multiple criteria for aggregation on pySpark Dataframe

Question

I have a pySpark dataframe that looks like this:

+-------------+----------+
|          sku|      date|
+-------------+----------+
|MLA-603526656|02/09/2016|
|MLA-603526656|01/09/2016|
|MLA-604172009|02/10/2016|
|MLA-605470584|02/09/2016|
|MLA-605502281|02/10/2016|
|MLA-605502281|02/09/2016|
+-------------+----------+

I want to group by sku, and then calculate the min and max dates. If I do this:

df_testing.groupBy('sku') \
    .agg({'date': 'min', 'date':'max'}) \
    .limit(10) \
    .show()

the behavior is the same as Pandas, where I only get the sku and max(date) columns. In Pandas I would normally do the following to get the results I want:

df_testing.groupBy('sku') \
    .agg({'day': ['min','max']}) \
    .limit(10) \
    .show()

However on pySpark this does not work, and I get a java.util.ArrayList cannot be cast to java.lang.String error. Could anyone please point me to the correct syntax?

Thanks.

Answer 1

You cannot use dict. Use:

>>> from pyspark.sql import functions as F
>>>
>>> df_testing.groupBy('sku').agg(F.min('date'), F.max('date'))