如何使用fold或Map函数在Pyspark中转换多个列？

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

# 创建SparkSession
spark = SparkSession.builder.appName("Example").getOrCreate()

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

# 打印原始DataFrame
df.show()

# 使用withColumn转换多个列
for column in df.columns:
    df = df.withColumn(column, col(column) + 1)

# 打印转换后的DataFrame
df.show()

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

# 创建SparkSession
spark = SparkSession.builder.appName("Example").getOrCreate()

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

# 打印原始DataFrame
df.show()

# 使用select和alias转换多个列
df = df.select([col(column) + 1 for column in df.columns])

# 打印转换后的DataFrame
df.show()

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

# 创建SparkSession
spark = SparkSession.builder.appName("Example").getOrCreate()

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

# 打印原始DataFrame
df.show()

# 使用reduce函数转换多个列
df = reduce(lambda df, column: df.withColumn(column, col(column) + 1), df.columns, df)

# 打印转换后的DataFrame
df.show()