Utilities

Helpers for NetworkX.

These are not imported into the base networkx namespace but can be accessed, for example, as

>>> import networkx
>>> networkx.utils.is_string_like('spam')
True

Helper functions

is_string_like(obj)	Check if obj is string.
flatten(obj[, result])	Return flattened version of (possibly nested) iterable object.
iterable(obj)	Return True if obj is iterable with a well-defined len()
is_list_of_ints(intlist)	Return True if list is a list of ints.
_get_fh(path[, mode])	Return a file handle for given path.

Data structures and Algorithms

UnionFind.union(*objects)

Find the sets containing the objects and merge them all.

Random sequence generators

pareto_sequence(n[, exponent])	Return sample sequence of length n from a Pareto distribution.
powerlaw_sequence(n[, exponent])	Return sample sequence of length n from a power law distribution.
uniform_sequence(n)	Return sample sequence of length n from a uniform distribution.
cumulative_distribution(distribution)	Return normalized cumulative distribution from discrete distribution.
discrete_sequence(n[, distribution, ...])	Return sample sequence of length n from a given discrete distribution or discrete cumulative distribution.

SciPy random sequence generators

scipy_pareto_sequence(n[, exponent])	Return sample sequence of length n from a Pareto distribution.
scipy_powerlaw_sequence(n[, exponent])	Return sample sequence of length n from a power law distribution.
scipy_poisson_sequence(n[, mu])	Return sample sequence of length n from a Poisson distribution.
scipy_uniform_sequence(n)	Return sample sequence of length n from a uniform distribution.
scipy_discrete_sequence(n[, distribution])	Return sample sequence of length n from a given discrete distribution