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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
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
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...