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Comparing performance of a for loop + yield and a generator expression. In reality this is a meaningless comparison because both instantly return a generator object.
import matplotlib.pyplot as plt
import timeit
# Initialize a list with 1M numbers
numbers = [i for i in range(0, 1000000)]
# Create a new list by squaring the numbers with for loop
def for_loop():
for num in numbers:
yield num ** 2
# Create a new list by squaring the numbers with set comprehension
def comprehension():
return (num ** 2 for num in numbers)
# Compute the runtime of a function
def measure_runtime(func, n_times):
total_runtime = 0.0
for i in range(n_times):
start = timeit.default_timer()
func()
stop = timeit.default_timer()
total_runtime += stop - start
return total_runtime / n_times
n_runs = 10
# Compute runtimes for both for loop and list comprehension approaches
loop_average = measure_runtime(for_loop, n_runs)
comprehension_average = measure_runtime(comprehension, n_runs)
print(
f"For loop yileds average runtime {loop_average} with {n_runs} iterations")
print(
f"Comprehension yileds average runtime {comprehension_average} with {n_runs} iterations")
fig, ax = plt.subplots()
approaches = ['For loop', 'Comprehension']
runtimes = [loop_average, comprehension_average]
rects = ax.bar(approaches, runtimes)
for rect, label in zip(rects, runtimes):
height = rect.get_height()
ax.text(rect.get_x() + rect.get_width() / 2,
height, label, ha='center', va='bottom')
plt.show()
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