如何在Python中实现通用树?这有内置的数据结构吗?
当前回答
class Node:
"""
Class Node
"""
def __init__(self, value):
self.left = None
self.data = value
self.right = None
class Tree:
"""
Class tree will provide a tree as well as utility functions.
"""
def createNode(self, data):
"""
Utility function to create a node.
"""
return Node(data)
def insert(self, node , data):
"""
Insert function will insert a node into tree.
Duplicate keys are not allowed.
"""
#if tree is empty , return a root node
if node is None:
return self.createNode(data)
# if data is smaller than parent , insert it into left side
if data < node.data:
node.left = self.insert(node.left, data)
elif data > node.data:
node.right = self.insert(node.right, data)
return node
def search(self, node, data):
"""
Search function will search a node into tree.
"""
# if root is None or root is the search data.
if node is None or node.data == data:
return node
if node.data < data:
return self.search(node.right, data)
else:
return self.search(node.left, data)
def deleteNode(self,node,data):
"""
Delete function will delete a node into tree.
Not complete , may need some more scenarion that we can handle
Now it is handling only leaf.
"""
# Check if tree is empty.
if node is None:
return None
# searching key into BST.
if data < node.data:
node.left = self.deleteNode(node.left, data)
elif data > node.data:
node.right = self.deleteNode(node.right, data)
else: # reach to the node that need to delete from BST.
if node.left is None and node.right is None:
del node
if node.left == None:
temp = node.right
del node
return temp
elif node.right == None:
temp = node.left
del node
return temp
return node
def traverseInorder(self, root):
"""
traverse function will print all the node in the tree.
"""
if root is not None:
self.traverseInorder(root.left)
print(root.data)
self.traverseInorder(root.right)
def traversePreorder(self, root):
"""
traverse function will print all the node in the tree.
"""
if root is not None:
print(root.data)
self.traversePreorder(root.left)
self.traversePreorder(root.right)
def traversePostorder(self, root):
"""
traverse function will print all the node in the tree.
"""
if root is not None:
self.traversePostorder(root.left)
self.traversePostorder(root.right)
print(root.data)
def main():
root = None
tree = Tree()
root = tree.insert(root, 10)
print(root)
tree.insert(root, 20)
tree.insert(root, 30)
tree.insert(root, 40)
tree.insert(root, 70)
tree.insert(root, 60)
tree.insert(root, 80)
print("Traverse Inorder")
tree.traverseInorder(root)
print("Traverse Preorder")
tree.traversePreorder(root)
print("Traverse Postorder")
tree.traversePostorder(root)
if __name__ == "__main__":
main()
其他回答
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=[])
class Tree(dict):
"""A tree implementation using python's autovivification feature."""
def __missing__(self, key):
value = self[key] = type(self)()
return value
#cast a (nested) dict to a (nested) Tree class
def __init__(self, data={}):
for k, data in data.items():
if isinstance(data, dict):
self[k] = type(self)(data)
else:
self[k] = data
作为一个字典,但提供尽可能多的嵌套字典。 试试下面的方法:
your_tree = Tree()
your_tree['a']['1']['x'] = '@'
your_tree['a']['1']['y'] = '#'
your_tree['a']['2']['x'] = '$'
your_tree['a']['3'] = '%'
your_tree['b'] = '*'
将传递一个嵌套的字典…就像树一样。
{'a': {'1': {'x': '@', 'y': '#'}, '2': {'x': '$'}, '3': '%'}, 'b': '*'}
... 如果你已经有字典了,它会把每一层都投射到一棵树上:
d = {'foo': {'amy': {'what': 'runs'} } }
tree = Tree(d)
print(d['foo']['amy']['what']) # returns 'runs'
d['foo']['amy']['when'] = 'now' # add new branch
这样,你就可以随心所欲地编辑/添加/删除每个词典级别。 遍历等所有dict方法仍然适用。
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。
bigtree是一个Python树实现,集成了Python列表、字典和pandas DataFrame。它是python式的,易于学习,并可扩展到许多类型的工作流。
bigtree有很多组成部分,即
从列表、字典和熊猫数据框架构建树 遍历树 修改树(移位/复制节点) 搜索树 辅助方法(克隆树,修剪树,获取两个树之间的差异) 导出树(打印到控制台,导出树到字典,熊猫数据框架,图像等) 其他树结构:二叉树! 其他图结构:有向无环图(dag)!
我还能说什么呢……是的,这也是有据可查的。
一些例子:
from bigtree import list_to_tree, tree_to_dict, tree_to_dot
# Create tree from list, print tree
root = list_to_tree(["a/b/d", "a/c"])
print_tree(root)
# a
# ├── b
# │ └── d
# └── c
# Query tree
root.children
# (Node(/a/b, ), Node(/a/c, ))
# Export tree to dictionary / image
tree_to_dict(root)
# {
# '/a': {'name': 'a'},
# '/a/b': {'name': 'b'},
# '/a/b/d': {'name': 'd'},
# '/a/c': {'name': 'c'}
# }
graph = tree_to_dot(root, node_colour="gold")
graph.write_png("tree.png")
来源/免责声明:我是bigtree的创造者;)
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 ->
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