Rocket Academy Bootcamp
  • 🚀Welcome to Bootcamp!
  • 🛠️Logistics
    • Course Schedules
    • Course Methodology
    • Required Software
    • LinkedIn Education Badge
  • 📚General Reference
    • Naming, Casing, and Commenting Conventions
    • VS Code Tips
    • Recommended Resources
  • 🪨0: Foundations
    • 0.1: Command Line
    • 0.2: Git
      • 0.2.1: Branches
    • 0.3: GitHub
      • 0.3.1: Pull Requests
    • 0.4: JavaScript
      • 0.4.1: ES6
      • 0.4.2: Common Syntax
      • 0.4.3: Reference vs Value
      • 0.4.4: Classes
      • 0.4.5: Destructuring and Spread Operator
      • 0.4.6: Promises
        • 0.4.6.1: Async Await
    • 0.5: Node.js
      • 0.5.1: Node Modules
      • 0.5.2: NPM
      • 0.5.3: Nodemon
  • 🖼️1: Frontend
    • 1.1: HTML
    • 1.2: CSS
      • 1.2.1: Layout
    • 1.3: React
      • Styling in ReactJs
      • Using Styling Libraries with React
      • React Deployment
    • 1.E: Exercises
      • 1.E.1: Recipe Site
      • 1.E.2: Portfolio Page
      • 1.E.3: World Clock
      • 1.E.4: High Card
      • 1.E.5: Guess The Word
    • 1.P: Frontend App
  • 🏭2: Full Stack
    • 2.1: Internet 101
      • 2.1.1: Chrome DevTools Network Panel
      • 2.1.2: HTTP Requests and Responses
    • 2.2: Advanced React
      • 2.2.1: AJAX
      • 2.2.2: React Router
      • 2.2.3: useContext
      • 2.2.4: useReducer
      • 2.2.5: Environmental Variables
      • 2.2.6: React useMemo - useCallback
    • 2.3: Firebase
      • 2.3.1: Firebase Realtime Database
      • 2.3.2: Firebase Storage
      • 2.3.3: Firebase Authentication
      • 2.3.4: Firebase Hosting
      • 2.3.5: Firebase Techniques
    • 2.E: Exercises
      • 2.E.1: Weather App
      • 2.E.2: Instagram Chat
      • 2.E.3: Instagram Posts
      • 2.E.4: Instagram Auth
      • 2.E.5: Instagram Routes
    • 2.P: Full-Stack App (Firebase)
  • 🤖3: Backend
    • 3.1: Express.js
      • 3.1.1 : MVC
    • 3.2: SQL
      • 3.2.1: SQL 1-M Relationships
      • 3.2.2: SQL M-M Relationships
      • 3.2.3: SQL Schema Design
      • 3.2.4: Advanced SQL Concepts
      • 3.2.5: SQL - Express
      • 3.2.6: DBeaver
    • 3.3: Sequelize
      • 3.3.1: Sequelize One-To-Many (1-M) Relationships
      • 3.3.2: Sequelize Many-To-Many (M-M) Relationships
      • 3.3.3: Advanced Sequelize Concepts
      • 3.3.4 Database Design
    • 3.4: Authentication
      • 3.4.1: JWT App
    • 3.5: Application Deployment
    • 3.E: Exercises
      • 3.E.1: Bigfoot JSON
      • 3.E.2: Bigfoot SQL
      • 3.E.3: Bigfoot SQL 1-M
      • 3.E.4: Bigfoot SQL M-M
      • 3.E.5: Carousell Schema Design
      • 3.E.6: Carousell Auth
    • 3.P: Full-Stack App (Express)
  • 🏞️4: Capstone
    • 4.1: Testing
      • 4.1.1: Frontend React Testing
      • 4.1.2: Backend Expressjs Testing
    • 4.2: Continuous Integration
      • 4.2.1 Continuous Deployment (Fly.io)
      • 4.2.2: Circle Ci
    • 4.3: TypeScript
    • 4.4: Security
    • 4.5: ChatGPT for SWE
    • 4.6: Soft Skills for SWE
    • 4.P: Capstone
  • 🧮Algorithms
    • A.1: Data Structures
      • A.1.1: Arrays
        • A.1.1.1: Binary Search
        • A.1.1.2: Sliding Windows
      • A.1.2: Hash Tables
      • A.1.3: Stacks
      • A.1.4: Queues
      • A.1.5: Linked Lists
      • A.1.6: Trees
      • A.1.7: Graphs
      • A.1.8: Heaps
    • A.2: Complexity Analysis
    • A.3: Object-Oriented Programming
    • A.4: Recursion
    • A.5: Dynamic Programming
    • A.6: Bit Manipulation
    • A.7: Python
  • 💼Interview Prep
    • IP.1: Job Application Strategy
    • IP.2: Resume
    • IP.3: Portfolio
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On this page
  • Learning Objectives
  • Introduction
  • Python Quirks
  • Exercises
  1. Algorithms

A.7: Python

PreviousA.6: Bit ManipulationNextInterview Prep

Learning Objectives

  1. Python is more commonly used than JavaScript for data and algorithm-related work due to its built-in data manipulation libraries and simpler syntax

  2. Learn basic Python syntax to be able to use Python for algorithm problems that are easier solved with Python's built-in libraries, such as algorithms involving queues and heaps

Introduction

Python is commonly used in data and algorithm-related work due to its built-in data manipulation libraries and simpler syntax. Luckily, Python is conceptually very similar to JavaScript, and most translations from Python to JavaScript and vice versa are purely syntactical translations.

This submodule aims to help us learn Python through exercises to solve algorithm problems that are easier solved with Python, such as algorithms involving queues and heaps.

Python Quirks

  1. Python variables adhere to , which means all local variables are accessible within the function in which they are declared. This is different from the that applies to let and const vars in JavaScript.

  2. Python == works like JS ===, in that both operators compare both value and data type.

Exercises

The following are a collection of concise exercises from (powered by ) that we believe will be helpful in learning Python for algorithms.

  5. (Here's a )

🧮
function scope
block scope
learnpython.org
datacamp.com
Hello, World!
Variables and Types
Lists
Basic Operators
String Formatting
nice article on String Interpolation
Basic String Operations
Conditions
Loops
Functions
Classes and Objects
Dictionaries