vector_norm

Returns the Lp norm of a float vector using the specified degree. Degree defaults to 2.0 (Euclidean norm) if unspecified.

For the corresponding Databricks SQL function, see vector_norm function.

Syntax

Python

from pyspark.sql import functions as dbf

dbf.vector_norm(vector=<vector>, degree=<degree>)

Parameters

Parameter	Type	Description
vector	pyspark.sql.Column or column name	Input vector column.
degree	pyspark.sql.Column or column name, optional	Norm degree (1.0 for L1, 2.0 for L2, float('inf') for infinity norm). Defaults to 2.0.

Parameter	Type	Description
vector	pyspark.sql.Column or column name	Input vector column.
degree	pyspark.sql.Column or column name, optional	Norm degree (1.0 for L1, 2.0 for L2, float('inf') for infinity norm). Defaults to 2.0.

Returns

pyspark.sql.Column: The Lp norm 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('v', ArrayType(FloatType()))])
df = spark.createDataFrame([([3.0, 4.0],)], schema)
df.select(dbf.vector_norm('v', dbf.lit(2.0).cast('float'))).first()[0]
# 5.0