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-forgeor
> pip install numpy-indexedDesign 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?
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Metadata
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Release files for numpy-indexed 0.3.7
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