time.perf_counter() function in Python

In this tutorial, we are going to learn about the time.perf_counter() method in Python. This function is part of the time module and provides the highest available resolution to measure a short duration.

The method time.perf_counter() returns a float value representing time in seconds. It includes time elapsed during sleep and is system-wide, making it ideal for measuring elapsed time in code execution.

Basic Usage

Let's start with a simple example to see how perf_counter() works ?

import time

# Get current performance counter value
print("Current time:", time.perf_counter())

Current time: 263.3530349

Measuring Execution Time

The most common use case is measuring how long a piece of code takes to execute. Here's how to measure execution time ?

import time

def is_prime(number):
    if number < 2:
        return False
    for i in range(2, int(number ** 0.5) + 1):
        if number % i == 0:
            return False
    return True

# Measure execution time
number = 17377
start_time = time.perf_counter()
result = is_prime(number)
end_time = time.perf_counter()

execution_time = end_time - start_time
print(f"Number {number} is prime: {result}")
print(f"Execution time: {execution_time:.6f} seconds")

Number 17377 is prime: True
Execution time: 0.000842 seconds

Comparing Multiple Operations

You can compare the performance of different approaches using perf_counter() ?

import time

# Method 1: Using list comprehension
start_time = time.perf_counter()
squares_list = [i**2 for i in range(10000)]
end_time = time.perf_counter()
time1 = end_time - start_time

# Method 2: Using regular loop
start_time = time.perf_counter()
squares_loop = []
for i in range(10000):
    squares_loop.append(i**2)
end_time = time.perf_counter()
time2 = end_time - start_time

print(f"List comprehension: {time1:.6f} seconds")
print(f"Regular loop: {time2:.6f} seconds")
print(f"Difference: {abs(time1 - time2):.6f} seconds")

List comprehension: 0.001245 seconds
Regular loop: 0.001876 seconds
Difference: 0.000631 seconds

Key Points

• High Resolution: Provides the highest available resolution for short duration measurements
• Monotonic: The reference point is undefined, but consecutive calls are guaranteed to be monotonic
• Includes Sleep: Unlike process_time(), it includes time elapsed during sleep
• Best for Benchmarking: Ideal for measuring elapsed time in performance testing

Conclusion

The time.perf_counter() function is the most accurate way to measure execution time in Python. Use it for benchmarking code performance and measuring elapsed time with the highest available resolution.