PySpark: Absolute value of a column. TypeError: a float is required

I have a dataframe df created as follow,

schema = StructType([StructField('Id', StringType(), False),
                     StructField('Value', FloatType(), False)])  
df = spark.createDataFrame([('a',5.0),('b',1.0),('c',-0.3)],schema)

It looks like

+---+-----+
| Id|Value|
+---+-----+
|  a|  5.0|
|  b|  1.0|
|  c| -0.3|
+---+-----+

Now I want to take absolute value of Value, which should return

+---+-----+
| Id|Value|
+---+-----+
|  a|  5.0|
|  b|  1.0|
|  c|  0.3|
+---+-----+

I've tried

df = df.withColumn('Value',math.fabs(df.Value))

But it complains TypeError: a float is required. However Value column was specified with FloatType().

Any clue on how to correctly do this? Thanks!