Umfassender Spickzettel für Python-Datenstrukturen

Umfassender Spickzettel für Python-Datenstrukturen

Inhaltsverzeichnis

1. Listen
2. Tupel
3. Sets
4. Wörterbücher
5. Strings
6. Arrays
7. Stapel
8. Warteschlangen
9. Verknüpfte Listen
10. Bäume
11. Haufenweise
12. Grafiken
13. Erweiterte Datenstrukturen

Listen

Listen sind geordnete, veränderliche Sequenzen.

Schaffung

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]

Gemeinsame Operationen

# Accessing 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

Fortgeschrittene Techniken

# 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

Tupel

Tupel sind geordnete, unveränderliche Sequenzen.

Schaffung

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

Gemeinsame Operationen

# 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)

Fortgeschrittene Techniken

# 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'}

Sets

Sets sind ungeordnete Sammlungen einzigartiger Elemente.

Schaffung

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}

Gemeinsame Operationen

# 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

Fortgeschrittene Techniken

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

# Set comparisons
is_subset = set1 <= set2
is_superset = set1 >= set2
is_disjoint = set1.isdisjoint(set2)

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

Wörterbücher

Wörterbücher sind veränderliche Abbildungen von Schlüssel-Wert-Paaren.

Schaffung

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)}

Gemeinsame Operationen

# 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

Fortgeschrittene Techniken

# 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)]

Saiten

Strings sind unveränderliche Folgen von Unicode-Zeichen.

Schaffung

single_quotes = 'Hello'
double_quotes = "World"
triple_quotes = '''Multiline
string'''
raw_string = r'C:\Users\name'
f_string = f"The answer is {40 + 2}"

Gemeinsame Operationen

# 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')

Fortgeschrittene Techniken

# 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'\u0061\u0062\u0063'

Arrays

Arrays sind kompakte Folgen numerischer Werte (aus dem Array-Modul).

Erstellung und Nutzung

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()

Stapel

Stacks können mithilfe von Listen odercollections.deque implementiert werden.

Implementierung und Nutzung

# 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

Warteschlangen

Warteschlangen können mitcollections.deque oder queue.Queue implementiert werden.

Implementierung und Nutzung

# 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

Verknüpfte Listen

Python verfügt nicht über eine integrierte verknüpfte Liste, kann aber implementiert werden.

Einfache Implementierung

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)

Bäume

Bäume können mithilfe benutzerdefinierter Klassen implementiert werden.

Einfache Binärbaum-Implementierung

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 < node.value:
            if node.left is None:
                node.left = TreeNode(value)
            else:
                self._insert_recursive(node.left, value)
        else:
            if node.right is None:
                node.right = TreeNode(value)
            else:
                self._insert_recursive(node.right, value)

Haufenweise

Heaps können mit dem Heapq-Modul implementiert werden.

Verwendung

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)

Grafiken

Grafiken können mithilfe von Wörterbüchern implementiert werden.

Einfache Implementierung

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)

Erweiterte Datenstrukturen

Versuchen Sie es

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

Disjunkte Menge (Union-Find)

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[xroot] = yroot
        elif self.rank[xroot] > self.rank[yroot]:
            self.parent[yroot] = xroot
        else:
            self.parent[yroot] = xroot
            self.rank[xroot] += 1

Dieses umfassende Cheatsheet deckt ein breites Spektrum an Python-Datenstrukturen ab, von den grundlegenden integrierten Typen bis hin zu fortgeschritteneren benutzerdefinierten Implementierungen. Jeder Abschnitt enthält Erstellungsmethoden, allgemeine Vorgänge und gegebenenfalls fortgeschrittene Techniken.
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