I have:
+---+-------+-------+
| id| var1| var2|
+---+-------+-------+
| a|[1,2,3]|[1,2,3]|
| b|[2,3,4]|[2,3,4]|
+---+-------+-------+
I want:
+---+-------+-------+-------+-------+-------+-------+
| id|var1[0]|var1[1]|var1[2]|var2[0]|var2[1]|var2[2]|
+---+-------+-------+-------+-------+-------+-------+
| a| 1| 2| 3| 1| 2| 3|
| b| 2| 3| 4| 2| 3| 4|
+---+-------+-------+-------+-------+-------+-------+
The solution provided by How to split a list to multiple columns in Pyspark?
df1.select('id', df1.var1[0], df1.var1[1], ...).show()
works, but some of my arrays are very long (max 332).
How can I write this so that it takes account of all length arrays?
This solution will work for your problem, no matter the number of initial columns and the size of your arrays. Moreover, if a column has different array sizes (eg [1,2], [3,4,5]), it will result in the maximum number of columns with null values filling the gap.
from pyspark.sql import functions as F
df = spark.createDataFrame(sc.parallelize([['a', [1,2,3], [1,2,3]], ['b', [2,3,4], [2,3,4]]]), ["id", "var1", "var2"])
columns = df.drop('id').columns
df_sizes = df.select(*[F.size(col).alias(col) for col in columns])
df_max = df_sizes.agg(*[F.max(col).alias(col) for col in columns])
max_dict = df_max.collect()[0].asDict()
df_result = df.select('id', *[df[col][i] for col in columns for i in range(max_dict[col])])
df_result.show()
>>>
+---+-------+-------+-------+-------+-------+-------+
| id|var1[0]|var1[1]|var1[2]|var2[0]|var2[1]|var2[2]|
+---+-------+-------+-------+-------+-------+-------+
| a| 1| 2| 3| 1| 2| 3|
| b| 2| 3| 4| 2| 3| 4|
+---+-------+-------+-------+-------+-------+-------+
