Mailman 3 December 2023 - Pandas-dev

pandas new infrastructure (OVH donation)
by Marc Garcia Sept. 21, 2024

Sept. 21, 2024

Hi all, pandas has received a donation from OVHcloud <https://www.ovhcloud.com/> to support the project infrastructure, with OVHcloud public cloud credits (an initial amount of 10,000 EUR for a period of one year). OVH is open to sponsor longer term and also other projects of the ecosystem (or NumFOCUS as a whole), but we started with this to have feedback at a smaller scale first. The credits will be used initially for: - Hosting of the pandas website - Running the pandas benchmarks - Speeding up the project CI I detail next what I have in mind to set up for each. If anyone is interested in getting involved, or has ideas, comments... please let me know. I'll publish updates here as there is progress on this. Website: I'm planning to experiment on splitting the website in two (it'll be transparent for users). The website and the stable docs which receive most of the traffic can probably be stored in Cloudflare pages. We're already using Cloudflare as a CDN, so instead of using it as a cache, we can publish the documents there. The rest of the docs (old versions and the dev version) can be hosted in bucket storage of the OVHcloud. Response times may be a bit slower, but our website is bigger than the Cloudflare quota, and having old docs rarely accessed in a CDN seems unnecessary anyway. - Benchmarks: OVHcloud instances have guaranteed hardware, and we'll be checking if this is enough for the results of the benchmarks to be consistent over runs, or if there is too much variability and we need dedicated hardware. If consistency is good enough that would be great, since our benchmarks mostly use one core, and using dedicated hardware is likely to be a decent waste of resources, since most servers will likely have 16 cores or more. We'll discuss with OVH if dedicated hardware is needed, as at the moment their public cloud doesn't offer it (there is an alpha for providing dedicated instances, but we need to check with them). - Faster CI: Our GitHub runners are small, and most builds take around one hour or more to finish. We should be able to use bigger OVH instances for our existing CI pretty easily, via their OpenStack API and CIrun.

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February 2023 bi-monthly community meeting (Wednesday February 8, UTC 18:00)
by Joris Van den Bossche Sept. 11, 2024

Sept. 11, 2024

Hi all, A late reminder that the next bi-monthly (twice a month) dev call is today in a bit more than 1 hour (Wednesday, February 8) at 18:00 UTC. Our calendar is at https://pandas.pydata.org/docs/development/meeting.html#calendar to check your local time. The pandas Community Meeting is a regular sync meeting for the project's maintainers which is open to the community. All are welcome to attend! Video Call: https://us06web.zoom.us/j/84484803210?pwd=TjUxNmcyNHcvcG9SNGJvbE53Y21GZz09 Meeting notes: https://docs.google.com/document/u/1/d/1tGbTiYORHiSPgVMXawiweGJlBw5dOkVJLY-… Joris

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Is it time for a bi-weekly pandas call?
by Marco Gorelli May 13, 2024

May 13, 2024

Currently, there's a monthly pandas call, which has been in place for several years. Seeing as there's now more people working on pandas as part of their jobs, might it be time to increase the frequency? E.g. to meet every 2 weeks, instead of once a month?

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ANN: pandas v2.2.0rc0
by Thomas Li Dec. 22, 2023

Dec. 22, 2023

We are pleased to announce a release candidate for pandas 2.2.0. If all goes well, we'll release pandas 2.2.0 in about two weeks. See the whatsnew( https://pandas.pydata.org/pandas-docs/version/2.2.0rc0/whatsnew/v2.2.0.html) for a list of all the changes. The release will be available on conda-forge and PyPI. The release can be installed from PyPI python -m pip install --upgrade --pre pandas==2.2.0rc0 Or from conda-forge conda install -c conda-forge/label/pandas_rc pandas==2.2.0rc0 Please report any issues with the release candidate on the pandas issue tracker.

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ANN: pandas v2.1.4
by Thomas Li Dec. 8, 2023

Dec. 8, 2023

We are pleased to announce the release of pandas v2.1.2. This is a patch release in the 2.1.x series and includes some regression fixes and bug fixes. We recommend that all users in the 2.1.x series upgrade to this version. See the release notes <https://pandas.pydata.org/pandas-docs/version/2.1.4/whatsnew/v2.1.4.html> for a list of all the changes. The release can be installed from PyPI python -m pip install --upgrade pandas==2.1.4 Or from conda-forge mamba install -c conda-forge pandas==2.1.4 Please report any issues with the release on the pandas issue tracker <https://github.com/pandas-dev/pandas/issues/new/choose>. Thanks to all the contributors <https://pandas.pydata.org/pandas-docs/version/2.1.4/whatsnew/v2.1.4.html#co…> who made this release possible.

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PDEP-8: In-place methods in pandas (i.e. deprecating the inplace keyword)
by Joris Van den Bossche Dec. 5, 2023

Dec. 5, 2023

Hi list, There is proposal to deprecate and eventually remove the `inplace` keyword (in most places): PDEP-8 (https://github.com/pandas-dev/pandas/pull/51466). This has been open for quite a while, and I just updated the text to reflect the latest state of the discussion, and wanted to bring this up on the mailing list here as well. Feedback goes to the PR: https://github.com/pandas-dev/pandas/pull/51466 If you want to see a rendered version of the text: https://jorisvandenbossche.github.io/pandas-website-preview/pdeps/0008-inpl… (our website does not yet render them before being merged) And posting the abstract here as well. The PDEP proposes: - The inplace parameter will be removed from any method which can never update the underlying values of a pandas object inplace or which alters the shape of the object, and where the inplace=True option is only syntactic sugar for reassigning the result to the calling DataFrame/Series. - As a consequence, the inplace parameter is only kept for those methods that can modify the underlying values of a pandas object inplace, such as fillna or replace. - With the introduction of Copy-on-Write (PDEP-7[1] <https://jorisvandenbossche.github.io/pandas-website-preview/pdeps/0008-inpl…>), users don’t need the inplace keyword to avoid a copy of the data. - For those methods that will keep the inplace=True option: - the method will do an attempt to do the operation inplace but still silently copy when needed (for Copy-on-Write), i.e. there is no guarantee it is actually done inplace. - the method will return the calling object (self), instead of the current None. For the full proposal and motivation, see the links above. Feedback welcome!

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