Mailman 3 October 2023 - NumPy-Discussion

Add to NumPy a function to compute cumulative sums from 0.
by john.dawson＠camlingroup.com March 20, 2024

March 20, 2024

`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`. … [View More]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) ``` [View Less]

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next NumPy triage meeting - now starting at 5 pm UTC
by Inessa Pawson March 17, 2024

March 17, 2024

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/… [View More]

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NEP 55 - Add a UTF-8 Variable-Width String DType to NumPy
by Nathan Feb. 12, 2024

Feb. 12, 2024

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: … [View More]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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Seeking feedback: design doc for `namedarray`, a lightweight array data structure with named dimensions
by Anderson Banihirwe Dec. 1, 2023

Dec. 1, 2023

: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 … [View More]

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*New Time* Next Documentation team meeting
by Mukulika Pahari Nov. 20, 2023

Nov. 20, 2023

Hi all, sorry for the late notice. Our next Documentation Team meeting will happen on *Monday, October 9* 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.… [View More]

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Function that searches arrays for the first element that satisfies a condition
by rosko37 Nov. 1, 2023

Nov. 1, 2023

I know this question has been asked before, both on this list as well as several threads on Stack Overflow, etc. It's a common issue. I'm NOT asking for how to do this using existing Numpy functions (as that information can be found in any of those sources)--what I'm asking is whether Numpy would accept inclusion of a function that does this, or whether (possibly more likely) such a proposal has already been considered and rejected for some reason. The task is this--there's a large array and … [View More]you want to find the next element after some index that satisfies some condition. Such elements are common, and the typical number of elements to be searched through is small relative to the size of the array. Therefore, it would greatly improve performance to avoid testing ALL elements against the conditional once one is found that returns True. However, all built-in functions that I know of test the entire array. One can obviously jury-rig some ways, like for instance create a "for" loop over non-overlapping slices of length slice_length and call something like np.where(cond) on each--that outer "for" loop is much faster than a loop over individual elements, and the inner loop at most will go slice_length-1 elements past the first "hit". However, needing to use such a convoluted piece of code for such a simple task seems to go against the Numpy spirit of having one operation being one function of the form func(arr)". A proposed function for this, let's call it "np.first_true(arr, start_idx, [stop_idx])" would be best implemented at the C code level, possibly in the same code file that defines np.where. I'm wondering if I, or someone else, were to write such a function, if the Numpy developers would consider merging it as a standard part of the codebase. It's possible that the idea of such a function is bad because it would violate some existing broadcasting or fancy indexing rules. Clearly one could make it possible to pass an "axis" argument to np.first_true() that would select an axis to search over in the case of multi-dimensional arrays, and then the result would be an array of indices of one fewer dimension than the original array. So np.first_true(np.array([1,5],[2,7],[9,10],cond) would return [1,1,0] for cond(x): x>4. The case where no elements satisfy the condition would need to return a "signal value" like -1. But maybe there are some weird cases where there isn't a sensible return value, hence why such a function has not been added. -Andrew Rosko [View Less]

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next NumPy Newcomers' Hour - 12 pm UTC
by Inessa Pawson Oct. 31, 2023

Oct. 31, 2023

Our next Newcomers' Hour will be held tomorrow, December 1st, at 12 pm UTC. Stop by to ask questions 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 Contributor Experience Lead | NumPy https://numpy.org/ GitHub: inessapawson

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Merging very limited weights support for quantiles/percentiles
by Sebastian Berg Oct. 27, 2023

Oct. 27, 2023

Hi all, there is a PR to merge very limited support for weights in quantiles, which given no further input I will probably merge based on sklearn devs saying that they will use it. This means, adding a `weights` kwarg [1]. See: https://github.com/numpy/numpy/pull/24254 Limited here means that it would only work for the "inverted_cdf" method (which is not the default one). Why is it very limited? Because this limited version is the only form we/I am pretty confident about getting it … [View More]

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next NumPy community meeting
by Inessa Pawson Oct. 22, 2023

Oct. 22, 2023

The next NumPy community meeting will be held this Wednesday, August 17th at 18:00 (6 pm) UTC. Join us via Zoom: https://us06web.zoom.us/j/83278611437?pwd=ekhoLzlHRjdWc0NOY2FQM0NPemdkZz09 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 meeting, visit: https://github.com/numpy/archive/blob/main/community_meetings/community-202… Cheers, … [View More]

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Preserving subclass on concatenation
by Dom Grigonis Oct. 14, 2023

Oct. 14, 2023

Hi everyone, Quick question. Is there a way to concatenate arrays while preserving subclasses or is it left for custom implementation due to ambiguities of this operation? Regards, DG

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