xgi.stats.NodeStat#

class NodeStat(network, view, func, args=None, kwargs=None)[source]#

Bases: IDStat

An arbitrary node-quantity mapping.

NodeStat objects represent a mapping that assigns a value to each node in a network. For more details, see the tutorial.

Attributes

name

Name of this stat.

Methods

`asdict`	Output the stat as a dict.
`aslist`	Output the stat as a list.
`asnumpy`	Output the stat as a numpy array.
`aspandas`	Output the stat as a pandas series.
`ashist`	Return the distribution of a numpy array.
`max`	The maximum value of this stat.
`mean`	The arithmetic mean of this stat.
`median`	The median of this stat.
`min`	The minimum value of this stat.
`std`	The standard deviation of this stat.
`var`	The variance of this stat.
`moment`	The statistical moments of this stat.
`argmin`	The ID corresponding to the minimum of the stat
`argmax`	The ID corresponding to the maximum of the stat
`argsort`	Get the list of IDs sorted by stat value.

argmax()#

The ID corresponding to the maximum of the stat

When the maximal value is not unique, returns first ID corresponding to the maximal value.

Returns:: The ID to which the maximum value corresponds.
Return type:: hashable

argmin()#

The ID corresponding to the minimum of the stat

When the minimum value is not unique, returns first ID corresponding to the minimum value.

Returns:: The ID to which the minimum value corresponds.
Return type:: hashable

argsort(reverse=False)#

Get the list of IDs sorted by stat value.

When values are not unique, the order of the IDs is preserved.

Parameters:: reverse (bool) – Whether the sorting should be ascending or descending.
Returns:: The IDs sorted in ascending or descending order.
Return type:: list

asdict()#

Output the stat as a dict.

Notes

All stats are stored as dicts and therefore this method incurs in no overhead as type conversion is not necessary.

ashist(bins=10, bin_edges=False, density=False, log_binning=False)#

Return the distribution of a numpy array.

Parameters:

vals (Numpy array) – The array of values
bins (int, list, or Numpy array) – The number of bins or the bin edges.
bin_edges (bool) – Whether to also output the min and max of each bin, by default, False.
density (bool) – Whether to normalize the resulting distribution.
log_binning (bool) – Whether to bin the values with log-sized bins. By default, False.

Returns:

A two-column table with “bin_center” and “value” columns, where “value” is a count or a probability. If bin_edges is True, outputs two additional columns, bin_lo and bin_hi, which outputs the left and right bin edges respectively.

Return type:

Pandas DataFrame

Notes

Originally from jkbren/networks-and-dataviz

aslist()#: Output the stat as a list.

asnumpy()#: Output the stat as a numpy array.

aspandas()#

Output the stat as a pandas series.

Notes

The name attribute of the returned series is set using the name property.

max()#: The maximum value of this stat.

mean()#: The arithmetic mean of this stat.

median()#: The median of this stat.

min()#: The minimum value of this stat.

moment(order=2, center=False)#

The statistical moments of this stat.

Parameters:

order (int (default 2)) – The order of the moment.
center (bool (default False)) – Whether to compute the centered (False) or uncentered/raw (True) moment.

property name#

Name of this stat.

The name of a stat is used to populate the keys of dictionaries in MultiStat objects, as well as the names of columns of pandas dataframes.

Examples

>>> import xgi
>>> H = xgi.Hypergraph([[1, 2, 3], [2, 3, 4, 5], [3, 4, 5]])
>>> da, d3 = H.nodes.degree, H.nodes.degree(order=3)
>>> da.name, d3.name
('degree', 'degree(order=3)')
>>> H.nodes.multi([da, d3]).asdict(transpose=True).keys()
dict_keys(['degree', 'degree(order=3)'])
>>> H.nodes.multi([da, d3]).aspandas().columns
Index(['degree', 'degree(order=3)'], dtype='object')

std()#

The standard deviation of this stat.

Notes

This implementation calculates the standard deviation with N in the denominator (NumPy’s default). This is in contrast to the sample standard deviation which normalizes by N-1. See https://www.allendowney.com/blog/2024/06/08/which-standard-deviation/ for more details.

sum()#: The sum of this stat.

var()#

The variance of this stat.

Notes

This implementation calculates the variance with N in the denominator. (NumPy’s default) This is in contrast to the sample variation which normalizes by N-1. See https://www.allendowney.com/blog/2024/06/08/which-standard-deviation/ for more details.