simple way to measure cell execution time in ipython notebook

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Okay, let's dive deep into the various ways you can measure cell execution time within an IPython (Jupyter) Notebook. We'll cover some simple, built-in options, as well as slightly more advanced techniques for more precise measurements. I'll provide explanations and code examples throughout.

*Why Measure Cell Execution Time?*

Understanding how long specific parts of your code take to run is crucial for:

*Performance Optimization:* Identifying bottlenecks – slow sections of your code that are consuming excessive resources.
*Algorithmic Comparisons:* Evaluating the efficiency of different algorithms or data structures for the same task.
*Resource Allocation:* Determining if your code requires more memory, CPU time, or specialized hardware.
*Reproducibility:* Documenting the expected runtime of your code for other users or collaborators.
*Debugging:* Tracking down performance regressions when you change your code.

*1. The `%time` Magic Command (Single Execution)*

The simplest way to measure the execution time of a single line or cell of code is by using the `%time` magic command. This is a built-in IPython feature that provides a quick snapshot of runtime information.

*`%time` (Single Line):* Place `%time` at the beginning of a line of code. It will execute that line once and report the time taken.

*`%%time` (Cell):* Place `%%time` at the top of a cell (as the very first line). It will execute the entire cell once and report the time taken.



*Output (Example):*

For `%time time.sleep(2)`:



For `%%time` cell:



*Explanation of the Output:*

*`CPU times:`* The amount of time the CPU spent executing your code. This is further divided into:
`user`: Time spent executing user code (your code).
`sys`: Time spent executing system code (e.g., operating system calls).
`total`: `user + sys`
*`Wall time:`* The actual elapsed time from the start to the end of the execution. Th ...

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