How to create new column with function in Spark Dataframe

Question

I'm trying to figure out the new dataframe API in Spark. I am facing an issue here that I have a dataframe with 2 columns, "ID" and "Amount". As a generic example, say I want to return a new column called "code" that returns a code based on the value of "Amt". I can write a function something like this:

def coder(myAmt:Integer):String {
  if (myAmt > 100) "Little"
  else "Big"
}
When I try to use it like this:

val DF = sqlContext.parquetFile("hdfs://temp/file.parquet")

DF.withColumn("Code", coder(DF("Amt")))
I get type mismatch errors

found   : org.apache.spark.sql.Column
required: Integer
 

I've tried changing the input type on my function to org.apache.spark.sql.Column but I then I start getting errors with the function compiling because it wants a boolean in the if statement.

Am I doing this wrong? Is there another way to do this than using withColumn?

Thanks in advance.

Answer 1

Let's say you have "Amt" column in your Schema:

import org.apache.spark.sql.functions._
val DF = sqlContext.parquetFile("hdfs://temp/file.parquet")
val coder: (Int => String) = (arg: Int) => {if (arg < 100) "little" else "big"}
val sqlfunc = udf(coder)
DF.withColumn("Code", sqlfunc(col("Amt")))
I guess withColumn is the right way to add a column.

You can even check out the details of a successful Spark developers with the Pyspark online training. 

Comments

This worked for me as I am supposed to replace the $ from the numeric values to get the sum. The replace function was not working for replacing the $ in Databricks DataFrame. Thanks the UDF worked for me.

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Answer 2

df = sqlContext.createDataFrame(
    [(1, "a", 25.0), (2, "B", -25.0)], ("c1", "c2", "c3"))

from pyspark.sql.functions import udf
from pyspark.sql.types import *

def valueToCategory(value):
   if   value == 1: return 1
   elif value == 2: return 2
   ...
   else: return 0

# NOTE: it seems that calls to udf() must be after SparkContext() is called
udfValueToCategory = udf(valueToCategory, StringType())
df_with_cat = df.withColumn("category", udfValueToCategory("c1"))

Answer 3

df.select('*', (df.column_name + 10).alias('new_column'))

Answer 4

new_col = []
for column in COLUMN_LIST:
    if column in df.columns:
        new_col.append(column)
    else:
        new_col.append(lit(None).cast(StringType()).alias('{0}'.format(column)))

df = df.select(new_col)

Answer 5

You can do it using udf:

a = F.udf(lambda :yourstring,StringType())
a.select(a().alias('new_column')

Answer 6

creator = udf(
    lambda val: val,
    StringType()
)
df.withColumn('new_col_name', creator(df.old_col))

Answer 7

DF.withColumn("new_col", DF.col("old_col") + 10)

Answer 8

import org.apache.spark.sql.functions.lit
    val addColumn :(String)=>String=(data:String)=>{data}
    val ColUDF= udf(addColumn)
     val output = inputDataFrame.withColumn("Name",ColUDF(lit("abcde")))

Answer 9

Dataset<Row> ​newDs = ds.withColumn("new_col",functions.lit(1));

Comments

Broken Link - link is not available

Answer 10

val df2 = dataFrame .withColumn("F", lit("foo")) .select("F", "A", "B", "C", "D", "E")

Answer 11

val coder: (Int => String) = v => if (v > 100) "Big" else "Small"
import org.apache.spark.sql.functions.udf
val coder_udf = udf(coder)
DF.withColumn("Code", coder_udf( DF.col("Amt")))

Answer 12

You don't even need to create a function. you can use the when method

DF.withColumn("Code",  when(DF("Amt") > 100 ,"Little").otherwise("Big"))

Answer 13

Hi,

withColumn() is used to add a new or update an existing column on DataFrame, here, we will see, how to add a new column by using an existing column. The withColumn() function takes two arguments, the first argument is the name of the new column and the second argument is the value of the column in Column type. 

df.withColumn("CopiedColumn",col("salary")* -1) .show(false)

Answer 14

Spark withColumn() is a DataFrame function that is used to add a new column to DataFrame, change the value of an existing column, convert the datatype of a column, derive a new column from an existing column, on this post, I will walk you through commonly used DataFrame column operations with Scala examples.

First, let’s create a simple DataFrame to work with.

  import spark.sqlContext.implicits._

  val data = Seq(("111",50000),("222",60000),("333",40000))
  val df = data.toDF("EmpId","Salary")
  df.show(false)

Yields below output

+-----+------+
|EmpId|Salary|
+-----+------+
|111  |50000 |
|222  |60000 |
|333  |40000 |
+-----+------+

Using withColumn() to Add a New Column

withColumn() is used to add a new or update an existing column on DataFrame, here, I will just explain how to add a new column by using an existing column. withColumn() function takes two arguments, the first argument is the name of the new column and the second argument is the value of the column in Column type.

  //Derive a new column from existing
  df.withColumn("CopiedColumn",col("salary")* -1)
    .show(false)

Here, we have added a new column CopiedColumn by multiplying -1 with an existing column Salary. This yields the below output.

+-----+------+------------+
|EmpId|Salary|CopiedColumn|
+-----+------+------------+
|111  |50000 |-50000      |
|222  |60000 |-60000      |
|333  |40000 |-40000      |
+-----+------+------------+

You can also add columns based on some conditions, please refer to Spark Case When and When Otherwise examples

Using Select to Add Column

The above statement can also be written using select() as below and this yields the same as the above output. You can also add multiple columns using select.