List = [1, 2, 3, 'GFG', 2.3] print(List) 

# Creating a List with # the use of multiple values List = ['Geeks', 'For', 'Geeks'] print('
List containing multiple values: ') print(List) # Creating a Multi-Dimensional List # (By Nesting a list inside a List) List2 = [['Geeks', 'For'], ['Geeks']] print('
Multi-Dimensional List: ') print(List2) # accessing a element from the # list using index number print('Accessing element from the list') print(List[0]) print(List[2]) # accessing a element using # negative indexing print('Accessing element using negative indexing') # print the last element of list print(List[-1]) # print the third last element of list print(List[-3]) 

# Creating a Tuple with # the use of Strings Tuple = ('Geeks', 'For') print('
Tuple with the use of String: ') print(Tuple) # Creating a Tuple with # the use of list list1 = [1, 2, 4, 5, 6] print('
Tuple using List: ') Tuple = tuple(list1) # Accessing element using indexing print('First element of tuple') print(Tuple[0]) # Accessing element from last # negative indexing print('
Last element of tuple') print(Tuple[-1]) print('
Third last element of tuple') print(Tuple[-3]) 

# Creating a Set with # a mixed type of values # (Having numbers and strings) Set = set([1, 2, 'Geeks', 4, 'For', 6, 'Geeks']) print('
Set with the use of Mixed Values') print(Set) # Accessing element using # for loop print('
Elements of set: ') for i in Set: print(i, end =' ') print() # Checking the element # using in keyword print('Geeks' in Set) 

# Same as {'a', 'b','c'} normal_set = set(['a', 'b','c']) print('Normal Set') print(normal_set) # A frozen set frozen_set = frozenset(['e', 'f', 'g']) print('
Frozen Set') print(frozen_set) # Uncommenting below line would cause error as # we are trying to add element to a frozen set # frozen_set.add('h') 

String = 'Welcome to GeeksForGeeks' print('Creating String: ') print(String) # Printing First character print('
First character of String is: ') print(String[0]) # Printing Last character print('
Last character of String is: ') print(String[-1]) 

# Creating a Dictionary Dict = {'Name': 'Geeks', 1: [1, 2, 3, 4]} print('Creating Dictionary: ') print(Dict) # accessing a element using key print('Accessing a element using key:') print(Dict['Name']) # accessing a element using get() # method print('Accessing a element using get:') print(Dict.get(1)) # creation using Dictionary comprehension myDict = {x: x**2 for x in [1,2,3,4,5]} print(myDict) 

import numpy as np a = np.array([[1,2,3,4],[4,55,1,2], [8,3,20,19],[11,2,22,21]]) m = np.reshape(a,(4, 4)) print(m) # Accessing element print('
Accessing Elements') print(a[1]) print(a[2][0]) # Adding Element m = np.append(m,[[1, 15,13,11]],0) print('
Adding Element') print(m) # Deleting Element m = np.delete(m,[1],0) print('
Deleting Element') print(m) 

# Creating bytearray a = bytearray((12, 8, 25, 2)) print('Creating Bytearray:') print(a) # accessing elements print('
Accessing Elements:', a[1]) # modifying elements a[1] = 3 print('
After Modifying:') print(a) # Appending elements a.append(30) print('
After Adding Elements:') print(a) 

# Node class class Node: # Function to initialize the node object def __init__(self, data): self.data = data # Assign data self.next = None # Initialize # next as null # Linked List class class LinkedList: # Function to initialize the Linked # List object def __init__(self): self.head = None 

# A simple Python program to introduce a linked list # Node class class Node: # Function to initialise the node object def __init__(self, data): self.data = data # Assign data self.next = None # Initialize next as null # Linked List class contains a Node object class LinkedList: # Function to initialize head def __init__(self): self.head = None # Code execution starts here if __name__=='__main__': # Start with the empty list llist = LinkedList() llist.head = Node(1) second = Node(2) third = Node(3)  '''  Three nodes have been created.  We have references to these three blocks as head,  second and third  llist.head second third  | | |  | | |  +----+------+ +----+------+ +----+------+  | 1 | None | | 2 | None | | 3 | None |  +----+------+ +----+------+ +----+------+  ''' llist.head.next = second; # Link first node with second  '''  Now next of first Node refers to second. So they  both are linked.  llist.head second third  | | |  | | |  +----+------+ +----+------+ +----+------+  | 1 | o-------->| 2 | null | | 3 | null |  +----+------+ +----+------+ +----+------+ ''' 두 번째.다음 = 세 번째 ; # 두 번째 노드를 세 번째 노드 '''와 연결합니다. 이제 두 번째 노드 다음은 세 번째 노드를 나타냅니다. 따라서 세 노드가 모두 연결됩니다.  lllist.head 두 번째 세 번째 | | |  | | |  +----+------+ +----+------+ +----+------+ | 1 | 아 -------->| 2 | 아 -------->| 3 | null |  +----+------+ +----+------+ +----+------+ ''' 

# A simple Python program for traversal of a linked list # Node class class Node: # Function to initialise the node object def __init__(self, data): self.data = data # Assign data self.next = None # Initialize next as null # Linked List class contains a Node object class LinkedList: # Function to initialize head def __init__(self): self.head = None # This function prints contents of linked list # starting from head def printList(self): temp = self.head while (temp): print (temp.data) temp = temp.next # Code execution starts here if __name__=='__main__': # Start with the empty list llist = LinkedList() llist.head = Node(1) second = Node(2) third = Node(3) llist.head.next = second; # Link first node with second second.next = third; # Link second node with the third node llist.printList() 

# A simple implementation of Priority Queue # using Queue. class PriorityQueue(object): def __init__(self): self.queue = [] def __str__(self): return ' '.join([str(i) for i in self.queue]) # for checking if the queue is empty def isEmpty(self): return len(self.queue) == 0 # for inserting an element in the queue def insert(self, data): self.queue.append(data) # for popping an element based on Priority def delete(self): try: max = 0 for i in range(len(self.queue)): if self.queue[i]>self.queue[max]: max = i item = self.queue[max] del self.queue[max] IndexError: print()exit() if __name__ == '__main__': myQueue = PriorityQueue(를 제외한 항목 반환 ) myQueue.insert(12) myQueue.insert(1) myQueue.insert(14) myQueue.insert(7) print(myQueue) myQueue.isEmpty()가 아닌 경우: print(myQueue.delete()) 

# importing 'heapq' to implement heap queue import heapq # initializing list li = [5, 7, 9, 1, 3] # using heapify to convert list into heap heapq.heapify(li) # printing created heap print ('The created heap is : ',end='') print (list(li)) # using heappush() to push elements into heap # pushes 4 heapq.heappush(li,4) # printing modified heap print ('The modified heap after push is : ',end='') print (list(li)) # using heappop() to pop smallest element print ('The popped and smallest element is : ',end='') print (heapq.heappop(li)) 

# A Python class that represents an individual node # in a Binary Tree class Node: def __init__(self,key): self.left = None self.right = None self.val = key 

# Python program to introduce Binary Tree # A class that represents an individual node in a # Binary Tree class Node: def __init__(self,key): self.left = None self.right = None self.val = key # create root root = Node(1) ''' following is the tree after above statement  1  /   None None''' root.left = Node(2); root.right = Node(3); ''' 2 and 3 become left and right children of 1  1  /   2 3  /  /  None None None None''' root.left.left = Node(4); '''4 becomes left child of 2  1  /   2 3  /  /  4 None None None /  None None''' 

# Python program to for tree traversals # A class that represents an individual node in a # Binary Tree class Node: def __init__(self, key): self.left = None self.right = None self.val = key # A function to do inorder tree traversal def printInorder(root): if root: # First recur on left child printInorder(root.left) # then print the data of node print(root.val), # now recur on right child printInorder(root.right) # A function to do postorder tree traversal def printPostorder(root): if root: # First recur on left child printPostorder(root.left) # the recur on right child printPostorder(root.right) # now print the data of node print(root.val), # A function to do preorder tree traversal def printPreorder(root): if root: # First print the data of node print(root.val), # Then recur on left child printPreorder(root.left) # Finally recur on right child printPreorder(root.right) # Driver code root = Node(1) root.left = Node(2) root.right = Node(3) root.left.left = Node(4) root.left.right = Node(5) print('Preorder traversal of binary tree is') printPreorder(root) print('
Inorder traversal of binary tree is') printInorder(root) print('
Postorder traversal of binary tree is') printPostorder(root) 

# Python program to print level # order traversal using Queue # A node structure class Node: # A utility function to create a new node def __init__(self ,key): self.data = key self.left = None self.right = None # Iterative Method to print the # height of a binary tree def printLevelOrder(root): # Base Case if root is None: return # Create an empty queue # for level order traversal queue = [] # Enqueue Root and initialize height queue.append(root) while(len(queue)>0): # 대기열의 앞 부분을 인쇄하고 # 대기열에서 제거합니다. print (queue[0].data) node = queue.pop(0) # node.left가 None이 아닌 경우 왼쪽 자식을 대기열에 넣습니다: queue.append(node.left ) # node.right가 None이 아닌 경우 오른쪽 자식을 큐에 넣습니다. queue.append(node.right) # 위 함수를 테스트하기 위한 드라이버 프로그램 root = Node(1) root.left = Node(2) root.right = Node(3) root.left.left = Node(4) root.left.right = Node(5) print ('이진 트리의 레벨 순서 탐색은 -') printLevelOrder(root) 

# Python3 Program to print BFS traversal # from a given source vertex. BFS(int s) # traverses vertices reachable from s. from collections import defaultdict # This class represents a directed graph # using adjacency list representation class Graph: # Constructor def __init__(self): # default dictionary to store graph self.graph = defaultdict(list) # function to add an edge to graph def addEdge(self,u,v): self.graph[u].append(v) # Function to print a BFS of graph def BFS(self, s): # Mark all the vertices as not visited visited = [False] * (max(self.graph) + 1) # Create a queue for BFS queue = [] # Mark the source node as # visited and enqueue it queue.append(s) visited[s] = True while queue: # Dequeue a vertex from # queue and print it s = queue.pop(0) print (s, end = ' ') # Get all adjacent vertices of the # dequeued vertex s. If a adjacent # has not been visited, then mark it # visited and enqueue it for i in self.graph[s]: if visited[i] == False: queue.append(i) visited[i] = True # Driver code # Create a graph given in # the above diagram g = Graph() g.addEdge(0, 1) g.addEdge(0, 2) g.addEdge(1, 2) g.addEdge(2, 0) g.addEdge(2, 3) g.addEdge(3, 3) print ('Following is Breadth First Traversal' ' (starting from vertex 2)') g.BFS(2) 

# Python3 program to print DFS traversal # from a given graph from collections import defaultdict # This class represents a directed graph using # adjacency list representation class Graph: # Constructor def __init__(self): # default dictionary to store graph self.graph = defaultdict(list) # function to add an edge to graph def addEdge(self, u, v): self.graph[u].append(v) # A function used by DFS def DFSUtil(self, v, visited): # Mark the current node as visited # and print it visited.add(v) print(v, end=' ') # Recur for all the vertices # adjacent to this vertex for neighbour in self.graph[v]: if neighbour not in visited: self.DFSUtil(neighbour, visited) # The function to do DFS traversal. It uses # recursive DFSUtil() def DFS(self, v): # Create a set to store visited vertices visited = set() # Call the recursive helper function # to print DFS traversal self.DFSUtil(v, visited) # Driver code # Create a graph given # in the above diagram g = Graph() g.addEdge(0, 1) g.addEdge(0, 2) g.addEdge(1, 2) g.addEdge(2, 0) g.addEdge(2, 3) g.addEdge(3, 3) print('Following is DFS from (starting from vertex 2)') g.DFS(2) 

# Python3 code to linearly search x in arr[]. # If x is present then return its location, # otherwise return -1 def search(arr, n, x): for i in range(0, n): if (arr[i] == x): return i return -1 # Driver Code arr = [2, 3, 4, 10, 40] x = 10 n = len(arr) # Function call result = search(arr, n, x) if(result == -1): print('Element is not present in array') else: print('Element is present at index', result) 

# Python3 Program for recursive binary search. # Returns index of x in arr if present, else -1 def binarySearch (arr, l, r, x): # Check base case if r>= l: mid = l + (r - l) // 2 # 요소가 중앙 자체에 존재하는 경우 if arr[mid] == x: return mid # 요소가 mid보다 작으면 # 존재할 수만 있습니다. 왼쪽 하위 배열 elif arr[mid]> x: return binarySearch(arr, l, mid-1, x) # 그렇지 않으면 요소는 # 오른쪽 하위 배열에만 존재할 수 있습니다. else: return binarySearch(arr, mid + 1, r, x ) else: # 요소가 배열에 없습니다 return -1 # 드라이버 코드 arr = [ 2, 3, 4, 10, 40 ] x = 10 # 함수 호출 결과 = binarySearch(arr, 0, len(arr)-1 , x) if result != -1: print('요소가 인덱스 %d에 있습니다' % 결과) else: print('요소가 배열에 없습니다') 

# Python program for implementation of Selection # Sort import sys A = [64, 25, 12, 22, 11] # Traverse through all array elements for i in range(len(A)): # Find the minimum element in remaining # unsorted array min_idx = i for j in range(i+1, len(A)): if A[min_idx]>A[j]: min_idx = j # 발견된 최소 요소를 # 첫 번째 요소와 교체 A[i], A[min_idx] = A[min_idx], A[i] # 위 테스트를 위한 드라이버 코드 print ('Sorted array ') for i in range(len(A)): print('%d' %A[i]), 

# Python program for implementation of Bubble Sort def bubbleSort(arr): n = len(arr) # Traverse through all array elements for i in range(n): # Last i elements are already in place for j in range(0, n-i-1): # traverse the array from 0 to n-i-1 # Swap if the element found is greater # than the next element if arr[j]>arr[j+1] : arr[j], arr[j+1] = arr[j+1], arr[j] # 위에서 테스트할 드라이버 코드 arr = [64, 34, 25, 12, 22, 11 , 90] bubbleSort(arr) print ('정렬된 배열은 다음과 같습니다:') for i in range(len(arr)): print ('%d' %arr[i]), 

# Python program for implementation of Insertion Sort # Function to do insertion sort def insertionSort(arr): # Traverse through 1 to len(arr) for i in range(1, len(arr)): key = arr[i] # Move elements of arr[0..i-1], that are # greater than key, to one position ahead # of their current position j = i-1 while j>= 0 및 키 < arr[j] : arr[j + 1] = arr[j] j -= 1 arr[j + 1] = key # Driver code to test above arr = [12, 11, 13, 5, 6] insertionSort(arr) for i in range(len(arr)): print ('% d' % arr[i]) 

# Python program for implementation of MergeSort def mergeSort(arr): if len(arr)>1: # 배열의 중앙 찾기 mid = len(arr)//2 # 배열 요소 나누기 L = arr[:mid] # 2개의 반으로 나누기 R = arr[mid:] # 전반부 정렬 mergeSort(L) # 후반부 정렬 mergeSort(R) i = j = k = 0 # 데이터를 임시 배열 L[] 및 R[]에 복사 while i < len(L) and j  < len(R): if L[i]  < R[j]: arr[k] = L[i] i += 1 else: arr[k] = R[j] j += 1 k += 1 # Checking if any element was left while i  < len(L): arr[k] = L[i] i += 1 k += 1 while j  < len(R): arr[k] = R[j] j += 1 k += 1 # Code to print the list def printList(arr): for i in range(len(arr)): print(arr[i], end=' ') print() # Driver Code if __name__ == '__main__': arr = [12, 11, 13, 5, 6, 7] print('Given array is', end='
') printList(arr) mergeSort(arr) print('Sorted array is: ', end='
') printList(arr) 

# Python3 program for implementation of Shell Sort def shellSort(arr): gap = len(arr) // 2 # initialize the gap while gap>0: i = 0 j = gap # 왼쪽에서 오른쪽으로 # j의 가능한 마지막 인덱스까지 배열을 확인합니다 while j < len(arr): if arr[i]>arr[j]: arr[i],arr[j] = arr[j],arr[i] i += 1 j += 1 # 이제 i번째 인덱스에서 왼쪽으로 돌아봅니다. # 값을 바꿉니다. 올바른 순서가 아닙니다. k = i 동안 k - 간격> -1: arr[k - 간격]> arr[k]인 경우: arr[k-gap],arr[k] = arr[k],arr[k-gap] k -= 1 gap //= 2 # 코드 확인을 위한 드라이버 arr2 = [12, 34, 54, 2, 3] print('input array:',arr2) shellSort(arr2) print('sorted array', arr2)