Updating a dataframe column in spark

Question

Looking at the new spark dataframe api, it is unclear whether it is possible to modify dataframe columns.

How would I go about changing a value in row x column y of a dataframe?

In pandas this would be df.ix[x,y] = new_value

Edit: Consolidating what was said below, you can't modify the existing dataframe as it is immutable, but you can return a new dataframe with the desired modifications.

If you just want to replace a value in a column based on a condition, like np.where:

from pyspark.sql import functions as F

update_func = (F.when(F.col('update_col') == replace_val, new_value)
                .otherwise(F.col('update_col')))
df = df.withColumn('new_column_name', update_func)

If you want to perform some operation on a column and create a new column that is added to the dataframe:

import pyspark.sql.functions as F
import pyspark.sql.types as T

def my_func(col):
    do stuff to column here
    return transformed_value

# if we assume that my_func returns a string
my_udf = F.UserDefinedFunction(my_func, T.StringType())

df = df.withColumn('new_column_name', my_udf('update_col'))

If you want the new column to have the same name as the old column, you could add the additional step:

df = df.drop('update_col').withColumnRenamed('new_column_name', 'update_col')

Answer 1

DataFrames are based on RDDs. RDDs are immutable structures and do not allow updating elements on-site. To change values, you will need to create a new DataFrame by transforming the original one either using the SQL-like DSL or RDD operations like map.

A highly recommended slide deck: Introducing DataFrames in Spark for Large Scale Data Science.

Answer 2

While you cannot modify a column as such, you may operate on a column and return a new DataFrame reflecting that change. For that you'd first create a UserDefinedFunction implementing the operation to apply and then selectively apply that function to the targeted column only. In Python:

from pyspark.sql.functions import UserDefinedFunction
from pyspark.sql.types import StringType

name = 'target_column'
udf = UserDefinedFunction(lambda x: 'new_value', StringType())
new_df = old_df.select(*[udf(column).alias(name) if column == name else column for column in old_df.columns])

new_df now has the same schema as old_df (assuming that old_df.target_column was of type StringType as well) but all values in column target_column will be new_value.

Answer 3

Just as maasg says you can create a new DataFrame from the result of a map applied to the old DataFrame. An example for a given DataFrame df with two rows:

val newDf = sqlContext.createDataFrame(df.map(row => 
  Row(row.getInt(0) + SOMETHING, applySomeDef(row.getAs[Double]("y")), df.schema)

Note that if the types of the columns change, you need to give it a correct schema instead of df.schema. Check out the api of org.apache.spark.sql.Row for available methods: https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/Row.html

[Update] Or using UDFs in Scala:

import org.apache.spark.sql.functions._

val toLong = udf[Long, String] (_.toLong)

val modifiedDf = df.withColumn("modifiedColumnName", toLong(df("columnName"))).drop("columnName")

and if the column name needs to stay the same you can rename it back:

modifiedDf.withColumnRenamed("modifiedColumnName", "columnName")

Answer 4

Commonly when updating a column, we want to map an old value to a new value. Here's a way to do that in pyspark without UDF's:

# update df[update_col], mapping old_value --> new_value
from pyspark.sql import functions as F
df = df.withColumn(update_col,
    F.when(df[update_col]==old_value,new_value).
    otherwise(df[update_col])).