Comparing two data frame with different number of columns in scala

Just convert column lists into sets, and use diff operations on these sets, like this:

df2.columns.toSet.diff(df1.columns.toSet)

Please note that the order of comparison matters, like, df1.columns.toSet.diff(df2.columns.toSet) won't produce a required diff. If you want to have diff independent of position, you can use something like this:

df2.columns.toSet.diff(df1.columns.toSet).union(
  df1.columns.toSet.diff(df2.columns.toSet))

In pyspark , You can use below logic .

dept = [("Finance",10), 
        ("Marketing",20), 
        ("Sales",30), 
        ("IT",40) 
      ]
deptColumns = ["dept_name","dept_id"]

dept1 = [("Finance",10,'999'), 
        ("Marketing",20,'999'), 
        ("Sales",30,'999'), 
        ("IT",40,'999') 
      ]
deptColumns1 = ["dept_name","dept_id","extracol"]

deptDF = spark.createDataFrame(data=dept, schema = deptColumns)
dept1DF = spark.createDataFrame(data=dept1, schema = deptColumns1)
deptDF_columns=deptDF.schema.names
dept1DF_columns=dept1DF.schema.names

list_difference = []
for item in dept1DF_columns:
  if item not in deptDF_columns:
     list_difference.append(item)

print(list_difference)

Tested code :