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全面的 Python 数据结构备忘单

全面的 python 数据结构备忘单

全面的 python 数据结构备忘单

目录

  1. 列表
  2. 元组
  3. 套装
  4. 词典
  5. 弦乐
  6. 数组
  7. 堆栈
  8. 排队
  9. 链接列表
  10. 图表
  11. 高级数据结构

列表

列表是有序的、可变的序列。

创建

empty_list = []
list_with_items = [1, 2, 3]
list_from_iterable = list("abc")
list_comprehension = [x for x in range(10) if x % 2 == 0]

常用操作

# <a style="color:#f60; text-decoration:underline;" href="https://www.codesou.cn/" target="_blank">access</a>ing elements
first_item = my_list[0]
last_item = my_list[-1]

# slicing
subset = my_list[1:4]  # elements 1 to 3
reversed_list = my_list[::-1]

# adding elements
my_list.append(4)  # add to end
my_list.insert(0, 0)  # insert at specific index
my_list.extend([5, 6, 7])  # add multiple elements

# removing elements
removed_item = my_list.pop()  # remove and return last item
my_list.remove(3)  # remove first occurrence of 3
del my_list[0]  # remove item at index 0

# other operations
length = len(my_list)
index = my_list.index(4)  # find index of first occurrence of 4
count = my_list.count(2)  # count occurrences of 2
my_list.sort()  # sort in place
sorted_list = sorted(my_list)  # return new sorted list
my_list.reverse()  # reverse in place

先进技术

# list as stack
stack = [1, 2, 3]
stack.append(4)  # push
top_item = stack.pop()  # pop

# list as queue (not efficient, use collections.deque instead)
queue = [1, 2, 3]
queue.append(4)  # enqueue
first_item = queue.pop(0)  # dequeue

# nested lists
matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
flattened = [item for sublist in matrix for item in sublist]

# list multiplication
repeated_list = [0] * 5  # [0, 0, 0, 0, 0]

# list unpacking
a, *b, c = [1, 2, 3, 4, 5]  # a=1, b=[2, 3, 4], c=5

元组

元组是有序的、不可变的序列。

创建

empty_tuple = ()
single_item_tuple = (1,)  # note the comma
tuple_with_items = (1, 2, 3)
tuple_from_iterable = tuple("abc")

常用操作

# accessing elements (similar to lists)
first_item = my_tuple[0]
last_item = my_tuple[-1]

# slicing (similar to lists)
subset = my_tuple[1:4]

# other operations
length = len(my_tuple)
index = my_tuple.index(2)
count = my_tuple.count(3)

# tuple unpacking
a, b, c = (1, 2, 3)

先进技术

# named tuples
from collections import namedtuple
point = namedtuple('point', ['x', 'y'])
p = point(11, y=22)
print(p.x, p.y)

# tuple as dictionary keys (immutable, so allowed)
dict_with_tuple_keys = {(1, 2): 'value'}


集合是独特元素的无序集合。

创建

empty_set = set()
set_with_items = {1, 2, 3}
set_from_iterable = set([1, 2, 2, 3, 3])  # {1, 2, 3}
set_comprehension = {x for x in range(10) if x % 2 == 0}

常用操作

# adding elements
my_set.add(4)
my_set.update([5, 6, 7])

# removing elements
my_set.remove(3)  # raises keyerror if not found
my_set.discard(3)  # no error if not found
popped_item = my_set.pop()  # remove and return an arbitrary element

# other operations
length = len(my_set)
is_member = 2 in my_set

# set operations
union = set1 | set2
intersection = set1 & set2
difference = set1 - set2
symmetric_difference = set1 ^ set2

先进技术

# frozen sets (immutable)
frozen = frozenset([1, 2, 3])

# set comparisons
is_subset = set1 = set2
is_disjoint = set1.isdisjoint(set2)

# set of sets (requires frozenset)
set_of_sets = {frozenset([1, 2]), frozenset([3, 4])}

词典

字典是键值对的可变映射。

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创建

empty_dict = {}
dict_with_items = {'a': 1, 'b': 2, 'c': 3}
dict_from_tuples = dict([('a', 1), ('b', 2), ('c', 3)])
dict_comprehension = {x: x**2 for x in range(5)}

常用操作

# accessing elements
value = my_dict['key']
value = my_dict.get('key', default_value)

# adding/updating elements
my_dict['new_key'] = value
my_dict.update({'key1': value1, 'key2': value2})

# removing elements
del my_dict['key']
popped_value = my_dict.pop('key', default_value)
last_item = my_dict.popitem()  # remove and return an arbitrary key-value pair

# other operations
keys = my_dict.keys()
values = my_dict.values()
items = my_dict.items()
length = len(my_dict)
is_key_present = 'key' in my_dict

先进技术

# dictionary unpacking
merged_dict = {**dict1, **dict2}

# default dictionaries
from collections import defaultdict
dd = defaultdict(list)
dd['key'].append(1)  # no keyerror

# ordered dictionaries (python 3.7+ dictionaries are ordered by default)
from collections import ordereddict
od = ordereddict([('a', 1), ('b', 2), ('c', 3)])

# counter
from collections import counter
c = counter(['a', 'b', 'c', 'a', 'b', 'b'])
print(c.most_common(2))  # [('b', 3), ('a', 2)]

弦乐

字符串是不可变的 unicode 字符序列。

创建

single_quotes = 'hello'
double_quotes = "world"
triple_quotes = '''multiline
string'''
raw_string = r'c:usersname'
f_string = f"the answer is {40 + 2}"

常用操作

# accessing characters
first_char = my_string[0]
last_char = my_string[-1]

# slicing (similar to lists)
substring = my_string[1:4]

# string methods
upper_case = my_string.upper()
lower_case = my_string.lower()
stripped = my_string.strip()
split_list = my_string.split(',')
joined = ', '.join(['a', 'b', 'c'])

# other operations
length = len(my_string)
is_substring = 'sub' in my_string
char_count = my_string.count('a')

先进技术

# string formatting
formatted = "{} {}".format("hello", "world")
formatted = "%s %s" % ("hello", "world")

# regular expressions
import re
pattern = r'd+'
matches = re.findall(pattern, my_string)

# unicode handling
unicode_string = u'u0061u0062u0063'

数组

数组是紧凑的数值序列(来自数组模块)。

创建和使用

from array import array
int_array = array('i', [1, 2, 3, 4, 5])
float_array = array('f', (1.0, 1.5, 2.0, 2.5))

# operations (similar to lists)
int_array.append(6)
int_array.extend([7, 8, 9])
popped_value = int_array.pop()

堆栈

堆栈可以使用lists或collections.deque来实现。

实施和使用

# using list
stack = []
stack.append(1)  # push
stack.append(2)
top_item = stack.pop()  # pop

# using deque (more efficient)
from collections import deque
stack = deque()
stack.append(1)  # push
stack.append(2)
top_item = stack.pop()  # pop

队列

队列可以使用collections.deque或queue.queue来实现。

实施和使用

# using deque
from collections import deque
queue = deque()
queue.append(1)  # enqueue
queue.append(2)
first_item = queue.popleft()  # dequeue

# using queue (thread-safe)
from queue import queue
q = queue()
q.put(1)  # enqueue
q.put(2)
first_item = q.get()  # dequeue

链表

python没有内置链表,但可以实现。

实施简单

class node:
    def __init__(self, data):
        self.data = data
        self.next = none

class linkedlist:
    def __init__(self):
        self.head = none

    def append(self, data):
        if not self.head:
            self.head = node(data)
            return
        current = self.head
        while current.next:
            current = current.next
        current.next = node(data)

树木

树可以使用自定义类来实现。

简单的二叉树实现

class treenode:
    def __init__(self, value):
        self.value = value
        self.left = none
        self.right = none

class binarytree:
    def __init__(self, root):
        self.root = treenode(root)

    def insert(self, value):
        self._insert_recursive(self.root, value)

    def _insert_recursive(self, node, value):
        if value 



<h2>
  
  
  堆
</h2>

<p>堆可以使用 heapq 模块来实现。</p>

<h3>
  
  
  用法
</h3>



<pre class="brush:php;toolbar:false">import heapq

# create a heap
heap = []
heapq.heappush(heap, 3)
heapq.heappush(heap, 1)
heapq.heappush(heap, 4)

# pop smallest item
smallest = heapq.heappop(heap)

# create a heap from a list
my_list = [3, 1, 4, 1, 5, 9]
heapq.heapify(my_list)

图表

图可以使用字典来实现。

实施简单

class graph:
    def __init__(self):
        self.graph = {}

    def add_edge(self, u, v):
        if u not in self.graph:
            self.graph[u] = []
        self.graph[u].append(v)

    def bfs(self, start):
        visited = set()
        queue = [start]
        visited.add(start)
        while queue:
            vertex = queue.pop(0)
            print(vertex, end=' ')
            for neighbor in self.graph.get(vertex, []):
                if neighbor not in visited:
                    visited.add(neighbor)
                    queue.append(neighbor)

高级数据结构

特里树

class trienode:
    def __init__(self):
        self.children = {}
        self.is_end = false

class trie:
    def __init__(self):
        self.root = trienode()

    def insert(self, word):
        node = self.root
        for char in word:
            if char not in node.children:
                node.children[char] = trienode()
            node = node.children[char]
        node.is_end = true

    def search(self, word):
        node = self.root
        for char in word:
            if char not in node.children:
                return false
            node = node.children[char]
        return node.is_end

不相交集(并查集)

class DisjointSet:
    def __init__(self, vertices):
        self.parent = {v: v for v in vertices}
        self.rank = {v: 0 for v in vertices}

    def find(self, item):
        if self.parent[item] != item:
            self.parent[item] = self.find(self.parent[item])
        return self.parent[item]

    def union(self, x, y):
        xroot = self.find(x)
        yroot = self.find(y)
        if self.rank[xroot]  self.rank[yroot]:
            self.parent[yroot] = xroot
        else:
            self.parent[yroot] = xroot
            self.rank[xroot] += 1

这份全面的备忘单涵盖了广泛的 python 数据结构,从基本的内置类型到更高级的自定义实现。每个部分都包含创建方法、常用操作以及适用的高级技巧。
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