./wip/py-dimarray, Numpy array with labelled dimensions and axes, dimension

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Branch: CURRENT, Version: 0.1.8, Package name: py312-dimarray-0.1.8, Maintainer: jihbed.research

dimarray is a package to handle numpy arrays with labelled dimensions and axes
Inspired from pandas, it includes advanced alignment and reshaping features
and as well as missing-value (NaN) handling.

The main difference with pandas is that it is generalized to N dimensions, and
behaves more closely to a numpy array. The axes do not have fixed names
('index', 'columns', etc...) but are given a meaningful name by the user
(e.g. 'time', 'items', 'lon' ...). This is especially useful for high
dimensional problems such as sensitivity analyses.

A natural I/O format for such an array is netCDF, common in geophysics,
which relies on the netCDF4 package, and supports metadata.

Required to run:
[devel/py-setuptools] [math/py-numpy] [lang/python37]

Required to build:
[pkgtools/cwrappers]

Master sites:

RMD160: 763c3a343578d149204baf0db8c542ea0638c7cb
Filesize: 88.394 KB

Version history: (Expand)

CVS history: (Expand)


   2014-07-22 20:42:18 by Kamel Derouiche | Files touched by this commit (4) 
Log message:
Import py27-dimarray-0.1.8 as wip/py-dimarray.

dimarray is a package to handle numpy arrays with labelled dimensions and axes
Inspired from pandas, it includes advanced alignment and reshaping features
and as well as missing-value (NaN) handling.

The main difference with pandas is that it is generalized to N dimensions, and
behaves more closely to a numpy array. The axes do not have fixed names
('index', 'columns', etc...) but are given a meaningful name by the user
(e.g. 'time', 'items', 'lon' ...). This is especially useful for high
dimensional problems such as sensitivity analyses.

A natural I/O format for such an array is netCDF, common in geophysics,
which relies on the netCDF4 package, and supports metadata.CVS: \ 
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