graphistry.plugins package¶
Subpackages¶
Submodules¶
graphistry.plugins.igraph module¶
-
graphistry.plugins.igraph.compute_igraph(self, alg, out_col=None, directed=None, params={})¶ Enrich or replace graph using igraph methods
- Parameters
alg (str) – Name of an igraph.Graph method like pagerank
out_col (Optional[str]) – For algorithms that generate a node attribute column, out_col is the desired output column name. When None, use the algorithm’s name. (default None)
directed (Optional[bool]) – During the to_igraph conversion, whether to be directed. If None, try directed and then undirected. (default None)
params (dict) – Any named parameters to pass to the underlying igraph method
- Returns
Plotter
- Return type
Example: Pagerank
- ::
import graphistry, pandas as pd edges = pd.DataFrame({‘s’: [‘a’,’b’,’c’,’d’], ‘d’: [‘c’,’c’,’e’,’e’]}) g = graphistry.edges(edges, ‘s’, ‘d’) g2 = g.compute_igraph(‘pagerank’) assert ‘pagerank’ in g2._nodes.columns
- Example: Pagerank with custom name
- ::
import graphistry, pandas as pd edges = pd.DataFrame({‘s’: [‘a’,’b’,’c’,’d’], ‘d’: [‘c’,’c’,’e’,’e’]}) g = graphistry.edges(edges, ‘s’, ‘d’) g2 = g.compute_igraph(‘pagerank’, out_col=’my_pr’) assert ‘my_pr’ in g2._nodes.columns
- Example: Pagerank on an undirected
- ::
import graphistry, pandas as pd edges = pd.DataFrame({‘s’: [‘a’,’b’,’c’,’d’], ‘d’: [‘c’,’c’,’e’,’e’]}) g = graphistry.edges(edges, ‘s’, ‘d’) g2 = g.compute_igraph(‘pagerank’, directed=False) assert ‘pagerank’ in g2._nodes.columns
- Example: Pagerank with custom parameters
- ::
import graphistry, pandas as pd edges = pd.DataFrame({‘s’: [‘a’,’b’,’c’,’d’], ‘d’: [‘c’,’c’,’e’,’e’]}) g = graphistry.edges(edges, ‘s’, ‘d’) g2 = g.compute_igraph(‘pagerank’, params={‘damping’: 0.85}) assert ‘pagerank’ in g2._nodes.columns
- Parameters
self (
Plottable) –
-
graphistry.plugins.igraph.from_igraph(self, ig, node_attributes=None, edge_attributes=None, load_nodes=True, load_edges=True, merge_if_existing=True)¶ Convert igraph object into Plotter
If base g has _node, _source, _destination definitions, use them
When merge_if_existing with preexisting nodes/edges df and shapes match ig, combine attributes
For merge_if_existing to work with edges, must set g._edge and have corresponding edge index attribute in igraph.Graph
- Parameters
ig (igraph) – Source igraph object
node_attributes (Optional[List[str]]) – Subset of node attributes to load; None means all (default)
edge_attributes (Optional[List[str]]) – Subset of edge attributes to load; None means all (default)
load_nodes (bool) – Whether to load nodes dataframe (default True)
load_edges (bool) – Whether to load edges dataframe (default True)
merge_if_existing (
bool) – Whether to merge with existing node/edge dataframes (default True)merge_if_existing – bool
- Returns
Plotter
- Return type
- Example: Convert from igraph, including all node/edge properties
- ::
import graphistry, pandas as pd edges = pd.DataFrame({‘s’: [‘a’, ‘b’, ‘c’, ‘d’], ‘d’: [‘b’, ‘c’, ‘d’, ‘e’], ‘v’: [101, 102, 103, 104]}) g = graphistry.edges(edges, ‘s’, ‘d’).materialize_nodes().get_degrees() assert ‘degree’ in g._nodes.columns g2 = g.from_igraph(g.to_igraph()) assert len(g2._nodes.columns) == len(g._nodes.columns)
- Example: Enrich from igraph, but only load in 1 node attribute
- ::
import graphistry, pandas as pd edges = pd.DataFrame({‘s’: [‘a’, ‘b’, ‘c’, ‘d’], ‘d’: [‘b’, ‘c’, ‘d’, ‘e’], ‘v’: [101, 102, 103, 104]}) g = graphistry.edges(edges, ‘s’, ‘d’).materialize_nodes().get_degree() assert ‘degree’ in g._nodes ig = g.to_igraph(include_nodes=False) assert ‘degree’ not in ig.vs ig.vs[‘pagerank’] = ig.pagerank() g2 = g.from_igraph(ig, load_edges=False, node_attributes=[g._node, ‘pagerank’]) assert ‘pagerank’ in g2._nodes asssert ‘degree’ in g2._nodes
-
graphistry.plugins.igraph.layout_igraph(self, layout, directed=None, bind_position=True, x_out_col='x', y_out_col='y', play=0, params={})¶ Compute graph layout using igraph algorithm. For a list of layouts, see layout_algs or igraph documentation.
- Parameters
layout (str) – Name of an igraph.Graph.layout method like sugiyama
directed (Optional[bool]) – During the to_igraph conversion, whether to be directed. If None, try directed and then undirected. (default None)
bind_position (bool) – Whether to call bind(point_x=, point_y=) (default True)
x_out_col (str) – Attribute to write x position to. (default ‘x’)
y_out_col (str) – Attribute to write x position to. (default ‘y’)
play (Optional[str]) – If defined, set settings(url_params={‘play’: play}). (default 0)
params (dict) – Any named parameters to pass to the underlying igraph method
- Returns
Plotter
- Return type
- Example: Sugiyama layout
- ::
import graphistry, pandas as pd edges = pd.DataFrame({‘s’: [‘a’,’b’,’c’,’d’], ‘d’: [‘b’,’c’,’d’,’e’]}) g = graphistry.edges(edges, ‘s’, ‘d’) g2 = g.layout_igraph(‘sugiyama’) assert ‘x’ in g2._nodes g2.plot()
- Example: Change which column names are generated
- ::
import graphistry, pandas as pd edges = pd.DataFrame({‘s’: [‘a’,’b’,’c’,’d’], ‘d’: [‘b’,’c’,’d’,’e’]}) g = graphistry.edges(edges, ‘s’, ‘d’) g2 = g.layout_igraph(‘sugiyama’, x_out_col=’my_x’, y_out_col=’my_y’) assert ‘my_x’ in g2._nodes assert g2._point_x == ‘my_x’ g2.plot()
- Example: Pass parameters to layout methods - Sort nodes by degree
- ::
import graphistry, pandas as pd edges = pd.DataFrame({‘s’: [‘a’,’b’,’c’,’d’], ‘d’: [‘b’,’c’,’d’,’e’]}) g = graphistry.edges(edges, ‘s’, ‘d’) g2 = g.get_degrees() assert ‘degree’ in g._nodes.columns g3 = g.layout_igraph(‘sugiyama’, params={‘layers’: ‘degree’}) g3.plot()
- Parameters
self (
Plottable) –
-
graphistry.plugins.igraph.to_igraph(self, directed=True, include_nodes=True, node_attributes=None, edge_attributes=None)¶ Convert current item to igraph Graph . See examples in from_igraph.
- Parameters
directed (bool) – Whether to create a directed graph (default True)
include_nodes (bool) – Whether to ingest the nodes table, if it exists (default True)
node_attributes (Optional[List[str]]) – Which node attributes to load, None means all (default None)
edge_attributes (Optional[List[str]]) – Which edge attributes to load, None means all (default None)
self (
Plottable) –