Applying Mapping Function on DataFrame

Question

I have just started using databricks/pyspark. Im using python/spark 2.1. I have uploaded data to a table. This table is a single column full of strings. I wish to apply a mapping function to each element in the column. I load the table into a dataframe:

df = spark.table("mynewtable")

The only way I could see was others saying was to convert it to RDD to apply the mapping function and then back to dataframe to show the data. But this throws up job aborted stage failure:

df2 = df.select("_c0").rdd.flatMap(lambda x: x.append("anything")).toDF()

All i want to do is just apply any sort of map function to my data in the table.For example append something to each string in the column, or perform a split on a char, and then put that back into a dataframe so i can .show() or display it.

Best Answer

Accepted Answer

You cannot:

Use flatMap because it will flatten the Row
You cannot use append because:
- tuple or Row have no append method
- append (if present on collection) is executed for side effects and returns None

I would use withColumn:

df.withColumn("foo", lit("anything"))

but map should work as well:

df.select("_c0").rdd.flatMap(lambda x: x + ("anything", )).toDF()

Edit (given the comment):

You probably want an udf

from pyspark.sql.functions import udfdef iplookup(s):return ... # Some lookup logiciplookup_udf = udf(iplookup)df.withColumn("foo", iplookup_udf("c0"))

Default return type is StringType, so if you want something else you should adjust it.

Applying Mapping Function on DataFrame

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