min_by

返回与 ord 参数中的最小值关联的 col 参数中的值。 此函数通常用于查找与 groupBy（一起使用时每个组中的最小 ord 参数值对应的 col 参数值）。 该函数是不确定的，因此，与 col 相同的值关联的输出顺序可能有所不同。

Syntax

from pyspark.sql import functions as sf

sf.min_by(col, ord)

参数

退货

pyspark.sql.Column：表示与最小值col关联的值的ord列对象。

例子

示例 1：将 min_by 与 groupBy 配合使用

import pyspark.sql.functions as sf
df = spark.createDataFrame([
    ("Java", 2012, 20000), ("dotNET", 2012, 5000),
    ("dotNET", 2013, 48000), ("Java", 2013, 30000)],
    schema=("course", "year", "earnings"))
df.groupby("course").agg(sf.min_by("year", "earnings")).sort("course").show()

+------+----------------------+
|course|min_by(year, earnings)|
+------+----------------------+
|  Java|                  2012|
|dotNET|                  2012|
+------+----------------------+

示例 2：对不同数据类型使用min_by

import pyspark.sql.functions as sf
df = spark.createDataFrame([
    ("Marketing", "Anna", 4), ("IT", "Bob", 2),
    ("IT", "Charlie", 3), ("Marketing", "David", 1)],
    schema=("department", "name", "years_in_dept"))
df.groupby("department").agg(
    sf.min_by("name", "years_in_dept")
).sort("department").show()

+----------+---------------------------+
|department|min_by(name, years_in_dept)|
+----------+---------------------------+
|        IT|                        Bob|
| Marketing|                      David|
+----------+---------------------------+

示例 3：使用min_by，其中 ord 具有多个最小值

import pyspark.sql.functions as sf
df = spark.createDataFrame([
    ("Consult", "Eva", 6), ("Finance", "Frank", 5),
    ("Finance", "George", 9), ("Consult", "Henry", 7)],
    schema=("department", "name", "years_in_dept"))
df.groupby("department").agg(
    sf.min_by("name", "years_in_dept")
).sort("department").show()

+----------+---------------------------+
|department|min_by(name, years_in_dept)|
+----------+---------------------------+
|   Consult|                        Eva|
|   Finance|                      Frank|
+----------+---------------------------+