take

Hi @simonaubertbd what's the expected output here? Assuming you have kept the alternative solution to use aggregate, do we need it to work the same as aggregate?

Hello @udit710 and thanks for your answer.

let's say I have

aggregate1 = inlineInput1.groupby([]).agg(weight_max=('weight', 'max')).reset_index()

would be simply 3

Any thoughts on this @rhshadrach? I concur with OP that this feature would be helpful in cases where the grouping columns are dynamically determined.

So you want the groupby to be a no op and just return the dataframe if no grouping columns are specified? I sort of get it, you dont want to split the dataframe on anything so it just passes through.

I don't see how overloading the method and muddying the API here is worth while. Just do the check yourself. I think it would be strange in some cases for groupby to return a data frame and a group by object in another.

The resulting object can still be a GroupBy object, as @udit710 implemented in #61168.

If I'm understanding the request right, @simonaubertbd desires for df.groupby([]).agg(...) to behave the same as df.agg(...). Here I am -1; the work it would take to get these to agree, and the ongoing maintenance to support, seems to me to be a non-starter.

On the other hand, I'm a bit more receptive to df.groupby([]) behaving the same as df.groupby(pd.Series(0, index=df.index)) (perhaps better would be to groupby np.zeros? Would have to run some benchmarks). However still here, it seems to me that having this live in user code rather than pandas is more explicit and readable.

Hello @rhshadrach

My bad, I may have been unclear. exactly like df.agg(...) would mean one row by aggregation if I'm right and this is typically what I don't want. I have in mind more something like

aggregate1 = inlineInput1.assign(d=0).groupby('d').agg(Age_max=('Age', 'max'), FirstName_count=('FirstName', 'count'), LastName_count=('LastName', 'count')).reset_index(drop=True)

About having this is user code, it took me a lot of time to deal with it, in developing as well as testing.

Thanks @simonaubertbd - then I believe your comment in #61160 (comment) should not be "simply 3" (the scalar), but rather a DataFrame with 3 as the value.

About having this is user code, it took me a lot of time to deal with it, in developing as well as testing.

I'm sympathetic, but still think adding a call to assign and reset_index is not onerous.

@rhshadrach This is a little more complex than that ;) The project I'm on is a python code generator so I had to deal with some conditional typescript and even finding the solution wasn't that easy (Well, I'm obviously not talking about days but more about hours.. also, I must acknowledge I'm kind of a newbie with pandas but when asked for more experimented devs, it wasn't that obvious for them).

That said, Yes, it was a dataframe with 3 as a value. Thanks for your remark, I should have been more specific.

Best regards,

Simon