The behavior you're observing is likely due to a combination of factors related to how Python executes code and how time is measured. Let's break down the issues and provide some recommendations for more accurate timing:

1. Resolution of time.time():
   The resolution of time.time() is typically around 1 microsecond on most systems. For very fast operations, this might not be accurate enough.
2. Overhead of function calls:
   Calling time.time() itself takes some time, which can be significant for very fast operations.
3. Python interpreter overhead:
   The Python interpreter introduces some overhead, especially when executing small pieces of code repeatedly.
4. System-level scheduling:
   The operating system may introduce delays between iterations of your loop, leading to inconsistent measurements.
5. JIT compilation (if using PyPy):
   If you're using PyPy, just-in-time compilation can cause timing variations.
   
   

For this you can try:

1. Use timeit module:
   The timeit module is designed for benchmarking small code snippets and handles many of the issues mentioned above.
2. Use time.perf_counter():
   For more precise timing, use time.perf_counter() instead of time.time(). It provides higher resolution and is monotonic.