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@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@python.org> wrote:

On Thu, Jun 28, 2018 at 8:25 AM, Nicolas Rolin <nicolas.rolin@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

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