pyspark.sql.functions.lit#
- pyspark.sql.functions.lit(col)[source]#
Creates a
Column
of literal value.New in version 1.3.0.
Changed in version 3.4.0: Supports Spark Connect.
- Parameters
- col
Column
, str, int, float, bool or list, NumPy literals or ndarray. the value to make it as a PySpark literal. If a column is passed, it returns the column as is.
Changed in version 3.4.0: Since 3.4.0, it supports the list type.
- col
- Returns
Column
the literal instance.
Examples
Example 1: Creating a literal column with an integer value.
>>> import pyspark.sql.functions as sf >>> df = spark.range(1) >>> df.select(sf.lit(5).alias('height'), df.id).show() +------+---+ |height| id| +------+---+ | 5| 0| +------+---+
Example 2: Creating a literal column from a list.
>>> import pyspark.sql.functions as sf >>> spark.range(1).select(sf.lit([1, 2, 3])).show() +--------------+ |array(1, 2, 3)| +--------------+ | [1, 2, 3]| +--------------+
Example 3: Creating a literal column from a string.
>>> import pyspark.sql.functions as sf >>> df = spark.range(1) >>> df.select(sf.lit("PySpark").alias('framework'), df.id).show() +---------+---+ |framework| id| +---------+---+ | PySpark| 0| +---------+---+
Example 4: Creating a literal column from a boolean value.
>>> import pyspark.sql.functions as sf >>> df = spark.createDataFrame([(True, "Yes"), (False, "No")], ["flag", "response"]) >>> df.select(sf.lit(False).alias('is_approved'), df.response).show() +-----------+--------+ |is_approved|response| +-----------+--------+ | false| Yes| | false| No| +-----------+--------+