如何在pyspark中将密集向量转换为数据帧？

from pyspark.ml.feature import VectorAssembler

assembler = VectorAssembler(
    inputCols=["col1", "col2", ...],  # 输入列的名称
    outputCol="features"  # 输出列的名称
)

output_df = assembler.transform(input_df)

from pyspark.sql import SparkSession
from pyspark.ml.feature import VectorAssembler

# 创建SparkSession
spark = SparkSession.builder.getOrCreate()

# 创建示例数据
data = [(1, 2, 3), (4, 5, 6), (7, 8, 9)]
input_df = spark.createDataFrame(data, ["col1", "col2", "col3"])

# 创建VectorAssembler对象
assembler = VectorAssembler(
    inputCols=["col1", "col2", "col3"],
    outputCol="features"
)

# 将密集向量转换为数据帧
output_df = assembler.transform(input_df)

# 打印结果
output_df.show()

+----+----+----+-------------+
|col1|col2|col3|     features|
+----+----+----+-------------+
|   1|   2|   3|[1.0,2.0,3.0]|
|   4|   5|   6|[4.0,5.0,6.0]|
|   7|   8|   9|[7.0,8.0,9.0]|
+----+----+----+-------------+