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