vector_l2_distance

Returns the Euclidean (L2) distance between two float vectors. The vectors must have the same dimension.

For the corresponding Databricks SQL function, see vector_l2_distance function.

Syntax

Python

from pyspark.sql import functions as dbf

dbf.vector_l2_distance(left=<left>, right=<right>)

Parameters

Parameter	Type	Description
left	pyspark.sql.Column or column name	First vector column.
right	pyspark.sql.Column or column name	Second vector column.

Parameter	Type	Description
left	pyspark.sql.Column or column name	First vector column.
right	pyspark.sql.Column or column name	Second vector column.

Returns

pyspark.sql.Column: L2 distance as a float value.

Examples

Python

from pyspark.sql import functions as dbf
from pyspark.sql.types import ArrayType, FloatType, StructType, StructField

schema = StructType([StructField('a', ArrayType(FloatType())), StructField('b', ArrayType(FloatType()))])
df = spark.createDataFrame([([1.0, 2.0, 3.0], [4.0, 5.0, 6.0])], schema)
df.select(dbf.vector_l2_distance('a', 'b')).first()[0]
# 5.196152...