CODEWITHSUNDEEP
jsondataframeapache-sparkpysparkexplode
Gadam
Gadam

Reputation: 3024

Split Array of Strings in a DataFrame into their own columns

I have a dataframe like this:

df.show()

+-----+ 
|col1 | 
+-----+ 
|[a,b]| 
|[c,d]|   
+-----+

How to convert it into a dataframe like below

+----+----+ 
|col1|col2| 
+----+----+ 
|   a|   b| 
|   c|   d|  
+----+----+

Upvotes: 1

Views: 55

Answers (1)

pissall
pissall

Reputation: 7419

It depends on the type of your "list":

If it is of type ArrayType():

df = spark.createDataFrame(spark.sparkContext.parallelize([['a', ["a","b","c"]], ['b', ["d","e","f"]]]), ["key", "col"])
df.printSchema()
df.show()
root
 |-- key: string (nullable = true)
 |-- col: array (nullable = true)
 |    |-- element: string (containsNull = true)
+---+---------+
|key|      col|
+---+---------+
|  a|[a, b, c]|
|  b|[d, e, f]|
+---+---------+
  • you can access the values like you would with python using []:
df.select("key", df.col[0], df.col[1], df.col[2]).show()
+---+------+------+------+
|key|col[0]|col[1]|col[2]|
+---+------+------+------+
|  a|     a|     b|     c|
|  b|     d|     e|     f|
+---+------+------+------+
  • If it is of type StructType(): (maybe you built your dataframe by reading a JSON)
df2 = df.select("key", F.struct(
        df.col[0].alias("col1"), 
        df.col[1].alias("col2"), 
        df.col[2].alias("col3")
    ).alias("col"))
df2.printSchema()
df2.show()

root
 |-- key: string (nullable = true)
 |-- col: struct (nullable = false)
 |    |-- col1: string (nullable = true)
 |    |-- col2: string (nullable = true)
 |    |-- col3: string (nullable = true)
+---+---------+
|key|      col|
+---+---------+
|  a|[a, b, c]|
|  b|[d, e, f]|
+---+---------+
  • you can directly 'split' the column using *:
df2.select('key', 'col.*').show()

+---+----+----+----+
|key|col1|col2|col3|
+---+----+----+----+
|  a|   a|   b|   c|
|  b|   d|   e|   f|
+---+----+----+----+

Upvotes: 2

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