The terms “coding blueprint” and “coding skeleton” often refer to the initial stages of code development, where a coder lays out the structure and logic of the code they’re planning to write. However, there can be subtle differences between the two:

  1. Coding Blueprint: This is typically an abstract representation of the code to be developed. It outlines the architecture of the code, modules, functions, and their interactions. It doesn’t usually contain any actual code, but rather a high-level design or pseudocode of how the application will be structured and how its components will interact. It serves as a guide during the actual coding process.

  2. Coding Skeleton: This is a basic version of the code which includes the major components and their interactions but leaves the implementation details empty or very basic. A coding skeleton provides a basic, runnable version of a program but with minimal functionality. It often includes function declarations, class definitions, and other basic structures. It serves as a starting point for filling in the actual code.

In simple terms, a coding blueprint is more like a plan or design, while a coding skeleton is like the basic structure of a building where you can see the rooms, but they are empty and need to be filled in. Both serve the purpose of providing a structured approach to coding and making the process more manageable.

Problem

For the Add Digits problem, I identified the following named coding constructs:

  • Reduce Number
    • Modulo Operation
  • Extract Digits
    • Integer Division
    • Floor Division
  • Track Sum
  • Accumulate Sum

Explain these in detail and give simple code examples to illustrate in Python3.

What is the purpose of modulo operation? Is it for digit extraction?

Add Strings

  • Charter to Integer Conversion
  • Integer to String Conversion

Assign Cookies

  • Maximize a Quantity
  • Resource Allocation
  • Two Pointers

Apply given rule to find matches between two sets. “Rule-Based Set Matching” could be a succinct name for this concept.

Sum in a Matrix

  • Traverse a Matrix
  • Track Maximum

3 Sum

  • Pairing Elements
  • Sorting Tuple

Buy Sell Stock

  • Running Maximum
  • Linear Scan

The linear scan is used in linear search.

Buy Sell Stock

  • Running Maximum
  • Up Swing

Misc

  • Accumulate Product

  1. Two Pointers: This is commonly used when dealing with arrays or linked lists, often used to find a pair of elements satisfying certain constraints.

  2. Sliding Window: This is often used when you need to track some kind of state in an array or list from one index to another, such as the sum of numbers in a subarray.

  3. Fast and Slow Pointers: This is a variant of the Two Pointers technique and is often used in linked list problems, for detecting cycles for instance.

  4. Merge Intervals: This pattern is used for solving problems that involve intervals. You typically need to find overlapping intervals or merge intervals, etc.

  5. Cyclic Sort: Useful when dealing with problems that involve sorting arrays containing numbers in a given range.

  6. Depth-First Search (DFS) and Breadth-First Search (BFS): These are two techniques for traversing trees and graphs.

  7. Two Heaps: In many problems, we are given a set of elements such that we can divide them into two parts. This pattern is an efficient approach to solve such problems.

  8. Subsets: Useful for problems where you need to find all the subsets of a given set.

  9. Modified Binary Search: A helpful pattern for problems involving binary search in a sorted array or list.

  10. Top ‘K’ Elements: Useful when you need to find the top ‘K’ elements in a list or array.

  11. K-way Merge: This pattern helps solve problems that involve a list of sorted arrays.

  12. 0/1 Knapsack (Dynamic Programming): Commonly used in optimization problems where you’re tasked with maximizing or minimizing a specific condition.