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))

> conda install numpy-indexed -c conda-forge

> pip install numpy-indexed