How to get the timing Execution Speed of Python Code?

Measuring the execution time of Python code is essential for performance optimization and benchmarking. Python provides several built-in modules like time and timeit to accurately measure code execution speed.

Using time Module

The time.perf_counter() function provides the highest available resolution and is recommended for measuring short durations ?

import time

t0 = time.perf_counter()
print("Hello")
t1 = time.perf_counter()
print("Time elapsed:", t1 - t0, "seconds")

Hello
Time elapsed: 2.7499999851558823e-05 seconds

Using time.time()

For wall-clock time measurement, you can use time.time() ?

import time

start = time.time()
# Simulate some work
sum(range(1000000))
end = time.time()
print(f"Execution time: {end - start:.6f} seconds")

Execution time: 0.021547 seconds

Using timeit Module

The timeit module provides more accurate measurements by running code multiple times and calculating statistical data ?

import timeit

def square_function(x):
    return x * x

# Measure execution time
execution_times = timeit.repeat("for x in range(100): square_function(x)", 
                                "from __main__ import square_function", 
                                number=10000, repeat=3)

print("Execution times:", execution_times)
print(f"Best time: {min(execution_times):.6f} seconds")

Execution times: [0.051234, 0.052891, 0.051456]
Best time: 0.051234 seconds

Using timeit.timeit() for Single Measurement

import timeit

# Time a simple expression
time_taken = timeit.timeit('sum(range(100))', number=10000)
print(f"Time for sum(range(100)): {time_taken:.6f} seconds")

Time for sum(range(100)): 0.034567 seconds

Comparison of Methods

Conclusion

Use timeit for accurate benchmarking and statistical analysis. For simple timing measurements, time.perf_counter() provides high-resolution timing suitable for most performance monitoring needs.