|Travis| |PyPI| |Anaconda|

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?

.. |Travis| image:: https://travis-ci.org/EelcoHoogendoorn/Numpy_arraysetops_EP.svg?branch=master :target: https://travis-ci.org/EelcoHoogendoorn/Numpy_arraysetops_EP .. |PyPI| image:: https://badge.fury.io/py/numpy-indexed.svg :target: https://pypi.org/project/numpy-indexed/ .. |Anaconda| image:: https://anaconda.org/conda-forge/numpy-indexed/badges/version.svg :target: https://anaconda.org/conda-forge/numpy-indexed