Modify nested property inside Struct column with PySpark

user_12705555 0
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I want to modify/filter on a property inside a struct. Let's say I have a dataframe with the following column :

#+------------------------------------------+
#|                 arrayCol                 |
#+------------------------------------------+
#| {"a" : "some_value", "b" : [1, 2, 3]}    |
#+------------------------------------------+

Schema:

struct<a:string, b:array<int>>

I want to filter out some values in 'b' property when value inside the array == 1

The result desired is the following :

#+------------------------------------------+
#|                 arrayCol                 |
#+------------------------------------------+
#| {"a" : "some_value", "b" : [2, 3]}       |
#+------------------------------------------+

Is it possible to do it without extracting the property, filter the values, and re-build another struct ?

pyspark apache-spark-sql apache-spark
2 Answers

Update:

For spark 3.1+, withField can be used to update the struct column without having to recreate all the struct. In your case, you can update the field b using filter function to filter the array values like this:

import pyspark.sql.functions as F

df1 = df.withColumn(
    'arrayCol',
    F.col('arrayCol').withField('b', F.filter(F.col("arrayCol.b"), lambda x: x != 1))
)

df1.show()
#+--------------------+
#|            arrayCol|
#+--------------------+
#|{some_value, [2, 3]}|
#+--------------------+

For older versions, Spark doesn’t support adding/updating fields in nested structures. To update a struct column, you'll need to create a new struct using the existing fields and the updated ones:

import pyspark.sql.functions as F

df1 = df.withColumn(
    "arrayCol",
    F.struct(
        F.col("arrayCol.a").alias("a"),
        F.expr("filter(arrayCol.b, x -> x != 1)").alias("b")
    )
)
user_13865510

One way would be to define a UDF:

Example:

import ast
from pyspark.sql import SparkSession
import pyspark.sql.functions as F
from pyspark.sql.types import StringType, MapType


def remove_value(col):
    col["b"] = str([x for x in ast.literal_eval(col["b"]) if x != 1])
    return col


if __name__ == "__main__":
    spark = SparkSession.builder.getOrCreate()
    df = spark.createDataFrame(
        [
            {
                "arrayCol": {
                    "a": "some_value",
                    "b": "[1, 2, 3]",
                },
            },
        ]
    )
    remove_value_udf = spark.udf.register(
        "remove_value_udf", remove_value, MapType(StringType(), StringType())
    )
    df = df.withColumn(
        "result",
        remove_value_udf(F.col("arrayCol")),
    )

Result:

root
 |-- arrayCol: map (nullable = true)
 |    |-- key: string
 |    |-- value: string (valueContainsNull = true)
 |-- result: map (nullable = true)
 |    |-- key: string
 |    |-- value: string (valueContainsNull = true)

+---------------------------------+------------------------------+              
|arrayCol                         |result                        |
+---------------------------------+------------------------------+
|{a -> some_value, b -> [1, 2, 3]}|{a -> some_value, b -> [2, 3]}|
+---------------------------------+------------------------------+
user_109479970
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