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Python for loop vs list comprehension runtime analysis on squaring a list of numbers with both approaches
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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(): | |
squared_nums = [] | |
for num in numbers: | |
squared_nums.append(num ** 2) | |
# Create a new list by squaring the numbers with list comprehension | |
def comprehension(): | |
squared_nums = [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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