如果userid具有某种顺序，那么可以使用rownumber函数。即使没有，也可以使用单调递增的\u id（）添加id。目前，我假设可以订购userid。然后你可以这样做：

from pyspark.sql import functions as F
from pyspark.sql.window import Window
df_merge = df1.select('userid').union(df2.select('userid'))
w=Window.orderBy('userid')
df_result = df_merge.withColumn('indexid',F.row_number().over(w))

编辑：在评论讨论之后。

# %% Test data and imports

import pyspark.sql.functions as F
from pyspark.sql import Window

df = sqlContext.createDataFrame([('a',100),('ab',50),('ba',300),('ced',60),('d',500)],schema=['userid','index'])
df1 = sqlContext.createDataFrame([('fgh',100),('ff',50),('fe',300),('er',60),('fi',500)],schema=['userid','dummy'])

# %%

# %% Merge the two dataframes, with a null columns as the index

df1=df1.withColumn('index', F.lit(None))
df_merge = df.select(df.columns).union(df1.select(df.columns))

# %%Define a window to arrange the newly added rows at the last and order them by userid

# %% The user id, even though random strings, can be ordered

w= Window.orderBy(F.col('index').asc_nulls_last(),F.col('userid'))# if possible add a partition column here, otherwise all your data will come in one partition, consider salting

# %% For the newly added rows, define index as the maximum value + increment of number of rows in main dataframe

df_final = df_merge.withColumn("index_new",F.when(~F.col('index').isNull(),F.col('index')).otherwise((F.last(F.col('index'),ignorenulls=True).over(w))+F.sum(F.lit(1)).over(w)))

# %% If number of rows in main dataframe is huge, then add an offset in the above line

df_final.show()
+------+-----+---------+
|userid|index|index_new|
+------+-----+---------+
|    ab|   50|       50|
|   ced|   60|       60|
|     a|  100|      100|
|    ba|  300|      300|
|     d|  500|      500|
|    er| null|      506|
|    fe| null|      507|
|    ff| null|      508|
|   fgh| null|      509|
|    fi| null|      510|
+------+-----+---------+