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Mastering Cell Execution Time in Jupyter Notebook: A Complete Guide

Measuring Cell Execution Time in Jupyter Notebook: A Comprehensive Guide

Jupyter Notebook is an incredibly useful tool for data scientists and engineers as it allows for experimentation and testing of code in an interactive environment. However, often times, it is necessary to measure the execution time of a cell to optimize and improve the code’s efficiency.

In this article, we will explore several methods for measuring cell execution time, including using the %time magic command, %timeit command, time module, and time.perf_counter(). Using %time Magic Command

The %time magic command is a built-in feature of Jupyter Notebook that allows us to measure the execution time of a single Python statement or expression.

To use this command, simply place `%time` before the line of code that you want to measure, and when you run the cell, it will output the time it took to complete the operation. For example, let’s try measuring the execution time of a loop in Python that sums up a list of integers:

“`

%time sum([i for i in range(10000)])

“`

The output would be something like:

“`

CPU times: user 602 s, sys: 76 s, total: 678 s

Wall time: 684 s

“`

The output is in microseconds (s).

The `user` and `sys` times represent the time spent by the CPU executing the code, and the `wall` time represents the time it took for the operation, including any overhead. %time vs %timeit Command

The `%timeit` command is similar to `%time` but is used to measure the average runtime of an operation over multiple runs, allowing for more accuracy.

It also measures the standard deviation of the runtimes, which can give us a better understanding of the variability of the output. For example, let’s try measuring the runtime of the same operation using `%timeit`:

“`

%timeit sum([i for i in range(10000)])

“`

The output would be something like:

“`

10000 loops, best of 3: 186 s per loop

“`

This output tells us that the average run time of the operation is 186 microseconds per loop, and it ran 10,000 loops, making this a more accurate measurement than `%time`.

Using the Time Module

The time module is a built-in Python module that provides several functions for working with time. One such function is `time.time()`, which returns the number of seconds that have elapsed since the epoch (January 1, 1970, 00:00:00 UTC).

To measure the execution time of a cell, we can store the current time before and after the operation and subtract them to get the elapsed time. We can also use the built-in `round` function to limit the output to a certain number of decimals.

Here is an example of using `time.time()` to measure the execution time of a loop:

“`

import time

start_time = time.time()

sum([i for i in range(10000)])

end_time = time.time()

elapsed_time = round(end_time – start_time, 2)

print(f”Elapsed time: {elapsed_time} seconds”)

“`

The output would be something like:

“`

Elapsed time: 0.0 seconds

“`

Using time.perf_counter()

The `time.perf_counter()` function returns a performance counter, which is a clock that measures the performance of the CPU and can provide a more accurate measurement of the elapsed time than `time.time()`. Here is an example of using `time.perf_counter()` to measure the execution time of a loop:

“`

import time

start_time = time.perf_counter()

sum([i for i in range(10000)])

end_time = time.perf_counter()

elapsed_time = round(end_time – start_time, 2)

print(f”Elapsed time: {elapsed_time} seconds”)

“`

The output would be something like:

“`

Elapsed time: 0.0 seconds

“`

Conclusion

Measuring the execution time of cells in Jupyter Notebook can help optimize and improve the efficiency of your code. In this article, we explored several methods for measuring cell execution time, including using %time magic command, %timeit command, time module, and time.perf_counter().

By using these methods, you can gain a better understanding of the time it takes for certain operations, allowing you to optimize your code and improve your workflow. In this article, we have explored several methods for measuring the execution time of cells in Jupyter Notebook, including using %time, %timeit, time module, and time.perf_counter().

By measuring the execution time of code, data scientists and engineers can optimize their workflow by finding ways to improve the code’s efficiency. Each method provides its advantages, such as %timeit that measures the average runtime of an operation over multiple runs, and time.perf_counter() that provides a more accurate measurement of elapsed time.

Overall, measuring cell execution time is a critical practice for any data scientist or engineer, and mastering the techniques we have discussed will benefit anyone looking to optimize their work.

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