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%timeit is a built-in magic command in Python's IPython and Jupyter Notebook environments that allows you to measure the execution time of a single statement or a small block of code. It's a convenient tool for quickly assessing the performance of your code, identifying bottlenecks, and comparing the speed of different implementations. %timeit runs the specified code multiple times and provides you with statistics about the execution time, such as the best, worst, and average times.
To use %timeit, open an IPython or Jupyter Notebook environment and follow these steps:
Launch an IPython or Jupyter Notebook session.
Open a code cell in your notebook.
Start your code with %%timeit (for a whole cell) or %timeit (for a single line).
Here's the basic syntax:
Replace your_code_here with the Python code you want to measure.
Let's look at a few examples to see how %timeit can be used:
Example 1: Measuring the execution time of a function
Suppose you have a function that you want to profile:
You can use %timeit to measure how long it takes to compute the 30th Fibonacci number:
Example 2: Measuring the execution time of a loop
You can also use %timeit to measure the execution time of a loop:
Example 3: Measuring the execution time of a single statement
You can measure the execution time of a single statement as well:
%timeit has various options that you can use to customize its behavior. Here are some common options:
You can use these options to tailor the %timeit command to your specific needs. For example:
This command will run your_code_here 100 times, repeat the measurement 5 times, and provide you with statistics based on the results.
The %timeit command provides you with detailed information about the execution time, including the best and worst times, the average time, and the standard deviation. The unit of time is typically milliseconds.
Here's an example output:
You should focus on the "best" time when comparing code performance because it represents the most optimal execution time.
In Python, %timeit is a valuable tool for measuring the performance of your code quickly and easily. By using this command, you can identify areas where your code may be running slowly and make optimizations accordingly. It's particularly helpful for profiling small code snippets, functions, or entire cells in Jupyter Notebook to ensure your code runs efficiently.
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