Pyspark replace strings in Spark dataframe column

Question

I'd like to perform some basic stemming on a Spark Dataframe column by replacing substrings. What's the quickest way to do this?

In my current use case, I have a list of addresses that I want to normalize. For example this dataframe:

id     address
1       2 foo lane
2       10 bar lane
3       24 pants ln

Would become

id     address
1       2 foo ln
2       10 bar ln
3       24 pants ln

Answer 1

For Spark 1.5 and later, I would suggest you to use the functions package and do something like this:

from pyspark.sql.functions import *

newDf = df.withColumn('address', regexp_replace('address', 'lane', 'ln'))

Crisp explanation:

• The function withColumn is called to add (or replace, if the name exists) a column to the data frame.

• The function regexp_replace will generate a new column by replacing all substrings that match the pattern.

If you wish to learn Pyspark visit this Pyspark Tutorial.