spark dataframe: explode list column

Question

I've got an output from Spark Aggregator which is List[Character]

case class Character(name: String, secondName: String, faculty: String)
val charColumn = HPAggregator.toColumn
val resultDF = someDF.select(charColumn)

So my dataframe looks like:

+-----------------------------------------------+
|               value                           |
+-----------------------------------------------+
|[[harry, potter, gryffindor],[ron, weasley ... |
+-----------------------------------------------+

Now I want to convert it to

+----------------------------------+
| name  | second_name | faculty    |
+----------------------------------+
| harry | potter      | gryffindor |
| ron   | weasley     | gryffindor |

How can I do that properly?

you want to have a list of names then list of second name then faculty or do you want to have a row for each value in the list? — Assaf Mendelson
– Assaf Mendelson, Commented Jul 31, 2018 at 9:34
can we see the schema of someDF? looking at the current scenario I guess you would need explode and split functions — Anahcolus
– Anahcolus, Commented Jul 31, 2018 at 10:56

Koedlt · Accepted Answer · 2023-11-28 12:25:05Z

This can be done using Explode and Split Dataframe functions.

Below is an example:

>>> df = spark.createDataFrame([[[['a','b','c'], ['d','e','f'], ['g','h','i']]]],["col1"])
>>> df.show(20, False)
+---------------------------------------------------------------------+
|col1                                                                 |
+---------------------------------------------------------------------+
|[WrappedArray(a, b, c), WrappedArray(d, e, f), WrappedArray(g, h, i)]|
+---------------------------------------------------------------------+

>>> from pyspark.sql.functions import explode
>>> out_df = df.withColumn("col2", explode(df.col1)).drop('col1')
>>>
>>> out_df .show()
+---------+
|     col2|
+---------+
|[a, b, c]|
|[d, e, f]|
|[g, h, i]|
+---------+

>>> out_df.select(out_df.col2[0].alias('c1'), out_df.col2[1].alias('c2'), out_df.col2[2].alias('c3')).show()
+---+---+---+
| c1| c2| c3|
+---+---+---+
|  a|  b|  c|
|  d|  e|  f|
|  g|  h|  i|
+---+---+---+

>>>

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