你可以试试:

from collections import defaultdict
def tree(): return defaultdict(tree)
users = tree()
users['harold']['username'] = 'hrldcpr'
users['handler']['username'] = 'matthandlersux'

建议在这里:https://gist.github.com/2012250

并没有内置树，但是可以通过从List继承Node类型并编写遍历方法来轻松地构造一个树。如果你这样做，我发现平分法很有用。

您还可以浏览PyPi上的许多实现。

如果我没记错的话，Python标准库不包含树数据结构，原因和。net基类库不包含树数据结构是一样的:内存的局部性降低了，导致缓存丢失更多。在现代处理器上，将大量内存放入缓存通常会更快，而“指针丰富”的数据结构会抵消这种好处。

如果您已经在使用networkx库，那么您可以使用它实现一个树。

NetworkX是一个用于创建、操作和研究的Python包 复杂网络的结构、动力学和功能。

因为“树”是(通常根)连接无环图的另一个术语，这些在NetworkX中被称为“树状图”。

你可能想要实现一个平面树(又名有序树)，其中每个兄弟姐妹都有一个唯一的秩，这通常通过标记节点来完成。

然而，图语言看起来不同于树语言，“扎根”树的方法通常是使用有向图，因此，虽然有一些非常酷的功能和相应的可视化可用，但如果你还没有使用networkx，它可能不是一个理想的选择。

一个构建树的例子:

import networkx as nx
G = nx.Graph()
G.add_edge('A', 'B')
G.add_edge('B', 'C')
G.add_edge('B', 'D')
G.add_edge('A', 'E')
G.add_edge('E', 'F')

该库允许每个节点是任何可哈希对象，并且不限制每个节点拥有的子节点的数量。

Python不像Java那样具有相当广泛的“内置”数据结构。但是，因为Python是动态的，所以很容易创建通用树。例如，二叉树可能是:

class Tree:
    def __init__(self):
        self.left = None
        self.right = None
        self.data = None

你可以这样使用它:

root = Tree()
root.data = "root"
root.left = Tree()
root.left.data = "left"
root.right = Tree()
root.right.data = "right"

如果每个节点需要任意数量的子节点，则使用子节点列表:

class Tree:
    def __init__(self, data):
        self.children = []
        self.data = data

left = Tree("left")
middle = Tree("middle")
right = Tree("right")
root = Tree("root")
root.children = [left, middle, right]

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)

我已经在我的网站https://web.archive.org/web/20120723175438/www.quesucede.com/page/show/id/python_3_tree_implementation上发布了一个Python 3树的实现

代码如下:

import uuid

def sanitize_id(id):
    return id.strip().replace(" ", "")

(_ADD, _DELETE, _INSERT) = range(3)
(_ROOT, _DEPTH, _WIDTH) = range(3)

class Node:

    def __init__(self, name, identifier=None, expanded=True):
        self.__identifier = (str(uuid.uuid1()) if identifier is None else
                sanitize_id(str(identifier)))
        self.name = name
        self.expanded = expanded
        self.__bpointer = None
        self.__fpointer = []

    @property
    def identifier(self):
        return self.__identifier

    @property
    def bpointer(self):
        return self.__bpointer

    @bpointer.setter
    def bpointer(self, value):
        if value is not None:
            self.__bpointer = sanitize_id(value)

    @property
    def fpointer(self):
        return self.__fpointer

    def update_fpointer(self, identifier, mode=_ADD):
        if mode is _ADD:
            self.__fpointer.append(sanitize_id(identifier))
        elif mode is _DELETE:
            self.__fpointer.remove(sanitize_id(identifier))
        elif mode is _INSERT:
            self.__fpointer = [sanitize_id(identifier)]

class Tree:

    def __init__(self):
        self.nodes = []

    def get_index(self, position):
        for index, node in enumerate(self.nodes):
            if node.identifier == position:
                break
        return index

    def create_node(self, name, identifier=None, parent=None):

        node = Node(name, identifier)
        self.nodes.append(node)
        self.__update_fpointer(parent, node.identifier, _ADD)
        node.bpointer = parent
        return node

    def show(self, position, level=_ROOT):
        queue = self[position].fpointer
        if level == _ROOT:
            print("{0} [{1}]".format(self[position].name,
                                     self[position].identifier))
        else:
            print("\t"*level, "{0} [{1}]".format(self[position].name,
                                                 self[position].identifier))
        if self[position].expanded:
            level += 1
            for element in queue:
                self.show(element, level)  # recursive call

    def expand_tree(self, position, mode=_DEPTH):
        # Python generator. Loosly based on an algorithm from 'Essential LISP' by
        # John R. Anderson, Albert T. Corbett, and Brian J. Reiser, page 239-241
        yield position
        queue = self[position].fpointer
        while queue:
            yield queue[0]
            expansion = self[queue[0]].fpointer
            if mode is _DEPTH:
                queue = expansion + queue[1:]  # depth-first
            elif mode is _WIDTH:
                queue = queue[1:] + expansion  # width-first

    def is_branch(self, position):
        return self[position].fpointer

    def __update_fpointer(self, position, identifier, mode):
        if position is None:
            return
        else:
            self[position].update_fpointer(identifier, mode)

    def __update_bpointer(self, position, identifier):
        self[position].bpointer = identifier

    def __getitem__(self, key):
        return self.nodes[self.get_index(key)]

    def __setitem__(self, key, item):
        self.nodes[self.get_index(key)] = item

    def __len__(self):
        return len(self.nodes)

    def __contains__(self, identifier):
        return [node.identifier for node in self.nodes
                if node.identifier is identifier]

if __name__ == "__main__":

    tree = Tree()
    tree.create_node("Harry", "harry")  # root node
    tree.create_node("Jane", "jane", parent = "harry")
    tree.create_node("Bill", "bill", parent = "harry")
    tree.create_node("Joe", "joe", parent = "jane")
    tree.create_node("Diane", "diane", parent = "jane")
    tree.create_node("George", "george", parent = "diane")
    tree.create_node("Mary", "mary", parent = "diane")
    tree.create_node("Jill", "jill", parent = "george")
    tree.create_node("Carol", "carol", parent = "jill")
    tree.create_node("Grace", "grace", parent = "bill")
    tree.create_node("Mark", "mark", parent = "jane")

    print("="*80)
    tree.show("harry")
    print("="*80)
    for node in tree.expand_tree("harry", mode=_WIDTH):
        print(node)
    print("="*80)