Comments (2)
Sorry, I've been unable to reproduce it:
>>> import numpy
>>> import awkward
>>> numpy.__version__
'1.18.5'
>>> awkward.__version__ # not my development version; a fresh copy from conda-forge
'0.12.22'
>>> awkward.JaggedArray.fromiter([[20.2, 10.1, 30.3, 50.5], [50.5], [60]])
<JaggedArray [[20.2 10.1 30.3 50.5] [50.5] [60.0]] at 0x7fa44f193c90>
>>> awkward.fromiter([[20.2, 10.1, 30.3, 50.5], [50.5], [60]])
<JaggedArray [[20.2 10.1 30.3 50.5] [50.5] [60.0]] at 0x7fa44f193d50>
But hopefully that narrows down your search—you might not be using the versions you think you're using (particularly in Jupyter; it sometimes gets packages from an entirely different installation) or maybe there's something else going on.
If you're just starting a project, you might want to consider Awkward1.
>>> import awkward1 as ak
>>> ak.Array([[20.2, 10.1, 30.3, 50.5], [50.5], [60]])
<Array [[20.2, 10.1, 30.3, 50.5, ... [60]] type='3 * var * float64'>
There's a ak.from_awkward0 and ak.to_awkward0 and you can load both Awkward0 and Awkward1 in the same project, so you can make a gradual transition if necessary.
from awkward-0.x.
Confirmed that it's a versioning issue instead(sorry!), thanks!
from awkward-0.x.
Related Issues (20)
- dynamically created methods are confusing for users HOT 1
- Achieve masking HOT 8
- AssertionError when Table is part of a list HOT 5
- Potential bug with subsequent masking HOT 2
- Reduction of empty elements HOT 2
- TLorentzVectorArray yields different values depending on masking order HOT 4
- IndexError when masking empty jaggedArray made from offsets HOT 1
- awkward method names HOT 9
- TypeError when using array.mean(weights) HOT 2
- Cyclic array? HOT 1
- broken link in readme HOT 1
- Installing awkward-numba in usermode breaks awkward HOT 3
- Syntax warning due to comparison of literals using is in Python 3.8 HOT 1
- Inconsistent Filesizes with .awkd Files HOT 6
- Bug in string comparison in StringArray HOT 1
- mean, std fail on ChunkedArrays HOT 1
- AttributeError when trying to read a particular format of awkward array HOT 5
- Small detail; broadcasting seems to work a little different to what is implied in the documentation. HOT 6
- Accumulate numpy arrays inside the loop HOT 1
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from awkward-0.x.