whatsnew/vX.X.X.rstfile if fixing a bug or adding a new feature.