Python timeit app for different methods of flattening a shallow list. list.extend is fastest across board. https://www.dropbox.com/s/lb7yojmcckndojm/flatten_graph.png

flatten_timeit.py

#!/usr/bin/env python2.6
#http://stackoverflow.com/questions/406121/flattening-a-shallow-list-in-python/408281#408281
"""Usage: %prog item_count"""
 
from __future__ import print_function
 
import collections
import itertools
import operator
from timeit import Timer
import sys
 
import matplotlib.pyplot as pyplot
 
def itertools_flatten(iter_lst):
    return list(itertools.chain(*iter_lst))
 
def itertools_iterable_flatten(iter_iter):
    return list(itertools.chain.from_iterable(iter_iter))
 
def reduce_flatten(iter_lst):
    return reduce(operator.add, map(list, iter_lst))
 
def reduce_lambda_flatten(iter_lst):
    return reduce(operator.add, map(lambda x: list(x), [i for i in iter_lst]))
 
def comprehension_flatten(iter_lst):
    return list(item for iter_ in iter_lst for item in iter_)
 
def iadd_flatten(aList):
    return reduce(operator.iadd, aList, [])
 
def list_iadd_flatten(aList):
    return reduce(list.__iadd__, aList, [])
 
def list_comprehension_flatten( aList ):
    return [y for x in aList for y in x]
 
def extend_flatten(aList):
    result = []
    extend = result.extend
    for sub in aList:
        extend(sub)
    return result
 
METHODS = ['itertools', 'itertools_iterable',
           'comprehension', 'iadd', 'list_iadd', 'list_comprehension', 'extend']
 
def _time_test_assert(iter_lst):
    """Make sure all methods produce an equivalent value.
    :raise AssertionError: On any non-equivalent value."""
    callables = (globals()[method + '_flatten'] for method in METHODS)
    results = [callable(iter_lst) for callable in callables]
    if not all(result == results[0] for result in results[1:]):
        raise AssertionError
 
def time_test(partition_count, item_count_per_partition, test_count=10000):
    """Run flatten methods on a list of :param:`partition_count` iterables.
    Normalize results over :param:`test_count` runs.
    :return: Mapping from method to (normalized) microseconds per pass.
    """
    iter_lst = [[dict()] * item_count_per_partition] * partition_count
    print('Partition count:    ', partition_count)
    print('Items per partition:', item_count_per_partition)
    _time_test_assert(iter_lst)
    test_str = 'flatten(%r)' % iter_lst
    result_by_method = {}
    for method in METHODS:
        setup_str = 'from %s import %s_flatten as flatten' % (__name__, method)
        t = Timer(test_str, setup_str)
        per_pass = test_count * t.timeit(number=test_count) / test_count
        print('%20s: %.2f usec/pass' % (method, per_pass))
        result_by_method[method] = per_pass
    return result_by_method
 
if __name__ == '__main__':
    if len(sys.argv) != 2:
        raise ValueError('Need a number of items to flatten')
    item_count = int(sys.argv[1])
    partition_counts = []
    pass_times_by_method = collections.defaultdict(list)
    for partition_count in xrange(1, item_count):
        if item_count % partition_count != 0:
            continue
        items_per_partition = item_count / partition_count
        result_by_method = time_test(partition_count, items_per_partition)
        partition_counts.append(partition_count)
        for method, result in result_by_method.iteritems():
            pass_times_by_method[method].append(result)
    for method, pass_times in pass_times_by_method.iteritems():
        pyplot.plot(partition_counts, pass_times, label=method)
    pyplot.legend()
    pyplot.title('Flattening Comparison for %d Items' % item_count)
    pyplot.xlabel('Number of Partitions')
    pyplot.ylabel('Microseconds')
    pyplot.savefig('p%d.png' % item_count)
    pyplot.show()