pyspark.sql.Column.eqNullSafe¶

Column.eqNullSafe(other: Union[Column, LiteralType, DecimalLiteral, DateTimeLiteral]) → Column¶

Equality test that is safe for null values.

Parameters

other: a value or Column

Notes

Unlike Pandas, PySpark doesn’t consider NaN values to be NULL. See the NaN Semantics for details.

Examples

>>> 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|
|           false|          false|            true|
|            true|          false|           false|
+----------------+---------------+----------------+

pyspark.sql.Column.endswith

pyspark.sql.Column.getField