Tutorial Playlist
200 Lessons1. Introduction to Python
2. Features of Python
3. How to install python in windows
4. How to Install Python on macOS
5. Install Python on Linux
6. Hello World Program in Python
7. Python Variables
8. Global Variable in Python
9. Python Keywords and Identifiers
10. Assert Keyword in Python
11. Comments in Python
12. Escape Sequence in Python
13. Print In Python
14. Python-if-else-statement
15. Python for Loop
16. Nested for loop in Python
17. While Loop in Python
18. Python’s do-while Loop
19. Break in Python
20. Break Pass and Continue Statement in Python
21. Python Try Except
22. Data Types in Python
23. Float in Python
24. String Methods Python
25. List in Python
26. List Methods in Python
27. Tuples in Python
28. Dictionary in Python
29. Set in Python
30. Operators in Python
31. Boolean Operators in Python
32. Arithmetic Operators in Python
33. Assignment Operator in Python
34. Bitwise operators in Python
35. Identity Operator in Python
36. Operator Precedence in Python
37. Functions in Python
38. Lambda and Anonymous Function in Python
39. Range Function in Python
40. len() Function in Python
41. How to Use Lambda Functions in Python?
42. Random Function in Python
43. Python __init__() Function
44. String Split function in Python
45. Round function in Python
46. Find Function in Python
47. How to Call a Function in Python?
48. Python Functions Scope
49. Method Overloading in Python
50. Method Overriding in Python
51. Static Method in Python
52. Python List Index Method
53. Python Modules
54. Math Module in Python
55. Module and Package in Python
56. OS module in Python
57. Python Packages
58. OOPs Concepts in Python
59. Class in Python
60. Abstract Class in Python
61. Object in Python
62. Constructor in Python
63. Inheritance in Python
64. Multiple Inheritance in Python
65. Encapsulation in Python
66. Data Abstraction in Python
67. Opening and closing files in Python
68. How to open JSON file in Python
69. Read CSV Files in Python
70. How to Read a File in Python
71. How to Open a File in Python?
72. Python Write to File
73. JSON Python
74. Python JSON – How to Convert a String to JSON
75. Python JSON Encoding and Decoding
76. Exception Handling in Python
77. Recursion in Python
78. Python Decorators
79. Python Threading
80. Multithreading in Python
81. Multiprocеssing in Python
82. Python Regular Expressions
83. Enumerate() in Python
84. Map in Python
85. Filter in Python
86. Eval in Python
87. Difference Between List, Tuple, Set, and Dictionary in Python
88. List to String in Python
89. Linked List in Python
90. Length of list in Python
91. Reverse a List in Python
92. Python List remove() Method
93. How to Add Elements in a List in Python
94. How to Reverse a List in Python?
95. Difference Between List and Tuple in Python
96. List Slicing in Python
97. Sort in Python
98. Merge Sort in Python
Now Reading
99. Selection Sort in Python
100. Sort Array in Python
101. Sort Dictionary by Value in Python
102. Datetime Python
103. Random Number in Python
104. 2D Array in Python
105. Abs in Python
106. Advantages of Python
107. Anagram Program in Python
108. Append in Python
109. Applications of Python
110. Armstrong Number in Python
111. Assert in Python
112. Binary Search in Python
113. Binary to Decimal in Python
114. Bool in Python
115. Calculator Program in Python
116. chr in Python
117. Control Flow Statements in Python
118. Convert String to Datetime Python
119. Count in python
120. Counter in Python
121. Data Visualization in Python
122. Datetime in Python
123. Extend in Python
124. F-string in Python
125. Fibonacci Series in Python
126. Format in Python
127. GCD of Two Numbers in Python
128. How to Become a Python Developer
129. How to Run Python Program
130. In Which Year Was the Python Language Developed?
131. Indentation in Python
132. Index in Python
133. Interface in Python
134. Is Python Case Sensitive?
135. Isalpha in Python
136. Isinstance() in Python
137. Iterator in Python
138. Join in Python
139. Leap Year Program in Python
140. Lexicographical Order in Python
141. Literals in Python
142. Matplotlib
143. Matrix Multiplication in Python
144. Memory Management in Python
145. Modulus in Python
146. Mutable and Immutable in Python
147. Namespace and Scope in Python
148. OpenCV Python
149. Operator Overloading in Python
150. ord in Python
151. Palindrome in Python
152. Pass in Python
153. Pattern Program in Python
154. Perfect Number in Python
155. Permutation and Combination in Python
156. Prime Number Program in Python
157. Python Arrays
158. Python Automation Projects Ideas
159. Python Frameworks
160. Python Graphical User Interface GUI
161. Python IDE
162. Python input and output
163. Python Installation on Windows
164. Python Object-Oriented Programming
165. Python PIP
166. Python Seaborn
167. Python Slicing
168. type() function in Python
169. Queue in Python
170. Replace in Python
171. Reverse a Number in Python
172. Reverse a string in Python
173. Reverse String in Python
174. Stack in Python
175. scikit-learn
176. Selenium with Python
177. Self in Python
178. Sleep in Python
179. Speech Recognition in Python
180. Split in Python
181. Square Root in Python
182. String Comparison in Python
183. String Formatting in Python
184. String Slicing in Python
185. Strip in Python
186. Subprocess in Python
187. Substring in Python
188. Sum of Digits of a Number in Python
189. Sum of n Natural Numbers in Python
190. Sum of Prime Numbers in Python
191. Switch Case in Python
192. Python Program to Transpose a Matrix
193. Type Casting in Python
194. What are Lists in Python?
195. Ways to Define a Block of Code
196. What is Pygame
197. Why Python is Interpreted Language?
198. XOR in Python
199. Yield in Python
200. Zip in Python
A fundamental sorting technique in Python called merge sort effectively manages huge datasets. In this thorough article, we'll go deep into merge sort, looking at its principles, Python implementation, time complexity, optimizations, and more. Let's start on the path to learning the skill of effective sorting. A well-liked and effective sorting algorithm in Python is called merge sort; it employs the divide-and-conquer strategy. This method entails breaking a problem down into numerous smaller issues. Then, each sub-problem is resolved on its own. Sub-problems are finally integrated to get the complete solution.
A traditional sorting algorithm based on the divide and conquer method is merge sort. The unsorted list is split into n sublists, each of which has one element, and each of these sublists is continuously merged to create a new sorted sublist until only one sublist is left. The sorted list is the last sublist. One of the most effective sorting algorithms is merge sort. Its foundation is the divide-and-conquer tactic. A list is constantly divided into several sublists using the merge sort algorithm until each sublist contains just one item.
At its core, merge sort operates by breaking down the problem into simpler subproblems, sorting them individually, and then merging the sorted sublists to obtain a fully sorted list. This approach ensures stability and predictable performance, making it a preferred choice in various applications. Merge sort is one of the most efficient sorting algorithms. It works on the principle of Divide and Conquer based on the idea of breaking down a list into several sub-lists until each sublist consists of a single element and merging those sublists in a manner that results in a sorted list.
Divide and conquer is a problem-solving strategy where a problem is divided into smaller, more manageable subproblems. In the case of merge sort, the unsorted list is divided into smaller sublists until each sublist contains only one element. These single-element sublists are then merged back together, ensuring that the merged list is sorted.
Let us have a rough understanding of merge sort:
Let's dive into Python implementations of merge sort. Below is a step-by-step guide to sorting an array using the merge sort algorithm.
As of now, we have a rough understanding of how merge sort is performed. For better understanding, let's dive deep into the algorithm followed by the code:
def merge_sort(arr):
if len(arr) > 1:
mid = len(arr) // 2 # Find the middle of the array
left_half = arr[:mid] # Divide the array into two halves
right_half = arr[mid:]
merge_sort(left_half) # Recursive call on the left half
merge_sort(right_half) # Recursive call on the right half
i = j = k = 0
# Copy data to temporary lists left_half[] and right_half[]
while i < len(left_half) and j < len(right_half):
if left_half[i] < right_half[j]:
arr[k] = left_half[i]
i = 1
else:
arr[k] = right_half[j]
j = 1
k = 1
# Check if any element was left
while i < len(left_half):
arr[k] = left_half[i]
i = 1
k = 1
while j < len(right_half):
arr[k] = right_half[j]
j = 1
k = 1
# Example usage
arr = [12, 11, 13, 5, 6, 7]
merge_sort(arr)
print("Sorted array is:", arr)
This Python program demonstrates merge sort on an array.
Merge sort exhibits a time complexity of O(nlogn) as it consistently divides the unsorted list into smaller sublists until each sublist contains only one element. The merging process takes linear time, resulting in a balanced and efficient performance across various input sizes.
While merge sort inherently offers stable and predictable performance, certain optimizations can further enhance its efficiency. One such optimization involves using insertion sort for small sublists. Insertion sort performs efficiently for small datasets, making it an ideal choice for optimizing merge sort for smaller inputs. Merge sort is one of the most efficient sorting algorithms. It works on the principle of Divide and Conquer based on the idea of breaking down a list into several sub-lists until each sublist consists of a single element and merging those sublists in a manner that results in a sorted list.
Implementing merge sort requires attention to detail. Ensuring the correct partitioning of the input list and the accurate merging of sublists are crucial steps in the implementation process. A precise implementation can be achieved by meticulously following the algorithm's steps. Merge Sort is a divide-and-conquer algorithm. It divides the input array into two halves, calls itself the two halves, and then merges the two sorted halves. The merge() function is used for merging two halves.
Merge sort can be extended to sort custom objects by defining a custom comparison function. This approach allows the algorithm to sort objects based on specific attributes, enabling diverse applications in real-world scenarios. Merge Sort divides an array into smaller subarrays, sorts them, and then merges them back together to achieve a sorted result. The code comprises two main functions: merge to combine two sorted arrays and mergesort to split and sort an array recursively.
In conclusion, mastering merge sort in Python equips developers with a powerful tool for tackling sorting challenges with precision and efficiency. Its elegant divide-and-conquer approach, coupled with stable performance, makes it an indispensable algorithm in the world of computer science. By understanding the intricacies detailed in this guide, developers are well-prepared to optimize their code, ensuring seamless sorting experiences for even the most extensive datasets and complex custom objects. Embrace merge sort and empower your applications with a sorting solution that stands the test of time.
Q1: Is merge sort suitable for large datasets?
Yes, merge sort is well-suited for large datasets due to its efficient divide-and-conquer strategy, resulting in a time complexity of O(nlogn). Merge sort can work well on any type of data set irrespective of its size (either large or small). whereas The quick sort cannot work well with large datasets. Next, it says that merge sort is not in place because it requires additional memory space to store the auxiliary arrays.
Q2: Can merge sort handle custom objects?
Certainly, merge sort can be extended to sort custom objects by defining a custom comparison function, allowing precise sorting based on specific object attributes. Sorting Array The merge sort algorithm divides the given array into roughly two halves and sorts them recursively.
Q3: What is the primary advantage of merge sort over other sorting algorithms?
Merge sort's primary advantage lies in its stable and consistent O(nlogn) time complexity, ensuring reliable performance across various input sizes and data distributions. Merge sort can be used with linked lists without taking up any more space. A merge sort algorithm is used to count the number of inversions in the list.
Q4. Why is merge sort used?
Use merge sort when there is a consideration for the stability of data. Stable sorting involves maintaining the order of identical values within an array. When compared with the unsorted data input, the order of identical values throughout an array in a stable sort is kept in the same position in the sorted output.
Q5. What is merge sort with two functions?
The Merge Sort algorithm operates through two functions: the mergeSort function itself that divides our input recursively and the merge function that sorts and stitches our divided halves back together into our sorted array output.
PAVAN VADAPALLI
Director of Engineering
Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working …Read More
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upGrad does not grant credit; credits are granted, accepted or transferred at the sole discretion of the relevant educational institution offering the diploma or degree. We advise you to enquire further regarding the suitability of this program for your academic, professional requirements and job prospects before enr...