如何在pyspark中转置只有一行和多列的数据帧？

from pyspark.sql import SparkSession
from pyspark.sql.functions import col

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

# 创建只有一行的数据帧
data = [(1, 2, 3)]
df = spark.createDataFrame(data, ["col1", "col2", "col3"])

# 转置单行数据帧为单列
df_transposed = df.select(col("col1"), col("col2"), col("col3")).toDF("values")
# 转置单列数据帧为多行
df_transposed = df_transposed.select(df_transposed.columns[0], *[(col(df_transposed.columns[0])[i]).alias(f"col{i+1}") for i in range(len(df_transposed.columns) - 1)])

# 显示转置后的数据帧
df_transposed.show()

from pyspark.sql import SparkSession

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

# 创建具有多列的数据帧
data = [(1, "A", 100), (2, "B", 200), (3, "C", 300)]
df = spark.createDataFrame(data, ["id", "category", "value"])

# 将列名转置为行值，并使用聚合函数将行值转置为列值
df_transposed = df.selectExpr("stack(3, 'id', id, 'category', category, 'value', value) as (attribute, val)") \
    .groupBy("attribute").pivot("attribute").agg({"val": "first"})

# 显示转置后的数据帧
df_transposed.show()