PySpark:使用过滤功能后取一列的平均值 [英] PySpark: Take average of a column after using filter function

问题描述

我正在使用以下代码来获取工资高于某个阈值的人的平均年龄.

I am using the following code to get the average age of people whose salary is greater than some threshold.

dataframe.filter(df['salary'] > 100000).agg({"avg": "age"})

列年龄是数字(浮点数)但我仍然收到此错误.

the column age is numeric (float) but still I am getting this error.

py4j.protocol.Py4JJavaError: An error occurred while calling o86.agg. 
: scala.MatchError: age (of class java.lang.String)

你知道不使用groupBy函数和SQL查询的任何其他方法来获得平均值等.

Do you know any other way to obtain the avg etc. without using groupBy function and SQL queries.

推荐答案

聚合函数应该是一个值和一个列名一个键:

Aggregation function should be a value and a column name a key:

dataframe.filter(df['salary'] > 100000).agg({"age": "avg"})

或者你可以使用pyspark.sql.functions:

from pyspark.sql.functions import col, avg

dataframe.filter(df['salary'] > 100000).agg(avg(col("age")))

也可以使用 CASE .. WHEN

from pyspark.sql.functions import when

dataframe.select(avg(when(df['salary'] > 100000, df['age'])))

这篇关于PySpark:使用过滤功能后取一列的平均值的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！