你可以编写类似下面的代码，这里我假设日期列是字符串类型的

from pyspark.sql.functions import date_format, to_date

data = [("20231030",), ("20231115",), ("20231225",)]
columns = ["date_string"]

df = spark.createDataFrame(data, columns)
df = df.withColumn("to_date_format", to_date(df["date_string"], "yyyyMMdd"))
df = df.withColumn("formatted_date", date_format(df["to_date_format"], "dd/MM/yyyy"))

df.show()
df.printSchema()

字符串
产出：

>>> df.show()
+-----------+--------------+--------------+
|date_string|to_date_format|formatted_date|
+-----------+--------------+--------------+
|   20231030|    2023-10-30|    30/10/2023|
|   20231115|    2023-11-15|    15/11/2023|
|   20231225|    2023-12-25|    25/12/2023|
+-----------+--------------+--------------+

>>> df.printSchema()
root
 |-- date_string: string (nullable = true)
 |-- to_date_format: date (nullable = true)
 |-- formatted_date: string (nullable = true)

型