您可以构建一些日期序列并将其全部分解：

maxdate = df.select(F.to_date(F.max('date'), 'yyyyMMdd').cast('string')).head()[0]

df2 = spark.sql(f"""
select explode(transform(sequence(trunc(to_date('{maxdate}'), 'quarter') - interval 2 months, trunc(to_date('{maxdate}'), 'quarter'), interval 1 month), x -> x - interval 1 day)) as custom_date, 'prev quarter' as info

union all

select explode(transform(sequence(trunc(to_date('{maxdate}'), 'month'), trunc(to_date('{maxdate}'), 'month') - interval 11 month, interval -1 month), x -> x - interval 1 day)) as custom_date, 'current year' as info

union all

select explode(transform(sequence(trunc(to_date('{maxdate}'), 'year'), trunc(to_date('{maxdate}'), 'year') - interval 11 month, interval -1 month), x -> x - interval 1 day)) as custom_date, 'prev year' as info
""")

df2.show(99)
+-----------+------------+
|custom_date|        info|
+-----------+------------+
| 2020-07-31|prev quarter|
| 2020-08-31|prev quarter|
| 2020-09-30|prev quarter|
| 2020-11-30|current year|
| 2020-10-31|current year|
| 2020-09-30|current year|
| 2020-08-31|current year|
| 2020-07-31|current year|
| 2020-06-30|current year|
| 2020-05-31|current year|
| 2020-04-30|current year|
| 2020-03-31|current year|
| 2020-02-29|current year|
| 2020-01-31|current year|
| 2019-12-31|current year|
| 2019-12-31|   prev year|
| 2019-11-30|   prev year|
| 2019-10-31|   prev year|
| 2019-09-30|   prev year|
| 2019-08-31|   prev year|
| 2019-07-31|   prev year|
| 2019-06-30|   prev year|
| 2019-05-31|   prev year|
| 2019-04-30|   prev year|
| 2019-03-31|   prev year|
| 2019-02-28|   prev year|
| 2019-01-31|   prev year|
+-----------+------------+