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Pseudocode

As explained in Get Programming: Learn to code with Python

Pseudocode is an informal notation combining natural language and programming-like constructs to describe the steps of an algorithm without requiring valid executable syntax.

Pseudocode is a mix of English and programming written before executable code. It turns a problem into ordered steps, showing where to get input, display output, make decisions, and repeat actions. section 2.3

Use pseudocode as a bridge from understanding a problem to organizing code:

  • Understand the task, including its inputs, outputs, and possible behaviors. section 12.1
  • Sketch the process, create test cases, and turn the important steps into words. section 12.1
  • Mark natural divisions such as initialization, algorithm steps, conditionals, loops, and cleanup. section 19.3
  • Use the outline to decide whether the problem needs to be broken into smaller pieces before coding. section 19.3

A small algorithm outline

Get a radius from the user. Apply a formula. Show the result. Repeat steps 1–3 until the user says to stop. This outline describes the program’s structure without requiring valid programming syntax. section 2.3

Figure 2.3. This is the ideal way to approach solving a problem with programming. Understand the problem before you write any code. Then test the code that you write and debug it as necessary. This process repeats until your code passes all tests.

Follow the cycle from understanding the problem to writing code, testing it, debugging it, and repeating until the tests pass. section 2.3

How Get Programming: Learn to code with Python covers it

How deep it goes
Develops pseudocode procedurally from sketches and test cases, stopping before valid executable syntax.
Examples
Uses a circle-area outline and capstone planning for name-mashing and a Scrabble-inspired program.
What it assumes
Assumes a problem to solve but teaches planning from sketches, simple examples, inputs, outputs, and expected behaviors.
What you can do after
Evaluate problem structure and organize smaller, testable code pieces before implementation.
How it connects
Connects pseudocode with flowcharts, algorithms, conditionals, loops, testing, and debugging.

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Related concepts

This explainer was generated by Manning's knowledge graph from the text of Get Programming: Learn to code with Python. The section links point to the parts of the book it draws on.