Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/17605#discussion_r114088222
--- Diff: python/pyspark/sql/column.py ---
@@ -171,6 +171,58 @@ def __init__(self, jc):
__ge__ = _bin_op("geq")
__gt__ = _bin_op("gt")
+ _eqNullSafe_doc = """
+ Equality test that is safe for null values.
+
+ :param other: a value or :class:`Column`
+
+ >>> from pyspark.sql import Row
+ >>> df1 = spark.createDataFrame([
+ ... Row(id=1, value='foo'),
+ ... Row(id=2, value=None)
+ ... ])
+ >>> df1.select(
+ ... df1['value'] == 'foo',
+ ... df1['value'].eqNullSafe('foo'),
+ ... df1['value'].eqNullSafe(None)
+ ... ).show()
+ +-------------+---------------+----------------+
+ |(value = foo)|(value <=> foo)|(value <=> NULL)|
+ +-------------+---------------+----------------+
+ | true| true| false|
+ | null| false| true|
+ +-------------+---------------+----------------+
+ >>> df2 = spark.createDataFrame([
+ ... Row(value = 'bar'),
+ ... Row(value = None)
+ ... ])
+ >>> df1.join(df2, df1["value"] == df2["value"]).count()
+ 0
+ >>> df1.join(df2, df1["value"].eqNullSafe(df2["value"])).count()
+ 1
+ >>> df2 = spark.createDataFrame([
+ ... Row(id=1, value=float('NaN')),
+ ... Row(id=2, value=42.0),
+ ... Row(id=3, value=None)
+ ... ])
+ >>> df2.select(
+ ... df2['value'].eqNullSafe(None),
+ ... df2['value'].eqNullSafe(float('NaN')),
+ ... df2['value'].eqNullSafe(42.0)
+ ... ).show()
+ +----------------+---------------+----------------+
+ |(value <=> NULL)|(value <=> NaN)|(value <=> 42.0)|
+ +----------------+---------------+----------------+
+ | false| true| false|
--- End diff --
In Pandas/numpy, the `nan`'s donât compare equal, i.e., `np.nan` !=
`np.nan`, but in Spark we treat them as equal. Shall we document it too?
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