Let's say I have a data frame which looks like this:
+---+-----------+-----------+
| id| address1| address2|
+---+-----------+-----------+
| 1|address 1.1|address 1.2|
| 2|address 2.1|address 2.2|
+---+-----------+-----------+
I would like to apply a custom function directly to the strings in the address1 and address2 columns, for example:
def example(string1, string2):
name_1 = string1.lower().split(' ')
name_2 = string2.lower().split(' ')
intersection_count = len(set(name_1) & set(name_2))
return intersection_count
I want to store the result in a new column, so that my final data frame would look like:
+---+-----------+-----------+------+
| id| address1| address2|result|
+---+-----------+-----------+------+
| 1|address 1.1|address 1.2| 2|
| 2|address 2.1|address 2.2| 7|
+---+-----------+-----------+------+
I've tried to execute it in a way I once applied a built-in function to the whole column, but I got an error:
>>> df.withColumn('result', example(df.address1, df.address2))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 2, in example
TypeError: 'Column' object is not callable
What am I doing wrong and how I can apply a custom function to strings in selected columns?
You have to use udf (user defined function) in spark
from pyspark.sql.functions import udf
example_udf = udf(example, LongType())
df.withColumn('result', example_udf(df.address1, df.address2))
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