Nsryjdtyk

I've got a list of objects which will number somewhere between the thousands & tens of thousands. These objects could be thought of as people which I'm looking to rank based on a score they have.

So first of all they're split into groups by age, then gender etc. At each point a ranking is provided corresponding to that age/gender category. The fields on the objects are age_group and gender. So you'd first collect everybody that's got the 30-39 age group, then all the men (M) and all women (W) from that age group.

Creating a new list at each of these points is very memory intensive so I'm attempting to use a generator & itertools to group using the original list. So I've got a function to do that;

def group_standings(_standings, field):

    """ sort list of standings by a given field """

    getter = operator.attrgetter(field)

    for k, g in itertools.groupby(_standings, getter):

        yield list(g)





def calculate_positions(standings):

    """

    sort standings by age_group then gender & set position based on point value 

    """

    for age_group in group_standings(standings, 'age_group'):



        for gender_group in group_standings(age_group, 'gender'):



            set_positions(

                standings=gender_group,

                point_field='points',

                position_field='position',

            )

For set_positions to function correctly it needs the whole group so that it can sort by the point_field value then set the position_field value.

Debugging the generator, groupby isn't collecting all objects matching the key as I'd expected. The output is something like;

DEBUG generating k 30-39

DEBUG generating g [<Standing object at 0x7fc86fedbe10>, <Standing object at 0x7fc86fedbe50>, <Standing object at 0x7fc86fedbe90>]



DEBUG generating k 20-29

DEBUG generating g [<Standing object at 0x7fc86fedbed0>]



DEBUG generating k 30-39

DEBUG generating g [<Standing object at 0x7fc86fedbf10>]



DEBUG generating k 20-29

DEBUG generating g [<Standing object at 0x7fc86fedbf50>, <Standing object at 0x7fc86fedbf90>, <Standing object at 0x7fc86fedbfd0>, <Standing object at 0x7fc856ecc050>, <Standing object at 0x7fc856ecc090>, <Standing object at 0x7fc856ecc0d0>, <Standing object at 0x7fc856ecc110>, <Standing object at 0x7fc856ecc150>, <Standing object at 0x7fc856ecc190>, <Standing object at 0x7fc856ecc1d0>]

To confirm, for set_positions to function, the list provided by the generator would need to contain all objects in the 20-29 age group, but as above, objects from that group are being found on multiple iterations of the list.

It happens because groupby function assumes that the input iterable is already sorted by key (see documentation). It's made for performance but is confusing.
Also, I wouldn't cast g to a list in group_standings function but applied it only when you pass gender_group to set_positions.

groupby works on adjacent elements

As per @MikhailBerlinkov's answer, groupby aggregates consecutive items only which are the same, optionally using a key argument for comparison.

It may help to see an example:

from itertools import groupby



L = [1, 1, 1, 2, 2, 2, 1, 1]



res = [list(j) for _, j in groupby(L)]



[[1, 1, 1], [2, 2, 2], [1, 1]]

As you can see, the groups of 1 values are split into two separate lists.

Sort before grouping

Instead, you can sort your list of objects before grouping. For a large list of objects, say of length n, this takes O(n log n) time. Here's an example (using same L as before):

L_sorted = sorted(L)



res = [list(j) for i, j in groupby(L_sorted)]



[[1, 1, 1, 1, 1], [2, 2, 2]]