Measuring Python Execution Time with the time Module

Measuring Python Execution Time with the time Module ⏱️

When you're optimizing code or comparing algorithms, measuring execution time is essential. Python's built-in time module makes it easy to track performance using time.time().

✅ Basic Execution Time Measurement

Record time before and after your code block, and calculate the difference.

import time

start = time.time()

# Code to measure
for _ in range(1000000):
    pass

end = time.time()
print(f"Execution Time: {end - start:.4f} seconds")

✅ Performance Comparison Example

Compare list creation using comprehension vs. for-loop with append.

# Comparing list creation methods
import time

start = time.time()
a = [i for i in range(1000000)]
end = time.time()
print("List comprehension:", end - start)

start = time.time()
b = []
for i in range(1000000):
    b.append(i)
end = time.time()
print("For-loop append:", end - start)

time.time() returns a float in seconds and is accurate enough for most basic timing needs. For more precise benchmarking, you can also explore time.perf_counter().

If you can’t measure it, you can’t improve it. Start timing, start optimizing — and build faster, smarter code. 🚀

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