这是一个通用的解决方案，即使json很混乱（元素的顺序不同或者缺少一些元素），它也能工作
你得先压平， regexp_replace 拆分“属性”列并最终透视。这也避免了新列名的硬编码。
构建Dataframe：

from pyspark.sql.types import *
from pyspark.sql import functions as F
from pyspark.sql.functions import col
from pyspark.sql.functions import *

schema = StructType([StructField("id", IntegerType()), StructField("start_ts", StringType()), StructField("end_ts", StringType()), \
    StructField("property", ArrayType(StructType(  [StructField("name", StringType()),  StructField("val", StringType())]    )))])

data = [[1, "2010", "2020", [["PID", "P123"], ["Eng", "PA111"], ["Town", "999"], ["Act", "123.1"]]],\
         [2, "2011", "2012", [["PID", "P456"], ["Eng", "PA222"], ["Town", "777"], ["Act", "234.1"]]]]

df = spark.createDataFrame(data,schema=schema)

df.show(truncate=False)
+---+--------+------+------------------------------------------------------+    
|id |start_ts|end_ts|property                                              |
+---+--------+------+------------------------------------------------------+
|1  |2010    |2020  |[[PID, P123], [Eng, PA111], [Town, 999], [Act, 123.1]]|
|2  |2011    |2012  |[[PID, P456], [Eng, PA222], [Town, 777], [Act, 234.1]]|
+---+--------+------+------------------------------------------------------+

展平和旋转：

df_flatten = df.rdd.flatMap(lambda x: [(x[0],x[1], x[2], y) for y in x[3]]).toDF(['id', 'start_ts', 'end_ts', 'property'])\
            .select('id', 'start_ts', 'end_ts', col("property").cast("string"))

df_split = df_flatten.select('id', 'start_ts', 'end_ts', regexp_replace(df_flatten.property, "[\[\]]", "").alias("replacced_col"))\
                .withColumn("arr", split(col("replacced_col"), ", "))\
                .select(col("arr")[0].alias("col1"), col("arr")[1].alias("col2"), 'id', 'start_ts', 'end_ts')

final_df = df_split.groupby(df_split.id,)\
                        .pivot("col1")\
                        .agg(first("col2"))\
                        .join(df,'id').drop("property")

输出：

final_df.show()
+---+-----+-----+----+----+--------+------+
| id|  Act|  Eng| PID|Town|start_ts|end_ts|
+---+-----+-----+----+----+--------+------+
|  1|123.1|PA111|P123| 999|    2010|  2020|
|  2|234.1|PA222|P456| 777|    2011|  2012|
+---+-----+-----+----+----+--------+------+

你能用数据代替截图吗？
同时，假设 df 我们需要做的是创建一个新的Dataframe，同时导出 vals 从上一个 property 数组到新列，并删除 property 最后一列：

from pyspark.sql.functions import col
output_df = df.withColumn("PID", col("property")[0].val).withColumn("EngID", col("property")[1].val).withColumn("TownIstat", col("property")[2].val).withColumn("ActiveEng", col("property")[3].val).drop("property")

万一 element 属于 ArrayType 使用以下选项：

from pyspark.sql.functions import col
output_df = df.withColumn("PID", col("property")[0][1]).withColumn("EngID", col("property")[1][1]).withColumn("TownIstat", col("property")[2][1]).withColumn("ActiveEng", col("property")[3][1]).drop("property")

explode将数组分解为新行，而不是新列，请参见：pyspark explode