How to transform nested dataframe schema in PySpark

Question

I have a dataframe with the following schema:

root                                                                            
|-- _1: struct (nullable = true)
|    |-- key: string (nullable = true)
|-- _2: struct (nullable = true)
|    |-- value: long (nullable = true)

I want to transform dataframe to the following schema:

root
|-- _1: struct (nullable = true)                                                                            
|    |-- key: string (nullable = true)
|    |-- value: long (nullable = true)

Community · Accepted Answer · 2020-06-20 09:12:55Z

2

Use struct:

pyspark.sql.functions.struct(*cols)

Creates a new struct column.

from pyspark.sql.functions import struct, col
from pyspark.sql import Row

df = spark.createDataFrame([Row(_1=Row(key="a"), _2=Row(value=1))])

result = df.select(struct(col("_1.key"), col("_2.value")).alias("_1"))

which gives:

result.printSchema()
# root
#  |-- _1: struct (nullable = false)
#  |    |-- key: string (nullable = true)
#  |    |-- value: long (nullable = true)

and

result.show()
# +-----+
# |   _1|
# +-----+
# |[a,1]|
# +-----+

edited Jun 20, 2020 at 9:12

CommunityBot

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answered Feb 15, 2018 at 23:22

Alper t. Turker

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1 Comment

jitendra Over a year ago

Aha! This is exactly what I was looking for. Thanks.

Anahcolus · Accepted Answer · 2018-02-16 00:58:42Z

If your dataframe is with following schema

root
 |-- _1: struct (nullable = true)
 |    |-- key: string (nullable = true)
 |-- _2: struct (nullable = true)
 |    |-- value: long (nullable = true)

Then you can use * to select all elements of struct columns into separate columns and then use struct inbuilt function to combine them back to one struct field

from pyspark.sql import functions as F
df.select(F.struct("_1.*", "_2.*").alias("_1"))

you should get your desired output dataframe

root
 |-- _1: struct (nullable = false)
 |    |-- key: string (nullable = true)
 |    |-- value: long (nullable = true)

Updated

More generalized form of above code if all the columns in original dataframe are struct is as below

df.select(F.struct(["{}.*".format(x) for x in df.columns]).alias("_1"))

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