在整行上使用 udf 过滤 Pyspark 数据框 [英] Filter Pyspark Dataframe with udf on entire row

问题描述

有没有办法选择整行作为一列输入到 Pyspark 过滤器 udf 中?

Is there a way to select the entire row as a column to input into a Pyspark filter udf?

我有一个复杂的过滤函数my_filter"，我想将其应用于整个 DataFrame:

I have a complex filtering function "my_filter" that I want to apply to the entire DataFrame:

my_filter_udf = udf(lambda r: my_filter(r), BooleanType())
new_df = df.filter(my_filter_udf(col("*"))

但是

col("*")

抛出错误，因为这不是有效的操作.

throws an error because that's not a valid operation.

我知道我可以将数据帧转换为 RDD，然后使用 RDD 的过滤方法，但我不想将其转换为 RDD，然后再转换回数据帧.我的 DataFrame 具有复杂的嵌套类型，因此当我再次尝试将 RDD 转换为数据帧时，模式推断失败.

I know that I can convert the dataframe to an RDD and then use the RDD's filter method, but I do NOT want to convert it to an RDD and then back into a dataframe. My DataFrame has complex nested types, so the schema inference fails when I try to convert the RDD into a dataframe again.

推荐答案

您应该静态地编写所有列.例如:

You should write all columns staticly. For example:

from pyspark.sql import functions as F

# create sample df
df = sc.parallelize([
     (1, 'b'),
     (1, 'c'),

 ]).toDF(["id", "category"])

#simple filter function
@F.udf(returnType=BooleanType())
def my_filter(col1, col2):
    return (col1>0) & (col2=="b")

df.filter(my_filter('id', 'category')).show()

结果:

+---+--------+
| id|category|
+---+--------+
|  1|       b|
+---+--------+

如果您有这么多列并且您确定要对列进行排序:

If you have so many columns and you are sure to order of columns:

cols = df.columns
df.filter(my_filter(*cols)).show()

产生相同的输出.

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