Handle string to array conversion in pyspark dataframe - apache-spark

Question

I have a file(csv) which when read in spark dataframe has the below values for print schema
-- list_values: string (nullable = true)
the values in the column list_values are something like:
[[[167, 109, 80, ...]]]
Is it possible to convert this to array type instead of string?
I tried splitting it and using code available online for similar problems:
df_1 = df.select('list_values', split(col("list_values"), ",\s*").alias("list_values"))
but if I run the above code the array which I get skips a lot of values in the original array i.e.
output of the above code is:
[, 109, 80, 69, 5...
which is different from original array i.e. (-- 167 is missing)
[[[167, 109, 80, ...]]]
Since I am new to spark I don't have much knowledge how it is done (For python I could have done ast.literal_eval but spark has no provision for this.
So I'll repeat the question again :
How can I convert/cast an array stored as string to array i.e.
'[]' to [] conversion

Answer 1

Suppose your DataFrame was the following:
df.show()
#+----+------------------+
#|col1| col2|
#+----+------------------+
#| a|[[[167, 109, 80]]]|
#+----+------------------+
df.printSchema()
#root
# |-- col1: string (nullable = true)
# |-- col2: string (nullable = true)
You could use pyspark.sql.functions.regexp_replace to remove the leading and trailing square brackets. Once that's done, you can split the resulting string on ", ":
from pyspark.sql.functions import split, regexp_replace
df2 = df.withColumn(
"col3",
split(regexp_replace("col2", r"(^\[\[\[)|(\]\]\]$)", ""), ", ")
)
df2.show()
#+----+------------------+--------------+
#|col1| col2| col3|
#+----+------------------+--------------+
#| a|[[[167, 109, 80]]]|[167, 109, 80]|
#+----+------------------+--------------+
df2.printSchema()
#root
# |-- col1: string (nullable = true)
# |-- col2: string (nullable = true)
# |-- col3: array (nullable = true)
# | |-- element: string (containsNull = true)
If you wanted the column as an array of integers, you could use cast:
from pyspark.sql.functions import col
df2 = df2.withColumn("col3", col("col3").cast("array<int>"))
df2.printSchema()
#root
# |-- col1: string (nullable = true)
# |-- col2: string (nullable = true)
# |-- col3: array (nullable = true)
# | |-- element: integer (containsNull = true)