PySpark: How to Transpose multiple columns in a Dataframe

Question

I am new to Spark and need a help with transposing the below input dataframe into the desired output df (Rows to Columns) using PySpark or Spark Sql.

Input Dataframe-

A    B    C   D

1    2    3   4

10   11   12  13
......
........

Required Output (transposed) data

A  1

B  2

C  3

D  4

A  11

B  12

C  13

D  14

....
......

It is better if I can pivot the input data (columns) as per our requirement.

Answer 1

Use stack function in spark.

Example:

df.show()
#+---+---+---+---+
#|  A|  B|  C|  D|
#+---+---+---+---+
#|  1|  2|  3|  4|
#| 10| 11| 12| 13|
#+---+---+---+---+

from pyspark.sql.functions import *

df.selectExpr("stack(4,'A',A,'B',B,'C',C,'D',D) as (key,value)").show()
#+---+-----+
#|key|value|
#+---+-----+
#|  A|    1|
#|  B|    2|
#|  C|    3|
#|  D|    4|
#|  A|   10|
#|  B|   11|
#|  C|   12|
#|  D|   13|
#+---+-----+

Answer 2

You can make a generalized function like below (inspired from my previous answer here):

def stack_multiple_col(df,cols=df.columns,output_columns=["col","values"]):
  """stacks multiple columns in a dataframe, 
     takes all columns by default unless passed a list of values"""
  return (f"""stack({len(cols)},{','.join(map(','.join,
         (zip([f'"{i}"' for i in cols],cols))))}) as ({','.join(output_columns)})""")

---

Sample Runs:

df.selectExpr(stack_multiple_col(df)).show()

+---+------+
|col|values|
+---+------+
|  A|     1|
|  B|     2|
|  C|     3|
|  D|     4|
|  A|    10|
|  B|    11|
|  C|    12|
|  D|    13|
+---+------+

df.selectExpr(stack_multiple_col(df,cols=['A','B'],output_columns=["A","B"])).show()
+---+---+
|  A|  B|
+---+---+
|  A|  1|
|  B|  2|
|  A| 10|
|  B| 11|
+---+---+

Answer 3

df = spark.createDataFrame([[1,2,3,4],[10,11,12,13]]).toDF('A','B','C','D')
+---+---+---+---+
|  A|  B|  C|  D|
+---+---+---+---+
|  1|  2|  3|  4|
| 10| 11| 12| 13|
+---+---+---+---+


df.select(
    F.explode(
        F.create_map(
            *reduce(lambda x, y: x+y,
                [[F.lit(col), col] for col in df.columns]
            )
        )
    )
)
+---+-----+
|key|value|
+---+-----+
|  A|    1|
|  B|    2|
|  C|    3|
|  D|    4|
|  A|   10|
|  B|   11|
|  C|   12|
|  D|   13|
+---+-----+