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adding notebooks for profiling modules #6
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Thank you!Thank you for your pull request 😃 🤖 This automated message can help you check the rendered files in your submission for clarity. If you have any questions, please feel free to open an issue in {sandpaper}. If you have files that automatically render output (e.g. R Markdown), then you should check for the following:
Rendered Changes🔍 Inspect the changes: https://github.com/ICR-RSE-Group/carpentry-pando-python/compare/md-outputs..md-outputs-PR-6 The following changes were observed in the rendered markdown documents: What does this mean?If you have source files that require output and figures to be generated (e.g. R Markdown), then it is important to make sure the generated figures and output are reproducible. This output provides a way for you to inspect the output in a diff-friendly manner so that it's easy to see the changes that occur due to new software versions or randomisation. ⏱️ Updated at 2025-09-22 15:31:49 +0000 |
| "source": [ | ||
| "# Pandas :\n", | ||
| "* Pandas is the most common Python package when working with tabular data (`csv`, `tsv` files).\n", | ||
| "* Pandas enhances performance if used well, otherwise it can harm performance\n", |
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I think this could be expanded more - what does it mean "if used well"?. Or add "in this section, we are going to look at Pandas use cases"
| "id": "7b1cbecd-8d60-4579-9a50-53b143c39c54", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Using pandas and numpy effectively\n", |
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I am not sure if this was intentional, but all the text in the notebook is different sizes because they use different numbers of #s. If that was not intentional, it would be good to make it slightly more consistent!
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some of them are intentional. I might have messed up some parts
| "source": [ | ||
| "# Using Numpy (efficiently) :\n", | ||
| "1. ## Numpy arrays are static:\n", | ||
| "* They are fixed in size (unlike Python lists).\n", |
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Maybe adding that they are contiguous blocks of memory could also be beneficial. Numpy is widely used in ML, especially with the PyTorch library because of that quick memory access
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I was planning to say this during the session without writing the detail. but go ahead and add the explanation
| "id": "e76fba2a-bf85-473f-a1a3-1390398b514e", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Conclusion 1: Avoid trying to resize Numpy arrays" |
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I think around this section, it would be beneficial to add the difference between Numpy arrays and Python lists, like they do in the course.
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im afraid we wont have time to go through the difference, it's a packed course.
But Add the section and then we decide on the spot if we do it or not
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@stacyrse do you want to do the changes in a separate commit? |
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