我推荐任何树(我是作者)。

例子:

from anytree import Node, RenderTree

udo = Node("Udo")
marc = Node("Marc", parent=udo)
lian = Node("Lian", parent=marc)
dan = Node("Dan", parent=udo)
jet = Node("Jet", parent=dan)
jan = Node("Jan", parent=dan)
joe = Node("Joe", parent=dan)

print(udo)
Node('/Udo')
print(joe)
Node('/Udo/Dan/Joe')

for pre, fill, node in RenderTree(udo):
    print("%s%s" % (pre, node.name))
Udo
├── Marc
│   └── Lian
└── Dan
    ├── Jet
    ├── Jan
    └── Joe

print(dan.children)
(Node('/Udo/Dan/Jet'), Node('/Udo/Dan/Jan'), Node('/Udo/Dan/Joe'))

anytree也有一个强大的API:

简单的树创建 简单树修改 预序树迭代 后序树迭代 解析相对节点路径和绝对节点路径 从一个节点移动到另一个节点。 树渲染(参见上面的例子) 节点连接/分离连接

我使用嵌套字典实现了树。这很容易做到，而且对我来说，它在相当大的数据集上很有效。我在下面发布了一个示例，你可以在谷歌代码中看到更多

  def addBallotToTree(self, tree, ballotIndex, ballot=""):
    """Add one ballot to the tree.

    The root of the tree is a dictionary that has as keys the indicies of all 
    continuing and winning candidates.  For each candidate, the value is also
    a dictionary, and the keys of that dictionary include "n" and "bi".
    tree[c]["n"] is the number of ballots that rank candidate c first.
    tree[c]["bi"] is a list of ballot indices where the ballots rank c first.

    If candidate c is a winning candidate, then that portion of the tree is
    expanded to indicate the breakdown of the subsequently ranked candidates.
    In this situation, additional keys are added to the tree[c] dictionary
    corresponding to subsequently ranked candidates.
    tree[c]["n"] is the number of ballots that rank candidate c first.
    tree[c]["bi"] is a list of ballot indices where the ballots rank c first.
    tree[c][d]["n"] is the number of ballots that rank c first and d second.
    tree[c][d]["bi"] is a list of the corresponding ballot indices.

    Where the second ranked candidates is also a winner, then the tree is 
    expanded to the next level.  

    Losing candidates are ignored and treated as if they do not appear on the 
    ballots.  For example, tree[c][d]["n"] is the total number of ballots
    where candidate c is the first non-losing candidate, c is a winner, and
    d is the next non-losing candidate.  This will include the following
    ballots, where x represents a losing candidate:
    [c d]
    [x c d]
    [c x d]
    [x c x x d]

    During the count, the tree is dynamically updated as candidates change
    their status.  The parameter "tree" to this method may be the root of the
    tree or may be a sub-tree.
    """

    if ballot == "":
      # Add the complete ballot to the tree
      weight, ballot = self.b.getWeightedBallot(ballotIndex)
    else:
      # When ballot is not "", we are adding a truncated ballot to the tree,
      # because a higher-ranked candidate is a winner.
      weight = self.b.getWeight(ballotIndex)

    # Get the top choice among candidates still in the running
    # Note that we can't use Ballots.getTopChoiceFromWeightedBallot since
    # we are looking for the top choice over a truncated ballot.
    for c in ballot:
      if c in self.continuing | self.winners:
        break # c is the top choice so stop
    else:
      c = None # no candidates left on this ballot

    if c is None:
      # This will happen if the ballot contains only winning and losing
      # candidates.  The ballot index will not need to be transferred
      # again so it can be thrown away.
      return

    # Create space if necessary.
    if not tree.has_key(c):
      tree[c] = {}
      tree[c]["n"] = 0
      tree[c]["bi"] = []

    tree[c]["n"] += weight

    if c in self.winners:
      # Because candidate is a winner, a portion of the ballot goes to
      # the next candidate.  Pass on a truncated ballot so that the same
      # candidate doesn't get counted twice.
      i = ballot.index(c)
      ballot2 = ballot[i+1:]
      self.addBallotToTree(tree[c], ballotIndex, ballot2)
    else:
      # Candidate is in continuing so we stop here.
      tree[c]["bi"].append(ballotIndex)

泛型树是一个具有零个或多个子节点的节点，每个子节点都是一个合适的(树)节点。它与二叉树不同，它们是不同的数据结构，尽管它们都有一些相同的术语。

Python中没有任何用于泛型树的内置数据结构，但很容易通过类实现。

class Tree(object):
    "Generic tree node."
    def __init__(self, name='root', children=None):
        self.name = name
        self.children = []
        if children is not None:
            for child in children:
                self.add_child(child)
    def __repr__(self):
        return self.name
    def add_child(self, node):
        assert isinstance(node, Tree)
        self.children.append(node)
#    *
#   /|\
#  1 2 +
#     / \
#    3   4
t = Tree('*', [Tree('1'),
               Tree('2'),
               Tree('+', [Tree('3'),
                          Tree('4')])])

如果您想要创建树数据结构，那么首先必须创建treeElement对象。如果您创建了treeElement对象，那么您可以决定树的行为。

下面是TreeElement类:

class TreeElement (object):

def __init__(self):
    self.elementName = None
    self.element = []
    self.previous = None
    self.elementScore = None
    self.elementParent = None
    self.elementPath = []
    self.treeLevel = 0

def goto(self, data):
    for child in range(0, len(self.element)):
        if (self.element[child].elementName == data):
            return self.element[child]

def add(self):

    single_element = TreeElement()
    single_element.elementName = self.elementName
    single_element.previous = self.elementParent
    single_element.elementScore = self.elementScore
    single_element.elementPath = self.elementPath
    single_element.treeLevel = self.treeLevel

    self.element.append(single_element)

    return single_element

现在，我们必须使用这个元素来创建树，在这个例子中我使用的是A*树。

class AStarAgent(Agent):
# Initialization Function: Called one time when the game starts
def registerInitialState(self, state):
    return;

# GetAction Function: Called with every frame
def getAction(self, state):

    # Sorting function for the queue
    def sortByHeuristic(each_element):

        if each_element.elementScore:
            individual_score = each_element.elementScore[0][0] + each_element.treeLevel
        else:
            individual_score = admissibleHeuristic(each_element)

        return individual_score

    # check the game is over or not
    if state.isWin():
        print('Job is done')
        return Directions.STOP
    elif state.isLose():
        print('you lost')
        return Directions.STOP

    # Create empty list for the next states
    astar_queue = []
    astar_leaf_queue = []
    astar_tree_level = 0
    parent_tree_level = 0

    # Create Tree from the give node element
    astar_tree = TreeElement()
    astar_tree.elementName = state
    astar_tree.treeLevel = astar_tree_level
    astar_tree = astar_tree.add()

    # Add first element into the queue
    astar_queue.append(astar_tree)

    # Traverse all the elements of the queue
    while astar_queue:

        # Sort the element from the queue
        if len(astar_queue) > 1:
            astar_queue.sort(key=lambda x: sortByHeuristic(x))

        # Get the first node from the queue
        astar_child_object = astar_queue.pop(0)
        astar_child_state = astar_child_object.elementName

        # get all legal actions for the current node
        current_actions = astar_child_state.getLegalPacmanActions()

        if current_actions:

            # get all the successor state for these actions
            for action in current_actions:

                # Get the successor of the current node
                next_state = astar_child_state.generatePacmanSuccessor(action)

                if next_state:

                    # evaluate the successor states using scoreEvaluation heuristic
                    element_scored = [(admissibleHeuristic(next_state), action)]

                    # Increase the level for the child
                    parent_tree_level = astar_tree.goto(astar_child_state)
                    if parent_tree_level:
                        astar_tree_level = parent_tree_level.treeLevel + 1
                    else:
                        astar_tree_level += 1

                    # create tree for the finding the data
                    astar_tree.elementName = next_state
                    astar_tree.elementParent = astar_child_state
                    astar_tree.elementScore = element_scored
                    astar_tree.elementPath.append(astar_child_state)
                    astar_tree.treeLevel = astar_tree_level
                    astar_object = astar_tree.add()

                    # If the state exists then add that to the queue
                    astar_queue.append(astar_object)

                else:
                    # Update the value leaf into the queue
                    astar_leaf_state = astar_tree.goto(astar_child_state)
                    astar_leaf_queue.append(astar_leaf_state)

你可以从对象中添加/删除任何元素，但要使结构为完整的。

您可以使用Python中的dataclasses模块创建Tree数据结构。

iter方法可用于使树可迭代，允许您通过改变yield语句的顺序来遍历树。

contains方法可用于检查树中是否存在特定值。

from dataclasses import dataclass

#               A
#              / \
#             B   C
#            / \   \
#           D   E   F
#          / \
#         G   H

@dataclass
class Node:
    data: str
    left: Node = None
    right: Node = None
    
    def __iter__(self):
        if self.left:
            yield from self.left
        
        yield self

        if self.right:
            yield from self.right

    def __contains__(self, other):
        for node in self:
            if node.data == other:
                return True
        return False
    

t = Node(
    'A', 
    Node(
        'B', 
        Node(
            'D', 
            Node('G'),
            Node('H'),
        ),
        Node('E'),
    ),  
    Node(
        'C', 
        right=Node('F'),
    ),
)
assert ('A' in t) is True
assert ('I' in t) is not True
for node in t:
    print(node.data, ' -> ', end='')
# G  -> D  -> H  -> B  -> E  -> A  -> C  -> F  -> 

嗨，你可以试试itertree(我是作者)。

该包与任何树包的方向相同，但关注点略有不同。在巨大的树(>100000个项目)上的性能要好得多，它处理迭代器具有有效的过滤机制。

>>>from itertree import *
>>>root=iTree('root')

>>># add some children:
>>>root.append(iTree('Africa',data={'surface':30200000,'inhabitants':1257000000}))
>>>root.append(iTree('Asia', data={'surface': 44600000, 'inhabitants': 4000000000}))
>>>root.append(iTree('America', data={'surface': 42549000, 'inhabitants': 1009000000}))
>>>root.append(iTree('Australia&Oceania', data={'surface': 8600000, 'inhabitants': 36000000}))
>>>root.append(iTree('Europe', data={'surface': 10523000 , 'inhabitants': 746000000}))
>>># you might use __iadd__ operator for adding too:
>>>root+=iTree('Antarktika', data={'surface': 14000000, 'inhabitants': 1100})

>>># for building next level we select per index:
>>>root[0]+=iTree('Ghana',data={'surface':238537,'inhabitants':30950000})
>>>root[0]+=iTree('Niger', data={'surface': 1267000, 'inhabitants': 23300000})
>>>root[1]+=iTree('China', data={'surface': 9596961, 'inhabitants': 1411780000})
>>>root[1]+=iTree('India', data={'surface': 3287263, 'inhabitants': 1380004000})
>>>root[2]+=iTree('Canada', data={'type': 'country', 'surface': 9984670, 'inhabitants': 38008005})    
>>>root[2]+=iTree('Mexico', data={'surface': 1972550, 'inhabitants': 127600000 })
>>># extend multiple items:
>>>root[3].extend([iTree('Australia', data={'surface': 7688287, 'inhabitants': 25700000 }), iTree('New Zealand', data={'surface': 269652, 'inhabitants': 4900000 })])
>>>root[4]+=iTree('France', data={'surface': 632733, 'inhabitants': 67400000 }))
>>># select parent per TagIdx - remember in itertree you might put items with same tag multiple times:
>>>root[TagIdx('Europe'0)]+=iTree('Finland', data={'surface': 338465, 'inhabitants': 5536146 })

创建的树可以被渲染:

>>>root.render()
iTree('root')
     └──iTree('Africa', data=iTData({'surface': 30200000, 'inhabitants': 1257000000}))
         └──iTree('Ghana', data=iTData({'surface': 238537, 'inhabitants': 30950000}))
         └──iTree('Niger', data=iTData({'surface': 1267000, 'inhabitants': 23300000}))
     └──iTree('Asia', data=iTData({'surface': 44600000, 'inhabitants': 4000000000}))
         └──iTree('China', data=iTData({'surface': 9596961,  'inhabitants': 1411780000}))
         └──iTree('India', data=iTData({'surface': 3287263, 'inhabitants': 1380004000}))
     └──iTree('America', data=iTData({'surface': 42549000, 'inhabitants': 1009000000}))
         └──iTree('Canada', data=iTData({'surface': 9984670, 'inhabitants': 38008005}))
         └──iTree('Mexico', data=iTData({'surface': 1972550, 'inhabitants': 127600000}))
     └──iTree('Australia&Oceania', data=iTData({'surface': 8600000, 'inhabitants': 36000000}))
         └──iTree('Australia', data=iTData({'surface': 7688287, 'inhabitants': 25700000}))
         └──iTree('New Zealand', data=iTData({'surface': 269652, 'inhabitants': 4900000}))
     └──iTree('Europe', data=iTData({'surface': 10523000, 'inhabitants': 746000000}))
         └──iTree('France', data=iTData({'surface': 632733, 'inhabitants': 67400000}))
         └──iTree('Finland', data=iTData({'surface': 338465, 'inhabitants': 5536146}))
     └──iTree('Antarktika', data=iTData({'surface': 14000000, 'inhabitants': 1100}))

过滤可以这样做:

>>>item_filter = Filter.iTFilterData(data_key='inhabitants', data_value=iTInterval(0, 20000000))
>>>iterator=root.iter_all(item_filter=item_filter)
>>>for i in iterator:
>>>    print(i)
iTree("'New Zealand'", data=iTData({'surface': 269652, 'inhabitants': 4900000}), subtree=[])
iTree("'Finland'", data=iTData({'surface': 338465, 'inhabitants': 5536146}), subtree=[])
iTree("'Antarktika'", data=iTData({'surface': 14000000, 'inhabitants': 1100}), subtree=[])