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Numpy extensions for set operations on nd-arrays, group_by operations, and related functionality

Project Description

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:

# 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

> conda install numpy-indexed -c conda-forge

or

> 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?
Release History

Release History

This version
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0.3.4

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0.3.3

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0.3.2

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0.3.1

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0.3.0

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0.2.19

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0.2.18

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0.2.16

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0.2.14

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0.2.12

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0.2.11

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0.2.10

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0.2.9

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0.2.8

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0.2.7

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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
numpy-indexed-0.3.4.zip (32.3 kB) Copy SHA256 Checksum SHA256 Source Jul 28, 2016

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