[Python-ideas] Allow a group by operation for dict comprehension

[David Mertz]

I agree with these recommendations. There are excellent 3rd party tools
that do what you want. This is way too much to try to shoehorn into a
comprehension.

I'd add one more option. You want something that behaves like SQL. Right in
the standard library is sqlite3, and you can create an in-memory DB to hope
the data you expect to group.

On Thu, Jun 28, 2018, 3:48 PM Wes Turner <wes.turner at gmail.com> wrote:

> PyToolz, Pandas, Dask .groupby()
>
> toolz.itertoolz.groupby does this succinctly without any
> new/magical/surprising syntax.
>
> https://toolz.readthedocs.io/en/latest/api.html#toolz.itertoolz.groupby
>
> From https://github.com/pytoolz/toolz/blob/master/toolz/itertoolz.py :
>
> """
> def groupby(key, seq):
>     """ Group a collection by a key function
>     >>> names = ['Alice', 'Bob', 'Charlie', 'Dan', 'Edith', 'Frank']
>     >>> groupby(len, names)  # doctest: +SKIP
>     {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']}
>     >>> iseven = lambda x: x % 2 == 0
>     >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8])  # doctest: +SKIP
>     {False: [1, 3, 5, 7], True: [2, 4, 6, 8]}
>     Non-callable keys imply grouping on a member.
>     >>> groupby('gender', [{'name': 'Alice', 'gender': 'F'},
>     ...                    {'name': 'Bob', 'gender': 'M'},
>     ...                    {'name': 'Charlie', 'gender': 'M'}]) #
> doctest:+SKIP
>     {'F': [{'gender': 'F', 'name': 'Alice'}],
>      'M': [{'gender': 'M', 'name': 'Bob'},
>            {'gender': 'M', 'name': 'Charlie'}]}
>     See Also:
>         countby
>     """
>     if not callable(key):
>         key = getter(key)
>     d = collections.defaultdict(lambda: [].append)
>     for item in seq:
>         d[key(item)](item)
>     rv = {}
>     for k, v in iteritems(d):
>         rv[k] = v.__self__
>     return rv
> """
>
> If you're willing to install Pandas (and NumPy, and ...), there's
> pandas.DataFrame.groupby:
>
>
> https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html
>
>
> https://github.com/pandas-dev/pandas/blob/v0.23.1/pandas/core/generic.py#L6586-L6659
>
>
> Dask has a different groupby implementation:
>
> https://gist.github.com/darribas/41940dfe7bf4f987eeaa#file-pandas_dask_test-ipynb
>
>
> https://dask.pydata.org/en/latest/dataframe-api.html#dask.dataframe.DataFrame.groupby
>
>
> On Thursday, June 28, 2018, Chris Barker via Python-ideas <
> python-ideas at python.org> wrote:
>
>> On Thu, Jun 28, 2018 at 8:25 AM, Nicolas Rolin <nicolas.rolin at tiime.fr>
>> wrote:
>>>
>>> I use list and dict comprehension a lot, and a problem I often have is
>>> to do the equivalent of a group_by operation (to use sql terminology).
>>>
>>
>> I don't know from SQL, so "group by" doesn't mean anything to me, but
>> this:
>>
>>
>>> For example if I have a list of tuples (student, school) and I want to
>>> have the list of students by school the only option I'm left with is to
>>> write
>>>
>>>     student_by_school = defaultdict(list)
>>>     for student, school in student_school_list:
>>>         student_by_school[school].append(student)
>>>
>>
>> seems to me that the issue here is that there is not way to have a
>> "defaultdict comprehension"
>>
>> I can't think of syntactically clean way to make that possible, though.
>>
>> Could itertools.groupby help here? It seems to work, but boy! it's ugly:
>>
>> In [*45*]: student_school_list
>>
>> Out[*45*]:
>>
>> [('Fred', 'SchoolA'),
>>
>>  ('Bob', 'SchoolB'),
>>
>>  ('Mary', 'SchoolA'),
>>
>>  ('Jane', 'SchoolB'),
>>
>>  ('Nancy', 'SchoolC')]
>>
>>
>> In [*46*]: {a:[t[0] *for* t *in* b] *for* a,b *in* groupby(sorted(student_school_list,
>> key=*lambda* t: t[1]), key=*lambda* t: t[
>>
>>     ...: 1])}
>>
>>     ...:
>>
>>     ...:
>>
>>     ...:
>>
>>     ...:
>>
>>     ...:
>>
>>     ...:
>>
>>     ...:
>>
>> Out[*46*]: {'SchoolA': ['Fred', 'Mary'], 'SchoolB': ['Bob', 'Jane'],
>> 'SchoolC': ['Nancy']}
>>
>>
>> -CHB
>>
>>
>> --
>>
>> Christopher Barker, Ph.D.
>> Oceanographer
>>
>> Emergency Response Division
>> NOAA/NOS/OR&R            (206) 526-6959   voice
>> 7600 Sand Point Way NE   (206) 526-6329   fax
>> Seattle, WA  98115       (206) 526-6317   main reception
>>
>> Chris.Barker at noaa.gov
>>
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