Luke Luke - 1 year ago 2648
Python Question

Pyspark replace strings in Spark dataframe column

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 Source

For Spark 1.5 or later, you can use the functions package:

from pyspark.sql.functions import *
newDf = df.withColumn('address', regexp_replace('address', 'lane', 'ln'))

Quick explanation:

  • The function withColumn is called to add (or replace, if the name exists) a column in the data frame.
  • The function regexp_replace will generate a new column by replacing all substrings that match the pattern.
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