Pyspark: Split multiple array columns into rows

Spark >= 2.4

You can replace zip_ udf with arrays_zip function

from pyspark.sql.functions import arrays_zip, col, explode

(df
    .withColumn("tmp", arrays_zip("b", "c"))
    .withColumn("tmp", explode("tmp"))
    .select("a", col("tmp.b"), col("tmp.c"), "d"))

Spark < 2.4

With DataFrames and UDF:

from pyspark.sql.types import ArrayType, StructType, StructField, IntegerType
from pyspark.sql.functions import col, udf, explode

zip_ = udf(
  lambda x, y: list(zip(x, y)),
  ArrayType(StructType([
      # Adjust types to reflect data types
      StructField("first", IntegerType()),
      StructField("second", IntegerType())
  ]))
)

(df
    .withColumn("tmp", zip_("b", "c"))
    # UDF output cannot be directly passed to explode
    .withColumn("tmp", explode("tmp"))
    .select("a", col("tmp.first").alias("b"), col("tmp.second").alias("c"), "d"))

With RDDs:

(df
    .rdd
    .flatMap(lambda row: [(row.a, b, c, row.d) for b, c in zip(row.b, row.c)])
    .toDF(["a", "b", "c", "d"]))

Both solutions are inefficient due to Python communication overhead. If data size is fixed you can do something like this:

from functools import reduce
from pyspark.sql import DataFrame

# Length of array
n = 3

# For legacy Python you'll need a separate function
# in place of method accessor 
reduce(
    DataFrame.unionAll, 
    (df.select("a", col("b").getItem(i), col("c").getItem(i), "d")
        for i in range(n))
).toDF("a", "b", "c", "d")

or even:

from pyspark.sql.functions import array, struct

# SQL level zip of arrays of known size
# followed by explode
tmp = explode(array(*[
    struct(col("b").getItem(i).alias("b"), col("c").getItem(i).alias("c"))
    for i in range(n)
]))

(df
    .withColumn("tmp", tmp)
    .select("a", col("tmp").getItem("b"), col("tmp").getItem("c"), "d"))

This should be significantly faster compared to UDF or RDD. Generalized to support an arbitrary number of columns:

# This uses keyword only arguments
# If you use legacy Python you'll have to change signature
# Body of the function can stay the same
def zip_and_explode(*colnames, n):
    return explode(array(*[
        struct(*[col(c).getItem(i).alias(c) for c in colnames])
        for i in range(n)
    ]))

df.withColumn("tmp", zip_and_explode("b", "c", n=3))

You'd need to use flatMap, not map as you want to make multiple output rows out of each input row.

from pyspark.sql import Row
def dualExplode(r):
    rowDict = r.asDict()
    bList = rowDict.pop('b')
    cList = rowDict.pop('c')
    for b,c in zip(bList, cList):
        newDict = dict(rowDict)
        newDict['b'] = b
        newDict['c'] = c
        yield Row(**newDict)

df_split = sqlContext.createDataFrame(df.rdd.flatMap(dualExplode))

One liner (for Spark>=2.4.0):

df.withColumn("bc", arrays_zip("b","c"))
  .select("a", explode("bc").alias("tbc"))
  .select("a", col"tbc.b", "tbc.c").show()

Import required:

from pyspark.sql.functions import arrays_zip

---

Steps -

1. Create a column bc which is an array_zip of columns b and c
2. Explode bc to get a struct tbc
3. Select the required columns a, b and c (all exploded as required).

Output:

> df.withColumn("bc", arrays_zip("b","c")).select("a", explode("bc").alias("tbc")).select("a", "tbc.b", col("tbc.c")).show()
+---+---+---+
|  a|  b|  c|
+---+---+---+
|  1|  1|  7|
|  1|  2|  8|
|  1|  3|  9|
+---+---+---+