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

## 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

### 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?

## Project details

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