Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/17827#discussion_r114244380
--- Diff: python/pyspark/sql/column.py ---
@@ -224,7 +224,39 @@ def __init__(self, jc):
https://spark.apache.org/docs/latest/sql-programming-guide.html#nan-semantics
.. versionadded:: 2.3.0
"""
+ _isNotDistinctFrom_doc = _eqNullSafe_doc.replace("eqNullSafe",
"isNotDistinctFrom")
+ _isDistinctFrom_doc = """
+ Inequality 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'].isDistinctFrom('foo'),
+ ... df1['value'].isDistinctFrom(None)
+ ... ).show()
+ +-------------------+---------------------+----------------------+
+ |(NOT (value = foo))|(NOT (value <=> foo))|(NOT (value <=> NULL))|
+ +-------------------+---------------------+----------------------+
+ | false| false| true|
+ | null| true| false|
+ +-------------------+---------------------+----------------------+
+
+ .. note:: Unlike Pandas, PySpark doesn't consider NaN values to be
NULL.
+ See the `NaN Semantics`_ for details.
+ .. _NaN Semantics:
+
https://spark.apache.org/docs/latest/sql-programming-guide.html#nan-semantics
+ .. versionadded:: 2.3.0
+ """
--- End diff --
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