Nicholas Smith
Nicholas Smith
I see what appears to be an inconsistency between using `keepdims=True` in a reducer and the behavior of `ak.singletons`: ```python >>> a = ak.Array([[3, 1, 2], [4, 5], []]) >>>...
Both functions in `awkward1._util` are used a few times in coffea. The [wrapper function](https://github.com/scikit-hep/awkward-1.0/blob/master/src/awkward1/_connect/_numpy.py#L41) that broadcasts and flattens inputs for numpy ufuncs was copied in a simplified form to coffea...
Proposing a new structure operation akin to pandas' [stack](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.stack.html) and [unstack](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.unstack.html) which pivots a record structure to a jagged array and vice versa. The main difference is that we do...
Currently, there is no convenient way for checking if a given array has masked entries if it is not a `MaskedArray` type, i.e. the mask methods are not universal. For...
I managed to end up with something like ```python a = ak.JaggedArray.fromcounts( np.array([1, 0, 3]), ak.MaskedArray( np.array([True, False, True, False]), np.arange(4), ) ) ``` which gives ``, and then proceeded...
```python >>> import awkward as ak >>> ak.__version__ '0.12.17' >>> a = ak.fromiter([[1, 2], [], [-2, 4], [4, -3], [-2, -1]]) >>> b = ak.fromiter([[2, 4], [1], [-1, 1], [],...
I would expect the following transformation between `arrays1` and `arrays3` to be an identity: ```python # http://scikit-hep.org/uproot/examples/HZZ.root fin = uproot.open("HZZ.root") tree = fin["events"] arrays1 = tree.lazyarrays("Electron_*") arrays2 = {k.replace('Electron_', ''):...
Take for example: ```python import hist import numpy as np h = hist.Hist.new.Regular(10, 0, 1, name="x").WeightedMean() ``` I can fill with ```python h.fill(x=np.array([0.1, 0.1, 0.2]), sample=np.array([1, 2, 3]), weight=np.array([1, 2,...
Copy of part 7 of #214 About categorical axes, it looks like the storage contains the outer product of growable categories: ``` h = bh.histogram( bh.axis.category([''], growth=True), bh.axis.category([''], growth=True), bh.axis.category([''],...
I ran into https://github.com/joerick/cibuildwheel/issues/402 while building wheels that now incorporate numpy See e.g. https://github.com/cms-nanoAOD/correctionlib/runs/2521360719?check_suite_focus=true#step:3:1003 Seems the current best solution is to drop pypy wheels for os x?