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Specifying number of loops in timeit – Python 2026 with Efficient Code

The number parameter in timeit controls how many times your code snippet runs during each measurement. Choosing the right value is crucial for getting accurate and meaningful benchmark results in 2026.

This March 15, 2026 guide explains how to properly set the number of loops and why it matters for efficient code optimization.

TL;DR — Key Takeaways 2026

number = how many times the statement runs in one timing
Too small → noisy and inaccurate results
Too large → includes system noise and takes too long
Best practice: Aim for total runtime between 0.2 and 2 seconds per repeat
Always use the same number when comparing two versions

1. Basic Usage of number Parameter


import timeit

# Default (number=1000000) - often too many for slow code
timeit.timeit('sum(range(1000))')

# Recommended: Control the number of loops
result = timeit.timeit(
    stmt='sum(range(10000))',
    number=5000                     # Run the code 5000 times per measurement
)

print(f"Best of 5: {result:.6f} seconds for 5000 loops")

2. Modern Best Practice Pattern (2026)


import timeit

def benchmark(stmt, setup="", number=1000, repeat=7):
    """Reliable benchmarking with smart loop count"""
    timer = timeit.Timer(stmt=stmt, setup=setup)
    times = timer.repeat(repeat=repeat, number=number)
    best_per_loop = min(times) / number
    
    print(f"{stmt[:50]:50} → {best_per_loop*1_000_000:8.2f} µs per loop "
          f"({number:,} loops)")
    return best_per_loop

# Usage examples
benchmark("sum(range(100000))")
benchmark("[x**2 for x in range(10000)]")
benchmark("np.sum(np.arange(100000)**2)", 
         setup="import numpy as np")

3. How to Choose the Right number of loops

Code Speed	Recommended number	Goal
Very fast (nanoseconds)	100,000 – 1,000,000	Reach ~0.5–2 seconds total
Medium (microseconds)	1,000 – 10,000	Stable measurement
Slower (milliseconds)	100 – 1,000	Avoid long wait times

4. Best Practices for number of loops in 2026

Target 0.2 – 2 seconds total runtime per repeat for best accuracy
Use the same number when comparing two different implementations
Start with 1000–5000 and adjust based on runtime
Combine with repeat=7 and take the minimum time
Never rely on default number=1_000_000 for real benchmarks

Conclusion — Specifying number of loops in timeit (2026)

Choosing the correct number of loops is a key part of reliable benchmarking. In 2026, developers who properly control timeit parameters get trustworthy performance data, make better optimization decisions, and write significantly more efficient code.

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