ArangoDB与Graphistry

我们探索了ArangoDB中的《权力的游戏》数据，以展示Arango的图支持如何与Graphistry快速互操作。

本教程分享两个示例转换：* 可视化完整图 * 可视化遍历查询的结果

每个都通过python-arango运行一个AQL查询，自动转换为pandas，并使用graphistry进行绘图。

设置

!pip install python-arango --user -q

from arango import ArangoClient
import pandas as pd
import graphistry

def paths_to_graph(paths, source='_from', destination='_to', node='_id'):
    nodes_df = pd.DataFrame()
    edges_df = pd.DataFrame()
    for graph in paths:
        nodes_df = pd.concat([ nodes_df, pd.DataFrame(graph['vertices']) ], ignore_index=True)
        edges_df = pd.concat([ edges_df, pd.DataFrame(graph['edges']) ], ignore_index=True)
    nodes_df = nodes_df.drop_duplicates([node])
    edges_df = edges_df.drop_duplicates([node])
    return graphistry.bind(source=source, destination=destination, node=node).nodes(nodes_df).edges(edges_df)

def graph_to_graphistry(graph, source='_from', destination='_to', node='_id'):
    nodes_df = pd.DataFrame()
    for vc_name in graph.vertex_collections():
        nodes_df = pd.concat([nodes_df, pd.DataFrame([x for x in graph.vertex_collection(vc_name)])], ignore_index=True)
    edges_df = pd.DataFrame()
    for edge_def in graph.edge_definitions():
        edges_df = pd.concat([edges_df, pd.DataFrame([x for x in graph.edge_collection(edge_def['edge_collection'])])], ignore_index=True)
    return graphistry.bind(source=source, destination=destination, node=node).nodes(nodes_df).edges(edges_df)

连接

# To specify Graphistry account & server, use:
# graphistry.register(api=3, username='...', password='...', protocol='https', server='hub.graphistry.com')
# For more options, see https://github.com/graphistry/pygraphistry#configure

client = ArangoClient(protocol='http', host='localhost', port=8529)
db = client.db('GoT', username='root', password='1234')

演示1：遍历可视化

• 使用 python-arango 的 traverse() 调用来遍历 Ned Stark 的后代

• 将结果路径转换为pandas和Graphistry

• 绘制图表，并使用名字而不是原始的Arango顶点ID

paths = db.graph('theGraph').traverse(
    start_vertex='Characters/4814',
    direction='outbound',
    strategy='breadthfirst'
)['paths']

g = paths_to_graph(paths)
g.bind(point_title='name').plot()

演示2：完整图表

• 在图上使用 python-arango 来识别并下载涉及的顶点/边集合

• 将结果转换为pandas和Graphistry

• 绘制图表，并使用名字而不是原始的Arango顶点ID

g = graph_to_graphistry( db.graph('theGraph') )
g.bind(point_title='name').plot()