我知道一种更广泛的方法，如下所示：

>>> from pyspark.sql.types import *
>>> from pyspark.sql.functions import col

# Consider df to be the dataframe from reading the JSON file.

>>> df.show()
+-------+------+
|details|header|
+-------+------+
|    def|   2.0|
+-------+------+

>>> df.printSchema()
root
 |-- details: string (nullable = true)
 |-- header: string (nullable = true)

# Convert all columns to integer type.

>>> df_parq=df.select(*(col(c).cast(IntegerType()).alias(c) for c in df.columns))
>>> df_parq.printSchema()
root
 |-- details: integer (nullable = true)
 |-- header: integer (nullable = true)

# Write file with modified column types to Parquet.

>>> df_parq.write.parquet('F:\Parquet\sample_out3')
>>> df_read_parq=spark.read.parquet('F:\Parquet\sample_out3')
>>> df_read_parq.printSchema()
root
 |-- details: integer (nullable = true)
 |-- header: integer (nullable = true)