PySpark：如何为所有列强制转换字符串数据类型

我的主要目标是将任何df的所有列都转换为字符串，这样比较就容易了。

我已经在下面尝试了多种建议的方法。但不能成功：

target_df = target_df.select([col(c).cast("string") for c in target_df.columns])

这给了错误：

pyspark.sql.utils.AnalysisException: "Can't extract value from SDV#155: need struct type but got string;"

我尝试的下一个是：

target_df = target_df.select([col(c).cast(StringType()).alias(c) for c in columns_list])

错误：

pyspark.sql.utils.AnalysisException: "Can't extract value from SDV#27: need struct type but got string;"

下一个方法是：

        for column in target_df.columns:
             target_df = target_df.withColumn(column, target_df[column].cast('string'))

错误：

pyspark.sql.utils.AnalysisException: "Can't extract value from SDV#27: need struct type but got string;"

在强制转换之前存在的几行代码：

        columns_list = source_df.columns.copy()
        target_df = target_df.toDF(*columns_list)

我正在尝试的示例df模式：

root
 |-- A: string (nullable = true)
 |-- S: string (nullable = true)
 |-- D: string (nullable = true)
 |-- F: string (nullable = true)
 |-- G: double (nullable = true)
 |-- H: double (nullable = true)
 |-- J: string (nullable = true)
 |-- K: string (nullable = true)
 |-- L: string (nullable = true)
 |-- M: string (nullable = true)
 |-- N: string (nullable = true)
 |-- B: string (nullable = true)
 |-- V: string (nullable = true)
 |-- C: string (nullable = true)
 |-- X: string (nullable = true)
 |-- Y: string (nullable = true)
 |-- U: double (nullable = true)
 |-- I: string (nullable = true)
 |-- R: string (nullable = true)
 |-- T: string (nullable = true)
 |-- Q: string (nullable = true)
 |-- E: double (nullable = true)
 |-- W: string (nullable = true)
 |-- AS: string (nullable = true)
 |-- DSC: string (nullable = true)
 |-- DCV: string (nullable = true)
 |-- WV: string (nullable = true)
 |-- SDV: string (nullable = true)
 |-- SDV.1: string (nullable = true)
 |-- WDV: string (nullable = true)
 |-- FWFV: string (nullable = true)
 |-- ERBVSER: string (nullable = true)