根据另一列的元素从 pyspark 数组中删除元素

Posted 2023-04-15

【中文标题】根据另一列的元素从 pyspark 数组中删除元素

【英文标题】：Remove element from pyspark array based on element of another column

【发布时间】：2019-11-13 11:15:16

【问题描述】：

我想验证一个数组是否包含 Pyspark (Spark

示例数据框：

column_1 <Array>           |    column_2 <String>
--------------------------------------------
["2345","98756","8794"]    |       8794
--------------------------------------------
["8756","45678","987563"]  |       1234
--------------------------------------------
["3475","8956","45678"]    |       3475
--------------------------------------------

我想比较两列 column_1 和 column_2。如果 column_1 包含 column_2 我应该从 column_1 中跳过它的值。 我做了一个 udf 从 column_1 中提取 column_2，但没有工作：

def contains(x, y):
        try:
            sx, sy = set(x), set(y)
            if len(sx) == 0:
                return sx
            elif len(sy) == 0:
                return sx
            else:
                return sx - sy            
        # in exception, for example `x` or `y` is None (not a list)
        except:
            return sx
    udf_contains = udf(contains, 'string')
    new_df = my_df.withColumn('column_1', udf_contains(my_df.column_1, my_df.column_2))  

预期结果：

column_1 <Array>           |    column_2 <String>
--------------------------------------------------
["2345","98756"]           |       8794
--------------------------------------------------
["8756","45678","987563"]  |       1234
--------------------------------------------------
["8956","45678"]           |       3475
--------------------------------------------------

知道有时我的 column_1 是 [] 而 column_2 是 null ，我该怎么做？谢谢

【问题讨论】：

检查udf_contains = udf(lambda x,y: [e for e in x if e != y], 'array<string>')

如果 x 可以为空或非列表。 udf(lambda x,y: [e for e in x if e != y] if isinstance(x, list) else x, 'array')

@jxc 我需要你的帮助 :) ***.com/questions/58875531/concatenate-array-pyspark/…

【参考方案1】：

Spark 2.4.0+

试试array_remove。从 spark 2.4.0 开始可用：

val df = Seq(
    (Seq("2345","98756","8794"), "8794"), 
    (Seq("8756","45678","987563"), "1234"), 
    (Seq("3475","8956","45678"), "3475"),
    (Seq(), "empty"),
    (null, "null")
).toDF("column_1", "column_2")
df.show(5, false)

df
    .select(
        $"column_1",
        $"column_2",
        array_remove($"column_1", $"column_2") as "diff"
    ).show(5, false)

它会返回：

+---------------------+--------+
|column_1             |column_2|
+---------------------+--------+
|[2345, 98756, 8794]  |8794    |
|[8756, 45678, 987563]|1234    |
|[3475, 8956, 45678]  |3475    |
|[]                   |empty   |
|null                 |null    |
+---------------------+--------+

+---------------------+--------+---------------------+
|column_1             |column_2|diff                 |
+---------------------+--------+---------------------+
|[2345, 98756, 8794]  |8794    |[2345, 98756]        |
|[8756, 45678, 987563]|1234    |[8756, 45678, 987563]|
|[3475, 8956, 45678]  |3475    |[8956, 45678]        |
|[]                   |empty   |[]                   |
|null                 |null    |null                 |
+---------------------+--------+---------------------+

对不起 scala，我想用 pyspark 做同样的事情很容易。

火花

%pyspark

from pyspark.sql.functions import udf
from pyspark.sql.types import ArrayType, StringType


data = [
    (["2345","98756","8794"], "8794"), 
    (["8756","45678","987563"], "1234"), 
    (["3475","8956","45678"], "3475"),
    ([], "empty"),
    (None,"null")    
    ]
df = spark.createDataFrame(data, ['column_1', 'column_2'])
df.printSchema()
df.show(5, False)

def contains(x, y):
    if x is None or y is None:
        return x
    else:
        sx, sy = set(x), set([y])
        return list(sx - sy)
udf_contains = udf(contains, ArrayType(StringType()))

df.select("column_1", "column_2", udf_contains("column_1", "column_2")).show(5, False)

结果：

root
 |-- column_1: array (nullable = true)
 |    |-- element: string (containsNull = true)
 |-- column_2: string (nullable = true)
+---------------------+--------+
|column_1             |column_2|
+---------------------+--------+
|[2345, 98756, 8794]  |8794    |
|[8756, 45678, 987563]|1234    |
|[3475, 8956, 45678]  |3475    |
|[]                   |empty   |
|null                 |null    |
+---------------------+--------+
+---------------------+--------+----------------------------+
|column_1             |column_2|contains(column_1, column_2)|
+---------------------+--------+----------------------------+
|[2345, 98756, 8794]  |8794    |[2345, 98756]               |
|[8756, 45678, 987563]|1234    |[8756, 987563, 45678]       |
|[3475, 8956, 45678]  |3475    |[8956, 45678]               |
|[]                   |empty   |[]                          |
|null                 |null    |null                        |
+---------------------+--------+----------------------------+

【讨论】：

感谢您的帮助，我只是这样做了：df.select(array_remove(df.data, 1)).collect()，但我得到“TypeError: 'Column' object is not可调用”可能是因为我使用了

@verojoucla 我用 pyspark 添加了 spark set("abc") > set(['a', 'c', 'b'])