Hi, Sebastian, On 22.02.20, 02:37, "NumPy-Discussion on behalf of Sebastian Berg" <numpy-discussion-bounces+hameerabbasi=yahoo....@python.org on behalf of sebast...@sipsolutions.net> wrote:
Hi all, When we create new datatypes, we have the option to make new choices for the new datatypes [0] (not the existing ones). The question is: Should every NumPy datatype have a scalar associated and should operations like indexing return a scalar or a 0-D array? This is in my opinion a complex, almost philosophical, question, and we do not have to settle anything for a long time. But, if we do not decide a direction before we have many new datatypes the decision will make itself... So happy about any ideas, even if its just a gut feeling :). There are various points. I would like to mostly ignore the technical ones, but I am listing them anyway here: * Scalars are faster (although that can be optimized likely) * Scalars have a lower memory footprint * The current implementation incurs a technical debt in NumPy. (I do not think that is a general issue, though. We could automatically create scalars for each new datatype probably.) Advantages of having no scalars: * No need to keep track of scalars to preserve them in ufuncs, or libraries using `np.asarray`, do they need `np.asarray_or_scalar`? (or decide they return always arrays, although ufuncs may not) * Seems simpler in many ways, you always know the output will be an array if it has to do with NumPy. Advantages of having scalars: * Scalars are immutable and we are used to them from Python. A 0-D array cannot be used as a dictionary key consistently [1]. I.e. without scalars as first class citizen `dict[arr1d[0]]` cannot work, `dict[arr1d[0].item()]` may (if `.item()` is defined, and e.g. `dict[arr1d[0].frozen()]` could make a copy to work. [2] * Object arrays as we have them now make sense, `arr1d[0]` can reasonably return a Python object. I.e. arrays feel more like container if you can take elements out easily. Could go both ways: * Scalar math `scalar = arr1d[0]; scalar += 1` modifies the array without scalars. With scalars `arr1d[0, ...]` clarifies the meaning. (In principle it is good to never use `arr2d[0]` to get a 1D slice, probably more-so if scalars exist.) From a usability perspective, one could argue that if the dimension of the array one is indexing into is known and the user isn't advanced, then the behavior expected is one of scalars and not 0D arrays. If, however, the input dimension is unknown, then the behavior switch at 0D and the need for an extra ellipsis to ensure array-ness makes things confusing to regular users. I am file with the current behavior of indexing, as anything else would likely be a large backwards-compat break. Note: array-scalars (the current NumPy scalars) are not useful in my opinion [3]. A scalar should not be indexed or have a shape. I do not believe in scalars pretending to be arrays. I personally tend towards liking scalars. If Python was a language where the array (array-programming) concept was ingrained into the language itself, I would lean the other way. But users are used to scalars, and they "put" scalars into arrays. Array objects are in some ways strange in Python, and I feel not having scalars detaches them further. Having scalars, however also means we should preserve them. I feel in principle that is actually fairly straight forward. E.g. for ufuncs: * np.add(scalar, scalar) -> scalar * np.add.reduce(arr, axis=None) -> scalar * np.add.reduce(arr, axis=1) -> array (even if arr is 1d) * np.add.reduce(scalar, axis=()) -> array I love this idea. Of course libraries that do `np.asarray` would/could basically chose to not preserve scalars: Their signature is defined as taking strictly array input. Cheers, Sebastian [0] At best this can be a vision to decide which way they may evolve. [1] E.g. PyTorch uses `hash(tensor) == id(tensor)` which is arguably strange. E.g. Quantity defines hash correctly, but does not fully ensure immutability for 0-D Quantities. Ensuring immutability in a world where "views" are a central concept requires a write-only copy. [2] Arguably `.item()` would always return a scalar, but it would be a second class citizen. (Although if it returns a scalar, at least we already have a scalar implementation.) [3] They are necessary due to technical debt for NumPy datatypes though. _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion