Question

我有一个数据框，其中包含类似于以下内容的列中的列表。所有列中列表的长度都不相同。

Name  Age  Subjects                  Grades
[Bob] [16] [Maths,Physics,Chemistry] [A,B,C]

我想以这样的方式爆炸数据框，使我得到以下输出-

Name Age Subjects Grades
Bob  16   Maths     A
Bob  16  Physics    B
Bob  16  Chemistry  C

我该如何实现？

Answer 1

这有效，

import pyspark.sql.functions as F
from pyspark.sql.types import *

df = sql.createDataFrame(
    [(['Bob'], [16], ['Maths','Physics','Chemistry'], ['A','B','C'])],
    ['Name','Age','Subjects', 'Grades'])
df.show()

+-----+----+--------------------+---------+
| Name| Age|            Subjects|   Grades|
+-----+----+--------------------+---------+
|[Bob]|[16]|[Maths, Physics, ...|[A, B, C]|
+-----+----+--------------------+---------+

将udf与zip一起使用。爆炸之前，explode所需的那些列必须先合并。

combine = F.udf(lambda x, y: list(zip(x, y)),
              ArrayType(StructType([StructField("subs", StringType()),
                                    StructField("grades", StringType())])))

df = df.withColumn("new", combine("Subjects", "Grades"))\
       .withColumn("new", F.explode("new"))\
       .select("Name", "Age", F.col("new.subs").alias("Subjects"), F.col("new.grades").alias("Grades"))
df.show()


+-----+----+---------+------+
| Name| Age| Subjects|Grades|
+-----+----+---------+------+
|[Bob]|[16]|    Maths|     A|
|[Bob]|[16]|  Physics|     B|
|[Bob]|[16]|Chemistry|     C|
+-----+----+---------+------+

Answer 2

PySpark在2.4中添加了arrays_zip函数，从而无需使用Python UDF压缩数组。

import pyspark.sql.functions as F
from pyspark.sql.types import *

df = sql.createDataFrame(
    [(['Bob'], [16], ['Maths','Physics','Chemistry'], ['A','B','C'])],
    ['Name','Age','Subjects', 'Grades'])
df = df.withColumn("new", F.arrays_zip("Subjects", "Grades"))\
       .withColumn("new", F.explode("new"))\
       .select("Name", "Age", F.col("new.Subjects").alias("Subjects"), F.col("new.Grades").alias("Grades"))
df.show()

+-----+----+---------+------+
| Name| Age| Subjects|Grades|
+-----+----+---------+------+
|[Bob]|[16]|    Maths|     A|
|[Bob]|[16]|  Physics|     B|
|[Bob]|[16]|Chemistry|     C|
+-----+----+---------+------+

Answer 3

您尝试过

df.select(explode(split(col("Subjects"))).alias("Subjects")).show()

您可以将数据帧转换为RDD。

对于RDD，您可以使用flatMap函数来分隔主题。

如何在pyspark中爆炸数据框的多列

3 个答案: