Comprehensive Python Data Structures Cheat sheet
Comprehensive Python Data Structures Cheat sheet
Table of Contents
- Lists
- Tuples
- Sets
- Dictionaries
- Strings
- Arrays
- Stacks
- Queues
- Linked Lists
- Trees
- Heaps
- Graphs
- Advanced Data Structures
Lists
Lists are ordered, mutable sequences.
Creation
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]
Common Operations
# 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
Advanced Techniques
# 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
Tuples
Tuples are ordered, immutable sequences.
Creation
empty_tuple = () single_item_tuple = (1,) # Note the comma tuple_with_items = (1, 2, 3) tuple_from_iterable = tuple("abc")
Common Operations
# 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)
Advanced Techniques
# 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 are unordered collections of unique elements.
Creation
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}
Common Operations
# 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
Advanced Techniques
# 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])}
Dictionaries
Dictionaries are mutable mappings of key-value pairs.
Creation
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)}
Common Operations
# 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
Advanced Techniques
# 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)]
Strings
Strings are immutable sequences of Unicode characters.
Creation
single_quotes = 'Hello' double_quotes = "World" triple_quotes = '''Multiline string''' raw_string = r'C:\Users\name' f_string = f"The answer is {40 + 2}"
Common Operations
# 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')
Advanced Techniques
# 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 are compact sequences of numeric values (from the array module).
Creation and Usage
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()
Stacks
Stacks can be implemented using lists or collections.deque.
Implementation and Usage
# 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
Queues
Queues can be implemented using collections.deque or queue.Queue.
Implementation and Usage
# 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
Linked Lists
Python doesn't have a built-in linked list, but it can be implemented.
Simple Implementation
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)
Trees
Trees can be implemented using custom classes.
Simple Binary Tree Implementation
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)
Heaps
Heaps can be implemented using the heapq module.
Usage
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)
Graphs
Graphs can be implemented using dictionaries.
Simple Implementation
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)
Advanced Data Structures
Trie
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
Disjoint Set (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
This comprehensive cheatsheet covers a wide range of Python data structures, from the basic built-in types to more advanced custom implementations. Each section includes creation methods, common operations, and advanced techniques where applicable.
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