If you don’t want to use type hints, then you can instead annotate all variables with Any, just like you did above. You can use this annotation to add type hints to your code. You need to annotate your variables to use a data class. _init_() is created automatically for data classes, based on annotated variables in the definition of the class. Note how data classes use syntax that looks similar to the class variable syntax that you saw earlier for defining all variables. name, 0 ) 21 22 # The rest of the code is unchanged _start_time = None 26 print ( f "Elapsed time: seconds" 14 logger : Any = print 15 _start_time : Any = field ( default = None, init = False, repr = False ) 16 17 def _post_init_ ( self ): 18 """Initialization: add timer to dict of timers""" 19 if self. start() to start it" ) 23 24 elapsed_time = time. _start_time is None : 22 raise TimerError ( f "Timer is not running. perf_counter () 18 19 def stop ( self ): 20 """Stop the timer, and report the elapsed time""" 21 if self. _start_time is not None : 15 raise TimerError ( f "Timer is running. _start_time = None 11 12 def start ( self ): 13 """Start a new timer""" 14 if self. However, when you look at the difference between two calls to perf_counter(), you can figure out how many seconds passed between the two calls:ġ # timer.py 2 3 import time 4 5 class TimerError ( Exception ): 6 """A custom exception used to report errors in use of Timer class""" 7 8 class Timer : 9 def _init_ ( self ): 10 self. Perf_counter() measures the time in seconds from some unspecified moment in time, which means that the return value of a single call to the function isn’t useful. Again, this is a performance counter that’s well-suited for timing parts of your code. Now you’ll add a bare-bones Python timer to the example with time.perf_counter(). The code may take a little while to run depending on your network, so you might want to use a Python timer to monitor the performance of the script. In this tutorial, you'll learn how to use a Python timer to monitor how quickly your programs are running. While many developers recognize Python as an effective programming language, pure Python programs may run more slowly than their counterparts in compiled languages like C, Rust, and Java. # Python Timer Functions: Three Ways to Monitor Your Code You can install realpython-reader on your system with pip: To learn more about the Real Python Reader and how it works, check out How to Publish an Open-Source Python Package to PyPI. The example that you’ll use in this tutorial is a short function that uses the realpython-reader package to download the latest tutorials available here on Real Python. If you already have code that you’d like to measure, then feel free to follow the examples with that instead. To better compare the different ways that you can add a Python timer to your code, you’ll apply different Python timer functions to the same code example throughout this tutorial. Later, you’ll compare this with other Python timer functions and learn why perf_counter() is usually the best choice. ( Source)įirst, you’ll use perf_counter() to create a Python timer. a clock with the highest available resolution to measure a short duration. Return the value (in fractional seconds) of a performance counter, i.e. For now, note what the documentation has to say about perf_counter(): You’ll learn more about these functions later. For example, perf_counter_ns() is the nanosecond version of perf_counter(). Python 3.7 introduced several new functions, like thread_time(), as well as nanosecond versions of all the functions above, named with an _ns suffix. If you check out the built-in time module in Python, then you’ll notice several functions that can measure time:
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