如何在可能为空的列上使用PySpark CountVectorizer

问题描述

我的Spark DataFrame中有一列:

I have a column in my Spark DataFrame:

 |-- topics_A: array (nullable = true)
 |    |-- element: string (containsNull = true)

我正在使用CountVectorizer:

I'm using CountVectorizer on it:

topic_vectorizer_A = CountVectorizer(inputCol="topics_A", outputCol="topics_vec_A")

我得到NullPointerExceptions，因为有时topic_A列包含空值.

I get NullPointerExceptions, because sometimes the topic_A column contains null.

有没有解决的办法?用零长度的数组填充它可以正常工作(尽管它会消耗很多数据量)-但我不知道如何在PySpark的Array列上执行fillNa.

Is there a way around this? Filling it with a zero-length array would work ok (although it will blow out the data size quite a lot) - but I can't work out how to do a fillNa on an Array column in PySpark.

推荐答案

我个人会使用NULL值删除列，因为那里没有有用的信息，但是您可以将空值替换为空数组.首先是一些进口:

Personally I would drop columns with NULL values because there is no useful information there but you can replace nulls with empty arrays. First some imports:

from pyspark.sql.functions import when, col, coalesce, array

您可以将特定类型的空数组定义为:

You can define an empty array of specific type as:

fill = array().cast("array<string>")

并将其与when子句组合:

topics_a = when(col("topics_A").isNull(), fill).otherwise(col("topics_A"))

或coalesce:

topics_a = coalesce(col("topics_A"), fill)

并将其用作:

df.withColumn("topics_A", topics_a)

因此带有示例数据:

df = sc.parallelize([(1, ["a", "b"]), (2, None)]).toDF(["id", "topics_A"])

df_ = df.withColumn("topics_A", topics_a)
topic_vectorizer_A.fit(df_).transform(df_)

结果将是:

+---+--------+-------------------+
| id|topics_A|       topics_vec_A|
+---+--------+-------------------+
|  1|  [a, b]|(2,[0,1],[1.0,1.0])|
|  2|      []|          (2,[],[])|
+---+--------+-------------------+

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