HyukjinKwon commented on a change in pull request #31207:
URL: https://github.com/apache/spark/pull/31207#discussion_r567530903
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File path: python/pyspark/sql/functions.py
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@@ -91,13 +92,58 @@ def lit(col):
Creates a :class:`Column` of literal value.
.. versionadded:: 1.3.0
+ .. versionchanged:: 3.2.0
+ Added support for complex type literals.
+
+ Parameters
+ ----------
+ col : bool, float, int, str, datetime.date, datetime.datetime, dict, list,
tuple
+ Object to be converted into :class:`Column`.
+
+ If it is a collection, conversion will be applied recursively. In such
case,
+ all stored values should be of compatible types.
+
+ The passed in object is returned directly if it is already a
:class:`Column`.
Examples
--------
>>> df.select(lit(5).alias('height')).withColumn('spark_user',
lit(True)).take(1)
[Row(height=5, spark_user=True)]
- """
- return col if isinstance(col, Column) else _invoke_function("lit", col)
+ >>> df.select(
+ ... lit({"height": 5}).alias("data"),
+ ... lit(["python", "scala"]).alias("languages")
+ ... ).take(1)
+ [Row(data={'height': 5}, languages=['python', 'scala'])]
+ """
+ if isinstance(col, Column):
+ return col
+
+ elif isinstance(col, list):
+ return array(*[lit(x) for x in col])
Review comment:
One thing I am a bit worried is that this will lazily check the type
(with type coercion) unlike Scala side ...
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