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How to Calculate Code Run Time in Python?

Published in Python Programming 3 mins read

To calculate code run time in Python, you essentially record the time before and after the code execution and then find the difference.

Here's a breakdown of the process with examples:

1. Using the time module:

The time module is the most common and straightforward way to measure code execution time.

   import time

   # Record the start time
   start_time = time.time()

   # Your code to be timed goes here
   for i in range(1000000):
       pass  # Example: A simple loop

   # Record the end time
   end_time = time.time()

   # Calculate the execution time
   execution_time = end_time - start_time

   print(f"The code took {execution_time} seconds to run.")
  • time.time(): Returns the current time in seconds since the epoch (the point where time begins for the system). It's a floating-point number, offering reasonable precision.

2. Using the timeit module:

The timeit module is specifically designed for measuring the execution time of small code snippets. It helps to avoid biases from other processes running on your system. It's excellent for comparing the performance of different ways to accomplish the same task.

   import timeit

   # Code to be timed (as a string)
   my_code = """
   for i in range(1000000):
       pass
   """

   # Measure the execution time
   execution_time = timeit.timeit(stmt=my_code, number=10) #Run 10 times

   print(f"The code took {execution_time / 10} seconds to run on average.") #Divide by number to get average
  • timeit.timeit(stmt, number): Executes the stmt (statement) a specified number of times (number) and returns the total execution time as a float. The number parameter is important; running the code multiple times helps to get a more stable measurement.

3. Considerations for Accuracy:

  • Warm-up: The first time a piece of code runs, it might take longer due to caching, JIT (Just-In-Time) compilation, or other initialization processes. Run the code a few times before starting the timing to "warm up" the system. The timeit module handles this automatically.
  • System Load: Other processes running on your computer can affect the timing results. Minimize background processes when measuring execution time.
  • Averaging: Run the code multiple times and calculate the average execution time to reduce the impact of random fluctuations. timeit does this.
  • Granularity: time.time() offers millisecond resolution, which is usually sufficient. For extremely short code snippets, you might need a more precise timer, but that's rarely necessary in most Python scripting scenarios.

4. Choosing the Right Method:

  • time module: Suitable for timing larger blocks of code or entire programs. Simple to use.
  • timeit module: Best for timing small code snippets and comparing the performance of different implementations. More robust against system noise.

In summary, calculating code run time in Python involves recording the start and end times using the time or timeit modules and then calculating the difference. The timeit module is preferred for more accurate timings of small code sections. Always consider factors like system load and warm-up effects to ensure reliable results.

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