pyspark - difference between select and agg - select

Question

What is the difference between the following two -
df.select(min("salary")).show()
and
df.agg({'salary':'min'}).show()
Also, what is the difference in these two -
df.groupBy("department").agg({'salary':'min'}).show()
and
df.groupBy("Company").min('salary').show()

Answer 1

In Spark, there are many different ways to write the same thing. It depends mostly if you prefer a SQL writting or a python writting.
df.select(min("salary")) is the equivalent of SQL :
select min(salary) from df
This query computes the min of the column salary without any group by clause.
It is equivalent to
from pyspark.sql import functions as F
df.groupBy().agg(F.min("salary"))
# OR
df.groupBy().agg({'salary':'min'})
As you can see, the groupBy is empty, so you do not group by anything. Python also can interpret the dict {'salary':'min'} which is equivalent to the function F.min("salary").
The method agg depends on the object. Applied to a Dataframe, it is equivalent to df.groupBy().agg. agg is also a method of the object GroupedData which is created when you do df.groupBy(). I added the link to the officiel doc where you can see the difference between the two methods.
When writting df.groupBy("department").agg({'salary':'min'}), you can specify in the method agg several different aggregation. When using just min, you are limited to one column. For example, you can do this :
from pyspark.sql import functions as F
df.groupBy("department").agg(F.min("salary"), F.max("age"))
# OR
df.groupBy("department").agg({'salary':'min', 'age':'max'})
# But you cannot do
df.groupBy("department").min("salary").max("age")
>> AttributeError: 'DataFrame' object has no attribute 'max'