ARROW-4472: [Website][Python] Blog post about string memory use work in Arrow 0.12 #3553
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Change-Id: I6e87debdd41565bf8921ea05b997568aa4eb0fa3
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Here's a published version https://wesm.github.io/arrow-site-test/blog/2019/02/04/python-string-memory-0.12/ test publishing the website really isn't that easy, and a number of things are broken... |
Change-Id: I6e8cd52878c06474ce45f4feb9713bb3f74cda53
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LGTM |
xhochy
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Feb 5, 2019
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Looks good, some small improvements but then this can go out tomorrow.
| We can use the `memory_profiler` Python package to easily get process memory | ||
| usage within a running Python application. | ||
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| ## Memory and Performance Benchmarks | ||
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| We can use the `memory_profiler` Python package to easily get process memory |
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| We can use the `memory_profiler` Python package to easily get process memory | |
| We can use the [`memory_profiler`][2] Python package to easily get process memory |
| provide fast and memory-efficient interoperability with pandas and other | ||
| popular libraries. | ||
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| [1]: https://www.slideshare.net/xhochy/extending-pandas-using-apache-arrow-and-numba No newline at end of file |
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| [1]: https://www.slideshare.net/xhochy/extending-pandas-using-apache-arrow-and-numba | |
| [1]: https://www.slideshare.net/xhochy/extending-pandas-using-apache-arrow-and-numba | |
| [2]: https://pypi.org/project/memory-profiler/ |
Change-Id: I872f47f6ba079c6f06e1f520a0cb7747411c897a
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Oops sorry I missed these edits @xhochy |
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Feb 8, 2019
…in Arrow 0.12 This blog shows how we were able to significant improve performance and memory use in common cases when converting from the Arrow string memory layout to pandas's native memory model based on NumPy arrays of Python objects. Author: Wes McKinney <wesm+git@apache.org> Closes #3553 from wesm/python-string-memory-0.12 and squashes the following commits: f0d684d <Wes McKinney> Update publication date 2bbb92d <Wes McKinney> Fix some base urls c624e55 <Wes McKinney> Draft blog post about string memory use work in Arrow 0.12
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This blog shows how we were able to significant improve performance and memory use in common cases when converting from the Arrow string memory layout to pandas's native memory model based on NumPy arrays of Python objects.