We can replace the step where we create a UDF with the decorator like this "decorator_times_four", udf_multiply_by_four("integers") Udf_multiply_by_four = fn.udf(multiply_by_four, T.IntegerType()) Multiply_by_four = curry_multiply_by_n(4) We do this by adding a wrapper function as seen here: First remember how we normally curry information into the function we want to use as a UDF. However “closuring in a variable’ does not really give the same sense of what is happening as ‘currying in a variable’. Closure is likely more technically correct.
As a side note I have heard this method referred to as closure as well. If you are not familiar with this concept you may want to look here. This seems handy, but can we still easily introduce additional parameters not contained in our dataframe by currying. Here fn.udf is applied as a decorator which saves us having to create a second function from our desired function. "decorator_times_three", decorator_multiply_by_three("integers") That is great but we can do this more compactly by using the decorator decorator_multiply_by_three(number): Then we use fn.udf to convert this into a User Defined Function which can be applied to a column in our dataframe. Here we create a function `multiply_by_two` (its trivial but this is just to demonstrate). "times_two", udf_multiply_by_two("integers") Udf_multiply_by_two = fn.udf(multiply_by_two, T.IntegerType()) Lets assume we have a dataframe df with a column of integers called “integers”. Lets start by remembering the standard way that we might create a UDF. However we might want to add in variables to create a more flexible UDF.
PYSPARK UDF EXAMPLE CODE
Decorators have some distinct advantages for code readability and compactness. As well as the standard ways of using UDFs covered previously, PySpark also has an decorator. These are called User Defined Functions, or UDFs, and I have written about them before. While Pyspark has a broad range of excellent data manipulation functions, on occasion you might want to create a custom function of your own.
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