从 PySpark 数组列中删除重复项 [英] Remove duplicates from PySpark array column

问题描述

我有一个包含 ArrayType(StringType()) 列的 PySpark 数据框.此列包含我需要删除的数组内的重复字符串.例如，一行条目可能看起来像 [milk、bread、milk、toast].假设我的数据框名为 df，我的列名为 arraycol.我需要类似的东西:

I have a PySpark Dataframe that contains an ArrayType(StringType()) column. This column contains duplicate strings inside the array which I need to remove. For example, one row entry could look like [milk, bread, milk, toast]. Let's say my dataframe is named df and my column is named arraycol. I need something like:

df = df.withColumn("arraycol_without_dupes", F.remove_dupes_from_array("arraycol"))

我的直觉是有一个简单的解决方案，但在浏览 stackoverflow 15 分钟后，我没有找到比分解列、删除完整数据帧上的重复项，然后再次分组更好的方法.必须有一种我没想到的更简单的方法，对吗?

My intution was that there exists a simple solution to this, but after browsing stackoverflow for 15 minutes I didn't find anything better than exploding the column, removing duplicates on the complete dataframe, then grouping again. There has got to be a simpler way that I just didn't think of, right?

我使用的是 Spark 2.4.0 版

I am using Spark version 2.4.0

推荐答案

对于 pyspark 2.4+ 版，您可以使用 pyspark.sql.functions.array_distinct:

For pyspark version 2.4+, you can use pyspark.sql.functions.array_distinct:

from pyspark.sql.functions import array_distinct
df = df.withColumn("arraycol_without_dupes", array_distinct("arraycol"))

<小时>

对于旧版本，您可以通过 API 函数使用 explode + groupBy 和 collect_set 执行此操作，但是 udf 在这里可能更有效:

---

For older versions, you can do this with the API functions using explode + groupBy and collect_set, but a udf is probably more efficient here:

from pyspark.sql.functions import udf

remove_dupes_from_array = udf(lambda row: list(set(row)), ArrayType(StringType()))
df = df.withColumn("arraycol_without_dupes", remove_dupes_from_array("arraycol"))

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