如何计算前三个值的窗口大小的美元滚动中位数?
输入数据
dollars timestampGMT
25 2017-03-18 11:27:18
17 2017-03-18 11:27:19
13 2017-03-18 11:27:20
27 2017-03-18 11:27:21
13 2017-03-18 11:27:22
43 2017-03-18 11:27:23
12 2017-03-18 11:27:24
预期输出数据
dollars timestampGMT rolling_median_dollar
25 2017-03-18 11:27:18 median(25)
17 2017-03-18 11:27:19 median(17,25)
13 2017-03-18 11:27:20 median(13,17,25)
27 2017-03-18 11:27:21 median(27,13,17)
13 2017-03-18 11:27:22 median(13,27,13)
43 2017-03-18 11:27:23 median(43,13,27)
12 2017-03-18 11:27:24 median(12,43,13)
下面的代码可以移动平均值,但是PySpark没有F.median()。
pyspark: rolling average using timeseries data
编辑1:挑战是中位数()函数不会退出。我不能做
df = df.withColumn('rolling_average', F.median("dollars").over(w))
如果我想要移动平均线,我可以做
df = df.withColumn('rolling_average', F.avg("dollars").over(w))
编辑2:尝试使用roximateQuantile()
windfun = Window().partitionBy().orderBy(F.col(date_column)).rowsBetween(-3, 0) sdf.withColumn("movingMedian", sdf.approxQuantile(col='a', probabilities=[0.5], relativeError=0.00001).over(windfun))
但是出现错误
AttributeError: 'list' object has no attribute 'over'
编辑3
请提供没有Udf的解决方案,因为它不会从催化剂优化中受益。
最佳答案
一种方法是收集$dollars
列作为每个窗口的列表,然后使用udf
计算所得列表的中位数:
from pyspark.sql.window import Window
from pyspark.sql.functions import *
import numpy as np
from pyspark.sql.types import FloatType
w = (Window.orderBy(col("timestampGMT").cast('long')).rangeBetween(-2, 0))
median_udf = udf(lambda x: float(np.median(x)), FloatType())
df.withColumn("list", collect_list("dollars").over(w)) \
.withColumn("rolling_median", median_udf("list")).show(truncate = False)
+-------+---------------------+------------+--------------+
|dollars|timestampGMT |list |rolling_median|
+-------+---------------------+------------+--------------+
|25 |2017-03-18 11:27:18.0|[25] |25.0 |
|17 |2017-03-18 11:27:19.0|[25, 17] |21.0 |
|13 |2017-03-18 11:27:20.0|[25, 17, 13]|17.0 |
|27 |2017-03-18 11:27:21.0|[17, 13, 27]|17.0 |
|13 |2017-03-18 11:27:22.0|[13, 27, 13]|13.0 |
|43 |2017-03-18 11:27:23.0|[27, 13, 43]|27.0 |
|12 |2017-03-18 11:27:24.0|[13, 43, 12]|13.0 |
+-------+---------------------+------------+--------------+
关于apache-spark - 如何使用Window()计算PySpark中的滚动中位数?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/46767807/