# seq is defined as a sequence of numbers result = sum(seq)
# seq is defined as a sequence of numbers result = sort(seq)
list
, array
, tuple
, string
, ...dict
class ListNode(object): """ Simple implementation of a singly linked list node. """ def __init__(self, data, succ=None): self.data = data self.succ = succ # populating the list head = ListNode(4) tail = head for i in range(3, 0, -1): tail.succ = ListNode(i) tail = tail.succ # using the list (simply print its elements) list_iterator = head while list_iterator: print(list_iterator.data) list_iterator = list_iterator.succ
double_linked_list.py
class ListNode(object): """ Simple implementation of a singly linked list node. """ def __init__(self, data, pred=None, succ=None): """ `succ`: the current node's successor `pred`: the current node's predecessor """ self.data = data self.pred = pred self.succ = succ # populating the list head = ListNode(10) tail = head for i in range(9, 0, -1): tail.succ = ListNode(i, tail) tail = tail.succ # iterate over the list list_iterator = head while list_iterator: print(list_iterator.data) list_iterator = list_iterator.succ # iterate from the back list_iterator = tail while list_iterator: print(list_iterator.data) list_iterator = list_iterator.pred
simple_tree.py
class TreeNode(object): """ Simple implementation of a binary tree. """ def __init__(self, data, left=None, right=None): self.data = data self.left = left self.right = right # populating the tree root = TreeNode(10) root.left = TreeNode(5) root.right = TreeNode(30) root.left.left = TreeNode(2) root.left.right = TreeNode(8) root.right.left = TreeNode(21) def print_tree(tree): """ Print the elements of a tree in no specific order. """ if tree: print(tree.data) print_tree(tree.left) print_tree(tree.right) print_tree(root)
graph.py
#!/usr/bin/env python3 """ One of the many ways to represent a graph in Python. """ import collections Vertex = collections.namedtuple("Vertex", ("id", "data")) Edge = collections.namedtuple("Edge", ("first", "second")) class Graph(object): """ This graph class uses two sets to store the vertices and edges. However, it would also be possible to use an adjacency matrix or adjacency list. Depending on the purpose of the graph and the algorithms using the graph, these alternative implementations might highly benefit the runtime. """ def __init__(self, vertici, edges): self.__vertici = set() self.__edges = set() for v in vertici: self.add_vertex(v) for e in edges: self.add_edge(e) def add_vertex(self, v): self.__vertici.add(v) def add_edge(self, e): self.__vertici.add(e.first) self.__vertici.add(e.second) self.__edges.add(e) def remove_vertex(self, v): try: self.__vertici.remove(v) except KeyError: pass to_remove = {e for e in self.__edges if v == e.first or v == e.second} self.__edges -= to_remove def is_adjacent(self, v1, v2): # this would be much faster with an adjacency matrix for e in self.__edges: if e.first == v1 and e.second == v2: return True if e.first == v2 and e.second == v1: return True return False # many more useful methods... v1 = Vertex('1', '') v2 = Vertex('2', '') v3 = Vertex('3', '') v4 = Vertex('4', '') v5 = Vertex('5', '') g = Graph(tuple(), (Edge(v1, v2), Edge(v1, v3), Edge(v2, v3), Edge(v3, v3), Edge(v3, v4), Edge(v4, v5), Edge(v5, v1))) print("1->2", g.is_adjacent(v1, v2)) print("1->5", g.is_adjacent(v1, v5)) print("1->5", g.is_adjacent(v1, v4))
graph = {1: [2, 3], 2: [3], 3: [3, 4], 4: [5], 5: [1]}