对python 树状嵌套结构的实现思路详解

Z时代
2024-01-10
分类：综合

原始数据

原始数据大致是这样子的：

每条数据中的四个数据分别是当前节点名称，节点描述（指代一些需要的节点属性),源节点（即最顶层节点），父节点（当前节点上一层节点）。


datas = [
 ["root", "根节点", "root", None],
 ["node1", "一级节点1", "root", "root"],
 ["node2", "一级节点2", "root", "root"],
 ["node11", "二级节点11", "root", "node1"],
 ["node12", "二级节点12", "root", "node1"],
 ["node21", "二级节点21", "root", "node2"],
 ["node22", "二级节点22", "root", "node2"],
]

节点类

抽象封装出一个节点类：


class Node(object):
 def __init__(self, name: str, desc, parent: str, children: list):
 """
 初始化
 :param name:
 :param desc:
 :param parent:
 :param children:
 """
 self.name = name
 self.desc = desc
 self.parent = parent
 self.children = children
 def get_nodes(self):
 """
 获取该节点下的全部结构字典
 """
 d = dict()
 d['name'] = self.name
 d['desc'] = self.desc
 d['parent'] = self.parent
 children = self.get_children()
 if children:
  d['children'] = [child.get_nodes() for child in children]
 return d
 def get_children(self):
 """
 获取该节点下的全部节点对象
 """
 return [n for n in nodes if n.parent == self.name]
 def __repr__(self):
 return self.name

将原始数据转换为节点对象


nodes = list()
for data in datas:
 node = Node(data[0], data[1], data[-1], [])
 nodes.append(node)

为各个节点建立联系


for node in nodes:
 children_names = [data[0] for data in datas if data[-1] == node.name]
 children = [node for node in nodes if node.name in children_names]
 node.children.extend(children)

测试


root = nodes[0]
print(root)
tree = root.get_nodes()
print(json.dumps(tree, indent=4))

运行结果：

原始数据也可以是字典的形式：


### fork_tool.py
import json
class Node(object):
 def __init__(self, **kwargs):
 """
 初始化
 :param nodes: 树的全部节点对象
 :param kwargs: 当前节点参数
 """
 self.forked_id = kwargs.get("forked_id")
 self.max_drawdown = kwargs.get("max_drawdown")
 self.annualized_returns = kwargs.get("annualized_returns")
 self.create_time = kwargs.get("create_time")
 self.desc = kwargs.get("desc")
 self.origin = kwargs.get("origin")
 self.parent = kwargs.get("parent")
 self.children = kwargs.get("children", [])
 def get_nodes(self, nodes):
 """
 获取该节点下的全部结构字典，即建立树状联系
 """
 d = dict()
 d['forked_id'] = self.forked_id
 d['max_drawdown'] = self.max_drawdown
 d['annualized_returns'] = self.annualized_returns
 d['create_time'] = self.create_time
 d['desc'] = self.desc
 d['origin'] = self.origin
 d['parent'] = self.parent
 children = self.get_children(nodes)
 if children:
  d['children'] = [child.get_nodes(nodes) for child in children]
 return d
 def get_children(self, nodes):
 """
 获取该节点下的全部节点对象
 """
 return [n for n in nodes if n.parent == self.forked_id]
 # def __repr__(self):
 # return str(self.desc)
def process_datas(datas):
 """
 处理原始数据
 :param datas:
 :return:
 """
 # forked_infos.append({"forked_id": str(forked_strategy.get("_id")),
 # "max_drawdown": max_drawdown,
 # "annualized_returns": annualized_returns,
 # "create_time": create_time, # 分支创建时间
 # "desc": desc,
 # "origin": origin,
 # "parent": parent,
 # "children": [],
 # })
 nodes = []
 # 构建节点列表集
 for data in datas:
 node = Node(**data)
 nodes.append(node)
 # 为各个节点对象建立类 nosql 结构的联系
 for node in nodes:
 children_ids = [data["forked_id"] for data in datas if data["parent"] == node.forked_id]
 children = [node for node in nodes if node.forked_id in children_ids]
 node.children.extend(children)
 return nodes
test_datas = [
 {'annualized_returns': 0.01,
 'children': [],
 'create_time': 1562038393,
 'desc': 'root',
 'forked_id': '5d1ad079e86117f3883f361e',
 'max_drawdown': 0.01,
 'origin': None,
 'parent': None},
 {'annualized_returns': 0.314,
 'children': [],
 'create_time': 1562060612,
 'desc': 'level1',
 'forked_id': '5d1b2744b264566d3f3f3632',
 'max_drawdown': 0.2,
 'origin': '5d1ad079e86117f3883f361e',
 'parent': '5d1ad079e86117f3883f361e'},
 {'annualized_returns': 0.12,
 'children': [],
 'create_time': 1562060613,
 'desc': 'level11',
 'forked_id': '5d1b2745e86117f3883f3632',
 'max_drawdown': None,
 'origin': '5d1ad079e86117f3883f361e',
 'parent': '5d1b2744b264566d3f3f3632'},
 {'annualized_returns': 0.09,
 'children': [],
 'create_time': 1562060614,
 'desc': 'level12',
 'forked_id': '5d1b2746b264566d3f3f3633',
 'max_drawdown': None,
 'origin': '5d1ad079e86117f3883f361e',
 'parent': '5d1b2744b264566d3f3f3632'},
 {'annualized_returns': None,
 'children': [],
 'create_time': 1562060614,
 'desc': 'level2',
 'forked_id': '5d1b2746e86117f3883f3633',
 'max_drawdown': None,
 'origin': '5d1ad079e86117f3883f361e',
 'parent': '5d1ad079e86117f3883f361e'},
 {'annualized_returns': None,
 'children': [],
 'create_time': 1562060627,
 'desc': 'level21',
 'forked_id': '5d1b2753b264566d3f3f3635',
 'max_drawdown': None,
 'origin': '5d1ad079e86117f3883f361e',
 'parent': '5d1b2746e86117f3883f3633'},
 {'annualized_returns': None,
 'children': [],
 'create_time': 1562060628,
 'desc': 'level211',
 'forked_id': '5d1b2754b264566d3f3f3637',
 'max_drawdown': None,
 'origin': '5d1ad079e86117f3883f361e',
 'parent': '5d1b2753b264566d3f3f3635'},
 {'annualized_returns': None,
 'children': [],
 'create_time': 1562060640,
 'desc': 'level212',
 'forked_id': '5d1b2760e86117f3883f3634',
 'max_drawdown': None,
 'origin': '5d1ad079e86117f3883f361e',
 'parent': '5d1b2753b264566d3f3f3635'},
]
if __name__ == "__main__":
 nodes = process_datas(test_datas)
 info = nodes[0].get_nodes(nodes)
 print(json.dumps(info, indent=4))