How to explode multiple columns of a dataframe in pyspark

问题

I have a dataframe which consists lists in columns similar to the following. The length of the lists in all columns is not same.

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

I want to explode the dataframe in such a way that i get the following output-

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

How can I achieve this?

回答1:

This works,

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

Use udf with zip. Those columns needed to explode have to be merged before exploding.

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

回答2:

PySpark has added an arrays_zip function in 2.4, which eliminates the need for a Python UDF to zip the arrays.

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

回答3:

Have you tried this

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

you can convert the data frame to an RDD.

For an RDD you can use a flatMap function to separate the Subjects.

来源：https://stackoverflow.com/questions/51082758/how-to-explode-multiple-columns-of-a-dataframe-in-pyspark

标签

python

dataframe

pyspark