如何使用Window（）計算Pyspark中的滾動中位數？

Question

如何計算以前3個值的窗口大小的美元滾動中值？

輸入數據

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 

期望輸出數據

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) 

下面代碼不移動平均但pyspark不必須F.median（）。

pyspark: rolling average using timeseries data

EDIT 1：目前的挑戰是中位數（）函數犯規退出。我不能做

df = df.withColumn('rolling_average', F.median("dollars").over(w)) 

如果我想移動平均線我可以做

df = df.withColumn('rolling_average', F.avg("dollars").over(w)) 

編輯2：使用approxQuantile嘗試（）

windfun = Window().partitionBy().orderBy(F.col(date_column)).rowsBetwe‌​en(-3, 0) sdf.withColumn("movingMedian", sdf.approxQuantile(col='a', probabilities=[0.5], relativeError=0.00001).over(windfun)) 

但得到錯誤

AttributeError: 'list' object has no attribute 'over' 

編輯3

請提供無Udf的解決方案，因爲它不會從催化劑優化中受益。

Answer 1

一種方法是收集$dollars列每個窗口的列表，然後通過計算得到的名單中位數的udf：

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   | 
+-------+---------------------+------------+--------------+