How to melt Spark DataFrame?

PySpark_Wide_to_Long.py

from pyspark.sql.functions import array, col, explode, lit, struct
from pyspark.sql import DataFrame
from typing import Iterable 

def melt(
        df: DataFrame, 
        id_vars: Iterable[str], value_vars: Iterable[str], 
        var_name: str="variable", value_name: str="value") -> DataFrame:
    """
    Convert :class:`DataFrame` from wide to long format.
    Source: https://stackoverflow.com/questions/41670103/how-to-melt-spark-dataframe
    """

    # -------------------------------------------------------------------------------
    # Create array<struct<variable: str, value: ...>>
    # -------------------------------------------------------------------------------
    _vars_and_vals = array(*(
        struct(lit(c).alias(var_name), col(c).alias(value_name)) 
        for c in value_vars))

    # -------------------------------------------------------------------------------
    # Add to the DataFrame and explode
    # -------------------------------------------------------------------------------
    _tmp = df.withColumn("_vars_and_vals", explode(_vars_and_vals))

    cols = id_vars + [
            col("_vars_and_vals")[x].alias(x) for x in [var_name, value_name]]
    return _tmp.select(*cols)
    
# -------------------------------------------------------------------------------
# Let's Implement Wide to Long in Pyspark!
# -------------------------------------------------------------------------------
melt(df_web_browsing_full_test, 
     id_vars=['ID_variable'], 
     value_vars=['VALUE_variable_1', 'VALUE_variable_2']).show()

I am unable to use this solution here. Could you perhaps help me with the logic?

from pyspark.sql.functions import array, col, explode, lit, struct

def melt(df, id_vars, value_vars, var_name, value_name):
    """Convert :class:`DataFrame` from wide to long format."""
    _vars_and_vals = F.array(*[F.struct(F.lit(c).alias(var_name),
                                        F.col(c).alias(value_name)) 
                               for c in value_vars])

    # Add to the DataFrame and explode
    _tmp = df.withColumn("_vars_and_vals", F.explode(_vars_and_vals))

    cols = id_vars + [F.col("_vars_and_vals")[x].alias(x) for x in [var_name, value_name]]
    return _tmp.select(*cols)

This function will fail if the variable has either a full stop in it or two adjacent spaces. Any idea how to exception them out?