unwrap_udt

Unwrap UDT data type column into its underlying type.

Syntax

import pyspark.sql.functions as sf

sf.unwrap_udt(col=<col>)

Parameters

Parameter	Type	Description
col	pyspark.sql.Column or str	The UDT column to unwrap.

Returns

pyspark.sql.Column: The underlying representation.

Examples

Example 1: Unwrap ML-specific UDT - VectorUDT.

from pyspark.sql import functions as sf
from pyspark.ml.linalg import Vectors

vec1 = Vectors.dense(1, 2, 3)
vec2 = Vectors.sparse(4, {1: 1.0, 3: 5.5})
df = spark.createDataFrame([(vec1,), (vec2,)], ["vec"])
df.select(sf.unwrap_udt("vec")).printSchema()

root
 |-- unwrap_udt(vec): struct (nullable = true)
 |    |-- type: byte (nullable = false)
 |    |-- size: integer (nullable = true)
 |    |-- indices: array (nullable = true)
 |    |    |-- element: integer (containsNull = false)
 |    |-- values: array (nullable = true)
 |    |    |-- element: double (containsNull = false)

Example 2: Unwrap ML-specific UDT - MatrixUDT.

from pyspark.sql import functions as sf
from pyspark.ml.linalg import Matrices

mat1 = Matrices.dense(2, 2, range(4))
mat2 = Matrices.sparse(2, 2, [0, 2, 3], [0, 1, 1], [2, 3, 4])
df = spark.createDataFrame([(mat1,), (mat2,)], ["mat"])
df.select(sf.unwrap_udt("mat")).printSchema()

root
 |-- unwrap_udt(mat): struct (nullable = true)
 |    |-- type: byte (nullable = false)
 |    |-- numRows: integer (nullable = false)
 |    |-- numCols: integer (nullable = false)
 |    |-- colPtrs: array (nullable = true)
 |    |    |-- element: integer (containsNull = false)
 |    |-- rowIndices: array (nullable = true)
 |    |    |-- element: integer (containsNull = false)
 |    |-- values: array (nullable = true)
 |    |    |-- element: double (containsNull = false)
 |    |-- isTransposed: boolean (nullable = false)