How to count number of days for every month in a date range

Question

I have data as below

+-----+------------+-------------+
| TYPE|DTIN_DATE   |DTOUT_DATE   |
+-----+------------+-------------+
|    A|  2021-03-22|   2021-05-26|
|    B|  2021-03-30|   2021-04-09|
+-----+------------+-------------+

I want to expand and count the number of days for every month between DTIN_DATE and DTOUT_DATE as below

+-----+------------+-------------+
| TYPE|MONTH       |NO_DAYS      |
+-----+------------+-------------+
|    A|  2021-03   |   10        |
|    A|  2021-04   |   30        |
|    A|  2021-05   |   26        |
|    B|  2021-03   |   2         |
|    B|  2021-04   |   9         |
+-----+------------+-------------+

Answer 1

You can create a sequence using 1 month as interval and then calculate days difference without grouping/ aggregating.

df = (df
    .withColumn(
        'month',
        F.explode(F.expr("sequence(date_trunc('MM', DTIN_DATE), date_trunc('MM', DTOUT_DATE), interval 1 month)")))
    .select(
        'TYPE',
        F.date_format('month', 'yyyy-MM').alias('MONTH'),
        (F.datediff(F.least('DTOUT_DATE', F.last_day('month')), F.greatest('DTIN_DATE', 'month')) + 1).alias('NO_DAYS'))
)

Full example:

from pyspark.sql import functions as F

df = spark.createDataFrame(
    [('A', '2021-03-22', '2021-05-26'),
     ('B', '2021-03-30', '2021-04-09')],
    ['TYPE', 'DTIN_DATE', 'DTOUT_DATE']
).withColumn('DTIN_DATE', F.col('DTIN_DATE').cast('date')
).withColumn('DTOUT_DATE', F.col('DTOUT_DATE').cast('date'))

df = (df
    .withColumn(
        'month',
        F.explode(F.expr("sequence(date_trunc('MM', DTIN_DATE), date_trunc('MM', DTOUT_DATE), interval 1 month)")))
    .select(
        'TYPE',
        F.date_format('month', 'yyyy-MM').alias('MONTH'),
        (F.datediff(F.least('DTOUT_DATE', F.last_day('month')), F.greatest('DTIN_DATE', 'month')) + 1).alias('NO_DAYS'))
)

df.show()
# +----+-------+-------+
# |TYPE|MONTH  |NO_DAYS|
# +----+-------+-------+
# |A   |2021-03|10     |
# |A   |2021-04|30     |
# |A   |2021-05|26     |
# |B   |2021-03|2      |
# |B   |2021-04|9      |
# +----+-------+-------+