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.

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...