vector_normalize

Normalizes a float vector to unit length using the specified norm degree. Degree defaults to 2.0 (Euclidean norm) if unspecified.

For the corresponding Databricks SQL function, see vector_normalize function.

Syntax

Python

from pyspark.sql import functions as dbf

dbf.vector_normalize(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 normalized vector as an array of floats.

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_normalize('v', dbf.lit(2.0).cast('float'))).first()[0]
# [0.6..., 0.8...]