[GitHub] spark pull request #17605: [SPARK-20290][MINOR][PYTHON][SQL] Add PySpark wra...

[viirya <gi...@git.apache.org>]

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 don\u2019t 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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