asl3 commented on code in PR #42332:
URL: https://github.com/apache/spark/pull/42332#discussion_r1283898250
##########
python/pyspark/sql/tests/test_utils.py:
##########
@@ -746,28 +748,123 @@ def test_assert_unequal_null_expected(self):
)
def test_assert_equal_exact_pandas_df(self):
+ df1 = pd.DataFrame(
+ data=np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)]), columns=["a",
"b", "c"]
+ )
+ df2 = pd.DataFrame(
+ data=np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)]), columns=["a",
"b", "c"]
+ )
+
+ assertDataFrameEqual(df1, df2, checkRowOrder=False)
Review Comment:
@zhengruifeng Yes, since `assertDataFrameEqual` asserts approximate equality
by default for float-like values, it will call the
[pandasutils.py](https://github.com/apache/spark/blob/4ed59d932631ba819d6b6071ee91408622a312db/python/pyspark/testing/pandasutils.py#L4)
`_assert_pandas_almost_equal` util to assert approximate equality for
float-like vals (instead of the pandasutils.py `_assert_pandas_equal`, which
has the same behavior as pandas assert_frame_equal).
The `assertPandasOnSparkEqual` from pandasutils.py calls
`_assert_pandas_equal` or `_assert_pandas_almost_equal` depending on user config
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