您可以使用最大隐藏差异来获取每个accountname的差异：

from pyspark.sql import functions as F, Window

df2 = df.withColumn(
    'new_column1',
    F.max(
        F.when(
            F.col('namespace') == 'Transversal',
            F.col('cost') - F.col('cost_to_pay')
        )
    ).over(Window.partitionBy('accountname'))
)

df2.show()
+-----------+-----------+----+-----------+-----------+
|accountname|  namespace|cost|cost_to_pay|new_column1|
+-----------+-----------+----+-----------+-----------+
| account001|        ns1|  93|          9|         68|
| account001|Transversal|  93|         25|         68|
| account002|        ns2|  50|         27|         38|
| account002|Transversal|  50|         12|         38|
+-----------+-----------+----+-----------+-----------+

如果 df 你的Dataframe在 groupby ，你可以找到 df_temp 使用：

df_temp = df.filter(F.col('namespace')=='Transversal')
df_temp = df_temp.withcolumn('new_column1', F.col('cost') - F.col('cost_to_pay'))
df_temp = df_temp.select('accountname', 'new_column1') ## keep only relevant columns

## you might want to have some extra checks, like droping duplicates, etc

## and finally join df_temp with you main dataframe df

df = df.join(df_temp, on='accountname', how='left')
df = df.na.fill({'accountname':'some predefined value, like 0}) ## if you wish to fill nulls