groups values applying an aggregation function

aggregate.py

table = [
    {'age': 32, 'gender': 'm', 'country': 'Germany', 'transactions': 4233},
    {'age': 23, 'gender': 'f', 'country': 'US',      'transactions': 11223},
    {'age': 31, 'gender': 'f', 'country': 'France',  'transactions': 3234},
    {'age': 41, 'gender': 'm', 'country': 'France',  'transactions': 2230},
    {'age': 19, 'gender': 'm', 'country': 'Germany', 'transactions': 42},
    {'age': 21, 'gender': 'f', 'country': 'France',  'transactions': 3315},
    {'age': 23, 'gender': 'm', 'country': 'Italy',   'transactions': 520}
]


def group_aggregate(groupby, field, agg, table):
    grouped = dict()

    for row in table:
        groupby_value = row.get(groupby)
        field_value = row.get(field)

        grouped[groupby_value] = agg([grouped[groupby_value], field_value]) if grouped.get(groupby_value) else field_value

    for k, v in grouped.items():
        print("{}: {}".format(k, v))


# SELECT SUM(transactions) FROM table GROUP BY gender
#
# m: 7025
# f: 17772
group_aggregate(groupby='gender',
                field='transactions',
                agg=sum,
                table=table)

# SELECT MAX(age) FROM table GROUP BY country
#
# Italy: 23
# France: 41
# US: 23
# Germany: 32
group_aggregate(groupby='country',
                field='age',
                agg=max,
                table=table)

# SELECT MAX(age) FROM table GROUP BY gender
#
# m: 19
# f: 21
group_aggregate(groupby='gender',
                field='age',
                agg=min,
                table=table)