Mailman 3 November 2023 - NumPy-Discussion

next NumPy community meeting
by Inessa Pawson 19 May '24

19 May '24

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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Add to NumPy a function to compute cumulative sums from 0.
by john.dawson＠camlingroup.com 20 Mar '24

20 Mar '24

`cumsum` computes the sum of the first k summands for every k from 1. Judging by my experience, it is more often useful to compute the sum of the first k summands for every k from 0, as `cumsum`'s behaviour leads to fencepost-like problems. https://en.wikipedia.org/wiki/Off-by-one_error#Fencepost_error For example, `cumsum` is not the inverse of `diff`. I propose adding a function to NumPy to compute cumulative sums beginning with 0, that is, an inverse of `diff`. It might be called `cumsum0`. The following code is probably not the best way to implement it, but it illustrates the desired behaviour. ``` def cumsum0(a, axis=None, dtype=None, out=None): """ Return the cumulative sum of the elements along a given axis, beginning with 0. cumsum0 does the same as cumsum except that cumsum computes the sum of the first k summands for every k from 1 and cumsum, from 0. Parameters ---------- a : array_like Input array. axis : int, optional Axis along which the cumulative sum is computed. The default (None) is to compute the cumulative sum over the flattened array. dtype : dtype, optional Type of the returned array and of the accumulator in which the elements are summed. If `dtype` is not specified, it defaults to the dtype of `a`, unless `a` has an integer dtype with a precision less than that of the default platform integer. In that case, the default platform integer is used. out : ndarray, optional Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output but the type will be cast if necessary. See :ref:`ufuncs-output-type` for more details. Returns ------- cumsum0_along_axis : ndarray. A new array holding the result is returned unless `out` is specified, in which case a reference to `out` is returned. If `axis` is not None the result has the same shape as `a` except along `axis`, where the dimension is smaller by 1. See Also -------- cumsum : Cumulatively sum array elements, beginning with the first. sum : Sum array elements. trapz : Integration of array values using the composite trapezoidal rule. diff : Calculate the n-th discrete difference along given axis. Notes ----- Arithmetic is modular when using integer types, and no error is raised on overflow. ``cumsum0(a)[-1]`` may not be equal to ``sum(a)`` for floating-point values since ``sum`` may use a pairwise summation routine, reducing the roundoff-error. See `sum` for more information. Examples -------- >>> a = np.array([[1, 2, 3], [4, 5, 6]]) >>> a array([[1, 2, 3], [4, 5, 6]]) >>> np.cumsum0(a) array([ 0, 1, 3, 6, 10, 15, 21]) >>> np.cumsum0(a, dtype=float) # specifies type of output value(s) array([ 0., 1., 3., 6., 10., 15., 21.]) >>> np.cumsum0(a, axis=0) # sum over rows for each of the 3 columns array([[0, 0, 0], [1, 2, 3], [5, 7, 9]]) >>> np.cumsum0(a, axis=1) # sum over columns for each of the 2 rows array([[ 0, 1, 3, 6], [ 0, 4, 9, 15]]) ``cumsum(b)[-1]`` may not be equal to ``sum(b)`` >>> b = np.array([1, 2e-9, 3e-9] * 1000000) >>> np.cumsum0(b)[-1] 1000000.0050045159 >>> b.sum() 1000000.0050000029 """ empty = a.take([], axis=axis) zero = empty.sum(axis, dtype=dtype, keepdims=True) later_cumsum = a.cumsum(axis, dtype=dtype) return concatenate([zero, later_cumsum], axis=axis, dtype=dtype, out=out) ```

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next NumPy triage meeting - now starting at 5 pm UTC
by Inessa Pawson 17 Mar '24

17 Mar '24

The next NumPy triage meeting will be held this Wednesday, January 11th at 5 pm UTC. (Please note the new time!) 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 Contributor Experience Lead | NumPy https://numpy.org/ GitHub: inessapawson

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next NumPy Newcomers' Hour - 10pm UTC
by Inessa Pawson 04 Mar '24

04 Mar '24

Our next Newcomers' Hour will be held this Thursday, November 16th 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 Optimization Team meeting
by Inessa Pawson 01 Mar '24

01 Mar '24

The next NumPy Optimization Team meeting will be held on Monday, November 20th at 4pm UTC. Join us via Zoom: https://numfocus-org.zoom.us/j/88162122074?pwd=a3h0cTZubzlLcTZzMVhCL1AxUVFM… . 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/dVdSlQ0TThWkOk0OkmGsmw?both. For the notes from the previous meetings, visit: https://github.com/numpy/archive/tree/main/optim_team_meetings. -- Cheers, Inessa Inessa Pawson GitHub: inessapawson

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NEP 55 - Add a UTF-8 Variable-Width String DType to NumPy
by Nathan 12 Feb '24

12 Feb '24

Hello all, I just opened a pull request to add NEP 55, see https://github.com/numpy/numpy/pull/24483. Per NEP 0, I've copied everything up to the "detailed description" section below. I'm looking forward to your feedback on this. -Nathan Goldbaum ========================================================= NEP 55 — Add a UTF-8 Variable-Width String DType to NumPy ========================================================= :Author: Nathan Goldbaum <ngoldbaum(a)quansight.com> :Status: Draft :Type: Standards Track :Created: 2023-06-29 Abstract -------- We propose adding a new string data type to NumPy where each item in the array is an arbitrary length UTF-8 encoded string. This will enable performance, memory usage, and usability improvements for NumPy users, including: * Memory savings for workflows that currently use fixed-width strings and store primarily ASCII data or a mix of short and long strings in a single NumPy array. * Downstream libraries and users will be able to move away from object arrays currently used as a substitute for variable-length string arrays, unlocking performance improvements by avoiding passes over the data outside of NumPy. * A more intuitive user-facing API for working with arrays of Python strings, without a need to think about the in-memory array representation. Motivation and Scope -------------------- First, we will describe how the current state of support for string or string-like data in NumPy arose. Next, we will summarize the last major previous discussion about this topic. Finally, we will describe the scope of the proposed changes to NumPy as well as changes that are explicitly out of scope of this proposal. History of String Support in Numpy ********************************** Support in NumPy for textual data evolved organically in response to early user needs and then changes in the Python ecosystem. Support for strings was added to numpy to support users of the NumArray ``chararray`` type. Remnants of this are still visible in the NumPy API: string-related functionality lives in ``np.char``, to support the obsolete ``np.char.chararray`` class, deprecated since NumPy 1.4 in favor of string DTypes. NumPy's ``bytes_`` DType was originally used to represent the Python 2 ``str `` type before Python 3 support was added to NumPy. The bytes DType makes the most sense when it is used to represent Python 2 strings or other null-terminated byte sequences. However, ignoring data after the first null character means the ``bytes_`` DType is only suitable for bytestreams that do not contain nulls, so it is a poor match for generic bytestreams. The ``unicode`` DType was added to support the Python 2 ``unicode`` type. It stores data in 32-bit UCS-4 codepoints (e.g. a UTF-32 encoding), which makes for a straightforward implementation, but is inefficient for storing text that can be represented well using a one-byte ASCII or Latin-1 encoding. This was not a problem in Python 2, where ASCII or mostly-ASCII text could use the Python 2 ``str`` DType (the current ``bytes_`` DType). With the arrival of Python 3 support in NumPy, the string DTypes were largely left alone due to backward compatibility concerns, although the unicode DType became the default DType for ``str`` data and the old ``string`` DType was renamed the ``bytes_`` DType. This change left NumPy with the sub-optimal situation of shipping a data type originally intended for null-terminated bytestrings as the data type for *all* python ``bytes`` data, and a default string type with an in-memory representation that consumes four times as much memory as needed for ASCII or mostly-ASCII data. Problems with Fixed-Width Strings ********************************* Both existing string DTypes represent fixed-width sequences, allowing storage of the string data in the array buffer. This avoids adding out-of-band storage to NumPy, however, it makes for an awkward user interface. In particular, the maximum string size must be inferred by NumPy or estimated by the user before loading the data into a NumPy array or selecting an output DType for string operations. In the worst case, this requires an expensive pass over the full dataset to calculate the maximum length of an array element. It also wastes memory when array elements have varying lengths. Pathological cases where an array stores many short strings and a few very long strings are particularly bad for wasting memory. Downstream usage of string data in NumPy arrays has proven out the need for a variable-width string data type. In practice, most downstream users employ ``object`` arrays for this purpose. In particular, ``pandas`` has explicitly deprecated support for NumPy fixed-width strings, coerces NumPy fixed-width string arrays to ``object`` arrays, and in the future may switch to only supporting string data via ``PyArrow``, which has native support for UTF-8 encoded variable-width string arrays [1]_. This is unfortunate, since `` object`` arrays have no type guarantees, necessitating expensive sanitization passes and operations using object arrays cannot release the GIL. Previous Discussions -------------------- The project last discussed this topic in depth in 2017, when Julian Taylor proposed a fixed-width text data type parameterized by an encoding [2]_. This started a wide-ranging discussion about pain points for working with string data in NumPy and possible ways forward. In the end, the discussion identified two use-cases that the current support for strings does a poor job of handling: * Loading or memory-mapping scientific datasets with unknown encoding, * Working with string data in a manner that allows transparent conversion between NumPy arrays and Python strings, including support for missing strings. As a result of this discussion, improving support for string data was added to the NumPy project roadmap [3]_, with an explicit call-out to add a DType better suited to memory-mapping bytes with any or no encoding, and a variable-width string DType that supports missing data to replace usages of object string arrays. Proposed work ------------- This NEP proposes adding ``StringDType``, a DType that stores variable-width heap-allocated strings in Numpy arrays, to replace downstream usages of the ``object`` DType for string data. This work will heavily leverage recent improvements in NumPy to improve support for user-defined DTypes, so we will also necessarily be working on the data type internals in NumPy. In particular, we propose to: * Add a new variable-length string DType to NumPy, targeting NumPy 2.0. * Work out issues related to adding a DType implemented using the experimental DType API to NumPy itself. * Support for a user-provided missing data sentinel. * A cleanup of ``np.char``, with the ufunc-like functions moved to a new namespace for functions and types related to string support. * An update to the ``npy`` and ``npz`` file formats to allow storage of arbitrary-length sidecar data. The following is out of scope for this work: * Changing DType inference for string data. * Adding a DType for memory-mapping text in unknown encodings or a DType that attempts to fix issues with the ``bytes_`` DType. * Fully agreeing on the semantics of a missing data sentinels or adding a missing data sentinel to NumPy itself. * Implement fast ufuncs or SIMD optimizations for string operations. While we're explicitly ruling out implementing these items as part of this work, adding a new string DType helps set up future work that does implement some of these items. If implemented this NEP will make it easier to add a new fixed-width text DType in the future by moving string operations into a long-term supported namespace. We are also proposing a memory layout that should be amenable to writing fast ufuncs and SIMD optimization in some cases, increasing the payoff for writing string operations as SIMD-optimized ufuncs in the future. While we are not proposing adding a missing data sentinel to NumPy, we are proposing adding support for an optional, user-provided missing data sentinel, so this does move NumPy a little closer to officially supporting missing data. We are attempting to avoid resolving the disagreement described in :ref:`NEP 26<NEP26>` and this proposal does not require or preclude adding a missing data sentinel or bitflag-based missing data support in the future. Usage and Impact ---------------- The DType is intended as a drop-in replacement for object string arrays. This means that we intend to support as many downstream usages of object string arrays as possible, including all supported NumPy functionality. Pandas is the obvious first user, and substantial work has already occurred to add support in a fork of Pandas. ``scikit-learn`` also uses object string arrays and will be able to migrate to a DType with guarantees that the arrays contains only strings. Both h5py [4]_ and PyTables [5]_ will be able to add first-class support for variable-width UTF-8 encoded string datasets in HDF5. String data are heavily used in machine-learning workflows and downstream machine learning libraries will be able to leverage this new DType. Users who wish to load string data into NumPy and leverage NumPy features like fancy advanced indexing will have a natural choice that offers substantial memory savings over fixed-width unicode strings and better validation guarantees and overall integration with NumPy than object string arrays. Moving to a first-class string DType also removes the need to acquire the GIL during string operations, unlocking future optimizations that are impossible with object string arrays. Performance *********** Here we briefly describe preliminary performance measurements of the prototype version of ``StringDType`` we have implemented outside of NumPy using the experimental DType API. All benchmarks in this section were performed on a Dell XPS 13 9380 running Ubuntu 22.04 and Python 3.11.3 compiled using pyenv. NumPy, Pandas, and the ``StringDType`` prototype were all compiled with meson release builds. Currently, the ``StringDType`` prototype has comparable performance with object arrays and fixed-width string arrays. One exception is array creation from python strings, performance is somewhat slower than object arrays and comparable to fixed-width unicode arrays:: In [1]: from stringdtype import StringDType In [2]: import numpy as np In [3]: data = [str(i) * 10 for i in range(100_000)] In [4]: %timeit arr_object = np.array(data, dtype=object) 3.55 ms ± 51.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) In [5]: %timeit arr_stringdtype = np.array(data, dtype=StringDType()) 12.9 ms ± 277 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) In [6]: %timeit arr_strdtype = np.array(data, dtype=str) 11.7 ms ± 150 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) In this example, object DTypes are substantially faster because the objects in the ``data`` list can be directly interned in the array, while ``StrDType`` and ``StringDType`` need to copy the string data and ``StringDType`` needs to convert the data to UTF-8 and perform additional heap allocations outside the array buffer. In the future, if Python moves to a UTF-8 internal representation for strings, the string loading performance of ``StringDType`` should improve. String operations have similar performance:: In [7]: %timeit np.array([s.capitalize() for s in data], dtype=object) 30.2 ms ± 109 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) In [8]: %timeit np.char.capitalize(arr_stringdtype) 38.5 ms ± 3.01 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) In [9]: %timeit np.char.capitalize(arr_strdtype) 46.4 ms ± 1.32 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) The poor performance here is a reflection of the slow iterator-based implementation of operations in ``np.char``. If we were to rewrite these operations as ufuncs, we could unlock substantial performance improvements. Using the example of the ``add`` ufunc, which we have implemented for the ``StringDType`` prototype:: In [10]: %timeit arr_object + arr_object 10 ms ± 308 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) In [11]: %timeit arr_stringdtype + arr_stringdtype 5.91 ms ± 18.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) In [12]: %timeit np.char.add(arr_strdtype, arr_strdtype) 65.9 ms ± 1.3 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) As described below, we have already updated a fork of Pandas to use a prototype version of ``StringDType``. This demonstrates the performance improvements available when data are already loaded into a NumPy array and are passed to a third-party library. Currently Pandas attempts to coerce all ``str`` data to ``object`` DType by default, and has to check and sanitize existing ``object `` arrays that are passed in. This requires a copy or pass over the data made unnecessary by first-class support for variable-width strings in both NumPy and Pandas:: In [13]: import pandas as pd In [14]: %timeit pd.Series(arr_stringdtype) 20.9 µs ± 341 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each) In [15]: %timeit pd.Series(arr_object) 1.08 ms ± 23.4 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each) We have also implemented a Pandas extension DType that uses ``StringDType`` under the hood, which is also substantially faster for creating Pandas data structures than the existing Pandas string DType that uses ``object`` arrays:: In [16]: %timeit pd.Series(arr_stringdtype, dtype='string[numpy]') 54.7 µs ± 1.38 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each) In [17]: %timeit pd.Series(arr_object, dtype='string[python]') 1.39 ms ± 1.16 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each) Backward compatibility ---------------------- We are not proposing a change to DType inference for python strings and do not expect to see any impacts on existing usages of NumPy, besides warnings or errors related to new deprecations or expiring deprecations in ``np.char``.

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Telling meson build which CBLAS/LAPACK (LAPACKE?) to use via pkgconfig module
by Dr. Thomas Orgis 25 Dec '23

25 Dec '23

Hi, I'm involved in packaging NumPy for http://pkgsrc.org/. We install a set of possible BLAS/CBLAS/LAPACK/LAPACKE packages side-by-side in the same prefix. This includes multiple variants of OpenBLAS with regard to multithreading (and indexing). For this purpose, we point software to use the build-time chosen BLAS implementation via BLAS_LIBS and similar variables, or, as seems to be appropriate for the new meson build of NumPy, via .pc files. A package depends on the generic BLAS library family and central user configuration chooses which one the packages should use during build. What would be the correct way to force the NumPy build to just use our BLAS choice, avoiding any automatisms that might surprise us? How agnostic is NumPy regarding the offered BLAS? Does it have to know that it is using OpenMP-parallelized OpenBLAS vs. the serial one (I'd imagine just setting OMP_NUM_THREADS handles paralellism) or MKL, Netlib, … ? It doesn't scale to have to tell it "openblas" or "netlib", as there is no universal vocabulary to name the variants, and NumPy doesn't even know openblas_openmp from serial openblas or openblas_pthread (right?). Basically, I want to do meson setup -Dcblas_pc=$CBLAS_PC with CBLAS_PC being the module name of one of $prefix/lib/pkgconfig/cblas.pc $prefix/lib/pkgconfig/openblas_pthread.pc $prefix/lib/pkgconfig/openblas_openmp.pc $prefix/lib/pkgconfig/openblas64_openmp.pc so that pkg-config does its thing without the NumPy build guessing around. Is that feasible already? Is it easily supportable with some changes to the build? I dream of a world where package build scripts don't have to add dozens of idiosyncratic lines to detect these libs. I'd like things to work like for CMake's FindBLAS with -DBLA_PREFER_PKGCONFIG and -DBLA_PKGCONFIG_BLAS=$BLAS_PC (see https://cmake.org/cmake/help/latest/module/FindBLAS.html since version 3.25). Can we have that? And: NumPy needs CBLAS … does it also need LAPACKE instead of LAPACK? These are differing libraries, possibly coming in differing binaries, even if your OpenBLAS builds also combine them. So I guess it should be -Dcblas_pc and -Dlapacke_pc, both being possibly identical. A build of the reference Netlib implementation provides four distinct libraries and .pc files: $prefix/lib/pkgconfig/cblas.pc $prefix/lib/pkgconfig/blas.pc $prefix/lib/pkgconfig/lapacke.pc $prefix/lib/pkgconfig/lapack.pc We do support installing openblas64 and friends alongside the others and I imagine just setting an ILP64 option and repective symbol suffix (none as of yet, as it's not a settled thing upstream) for the NumPy build if a 64 variant is chosen by the user. I wonder a bit if there are possible pitfalls combining other libraries with Python and indirectly some incompatible BLAS variant via NumPy … but one point of our user choice is that they could ensure that all packages really use the same BLAS. Alrighty then, Thomas -- Dr. Thomas Orgis HPC @ Universität Hamburg

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NEP 55 Updates and call for testing
by Nathan 08 Dec '23

08 Dec '23

Hi all, This week I updated NEP 55 to reflect the changes I made to the prototype since I initially sent out the NEP. The updated NEP is available on the NumPy website: https://numpy.org/neps/nep-0055-string_dtype.html. Updates to the NEP ++++++++++++++++++ The changes since the original version of the NEP focus on fully defining the C API surface we would like to add to the NumPy C API and an implementation of a per-dtype-instance arena allocator to manage heap allocations. This enabled major improvements to the prototype, including implementing the small string optimization and locking all access to heap memory behind a fine-grained mutex which should prevent seg faults or memory corruption in a multithreaded context. Thanks to Warren Weckesser for his proof of concept code and help with the small string optimization implementation, he has been added as an author to reflect his contributions. With these changes the stringdtype prototype is feature complete. Call to Review NEP 55 +++++++++++++++++++++ I'm requesting another round of review on the NEP with an eye toward acceptance before the NumPy 2.0 release branch is created from main. If I can manage it, my plan is to have a pull request open that merges the stringdtype codebase into NumPy before the branch is created. That said, if we decide that we need more time, or if some issue comes up, I'm happy with this going into main after the NumPy 2.0 release branch is created. The most significant feedback we have not addressed from the last round of review was Warren's suggestion to add a default missing data sentinel to NumPy itself. For reasons outlined in the NEP and in my reply to Warren from earlier this year, we do not want to add a missing data singleton to NumPy, instead leaving it to users to choose the missing data semantics they prefer. Otherwise I believe the current draft addresses all outstanding feedback from the last round of review. Help me Test the Prototype! +++++++++++++++++++++++++++ If anyone has time and interest, I would also very much appreciate some testing and tire-kicking on the stringdtype prototype, available at https://github.com/numpy/numpy-user-dtypes. There is a README with build instructions here: https://github.com/numpy/numpy-user-dtypes/blob/main/stringdtype/README.md If you have a Python development environment with a C compiler, it should be straightforward to build, install, and test the prototype. Note that you must have `NUMPY_EXPERIMENTAL_DTYPE_API=1` set in your shell environment or via `os.environ` to import stringdtype without error. I'm particularly interested to hear experiences converting code to use stringdtype. This could be code using fixed-width strings in a situation where a variable-length string array makes more sense or code using object string arrays. Are there pain points that aren't discussed in the NEP or existing workflows that cannot be adapted to use stringdtype? As far as I'm aware there aren't, but more testing will help catch issues before we've stabilized everything. My fork of pandas might be a source of inspiration for porting an existing non-trivial codebase that used object string arrays: https://github.com/pandas-dev/pandas/compare/main...ngoldbaum:pandas:string… Thanks all for your time, attention, and help reviewing the NEP! -Nathan

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Seeking feedback: design doc for `namedarray`, a lightweight array data structure with named dimensions
by Anderson Banihirwe 01 Dec '23

01 Dec '23

:wave:t5: folks, [there has been growing interest in a lightweight array structure](https://github.com/pydata/xarray/issues/3981) that's in the same vein as [xarray's Variable](https://docs.xarray.dev/en/stable/generated/xarray.Variable.html). we've put together a design doc for `namedarray`, and we could use your feedback/input. ## what is `namedarray`? in essence, `namedarray` aims to be a lighter version of xarray's Variable—shedding some of the heavier dependencies (e.g. Pandas) but still retaining the goodness of named dimensions. ## what makes it special? * **Array Protocol Compatibility**: we are planning to make it compatible with existing array protocols and the new [Python array API standard](https://data-apis.org/array-api/latest/). * **Duck-Array Objects**: designed to wrap around multiple duck-array objects, like NumPy, Dask, Sparse, Pint, CuPy, and PyTorch. ## why are we doing this? the goal is to bridge the gap between power and simplicity, providing a lightweight alternative for scientific computing tasks that don't require the full firepower of Xarray (`DataArray` and `Dataset`). ## share your thoughts We've put together a design doc that goes into the nitty-gritty of `namedarray`. your insights could be invaluable in making this initiative a success. please give it a read and share your thoughts [here](https://github.com/pydata/xarray/discussions/8080) * **Design Doc**: [namedarray Design Document](https://github.com/pydata/xarray/blob/main/design_notes/named_arr… cross posting from [Scientifc Python Discourse](https://discuss.scientific-python.org/t/seeking-feedback-design-…

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Next Newcomers' Hour: Thursday Nov 30th, 12pm UTC
by Melissa Mendonça 29 Nov '23

29 Nov '23

Hi all! Our next Newcomers' Hour will be held this Thursday, November 30th at 12 pm (noon) 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://numfocus-org.zoom.us/j/82563808729?pwd=ZFU3Z2dMcXBGb05YemRsaGE1OW5n… Hope to see you around! - Melissa

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