Yikun commented on code in PR #36356:
URL: https://github.com/apache/spark/pull/36356#discussion_r858401383
##########
python/pyspark/pandas/tests/test_dataframe.py:
##########
@@ -1814,8 +1814,12 @@ def test_nlargest(self):
index=np.random.rand(7),
)
psdf = ps.from_pandas(pdf)
- self.assert_eq(psdf.nlargest(5, columns="a"), pdf.nlargest(5,
columns="a"))
- self.assert_eq(psdf.nlargest(5, columns=["a", "b"]), pdf.nlargest(5,
columns=["a", "b"]))
+ # see also: https://github.com/pandas-dev/pandas/issues/46589
+ if not (LooseVersion("1.4.0") <= LooseVersion(pd.__version__) <=
LooseVersion("1.4.2")):
+ self.assert_eq(psdf.nlargest(5, columns="a"), pdf.nlargest(5,
columns="a"))
+ self.assert_eq(
+ psdf.nlargest(5, columns=["a", "b"]), pdf.nlargest(5,
columns=["a", "b"])
+ )
Review Comment:
If you still think I need to compare with real results rather than skip, I'd
also like to change. We need to change `index=np.random.rand(7)` to a certain
range, and construct a result df.
Because this is only failed with panda 1.4.0~1.4.2, so I thought skip is
enough.
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