Hacktoberfest 2026：維護者為十月標記出來的 issue，仍然開放、適合新手。 瀏覽 Hacktoberfest issue

Syntax for typing multi-dimensional arrays

未關閉
  • 哪裡不對？
#516 8 則留言 17 個 reaction 已指派 0 人 在 GitHub 檢視

維護者通常 1 天內回覆

還沒有人認領這個 Issue。

評估

難度
5/5
預估耗時
一週以上
新手友好度
25/100
Issue 類型
功能
描述清晰度
需要釐清
活躍度
停滯
技術堆疊
numpy, python
領域
developer-experience, tooling

研究方向

從較大型的多維陣列 issue (#513) 和連結的設計文件開始，尤其是其中的「Possible syntax」區段。比較提議的 dtype 和 shape 形式，包括泛型引數和可變參數，並檢視現有討論。完成的標準是達成並記錄一套已確定的語法，以支援所述的可選和泛型情況。

由索引模型根據 Issue 內容生成。

描述

topic: feature

As part of the larger project for multi-dimensional arrays (https://github.com/python/typing/issues/513), one of the first questions I would like to settle is what syntax for typing data-types and shapes should look like.

Both dtype and shape should be optional, and it should be possible to define multi-dimensional arrays for which either or both of these are generic:

  • dtype: indicates the data type for array elements, e.g., np.float64
  • shape: indicates the shape of the multi-dimensional array, a tuple of zero or more integers. We would like to support integer and variable sized dimensions, and variable numbers of dimensions. These are most naturally represented with indexing by a variadic number of integer, variable, colon : and/or ellipsis ... arguments, e.g., NDArray[1, N, :, ...] for an array with dimensions of size 1, size N, and arbitrary size, followed by 0 or more arbitrary sized dimensions.

For NumPy, ideally we would like to add basic typing support for dtype (using Generic) even before typing for shape is possible. But we'd like to know what the ultimate syntax should look like, so we don't paint ourselves into a corner.

One key question: can we safely rely on using a single generic argument for dtypes (e.g., np.ndarray[np.float64]) as indicating an array without any shape constraints?

My doc (same as in the master issue) considers a number of options under the "Possible syntax" section.

So far, I think the best option is some variation of "two generic arguments", for dtype and shape. But this could quickly get annoyingly verbose when sprinkled all over a code-base, e.g., np.ndarray[np.float32, Shaped[..., N, M]]:

  • It would be nice to support syntax like np.ndarray[np.float32] (the multi-dimensional equivalent of List[float]) as an alias for np.ndarray[np.float32, Any], but we don't yet have optional arguments for generics (variadic arguments are a somewhat awkward fit for a single argument).
  • It would also be nice to allow omitting Shaped[], e.g., by writing dimensions as variadic generics to the array type like np.ndarray[np.float32, ..., N, M]. One possible ambiguity is how to specify scalar arrays: np.ndarray[np.float32,] looks very similar to np.ndarray[np.float32]. But scalar arrays are rare enough that these could potentially be resolved by disallowing np.ndarray[np.float32,] in favor of requiring np.ndarray[np.float32, Shape[()]].
主要語言
Python
星號
1.8k
分支
302
平均合併
4 小時 16 分鐘
30 天內合併 PR
7

環境準備

這個專案沒有提供開發容器、Dockerfile 或貢獻指南，環境需要你自己搭建：先看它的 README，通用步驟見我們的新手貢獻指南。

從這裡開始

  1. 先讀完整個 Issue，再讀專案的貢獻指南。
  2. 在 Issue 下留言說明你要接手 —— 這能避免兩個人做同樣的事。
  3. Fork 儲存庫，在一個分支上完成修改。
  4. 送出 Pull Request，並在描述裡引用這個 Issue 編號。

python/typing 的其他 Issue

查看 python/typing 的全部 Issue

相似的 Issue

更多 Python Issue

把新 issue 寄到你的電子郵件信箱

精選適合新手參與的 GitHub issue 摘要。