CODEWITHSUNDEEP
apache-sparkpysparkapache-spark-sql
SmitM
SmitM

Reputation: 1376

Pyspark DataFrame: Split column with multiple values into rows

I have a dataframe (with more rows and columns) as shown below.

Sample DF:

from pyspark import Row
from pyspark.sql import SQLContext
from pyspark.sql.functions import explode

sqlc = SQLContext(sc)

df = sqlc.createDataFrame([Row(col1 = 'z1', col2 = '[a1, b2, c3]', col3 = 'foo')])
# +------+-------------+------+
# |  col1|         col2|  col3|
# +------+-------------+------+
# |    z1| [a1, b2, c3]|   foo|
# +------+-------------+------+

df
# DataFrame[col1: string, col2: string, col3: string]

What I want:

+-----+-----+-----+
| col1| col2| col3|
+-----+-----+-----+
|   z1|   a1|  foo|
|   z1|   b2|  foo|
|   z1|   c3|  foo|
+-----+-----+-----+

I tried to replicate the RDD solution provided here: Pyspark: Split multiple array columns into rows

(df
    .rdd
    .flatMap(lambda row: [(row.col1, col2, row.col3) for col2 in row.col2)])
    .toDF(["col1", "col2", "col3"]))

However, it is not giving the required result

Edit: The explode option does not work because it is currently stored as string and the explode function expects an array

Upvotes: 6

Views: 10687

Answers (2)

abiratsis
abiratsis

Reputation: 7336

Pault's solution should work perfectly fine although here is another solution which uses regexp_extract instead (you don't really need to replace anything in this case) and it can handle arbitrary number of spaces:

from pyspark.sql.functions import col, explode, regexp_extract,regexp_replace, split

df.withColumn("col2", 
              explode(
                  split(
                      regexp_extract(
                        regexp_replace(col("col2"), "\s", ""), "^\[(.*)\]$", 1), ","))) \
.show()

Explanation:

  • Initially regexp_replace(col("col2"), "\s", "") will replace all spaces with empty string.
  • Next regexp_extract will extract the content of the column which start with [ and ends with ].
  • Then we execute split for the comma separated values and finally explode.

Upvotes: 2

pault
pault

Reputation: 43544

You can use explode but first you'll have to convert the string representation of the array into an array.

One way is to use regexp_replace to remove the leading and trailing square brackets, followed by split on ", ".

from pyspark.sql.functions import col, explode, regexp_replace, split

df.withColumn(
    "col2", 
    explode(split(regexp_replace(col("col2"), "(^\[)|(\]$)", ""), ", "))
).show()
#+----+----+----+
#|col1|col2|col3|
#+----+----+----+
#|  z1|  a1| foo|
#|  z1|  b2| foo|
#|  z1|  c3| foo|
#+----+----+----+

Upvotes: 4

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