How Do I Convert an Array (i.e., List) Column to Vector in Apache Spark?

Converting an array (list) column to a vector in Apache Spark is a common preprocessing step in machine learning pipelines. In Spark, vectors are used to store features for machine learning algorithms. Depending on the language you’re using, the steps can be slightly different. Below, I’ll provide a detailed explanation and code snippets for both PySpark (Python) and Scala.

Using PySpark (Python)

In PySpark, you can convert an array column to a vector using the VectorUDT and functions provided in the pyspark.ml.linalg module. Here’s a step-by-step guide and code example:

Step-by-Step Guide

1. Import the required modules: VectorUDT, Vectors, udf from PySpark.
2. Define a UDF (User Defined Function) to convert an array to a vector.
3. Apply the UDF to the DataFrame column.

Code Example

from pyspark.sql import SparkSession
from pyspark.sql.functions import udf
from pyspark.sql.types import ArrayType, DoubleType
from pyspark.ml.linalg import Vectors, VectorUDT

# Create Spark session
spark = SparkSession.builder.appName("ArrayToVector").getOrCreate()

# Sample data
data = [(1, [1.0, 2.0, 3.0]), (2, [4.0, 5.0, 6.0])]
columns = ["id", "features_array"]

# Create DataFrame
df = spark.createDataFrame(data, columns)

# Define UDF to convert array to vector
array_to_vector_udf = udf(lambda x: Vectors.dense(x), VectorUDT())

# Apply UDF to convert array column to vector column
df_with_vector = df.withColumn("features_vector", array_to_vector_udf(df["features_array"]))

# Show the DataFrame
df_with_vector.show()

+---+--------------+--------------+
| id|features_array|features_vector|
+---+--------------+--------------+
|  1|  [1.0, 2.0, 3.0]| [1.0,2.0,3.0]|
|  2|  [4.0, 5.0, 6.0]| [4.0,5.0,6.0]|
+---+--------------+--------------+

Using Scala

In Scala, you can also convert an array column to a vector using the VectorUDT and functions provided in the org.apache.spark.ml.linalg package.

Step-by-Step Guide

1. Import the required modules: org.apache.spark.ml.linalg.Vectors and org.apache.spark.sql.functions.udf.
2. Define a UDF (User Defined Function) to convert an array to a vector.
3. Apply the UDF to the DataFrame column.

Code Example

import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions.udf
import org.apache.spark.ml.linalg.{Vectors, VectorUDT}

// Create Spark session
val spark = SparkSession.builder.appName("ArrayToVector").getOrCreate()

// Sample data
val data = Seq((1, Array(1.0, 2.0, 3.0)), (2, Array(4.0, 5.0, 6.0)))
val columns = Seq("id", "features_array")

// Create DataFrame
val df = spark.createDataFrame(data).toDF(columns: _*)

// Define UDF to convert array to vector
val arrayToVectorUDF = udf((array: Seq[Double]) => Vectors.dense(array.toArray), new VectorUDT())

// Apply UDF to convert array column to vector column
val df_with_vector = df.withColumn("features_vector", arrayToVectorUDF(df("features_array")))

// Show the DataFrame
df_with_vector.show()

+---+--------------+--------------+
| id|features_array|features_vector|
+---+--------------+--------------+
|  1|  [1.0, 2.0, 3.0]| [1.0,2.0,3.0]|
|  2|  [4.0, 5.0, 6.0]| [4.0,5.0,6.0]|
+---+--------------+--------------+

This conversion is essential when you’re constructing feature vectors for machine learning algorithms, primarily if you’re working within the Spark MLlib library.