本文介绍了countDistinct和distinct.count之间的区别的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!

问题描述

为什么我为..agg(countDistinct("member_id") as "count")..distinct.count获得不同的输出?select count(distinct member_id)select distinct count(member_id)之间的区别相同吗?

Why do I get different outputs for ..agg(countDistinct("member_id") as "count") and ..distinct.count?Is the difference the same as between select count(distinct member_id) and select distinct count(member_id)?

推荐答案

df.agg(countDistinct("member_id") as "count")

返回member_id列的不同值的数量,而忽略所有其他列,而

returns the number of distinct values of the member_id column, ignoring all other columns, while

df.distinct.count

将计算DataFrame中不同的记录的数量-其中"distinct"表示 all 列的值相同.

will count the number of distinct records in the DataFrame - where "distinct" means identical in values of all columns.

例如,DataFrame:

So, for example, the DataFrame:

+-----------+---------+
|member_name|member_id|
+-----------+---------+
|          a|        1|
|          b|        1|
|          b|        1|
+-----------+---------+

只有一个不同的member_id值,但是有两个不同的记录,因此agg选项将返回1,而后者将返回2.

has only one distinct member_id value but two distinct records, so the agg option would return 1 while the latter would return 2.

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08-26 17:56
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