Pyspark:按列加权平均

例如,我有一个这样的数据集

test = spark.createDataFrame([
    (0, 1, 5, "2018-06-03", "Region A"),
    (1, 1, 2, "2018-06-04", "Region B"),
    (2, 2, 1, "2018-06-03", "Region B"),
    (3, 3, 1, "2018-06-01", "Region A"),
    (3, 1, 3, "2018-06-05", "Region A"),
])\
  .toDF("orderid", "customerid", "price", "transactiondate", "location")
test.show()

我可以通过

overall_stat = test.groupBy("customerid").agg(count("orderid"))\
  .withColumnRenamed("count(orderid)", "overall_count")
temp_result = test.groupBy("customerid").pivot("location").agg(count("orderid")).na.fill(0).join(overall_stat, ["customerid"])

for field in temp_result.schema.fields:
    if str(field.name) not in ['customerid', "overall_count", "overall_amount"]:
        name = str(field.name)
        temp_result = temp_result.withColumn(name, col(name)/col("overall_count"))
temp_result.show()

数据是这样的

现在,我想计算加权平均值 overall_count ,我该怎么做?

结果应该是 (0.66*3+1*1)/4 对于区域A,以及 (0.33*3+1*1)/4 对于区域B

我的想法:

当然可以通过将数据转换成python/panda然后进行一些计算来实现,但是在什么情况下应该使用Pyspark呢?

我可以得到一些像

temp_result.agg(sum(col("Region A") * col("overall_count")), sum(col("Region B")*col("overall_count"))).show()

但感觉不对,特别是如果有很多 region 数一数。