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This package contains functionality for indexed operations on numpy ndarrays, providing efficient vectorized functionality such as grouping and set operations.

Project description

Home-page: https://github.com/EelcoHoogendoorn/Numpy_arraysetops_EP
Author: Eelco Hoogendoorn
Author-email: hoogendoorn.eelco@gmail.com
License: Freely Distributable
Description: |Build Status| |Build status|

Numpy indexed operations
========================

This package contains functionality for indexed operations on numpy ndarrays, providing efficient vectorized functionality such as grouping and set operations.

* Rich and efficient grouping functionality:

- splitting of values by key-group
- reductions of values by key-group

* Generalization of existing array set operation to nd-arrays, such as:

- unique
- union
- difference
- exclusive (xor)
- contains / in (in1d)

* Some new functions:

- indices: numpy equivalent of list.index
- count: numpy equivalent of collections.Counter
- mode: find the most frequently occuring items in a set
- multiplicity: number of occurrences of each key in a sequence
- count\_table: like R's table or pandas crosstab, or an ndim version of np.bincount

Some brief examples to give an impression hereof:

.. code:: python

# three sets of graph edges (doublet of ints)
edges = np.random.randint(0, 9, (3, 100, 2))
# find graph edges exclusive to one of three sets
ex = exclusive(*edges)
print(ex)
# which edges are exclusive to the first set?
print(contains(edges[0], ex))
# where are the exclusive edges relative to the totality of them?
print(indices(union(*edges), ex))
# group and reduce values by identical keys
values = np.random.rand(100, 20)
# and so on...
print(group_by(edges[0]).median(values))

Installation
------------

.. code:: python

> conda install numpy-indexed -c conda-forge

or

.. code:: python

> pip install numpy-indexed

See: https://pypi.python.org/pypi/numpy-indexed

Design decisions:
-----------------

This package builds upon a generalization of the design pattern as can
be found in numpy.unique. That is, by argsorting an ndarray, many
subsequent operations can be implemented efficiently and in a vectorized
manner.

The sorting and related low level operations are encapsulated into a
hierarchy of Index classes, which allows for efficient lookup of many
properties for a variety of different key-types. The public API of this
package is a quite thin wrapper around these Index objects.

The two complex key types currently supported, beyond standard sequences
of sortable primitive types, are ndarray keys (i.e, finding unique
rows/columns of an array) and composite keys (zipped sequences). For the
exact casting rules describing valid sequences of key objects to index
objects, see as\_index().

Todo and open questions:
------------------------

- There may be further generalizations that could be built on top of
these abstractions. merge/join functionality perhaps?

.. |Build Status| image:: https://travis-ci.org/EelcoHoogendoorn/Numpy_arraysetops_EP.svg?branch=master
:target: https://travis-ci.org/EelcoHoogendoorn/Numpy_arraysetops_EP
.. |Build status| image:: https://ci.appveyor.com/api/projects/status/h7w191ovpa9dcfum?svg=true
:target: https://ci.appveyor.com/project/clinicalgraphics/numpy-arraysetops-ep

Keywords: numpy group_by set-operations indexing
Platform: Any
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Topic :: Utilities
Classifier: Topic :: Scientific/Engineering
Classifier: License :: Freely Distributable
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5

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