如何在pyspark中分解数据框的多个列

Question

我有一个数据框,其中包含类似于以下列的列表.所有列中列表的长度不相同.

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

我希望以这样的方式分解数据帧,以便获得以下输出 -

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

我怎样才能做到这一点？

Answer 1

PySparkarrays_zip在 2.4 中添加了一个函数，从而无需 Python UDF 来压缩数组。

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 2

参加聚会迟到了:-)

最简单的方法是使用inline没有 python API 但受selectExpr.

df.selectExpr('Name[0] as Name','Age[0] as Age','inline(arrays_zip(Subjects,Grades))').show()

df.selectExpr('Name[0] as Name','Age[0] as Age','inline(arrays_zip(Subjects,Grades))').show()

Answer 3

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]|
+-----+----+--------------------+---------+

使用udf带zip.这些列需要explode在爆炸之前合并.

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|
+-----+----+---------+------+