如何使用Window()计算PySpark中的滚动中位数? [英] How to calculate rolling median in PySpark using Window()?
问题描述
对于前三个值的窗口大小,如何计算美元的滚动中位数?
How do I calculate rolling median of dollar for a window size of previous 3 values?
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
预期的输出数据
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PySpark没有F.median().
Below code does moving avg but PySpark doesn't have F.median().
挑战是中值()函数不会退出.我不能
EDIT 1: The challenge is median() function doesn't exit. I cannot do
df = df.withColumn('rolling_average', F.median("dollars").over(w))
如果我想要移动平均线,我可以做
If I wanted moving average I could have done
df = df.withColumn('rolling_average', F.avg("dollars").over(w))
使用roximateile()尝试
EDIT 2: Tried using approxQuantile()
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))
但是出错了
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.
Please give solution without Udf since it won't benefit from catalyst optimization.
推荐答案
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:>
One way is to collect the $dollars
column as a list per window, and then calculate the median of the resulting lists using an 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 |
+-------+---------------------+------------+--------------+
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