Mailman 3 March 2022 - NumPy-Discussion

Adding "weights" option to quantile/percentile
by Chun-Wei Yuan March 10, 2022

March 10, 2022

Dear all, I have a long-standing PR (#9211 <https://github.com/numpy/numpy/pull/9211>) about adding a "weights" option to the quantile/percentile methods' API. The code is tested for all 13 different interpolation methods, and I welcome any additional testing suggestions. A couple of remarks: 1.) weights can be any positive real number, and there's no distinction between frequency weights and probability weights. Underneath, along the data axis you're applying weights to, if any weight is less than 1, then the code will renormalize the weights such that the minimum is 1. For example, if weights=[0.1, 0.9], then the quantiles will be computed as if weights=[1, 9]. If weights=[0.25, 0.75], then they'll be treated as if weights=[1, 3]. In other words, probability weights are converted to frequency weights for the calculation. If another function using quantile() wants to make that distinction between frequency/probability weights in its API, it is free to do so. But right now I do not see any reason for it in quantile()/percentile(). 2.) The use of np.vectorize(). In computing quantiles, it's necessary to sort the data values along the data axis, and rearrange the corresponding weights according to the sort order. Hence each column down the data axis has a different rearrangement. vectorize() allows for such synchronized operations between equal-shaped arrays. I don't see that from, say, apply_along_axis(). Lastly, I'll be sure to add more docs/comments to better explain the steps in the code. Best, Chun

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NumPy Development Meeting Wednesday - Triage Focus
by Sebastian Berg March 8, 2022

March 8, 2022

Hi all, Our bi-weekly triage-focused NumPy development meeting is Wednesday, March 9th at 17:00 UTC (9:00am Pacific Time). Everyone is invited to join in and edit the work-in-progress meeting topics and notes: https://hackmd.io/68i_JvOYQfy9ERiHgXMPvg I encourage everyone to notify us of issues or PRs that you feel should be prioritized, discussed, or reviewed. Best regards Sebastian

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Numpy Participation in Google Season of Docs 2022
by bhavuk kalra March 8, 2022

March 8, 2022

Good Day Everyone in the mailing list and the Numpy Community, I am a recent joiner on the mailing list. I tried to see other messages in different threads to see if this question was discussed anywhere else. But I couldn't find any. So, taking this opportunity to ask. Query 1: Will the Numpy community participate in the Google Season of Docs 2022 as it did in 2021. I have also come to know that there are bi-weekly community meetings within Numpy. The most recent one happened on 2nd June. I am looking forward to joining the most upcoming one. Query 2: Is the community maintaining a summary for each meeting. In short, noting down what essential points were discussed in single line format? If it already exists, I would I love to be caught up with them. Regards, Bhavuk Kalra

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NumPy 1.22.3 released
by Charles R Harris March 7, 2022

March 7, 2022

Hi All, On behalf of the NumPy team, I'm pleased to announce the release of NumPy 1.22.3. NumPy 1.22.3 is a maintenance release that fixes bugs discovered after the 1.22.2 release. The most noticeable fixes may be those for DLPack. One that may cause some problems is disallowing strings as inputs to logical ufuncs. It is still undecided how strings should be treated in those functions and it was thought best to simply disallow them until a decision was reached. That should not cause problems with older code. The Python versions supported in this release are 3.8-3.10. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/1.22.3>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v1.22.3>. Note that the Mac wheels are now based on OS X 10.14 rather than 10.9 that was used in previous NumPy release cycles. 10.14 is the oldest release supported by Apple. Linux users will need pip >= 0.19.3 in order to install the manylinux2014 wheels. A recent version of pip is needed to install the universal2 macos wheels. *Contributors* A total of 9 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - @GalaxySnail + - Alexandre de Siqueira - Bas van Beek - Charles Harris - Melissa Weber Mendonça - Ross Barnowski - Sebastian Berg - Tirth Patel - Matthieu Darbois *Pull requests merged* A total of 10 pull requests were merged for this release. - #21048: MAINT: Use "3.10" instead of "3.10-dev" on travis. - #21106: TYP,MAINT: Explicitly allow sequences of array-likes in ``np.concatenate`` - #21137: BLD,DOC: skip broken ipython 8.1.0 - #21138: BUG, ENH: np._from_dlpack: export correct device information - #21139: BUG: Fix numba DUFuncs added loops getting picked up - #21140: BUG: Fix unpickling an empty ndarray with a non-zero dimension... - #21141: BUG: use ThreadPoolExecutor instead of ThreadPool - #21142: API: Disallow strings in logical ufuncs - #21143: MAINT, DOC: Fix SciPy intersphinx link - #21148: BUG,ENH: np._from_dlpack: export arrays with any strided size-1... Cheers, Charles Harris

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[Job] NumPy Open Source Developer at NVIDIA
by Mike McCarty March 4, 2022

March 4, 2022

Hi all, I'm excited to share that I'm hiring a remote NumPy developer at NVIDIA! The majority of their time will be focused on open source contributions to the NumPy project, so I wanted to share the opportunity on this list. We are continuing to expand our support of the PyData ecosystem and looking to hire strong engineers who are or can become contributors to NumPy, Pandas, Scikit-learn, SciPy, and NetworkX. Please see the job posting<https://nvidia.wd5.myworkdayjobs.com/NVIDIAExternalCareerSite/job/US-CA-Rem…> for more details. Non-US based applicants are eligible in certain countries. I can follow up with individuals to confirm eligibility. Thanks, Mike

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Depending on NumPy: to future pin or not
by Stefan van der Walt March 3, 2022

March 3, 2022

Hi all, There's a discussion at https://github.com/numpy/numpy/pull/21144 on whether we should recommend to libraries that depend on NumPy to limit the number of future NumPy releases their package will operate with. If this is something you've spent time thinking about, please weigh in. Thanks, Stéfan

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ENH: Use sequence of ints instead of tuple of ints in the axis parameter for mean, sum, min, max, etc. #21136
by Aleksandr Kadykov March 2, 2022

March 2, 2022

Dear NumPy developers, First, I would like to thank you for all your efforts in developing NumPy, it is a really amazing project! NumPy has many functions that support operations over multiple dimensions like min, max, mean, average, sum, etc. All of them has axis parameter to specify the axes along which the function has to be applied. In most cases, the accepted types for this parameter are None, int, and a tuple of ints, like in ptp, percentile, nanpercentile, quantile, nanquantile, average, mean, std, var, nanmean, nanstd, nanvar, sum, prod, nanprod, nansum, and gradient. Is there any reason not to use a sequence of ints for the axis parameter where a tuple of ints is allowed? For instance, the functions median and nanmedian support a sequence of ints, therefore users can use sets and frozensets to skip unnecessary checks for repeated values. Thank you. Best regards, Aleksandr Kadykov

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NumPy Development Meeting Wednesday - Triage Focus
by Sebastian Berg March 1, 2022

March 1, 2022

Hi all, Our bi-weekly triage-focused NumPy development meeting is Wednesday, March 2nd at 17:00 UTC (9:00am Pacific Time). Everyone is invited to join in and edit the work-in-progress meeting topics and notes: https://hackmd.io/68i_JvOYQfy9ERiHgXMPvg I encourage everyone to notify us of issues or PRs that you feel should be prioritized, discussed, or reviewed. Best regards Sebastian

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