itertools.groupby() / itertools.starmap() / itertools.accumulate()

import itertools

# Group consecutive elements by key
itertools.groupby(iterable, key=None)

# Unpack arguments and map to a function
itertools.starmap(function, iterable)

# Cumulative calculation
itertools.accumulate(iterable, func=operator.add, initial=None)

import itertools
import operator

# groupby: group by key (must be sorted first)
data = [
    {'name': 'Tanaka', 'dept': 'Sales'},
    {'name': 'Suzuki', 'dept': 'Sales'},
    {'name': 'Yamada', 'dept': 'Dev'},
    {'name': 'Sato',   'dept': 'Dev'},
    {'name': 'Ito',    'dept': 'Sales'},  # not sorted, so becomes a separate group
]

# Sort by department, then group
sorted_data = sorted(data, key=lambda x: x['dept'])
for dept, members in itertools.groupby(sorted_data, key=lambda x: x['dept']):
    names = [m['name'] for m in members]
    print(f"{dept}: {names}")
# Dev: ['Yamada', 'Sato']
# Sales: ['Tanaka', 'Suzuki', 'Ito']

# groupby: group consecutive identical characters in a string
for char, group in itertools.groupby('AAABBCCDD'):
    print(char, list(group))
# A ['A', 'A', 'A']
# B ['B', 'B']
# C ['C', 'C']
# D ['D', 'D']

# starmap: unpack tuples and pass as arguments to a function
pairs = [(2, 3), (4, 2), (10, 3)]
result = list(itertools.starmap(pow, pairs))
print(result)   # [8, 16, 1000]  (2^3, 4^2, 10^3)

# accumulate: running sum
nums = [1, 2, 3, 4, 5]
acc_sum = list(itertools.accumulate(nums))
print(acc_sum)  # [1, 3, 6, 10, 15]

# accumulate: running product
acc_prod = list(itertools.accumulate(nums, operator.mul))
print(acc_prod) # [1, 2, 6, 24, 120]  (1!, 2!, 3!, 4!, 5!)

# accumulate: running maximum
nums2 = [3, 1, 4, 1, 5, 9, 2, 6]
acc_max = list(itertools.accumulate(nums2, max))
print(acc_max)  # [3, 3, 4, 4, 5, 9, 9, 9]

groupby() works similarly to SQL's GROUP BY, but it only groups consecutive elements, so you must sort the data by key beforehand. Also note that a group's iterator becomes exhausted once you advance to the next group — convert it to a list with list() if you need to reuse the values.

starmap() is similar to map(), but it unpacks each element (assumed to be a tuple) and passes its items as separate arguments to the function. It offers a cleaner alternative to patterns like map(func, zip(a, b)).

accumulate() accepts a custom func since Python 3.3, and supports an initial parameter for a starting value since Python 3.8. It is useful for a wide variety of running aggregations, including cumulative sums, products, and running maximums.