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')
Answers:
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.
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.
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")
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])).
importing col, when from pyspark.sql.functions and updating fifth column to integer(0,1,2) based on the string(string a, string b, string c) into a new DataFrame.
from pyspark.sql.functions import col, when
data_frame_temp = data_frame.withColumn("col_5",when(col("col_5") == "string a", 0).when(col("col_5") == "string b", 1).otherwise(2))
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')
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.
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.
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")
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])).
importing col, when from pyspark.sql.functions and updating fifth column to integer(0,1,2) based on the string(string a, string b, string c) into a new DataFrame.
from pyspark.sql.functions import col, when
data_frame_temp = data_frame.withColumn("col_5",when(col("col_5") == "string a", 0).when(col("col_5") == "string b", 1).otherwise(2))