Pyspark：在 groupBy 之后获得百分比结果

Question

例如，这是我的测试数据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()我可以得到这样的汇总数据test.groupBy("customerid", "location").agg(sum("price")).show()在此处输入图片说明但我也想要百分比数据，像这样+----------+--------+----------+ |customerid|location|sum(price)| percentage+----------+--------+----------+ |         1|Region B|         2|    20%|         1|Region A|         8|    80%|         3|Region A|         1|    100%|         2|Region B|         1|    100%+----------+--------+----------+我想知道我该怎么做？也许使用窗口功能？我可以将数据透视表变成这样吗？（带有百分比和总和列）

Answer 1

这回答了问题的原始版本。

在 SQL 中，您可以使用窗口函数：

select customerid, location, sum(price),

       (sum(price) / sum(sum(price)) over (partition by customerid) as ratio

from t

group by customerid, location;

Answer 2

这是解决您问题的干净代码：

from pyspark.sql import functions as F

from pyspark.sql.window import Window

(test.groupby("customerid", "location")

      .agg(F.sum("price").alias("t_price"))

      .withColumn("perc", F.col("t_price") / F.sum("t_price").over(Window.partitionBy("customerid")))