Max Value in N days before end of week/month/quarter

I have a dataframe `df` which contains daily data for many `id`s, sample:

```| yyyy_mm_dd | id   | availability |
|------------|------|--------------|
| 2020-01-01 | 1334 | 300          |
| 2020-01-02 | 1334 | 676          |
| 2020-01-03 | 1334 | 463          |
| ...        | 1334 | ...          |
| 2020-03-29 | 1334 | 564          |
| 2020-03-30 | 1334 | 765          |
| 2020-03-31 | 1334 | 7564         |
| ...        | 1334 | ...          |
| 2020-06-28 | 1334 | 4634         |
| 2020-06-29 | 1334 | 65           |
| 2020-06-30 | 1334 | 643          |
| ...        | 1334 | ...          |
```

How could I identify the maximum `availability` in the last N days prior to end of a quarter? I.e. if N = 3 then I would like to find the maximum `availability` per `id`, in the last 3 days to the prior of each quarter end.

I know I could hardcode the dates but I would like to avoid that if possible. Is this something that windowing could help with?

Expected output based on above input:

```| id   | year | quarter | max_availability |
|------|------|---------|------------------|
| 1334 | 2020 | 01      | 7564             |
| 1334 | 2020 | 02      | 4634             |
```

You can try this code below, which gets the last 10 rows before the end of a quarter and calculates the maximum availability grouped by id.

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

df2 = df.withColumn(
'rn',
F.row_number().over(
Window.partitionBy(
'id',
F.quarter('yyyy_mm_dd'),
F.year('yyyy_mm_dd')
).orderBy(F.desc('yyyy_mm_dd'))
)
).filter('rn <= 10').groupBy(
'id',
F.quarter('yyyy_mm_dd').alias('quarter'),
F.year('yyyy_mm_dd').alias('year')
).agg(
F.max('availability').alias('max_availability')
)

df2.show()
+----+-------+----+----------------+
|  id|quarter|year|max_availability|
+----+-------+----+----------------+
|1334|      1|2020|            7564|
|1334|      2|2020|            4634|
+----+-------+----+----------------+
```