Pivot duplicate values into 2 different columns

user_14051622 0
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I have the following data imported into PySpark dataframe:

from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql.types import *
spark = SparkSession.builder.master("local[4]").appName("pyspark").getOrCreate()
df = spark.read.csv("example.csv")
df.show()

#+--+------+
#|id|answer|
#+--+------+
#| 1|a     |
#| 1|b     |
#| 2|c     |
#| 2|d     |
#| 3|e     |
#| 4|f     |
#| 4|g     |
#+--+------+

Some id might have duplicated, some might not. I would like to have the following output:

+--+------+------+
|id|first |second|
+--+------+------+
| 1|a     |b     |
| 2|c     |d     |
| 3|e     |Null  |
| 4|f     |g     |
+--+------+------+
pyspark apache-spark-sql apache-spark pivot duplicates
1 Answers

I would use groupBy and aggregate using collect_list. I've added array_sort just for more determinism.

from pyspark.sql import functions as F

df = df.groupBy('id').agg(F.array_sort(F.collect_list('answer')).alias('list'))
df = df.select(
    'id',
    F.element_at('list', 1).alias('first'),
    F.element_at('list', 2).alias('second'),
)
df.show()
# +---+-----+------+
# | id|first|second|
# +---+-----+------+
# |  1|    a|     b|
# |  2|    c|     d|
# |  3|    e|  null|
# |  4|    f|     g|
# +---+-----+------+
user_27535010
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