Pyspark：在groupBy

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()

但是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

• 我该怎么办？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）

---

我只在How to get percentage of counts of a column after groupby in Pandas上找到一个熊猫的例子

更新：

在@Gordon Linoff的帮助下，我可以通过

获得百分比

from pyspark.sql.window import Window
test.groupBy("customerid", "location").agg(sum("price"))\
  .withColumn("percentage", col("sum(price)")/sum("sum(price)").over(Window.partitionBy(test['customerid']))).show()

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

这是您的问题的干净代码：

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")))