BUG: sorting with large float and multiple columns incorrect #14944
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jreback
reviewed
Dec 21, 2016
| columns=("int", "float")) | ||
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| df2 = df.sort_values(["int", "float"]) | ||
| assert is_sorted(df2.loc[:, "int"]) |
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use assert_frame_equal and construct the expected result.
jreback
changed the title
jreback
added
Bug
Numeric Operations
having the int equivalent of NaT in an int64 column caused wrong
sorting because this special value was considered as "missing
value".
uweschmitt
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fix-gh-14922
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having the int equivalent of NaT in an int64 column caused wrong
sorting because this special value was considered as "missing
value".
Current coverage is 84.65% (diff: 100%)@@ master #14944 diff @@
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Lines 51021 51031 +10
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+ Hits 43188 43199 +11
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jreback
reviewed
Dec 21, 2016
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| from pandas.util.testing import (assert_series_equal, | ||
| assert_frame_equal, | ||
| assertRaisesRegexp) | ||
| assertRaisesRegexp, | ||
| is_sorted) |
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you don't need to import the is_sorted
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can you add a similar tests which uses datetimes and a NaT as well (just to verify that it doesn't break). further pls check both ascending and descending. |
jreback
reviewed
Dec 21, 2016
| def test_sort_nat_values_in_int_column(self): | ||
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| # GH 14922, sorting with large float and multiple columns incorrect | ||
| int_values = (2, int(NaT)) |
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use np.iinfo(np.int64).min instead (and NaT) is already an int
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Thought NaT is implicitly indicating what went wrong...
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can you add a whatsnew note (0.20.0, bug fixes) |
jreback
reviewed
Dec 22, 2016
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| # and now check if NaT is still considered as "na" for datetime64 | ||
| # columns: | ||
| df = DataFrame(dict(int=int_values, float=float_values), |
jreback
reviewed
Dec 22, 2016
| df = DataFrame(dict(datetime=[Timestamp("2016-01-01"), NaT], | ||
| float=float_values), columns=["datetime", "float"]) | ||
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| # check if the dtype is datetime64[ns]: |
jreback
reviewed
Dec 22, 2016
| df = DataFrame(dict(datetime=[Timestamp("2016-01-01"), NaT], | ||
| float=float_values), columns=["datetime", "float"]) | ||
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| assert df["datetime"].dtypes == np.dtype("datetime64[ns]"),\ |
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this line here is not needed (the assert of datetime)
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Ok now ? |
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thanks! |
ShaharBental
pushed a commit
to ShaharBental/pandas
that referenced
this pull request
Dec 26, 2016
closes pandas-dev#14922 Having the `int` equivalent of `NaT` in an `int64` column caused wrong sorting because this special value was considered as "missing value". Author: Uwe <[email protected]> Closes pandas-dev#14944 from uweschmitt/fix-gh-14922 and squashes the following commits: c244438 [Uwe] further cleanup tests 4f28026 [Uwe] fixed typo in whatsnew/v0.20.0.txt 60cca5d [Uwe] add fix of GH14922 to release notes for 0.20.0 04dcbe8 [Uwe] further test cleanup 21e610c [Uwe] extended tests + minor cleanup 358a31e [Uwe] Merge branch 'fix-pandas-devgh-14922' of github.com:uweschmitt/pandas into fix-pandas-devgh-14922 03699c6 [Uwe] Fix GH 14922 1afdbb8 [Uwe] Fix GH 14922
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Fixes #14922
Having the
intequivalent ofNaTin anint64column caused wrong sorting because this special value was considered as "missing value".git diff upstream/master | flake8 --diff