Understanding Insertion Sort: A Question-Driven Approach
In this blog post, we'll take a question-driven approach to understand the fundamentals of the insertion sort algorithm. I came up with this approach when I was trying to find a better way to understand the insertion algorithm and others that I will soon be learning about. I wanted to build a strategy that I could apply to most, if not all, of the algorithms I will be learning. While I was thinking about this, I was sure that I might have to use first principles thinking
Inspired by first principles thinking, this approach involves first trying to grasp the algorithm, whether our initial understanding is vague or clear. We then identify the tiny concepts or mechanics involved that make up the algorithm. By forming questions around these mechanics or tiny concepts. We are essentially trying to understand the working of the algorithm from small different perspectives with a focus on trying to solve the questions that we formed on our own.
The answer you form may or may not initially resemble the syntax used in the actual algorithm. The goal should be to answer the question on your own, regardless of whether the syntax is close or not. Once you have a clear understanding, you can then convert, merge your answer(s) to use syntax, similar to the actual implementation of the algorithm. I believe this process allows you to explore alternative forms of code , grasp why a specific syntax is being used, deal with edge cases in a better way on your own.
I believe this method ensures that we understand the theory and the reasoning behind each line of code, making the implementation process more intuitive and meaningful. The following questions and the thought process which i went through helped me understand Insertion Sort better and enabled me to code it effectively.
For you, the questions might be different; they could be more, fewer, or entirely different. Some might say this is akin to reverse engineering, whatever you call it, this method enabled me get a thorough understanding of the Insertion Sort algorithm. I hope it does the same for you for any other other algorithm. Soo, let’s dive in!
Insertion Sort Implementation
This is the form of code we will eventually implement for Insertion Sort.
def insertion_sort(values): for new_value_index in range(1,len(values)): new_value = values[new_value_index] index = new_value_index-1 while index>=0: if values[index]<new_value:break values[index+1] = values[index] index-=1 values[index+1] = new_value
Questions
Given a sorted list, using while loop, print values from right to left.
values = [4,8,12,16,20,24,30] # given a sorted list, using while loop, print values from right to left. index = len(values)-1 while index>=0: print(values[index],end = " ") index-=1
Given a sorted list and a new value, find the index at which the new value is to be inserted to keep the list sorted.
values = [4, 8, 12, 16, 20, 24] new_value = 14 # using while loop, if traversing from right to left index = len(values)-1 while index>=0: if values[index]<new_value: break index-=1 print(values,new_value,index)
Given a sorted list and a new value, insert the new value to the list so it remains sorted.
values = [4, 8, 12, 16, 20, 24] new_value = 14 # if traversal from right to left index = len(values)-1 while index>=0: if values[index]<new_value:break index-=1 values = values[:index+1] + [new_value] + values[index+1:] print(values)
Given a sorted list, then appended with a new value, move the new value to the given index position.
values = [4, 8, 12, 16, 20, 24, 30] new_value = 14 values.append(new_value) given_index = 3 # above given n = len(values)-1 index = n-1 while index>given_index: values[index+1] = values[index] index-=1 print(values) values[given_index+1] = new_value print(values)
Given a sorted list, then appended with a new value, sort the list.
values = [4, 8, 12, 16, 20, 24, 30] new_value = 14 values.append(new_value) print(values) ### given a sorted list, then appended with new value, sort the list #### n = len(values)-1 new_value = values[-1] # find the index at which the value is to be inserted # right to left index = n-1 while index>=0: if values[index]<new_value:break index-=1 given_index = index print("given_index : " , given_index) # move the values forward by one step until we reach the given index index = n-1 while index>given_index: values[index+1] = values[index] index-=1 values[index+1] = new_value print(values)
Given a sorted list, then appended with a new value(s), sort the list.
values = [4, 8, 12, 16, 20, 24, 30] new_values = [14,32] values += new_values print(values) # given a sorted list, then appended with two new value(s), sort the list n = len(values)-1 new_value_start_index = n - 1 print(new_value_start_index, values[new_value_start_index]) for new_value_index in range(new_value_start_index,len(values)): new_value = values[new_value_index] index = new_value_index-1 while index>=0: if values[index]<new_value: break values[index+1] = values[index] index-=1 values[index+1] = new_value print(values)
Given a list, sort it.
import random values = random.sample(range(10,90), k = 10) values
print(values) for new_value_index in range(1,len(values)): new_value = values[new_value_index] index = new_value_index-1 while index>=0: if values[index]<new_value:break values[index+1] = values[index] index-=1 values[index+1] = new_value print(values)
Insertion Sort Implementation
def insertion_sort(values): for new_value_index in range(1,len(values)): new_value = values[new_value_index] index = new_value_index-1 while index>=0: if values[index]<new_value:break values[index+1] = values[index] index-=1 values[index+1] = new_value
Additional Resources
While I initially worked through a comprehensive set of questions to understand the algorithm better, the above are a set of questions that I think are important to understand Insertion Sort in a better way. Including all of the questions that I worked on would make the post quite lengthy.
For those interested in seeing all of the questions, I have created a Jupyter Notebook containing the full set of questions with my own answers, which enabled me to understand the implementation of Insertion Sort completely.
I encourage you to check out the notebook if you want to delve further.
Corrections and suggestions are welcome.
The above is the detailed content of Understanding Insertion Sort: A Question-Driven Approach. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics











Python is suitable for data science, web development and automation tasks, while C is suitable for system programming, game development and embedded systems. Python is known for its simplicity and powerful ecosystem, while C is known for its high performance and underlying control capabilities.

Python excels in gaming and GUI development. 1) Game development uses Pygame, providing drawing, audio and other functions, which are suitable for creating 2D games. 2) GUI development can choose Tkinter or PyQt. Tkinter is simple and easy to use, PyQt has rich functions and is suitable for professional development.

You can learn basic programming concepts and skills of Python within 2 hours. 1. Learn variables and data types, 2. Master control flow (conditional statements and loops), 3. Understand the definition and use of functions, 4. Quickly get started with Python programming through simple examples and code snippets.

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

You can learn the basics of Python within two hours. 1. Learn variables and data types, 2. Master control structures such as if statements and loops, 3. Understand the definition and use of functions. These will help you start writing simple Python programs.

To maximize the efficiency of learning Python in a limited time, you can use Python's datetime, time, and schedule modules. 1. The datetime module is used to record and plan learning time. 2. The time module helps to set study and rest time. 3. The schedule module automatically arranges weekly learning tasks.

Python excels in automation, scripting, and task management. 1) Automation: File backup is realized through standard libraries such as os and shutil. 2) Script writing: Use the psutil library to monitor system resources. 3) Task management: Use the schedule library to schedule tasks. Python's ease of use and rich library support makes it the preferred tool in these areas.

Python is widely used in the fields of web development, data science, machine learning, automation and scripting. 1) In web development, Django and Flask frameworks simplify the development process. 2) In the fields of data science and machine learning, NumPy, Pandas, Scikit-learn and TensorFlow libraries provide strong support. 3) In terms of automation and scripting, Python is suitable for tasks such as automated testing and system management.
