Selecting values from non-null columns in a PySpark DataFrame

There is a pyspark dataframe with missing values:

tbl = sc.parallelize([
        Row(first_name='Alice', last_name='Cooper'),             
        Row(first_name='Prince', last_name=None),
        Row(first_name=None, last_name='Lenon')
    ]).toDF()
tbl.show()

Here's the table:

  +----------+---------+
  |first_name|last_name|
  +----------+---------+
  |     Alice|   Cooper|
  |    Prince|     null|
  |      null|    Lenon|
  +----------+---------+

I would like to create a new column as follows:

• if first name is None, take the last name
• if last name is None, take the first name
• if they are both present, concatenate them
• we can safely assume that at least one of them is present

I can construct a simple function:

def combine_data(row):
    if row.last_name is None:
        return row.first_name
    elif row.first_name is None:
        return row.last_name
    else:
        return '%s %s' % (row.first_name, row.last_name)
tbl.map(combine_data).collect()

I do get the correct result, but I can't append it to the table as a column: tbl.withColumn('new_col', tbl.map(combine_data)) results in AssertionError: col should be Column

What is the best way to convert the result of map to a Column? Is there a preferred way to deal with null values?