Mailman 3 April 2024 - NumPy-Discussion

Fixing definition of reduceat for Numpy 2.0?
by Martin Ling Nov. 24, 2024

Nov. 24, 2024

Hi folks, I don't follow numpy development in much detail these days but I see that there is a 2.0 release planned soon. Would this be an opportunity to change the behaviour of 'reduceat'? This issue has been open in some form since 2006! https://github.com/numpy/numpy/issues/834 The current behaviour was originally inherited from Numeric, and makes reduceat often unusable in practice, even where it should be the perfect, concise, efficient solution. But it has been impossible to change it without breaking compatibіlity with existing code. As a result, horrible hacks are needed instead, e.g. my answer here: https://stackoverflow.com/questions/57694003 Is this something that could finally be fixed in 2.0? Martin

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New matvec and vecmat functions
by Marten van Kerkwijk Nov. 22, 2024

Nov. 22, 2024

Hi All, I have a PR [1] that adds `np.matvec` and `np.vecmat` gufuncs for matrix-vector and vector-matrix calculations, to add to plain matrix-matrix multiplication with `np.matmul` and the inner vector product with `np.vecdot`. They call BLAS where possible for speed. I'd like to hear whether these are good additions. I also note that for complex numbers, `vecmat` is defined as `x†A`, i.e., the complex conjugate of the vector is taken. This seems to be the standard and is what we used for `vecdot` too (`x†x`). However, it is *not* what `matmul` does for vector-matrix or indeed vector-vector products (remember that those are possible only if the vector is one-dimensional, i.e., not with a stack of vectors). I think this is a bug in matmul, which I'm happy to fix. But I'm posting here in part to get feedback on that. Thanks! Marten [1] https://github.com/numpy/numpy/pull/25675 p.s. Separately, with these functions available, in principle these could be used in `__matmul__` (and thus for `@`) and the specializations in `np.matmul` removed. But that can be a separate PR (if it is wanted at all).

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next NumPy community meeting
by Inessa Pawson May 19, 2024

May 19, 2024

The next NumPy community meeting will be held this Wednesday, November 8th at 5pm UTC. Join us via Zoom: https://numfocus-org.zoom.us/s/83278611437?pwd=ekhoLzlHRjdWc0NOY2FQM0NPemdk… . Everyone is welcome and encouraged to attend. To add to the meeting agenda the topics you’d like to discuss, follow the link: https://hackmd.io/76o-IxCjQX2mOXO_wwkcpg?both. For the notes from the previous meetings, visit: https://github.com/numpy/archive/tree/main/community_meetings. -- Cheers, Inessa Inessa Pawson GitHub: inessapawson

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New feature
by Alexei Lisitsa May 11, 2024

May 11, 2024

I created an extension to numpy called numpy_list that allows to create numpy array from predefined lists for now it supports arithmetic and geometric progressions You can see info there https://github.com/alessionuovo/mionumpy/tree/list_operations/numpy_extensi… Thanks Alexei

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next Documentation team meeting
by Mukulika Pahari May 6, 2024

May 6, 2024

Hi all, Our next Documentation Team meeting will happen on *Monday, December 4* at *11PM UTC*. If this time slot is inconvenient for you to join, please let me know in the replies or Slack and we will try to add another time slot. All are welcome - you don't need to already be a contributor to join. If you have questions or are curious about what we're doing, we'll be happy to meet you! If you wish to join on Zoom, use this (updated) link: https://numfocus-org.zoom.us/j/85016474448?pwd=TWEvaWJ1SklyVEpwNXUrcHV1YmFJQ... Here's the permanent hackmd document with the meeting notes (still being updated): https://hackmd.io/oB_boakvRqKR-_2jRV-Qjg Hope to see you around! Best wishes, Mukulika

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next NumPy Newcomers' Hour - April 4th, 2024 at 10pm UTC
by Inessa Pawson April 30, 2024

April 30, 2024

Our next Newcomers' Hour will be held TODAY, April 4th at 10pm UTC. Stop by to ask questions, share your progress, celebrate success, or just to say hi. To add to the meeting agenda the topics you’d like to discuss, follow the link: https://hackmd.io/3f3otyyuTte3FU9y3QzsLg?both. Join the meeting via Zoom: https://us06web.zoom.us/j/82563808729?pwd=ZFU3Z2dMcXBGb05YemRsaGE1OW5nQT09. -- Cheers, Inessa Inessa Pawson GitHub: inessapawson

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next NumPy triage meeting - April 3rd, 2024 at 6pm UTC
by Inessa Pawson April 28, 2024

April 28, 2024

The next NumPy triage meeting will be held this Wednesday, April 3rd at 6pm UTC. This is a meeting where we synchronously triage prioritized PRs and issues. Join us via Zoom: https://numfocus-org.zoom.us/j/82096749952?pwd=MW9oUmtKQ1c3a2gydGk1RTdYUUVX… . Everyone is welcome to attend and contribute to a conversation. Please notify us of issues or PRs that you’d like to have reviewed by adding a GitHub link to them in the meeting agenda: https://hackmd.io/68i_JvOYQfy9ERiHgXMPvg. -- Cheers, Inessa Inessa Pawson GitHub: inessapawson

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np.where and ZeroDivisionError: float division by zero
by 840362492＠qq.com April 26, 2024

April 26, 2024

0 In my code, I use the following calculation for a column in the dataframe: np.where(df_score['number'] ! = 0, 100 - ((100 * df_score[rank_column] -50)/df_score['number']), None),I have used df_score['number']! = 0, but the code is still wrong, ZeroDivisionError: float division by zero, even if I put df_score['number']! = 0 changed to df_score['number'] > 0, why? pandas version:1.1.5 numpy version:1.24.4 Here are my numbers： 12.00000 12.00000 12.00000 12.00000 12.00000 0.00000 0.00000 0.00000 0.00000 0.00000 12.00000 12.00000 12.00000 I want to know why it went wrong and what should be done to fix it? Thank you for your help

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(no subject)
by Shreya Nalawade April 23, 2024

April 23, 2024

Dear Sir/Madam I want to contribute to the GSOD Numpy. Till when are the proposal accepted plus what should be the format of the proposals. Kindly guide me through the organisation policies and contribution guidelines.

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JSON format for multi-dimensional data
by philippe＠loco-labs.io April 17, 2024

April 17, 2024

Hi community, This memo is a proposal to implement a compact and reversible (lossless round-trip) JSON interface for multi-dimensional data and in particular for Numpy (see issue #12481). The links to the documents are at the end of the memo. The JSON-NTV (Named and Typed value) format is a JSON format which integrates a notion of type. This format has also been implemented for tabular data (see NTV-pandas package available in the pandas ecosystem and the PDEP12 specification). . The use of this format has the following advantages: - Taking into account data types not known to Numpy, - Reversible format (lossless round-trip) - Interoperability with other tools for tabular or multi-dimensional data (e.g. pandas, Xarray) - Ease of sharing Json format - Binary coding possible (e.g. CBOR format) - Format integrating data of different nature The associated Jupyter Notebook presents some key points of this proposal (first draft): Summary: - introduction - benefits - multi-dimensionnal data - Multi-dimensional types - Format JSON - Using the NTV format - Equivalence of tabular format and multidimensional format - Astropy specific points - Units and quantities - Coordinates - Tables - Other structures This subject seems important to me (in particular for interoperability issues) and I would like to have your feedback before working on the implementation. Especially, - do you think this “semantic” format is interesting to use? - do you have any particular expectations or subjects that I need to study beforehand? - do you have any examples or test cases to offer me? And of course, any type of remark and comment is welcome. Thanks in advance ! links: - Jupyter notebook : https://nbviewer.org/github/loco-philippe/Environmental-Sensing/blob/main/p… - JSON-NTV format : https://www.ietf.org/archive/id/draft-thomy-json-ntv-02.html - JSON-NTV overview : https://nbviewer.org/github/loco-philippe/NTV/blob/main/example/example_ntv… - NTV tabular format : https://www.ietf.org/archive/id/draft-thomy-ntv-tab-00.html#name-tabular-st… - NTV-pandas package : https://github.com/loco-philippe/ntv-pandas/blob/main/README.md - NTV-pandas examples : https://nbviewer.org/github/loco-philippe/ntv-pandas/blob/main/example/exam… - Pandas specification - PDEP12 : https://pandas.pydata.org/pdeps/0012-compact-and-reversible-JSON-interface.…

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