计算pyspark数据框列上的百分位数

时间:2018-09-19 12:03:16

标签: dataframe pyspark quantile percentile

我有一个PySpark数据框,其中包含一个ID,然后包含几个要为其计算95%点的变量。

printSchema()的一部分:

root
 |-- ID: string (nullable = true)
 |-- MOU_G_EDUCATION_ADULT: double (nullable = false)
 |-- MOU_G_EDUCATION_KIDS: double (nullable = false)

我找到了How to derive Percentile using Spark Data frame and GroupBy in python,但这失败并显示一条错误消息:

perc95_udf = udf(lambda x: x.quantile(.95))


fanscores = genres.withColumn("P95_MOU_G_EDUCATION_ADULT", perc95_udf('MOU_G_EDUCATION_ADULT')) \
                      .withColumn("P95_MOU_G_EDUCATION_KIDS", perc95_udf('MOU_G_EDUCATION_KIDS'))

fanscores.take(2) 

AttributeError:“浮动”对象没有属性“分位数”

我已经尝试过的其他UDF试验:

def percentile(quantiel,kolom):
    x=np.array(kolom)
    perc=np.percentile(x, quantiel)
    return perc

percentile_udf = udf(percentile, LongType())


fanscores = genres.withColumn("P95_MOU_G_EDUCATION_ADULT", percentile_udf(quantiel=95, kolom=genres.MOU_G_EDUCATION_ADULT)) \
                  .withColumn("P95_MOU_G_EDUCATION_KIDS", percentile_udf(quantiel=95, kolom=genres.MOU_G_EDUCATION_KIDS))

fanscores.take(2)   

给出错误:“ TypeError:wrapper()得到了意外的关键字参数'quantiel'”

我的最终审判:

import numpy as np

def percentile(quantiel):
    return udf(lambda kolom: np.percentile(np.array(kolom), quantiel))

fanscores = genres.withColumn("P95_MOU_G_EDUCATION_ADULT", percentile(quantiel=95)(genres.MOU_G_EDUCATION_ADULT)) \
                  .withColumn("P95_MOU_G_EDUCATION_KIDS", percentile(quantiel=95) (genres.MOU_G_EDUCATION_KIDS))

fanscores.take(2)  

给出错误:

PickleException:预期用于构造ClassDict的零参数(对于numpy.dtype)

我该如何解决?

1 个答案:

答案 0 :(得分:1)

df.selectExpr('percentile(MOU_G_EDUCATION_ADULT, 0.95)').show()

对于大型数据集,请考虑使用percentile_approx()