Comparing Times in Python 2026 with Efficient Code
Running benchmarks is only half the job — comparing the results properly is what drives real performance improvements. In 2026, with faster interpreters and free-threading, learning how to accurately compare timing results is essential for making confident optimization decisions.
This March 15, 2026 guide shows the best ways to compare timeit and other timing results effectively.
TL;DR — Key Takeaways 2026
- Always compare relative improvement (e.g., 3.2x faster) rather than absolute times
- Use the same
numberandrepeatvalues when comparing versions - Calculate speedup ratio and percentage improvement
- Visualize comparisons with tables or simple charts
- Consider hardware, Python version, and environment in comparisons
1. Basic Time Comparison
import timeit
def version1():
return sum(i**2 for i in range(10000))
def version2():
import numpy as np
return np.sum(np.arange(10000)**2)
# Compare fairly
time1 = timeit.timeit(version1, number=1000)
time2 = timeit.timeit(version2, number=1000)
print(f"Version 1: {time1:.6f} seconds")
print(f"Version 2: {time2:.6f} seconds")
print(f"Speedup: {time1 / time2:.2f}x faster")
2. Modern Comparison Function (2026 Recommended)
def compare_times(name1, func1, name2, func2, number=1000, repeat=7):
t1 = min(timeit.Timer(func1).repeat(repeat=repeat, number=number)) / number
t2 = min(timeit.Timer(func2).repeat(repeat=repeat, number=number)) / number
speedup = t1 / t2
improvement = (t1 - t2) / t1 * 100
print(f"{'='*60}")
print(f"{name1:30} → {t1*1_000_000:8.2f} µs")
print(f"{name2:30} → {t2*1_000_000:8.2f} µs")
print(f"Speedup: {speedup:.2f}x faster ({improvement:.1f}% improvement)")
return speedup
# Usage
compare_times(
"List Comprehension",
lambda: [x**2 for x in range(10000)],
"NumPy Vectorized",
lambda: __import__('numpy').sum(__import__('numpy').arange(10000)**2)
)
3. Best Practices for Comparing Times in 2026
- Use identical conditions — same
number, same data, same environment - Take the minimum time from multiple repeats
- Calculate both speedup ratio and percentage improvement
- Include context — Python version, hardware, data size
- Save results for historical comparison
- Visualize with simple tables or charts when possible
Conclusion — Comparing Times in Python 2026
Comparing timing results properly is the key to successful performance optimization. In 2026, developers who systematically measure, compare, and record performance gains build faster, more efficient, and more maintainable applications. Never optimize without measuring — and never measure without comparing.
Next steps:
- Use the
compare_times()helper function in your optimization workflow - Related articles: Using timeit in Python 2026 • Efficient Python Code 2026