如何在Pyspark Dataframe中的特定索引中添加行或替换？

from pyspark.sql import SparkSession

spark = SparkSession.builder.getOrCreate()
target_df = spark.createDataFrame([], schema)  # schema为目标DataFrame的结构

target_df = target_df.union(original_df.filter(~condition))  # condition为需要替换的特定索引行的条件

target_df = target_df.union(new_row)  # new_row为需要添加或替换的新行

from pyspark.sql import SparkSession

spark = SparkSession.builder.getOrCreate()

# 创建目标DataFrame
target_df = spark.createDataFrame([], schema)  # schema为目标DataFrame的结构

# 添加或替换行
target_df = target_df.union(original_df.filter(~condition))  # condition为需要替换的特定索引行的条件
target_df = target_df.union(new_row)  # new_row为需要添加或替换的新行

from pyspark.sql.functions import monotonically_increasing_id

original_df = original_df.withColumn("index", monotonically_increasing_id())

from pyspark.sql.functions import when

# 使用when函数替换特定索引的行
target_df = original_df.withColumn("column1", when(condition, new_value).otherwise(original_df["column1"]))

from pyspark.sql import SparkSession
from pyspark.sql.functions import monotonically_increasing_id, when

spark = SparkSession.builder.getOrCreate()

# 添加自增索引列
original_df = original_df.withColumn("index", monotonically_increasing_id())

# 替换特定索引的行
target_df = original_df.withColumn("column1", when(condition, new_value).otherwise(original_df["column1"]))