There is no need for an UDF here. Column already provides cast method with DataType instance :

from pyspark.sql.types import DoubleType

changedTypedf = joindf.withColumn("label", joindf["show"].cast(DoubleType()))

or short string:

changedTypedf = joindf.withColumn("label", joindf["show"].cast("double"))

where canonical string names (other variations can be supported as well) correspond to simpleString value. So for atomic types:

from pyspark.sql import types 

for t in ['BinaryType', 'BooleanType', 'ByteType', 'DateType', 
          'DecimalType', 'DoubleType', 'FloatType', 'IntegerType', 
           'LongType', 'ShortType', 'StringType', 'TimestampType']:
    print(f"{t}: {getattr(types, t)().simpleString()}")

BinaryType: binary
BooleanType: boolean
ByteType: tinyint
DateType: date
DecimalType: decimal(10,0)
DoubleType: double
FloatType: float
IntegerType: int
LongType: bigint
ShortType: smallint
StringType: string
TimestampType: timestamp

and for example complex types

types.ArrayType(types.IntegerType()).simpleString()

'array<int>'

types.MapType(types.StringType(), types.IntegerType()).simpleString()

'map<string,int>'