您可以始终使用UDF来满足自定义需求，例如：

spark = SparkSession.builder.appName("DateDataFrame").getOrCreate()
data = [
    ("The error is in %s value is %s.", "xx", "z"),
    ("The new cond is in %s is %s.", "y", "ww"),
]
df = spark.createDataFrame(data, ['ErrorDescBefore', 'name', 'value'])

format_udf = udf(lambda str, name, value: str.replace('%s', name, 1).replace('%s', value, 1))

df.withColumn("ErrorDescAfter", format_udf(col("ErrorDescBefore"), col("name"), col("value"))).show(truncate=False)

结果：

+-------------------------------+----+-----+------------------------------+
|ErrorDescBefore                |name|value|ErrorDescAfter                |
+-------------------------------+----+-----+------------------------------+
|The error is in %s value is %s.|xx  |z    |The error is in xx value is z.|
|The new cond is in %s is %s.   |y   |ww   |The new cond is in y is ww.   |
+-------------------------------+----+-----+------------------------------+

您可以将ErrorDescBefore拆分为一个数组，其中%s作为分隔符，然后使用concat函数将其元素与name和value连接起来。

import pyspark.sql.functions as F

...
df = df.withColumn('ErrorDescAfter', F.split('ErrorDescBefore', '%s')).withColumn(
    'ErrorDescAfter',
    F.concat(F.col('ErrorDescAfter')[0], 'name', F.col('ErrorDescAfter')[1], 'value', F.col('ErrorDescAfter')[2])
)

如果您知道ErrorDescBefore列中的格式将保持一致，则可以在字符串%s上执行split ErrorDescBefore，并将每个项与name和value列连接起来：

df.withColumn(
    'ErrorDescAfter',
    F.concat(
        F.split(F.col('ErrorDescBefore'), '%s').getItem(0),
        F.col('name'),
        F.split(F.col('ErrorDescBefore'), '%s').getItem(1),
        F.col('value'),
        F.split(F.col('ErrorDescBefore'), '%s').getItem(2),
    )
)

+-------------------------------+----+-----+------------------------------+
|ErrorDescBefore                |name|value|ErrorDescAfter                |
+-------------------------------+----+-----+------------------------------+
|The error is in %s value is %s.|xx  |z    |The error is in xx value is z.|
|The new cond is in %s is %s.   |y   |ww   |The new cond is in y is ww.   |
+-------------------------------+----+-----+------------------------------+