How can I use a function in dataframe withColumn function in Pyspark?

I have the some dictionaries and a function defined:

dict_TEMPERATURE = {(0, 70): 'Low', (70.01, 73.99): 'Normal-Low',(74, 76): 'Normal', (76.01, 80): 'Normal-High', (80.01, 300): 'High'}
...
hierarchy_dict = {'TEMP': dict_TEMPERATURE, 'PRESS': dict_PRESSURE, 'SH_SP': dict_SHAFT_SPEED, 'POI': dict_POI, 'TRIG': dict_TRIGGER}



def function_definition(valor, atributo):

    dict_atributo = hierarchy_dict[atributo]
    valor_generalizado = None

    if isinstance(valor, (int, long, float, complex)):

        for key, value in dict_atributo.items():

            if(isinstance(key, tuple)):
                lista = list(key)

                if (valor > key[0] and valor < key[1]):
                    valor_generalizado = value

    else: # if it is not numeric
        valor_generalizado = dict_atributo.get(valor)


    return valor_generalizado

What this function basically do is: check the value which is passed as an argument to the "function_definition" function, and replace its value according to its dictionary's references.

So, if I call "function_definition(60, 'TEMP')" it will return 'LOW'.

On the other hand, I have a dataframe with the next structure (this is an example):

+----+-----+-----+---+----+
|TEMP|SH_SP|PRESS|POI|TRIG|
+----+-----+-----+---+----+
|   0|    1|    2|  0|   0|
|   0|    2|    3|  1|   1|
|   0|    3|    4|  2|   1|
|   0|    4|    5|  3|   1|
|   0|    5|    6|  4|   1|
|   0|    1|    2|  5|   1|
+----+-----+-----+---+----+

What I want to do is to replace the values of one column of the dataframe based on the function defined above, so I have the next code-line:

dataframe_new = dataframe.withColumn(atribute_name, function_definition(dataframe[atribute_name], atribute_name))

But I get the next error message when executing it:

AssertionError: col should be Column

What is wrong in my code? How could do that?