IIUC，你需要wide to long类型的转换，可以通过pyspark中的stack来实现

IIUC, you need wide to long kind of transformation which can be achieved by stack in pyspark

我创建了一个包含 5 个月数据的示例数据框

I created a sample dataframe with 5 months data

df = spark.createDataFrame([(1,10,20,30,40,50,10,20,30,40,50),(2,10,20,30,40,50,10,20,30,40,50)],['cust','Measrue1_month1','Measrue1_month2','Measrue1_month3','Measrue1_month4','Measrue1_month5','Measrue2_month1','Measrue2_month2','Measrue2_month3','Measrue2_month4','Measrue2_month5'])

现在生成堆栈操作的子句.可以以更好的方式完成，但这里是最简单的例子

Now generating the clause for stack operation. Can be done in better ways but here is the most simplest example

Measure1 = [i for i in df.columns if i.startswith('Measrue1')]
Measure2 = [i for i in df.columns if i.startswith('Measrue2')]
final = []
for i in Measure1:
    for j in Measure2:
        if(i.split('_')[1]==j.split('_')[1]):
            final.append((i,j))
rows = len(final)
values = ','.join([f"'{i.split('_')[1]}',{i},{j}" for i,j in final])

现在实际应用堆栈操作

df.select('cust',expr(f'''stack({rows},{values})''').alias('Month','Measure1','Measure2')).show()

+----+------+--------+--------+
|cust| Month|Measure1|Measure2|
+----+------+--------+--------+
|   1|month1|      10|      10|
|   1|month2|      20|      20|
|   1|month3|      30|      30|
|   1|month4|      40|      40|
|   1|month5|      50|      50|
|   2|month1|      10|      10|
|   2|month2|      20|      20|
|   2|month3|      30|      30|
|   2|month4|      40|      40|
|   2|month5|      50|      50|
+----+------+--------+--------+