Imagine that you have:
keys = [\'name\', \'age\', \'food\']
values = [\'Monty\', 42, \'spam\']
What is the simplest way to produce the foll
I had this doubt while I was trying to solve a graph-related problem. The issue I had was I needed to define an empty adjacency list and wanted to initialize all the nodes with an empty list, that's when I thought how about I check if it is fast enough, I mean if it will be worth doing a zip operation rather than simple assignment key-value pair. After all most of the times, the time factor is an important ice breaker. So I performed timeit operation for both approaches.
import timeit
def dictionary_creation(n_nodes):
dummy_dict = dict()
for node in range(n_nodes):
dummy_dict[node] = []
return dummy_dict
def dictionary_creation_1(n_nodes):
keys = list(range(n_nodes))
values = [[] for i in range(n_nodes)]
graph = dict(zip(keys, values))
return graph
def wrapper(func, *args, **kwargs):
def wrapped():
return func(*args, **kwargs)
return wrapped
iteration = wrapper(dictionary_creation, n_nodes)
shorthand = wrapper(dictionary_creation_1, n_nodes)
for trail in range(1, 8):
print(f'Itertion: {timeit.timeit(iteration, number=trails)}\nShorthand: {timeit.timeit(shorthand, number=trails)}')
For n_nodes = 10,000,000 I get,
Iteration: 2.825081646999024 Shorthand: 3.535717916001886
Iteration: 5.051560923002398 Shorthand: 6.255070794999483
Iteration: 6.52859034499852 Shorthand: 8.221581164998497
Iteration: 8.683652416999394 Shorthand: 12.599181543999293
Iteration: 11.587241565001023 Shorthand: 15.27298851100204
Iteration: 14.816342867001367 Shorthand: 17.162912737003353
Iteration: 16.645022411001264 Shorthand: 19.976680120998935
You can clearly see after a certain point, iteration approach at n_th step overtakes the time taken by shorthand approach at n-1_th step.