pixegamiLearn how to build a RAG (Retrieval Augmented Generation) app in Python that can let you query/chat with your PDFs using generative AI.
This project contains some more advanced topics, like how to run RAG apps locally (with Ollama), how to update a vector DB with new items, how to use RAG with PDFs (or any other files), and how to test the quality of AI generated responses.
π Chapters 00:00 Introduction 01:06 RAG Recap 03:22 Loading PDF Data 05:08 Generate Embeddings 07:16 How To Store and Update Data 10:46 Updating Database 11:45 Running RAG Locally 15:12 Unit Testing AI Output 20:29 Wrapping Up
Python RAG Tutorial (with Local LLMs): AI For Your PDFspixegami2024-04-17 | Learn how to build a RAG (Retrieval Augmented Generation) app in Python that can let you query/chat with your PDFs using generative AI.
This project contains some more advanced topics, like how to run RAG apps locally (with Ollama), how to update a vector DB with new items, how to use RAG with PDFs (or any other files), and how to test the quality of AI generated responses.
π Chapters 00:00 Introduction 01:06 RAG Recap 03:22 Loading PDF Data 05:08 Generate Embeddings 07:16 How To Store and Update Data 10:46 Updating Database 11:45 Running RAG Locally 15:12 Unit Testing AI Output 20:29 Wrapping UpNano Banana πFull Guide and Best Practices for AI Image Generationpixegami2025-09-15 | Learn how to get AMAZING results when you generate or edit images with Nano Banana. This video will walk you through Google's official image generation guide, prompt templates, and best practices β with real examples.
π Chapters 00:00 Nano Banana Guide 01:07 Generate Realistic Images 02:41 Generate Illustrations and Anime 04:01 Generate Product Photos 04:59 Editing Images with Text 06:03 Combining Multiple Images 07:26 6x Best Practices for Nano Banana
#pixegami #nanobananaCreate AMAZING Images with Googles Nano Banana API in Pythonpixegami2025-09-01 | Nano Banana is an INSANELY good AI image model by Google.
It's also known as "Gemini 2.5 Flash Image", and is available via API. In this project, I'll show you how to use the API in Python, to create your own image generation app.
π Chapters 00:00 What is Nano Banana? 01:29 Gemini API Key 02:54 Project Setup 03:45 Nano Banana API Code 07:05 Edit and Merge ImageUV: The Python Tool Everyone Is Switching Topixegami2025-07-31 | UV is a fast, powerful all-in-one package manager for Python that replaces tools like pip and virtualenv. Let's take a look at what it is, how to use it, and why many Python developers are switching to this.
π Links π UV GitHub: github.com/astral-sh/uv π How To Install: https://docs.astral.sh/uv/getting-started/installation
π Chapters 00:00 What is UV? 01:14 Project and Environment Setup 05:44 Package Management 08:59 Run Tools with uvx 11:03 UV SpeedGemini CLI: How To Get Startedpixegami2025-07-08 | Gemini CLI is a free and open source AI agent from Google.
It is a general purpose AI agent that runs from your terminal, and can autonomously work on your project for you. It is extremely powerful, it can connect to external tools via MCP, and it is free to use. But it is also new, so let's take a quick first look at how to use Gemini CLI, and some example use cases.
π Chapters 00:00 What is Gemini CLI? 03:36 Setting Up Gemini CLI 05:05 Configuring GEMINI.md 06:27 Example Use Cases 09:32 Connecting External Tools 12:47 Pricing and Models
#pixegami #aiClaude Code Will 10x Your Productivitypixegami2025-07-01 | Claude Code is a powerful, general purpose AI agent that runs in your terminal. It is super powerful, and it could change the way we build software.
Learn how to install Claude Code, how to configure it, and how to get started using it effectively.
π Chapters 00:00 Introduction 02:20 What Is Claude Code? 04:58 Install Claude Code 07:02 Example Use Cases 11:54 Configuring Claude 13:41 Custom Workflows 15:31 Using External Tools 19:59 Model and Pricing
#pixegamiBuild an MCP Server in 20 Minutes (with Python)pixegami2025-06-24 | Learn how to build an MCP server in Python and connect it to Claude Desktop and use it as an AI agent. This MCP server will let you fetch real time data, and create/modify spreadsheets via Google sheets.
This is a great project for any Python developers that want to get started with MCP server development, and automation with AI agents!
π Chapters 00:00 Introduction 02:33 MCP Server Overview 04:31 Create an MCP Server 08:58 Google Sheets MCP 13:29 Using MCP tools in Claude Desktop
#pixegami #aiWhat is MCP? Model Context Protocol Explainedpixegami2025-06-17 | MCP (Model Context Protocol) gives AI agents superpowers.
It is an open standard that lets AI agents (LLMs) interact with the outside world β sort of a universal API (or a USB-C) for LLMs. Let's take a look at the problem it solves, how it works, and some examples.
With MCP servers, you will be able to enable AI agents to do anything that you could do with an external API: update spreadsheets, make a phone call, crawl the web, play a game (and probably thousands of other things).
π Chapters 00:00 What Is MCP? 01:21 The Problem MCP Solves 04:36 How MCP Works 08:55 An Example Agent
#pixegami #aiFastAPI for AI: Build an AI Endpoint in 30 Minutespixegami2025-06-10 | Learn how to build a FastAPI endpoint for your AI app in 30 mins.
We'll be using FastAPI (Python), and Gemini as our AI LLM model. Then we'll take a look at how to secure the endpoint with rate limiting, and with JWT tokens for authentication.
π Chapters 00:00 - Introduction 01:29 - Project Overview 02:34 - Setting Up 04:13 - Create a FastAPI App 06:46 - AI Integration (Gemini) 08:11 - Custom System Prompt 11:16 - Run the Server 12:51 - Rate Limiting 16:03 - Authentication with JWT 22:38 - Finished Project
#pixegamiBuild a Simple RAG Pipeline in 30 Minutes!pixegami2025-06-03 | Learn how to a simple, modular RAG pipeline in Python. This will let you add documents, generate embeddings, and evaluate (test) the pipeline quality, end-to-end with a single CLI command.
π Chapters 00:00 - Introduction 02:01 - Pipeline Architecture 04:43 - Set Up the Database 09:05 - Set Up Core Components 13:14 - Evaluation 18:04 - Connect the Pipeline 20:35 - Run the Pipeline 23:39 - Upgrade and Run Again!
#pixegamiRun DeepSeek R1 Locally β Free AI That Actually Works!pixegami2025-02-01 | Here are 3 quick and easy ways to run the open-source Deepseek-R1 LLM model locally on your computer.
π Chapters 00:00 How To Run Deepseek Locally 00:52 Run Deepseek-R1 with LM Studio 02:20 Run Deepseek-R1 with Ollama 04:01 Run Deepseek-R1 with PythonSupabase Full Project β‘οΈ The FASTEST Way to Ship a SaaS App?pixegami2025-01-27 | Deploy a fully functioning SaaS application with Supabase and NextJS!
In this video I'll show you how Supabase works, walk you through how to apply its awesome features (databases, auth, storage) to a SaaS project. The code is free for you to download and clone from GitHub. Enjoy!
π Chapters 00:00 Introduction and Demo 02:19 Supabase Project Overview 05:11 Setting Up 08:03 Create Supabase Project 10:10 Supabase CLI 11:54 Google OAuth 14:38 OpenAI API Key 15:32 Stripe Integration 22:12 Add Database 27:28 Add Storage 28:49 Deploy Edge Functions 32:15 Run the Project 34:03 Run Integration Tests 35:02 Wrapping UpHow Good is Cursor AI? Pros and Cons with Real Examplespixegami2024-10-21 | Cursor is an AI code editor. In this video, we're going to try it out by creating a project from scratch. At the end I'll review my experience with this new tool from my perspective as a software engineer.
π Chapters 00:00 Introduction 00:43 How Cursor Works 02:17 Project Overview 02:50 Starting a Project 06:28 Adding New Features 10:33 Refine Map Results 13:08 Expanding Scope 14:46 Difficult Bug 17:57 Build a Flask Server 20:08 Adding CSS Style 23:37 Optimize Performance 26:03 Project Retrospective 28:35 Cursor Pros and Cons 31:38 VerdictHow To Build an AI Agent That Can Use Tools | Python + Claude 3.5 Sonnetpixegami2024-10-07 | Learn how to build a Python AI chat/agent that can use tools (custom functions). In this video, our AI agent will help us making bookings/reservations based on data from a CSV file. We will be using Claude Sonnet 3.5 as our LLM.
π Chapters 00:00 Introduction 01:18 Project Demo 03:45 Tool-Use Concepts 06:33 Project Setup 09:11 Creating Tools 13:34 Tool Schemas 16:48 Tool Helper Functions 17:58 Integrate Tools with AI 23:15 Project Code Walkthrough 26:34 Demo (With Broken Edge Case) 27:59 Final Project Demo 29:21 Wrapping UpClaude 3.5 API in Python β’ Explore AWESOME Use Cases!pixegami2024-07-22 | Learn to use Claude 3.5 Sonnet API in Python with this tutorial. We will have 3 example use-cases: a simple chat interface, tool-use (to make a local artifacts feature), and using vision to read a screenshot. All of this will be done via the API (Python SDK).
π Chapters 00:00 - Introduction 03:07 - Setting Up 04:42 - Chat Example 06:53 - Tool-Use (Function Calling) 09:20 - Creating Tools 11:04 - Using Tools 16:23 - Vision API 19:18 - Wrapping UpHow To Build a Web UI for your AI App (Clean & Functional)pixegami2024-07-08 | Learn how to build a clean and functional NextJS frontend for your RAG/AI API.
The AI app is a RAG (Retrieval Augmented Generation) app, built using Python and FastAPI, and hosted on AWS. The frontend will be using NextJS, TailwindCSS, and shadcn/ui components. The site will be fully static (free/cheap to host, without a dedicated server) and can generally be adapted to any kind of API backend.
π Chapters 00:00 - Introduction 02:43 - Architecture Overview 06:24 - Generate an API Client 09:32 - Implement "ViewQuery" Page 15:27 - Rendering Components 19:31 - Implement "SubmitQuery" Form 25:07 - Implement "QueryList" Component 28:26 - Final LayoutHow To Deploy Your RAG/AI App On AWS (Step by Step)pixegami2024-06-12 | Learn how to deploy your Python RAG/AI project to the cloud in this advanced tutorial. We will turn the app into a public API, and host it on AWS Lambda (which makes it scalable and high-performance).
π Chapters 00:00 - Introduction 04:12 - RAG Recap 11:25 - Project Architecture 13:57 - Adding FastAPI 18:44 - Building a Docker Image 20:54 - Deployment Hacks 25:53 - Local Testing With Docker 29:17 - Build AWS Infrastructure with CDK 42:12 - Creating an Async API 54:11 - Wrapping UpOllama: Run LLMs Locally On Your Computer (Fast and Easy)pixegami2024-04-08 | With Ollama, you can run local, open-source LLMs on your own computer easily and for free. This tutorial walks through how to install and use Ollama, how to access it via a local REST API, and how to use it in a Python app (using a client library like Langchain).
π Chapters 00:00 How To Run LLMs Locally 01:07 Install Ollama 02:45 Ollama Server and API 04:15 Using Ollama Via LangchainUsing Python and AI to Automate My Video Editing Processpixegami2024-04-01 | This is a behind-the-scenes breakdown of my current process for making videos (as of 2024).
In this video I'll show all the steps I take as I make these videos, the equipment I use, and some of the custom AI and automation tooling I've created to help make the process go faster (originally it was 1-2 months, now it's down to about 7 days).
I use a lot of automation (with Python and AI tools) to do a lot of the time-consuming work, such slide generation, editing, translation, and generating video meta-data.
π Chapters 00:00 - Introduction 00:45 - Video Production Process 01:59 - Project Idea 02:52 - Coding 04:21 - Writing 04:55 - Generating Slides 08:44 - Recording 09:44 - Editing 10:20 - Automated First Edit 14:05 - Translation Process 14:55 - Chapter Labelling 15:45 - Process DemoPython Langchain Tutorial: Use 3 Different LLMs in 10 Minspixegami2024-01-15 | Learn how to easily switch between LLMs in Langchain for your Python applications: OpenAI's GPT-4, Amazon Bedrock (Claude V2), and Google Gemini Pro.
Or if none of these suit your needs, you can also implement your own interface.
π Chapters 00:00 Introduction 01:43 Getting Started 03:18 Creating the Base App 05:33 How To Use OpenAI GPT-4 07:42 How To Use Claude/Llama2 (via AWS) 09:33 How To Use Google Gemini Pro 10:27 Custom LLMs
In this video, I'll share what I think are the top 5 skills to invest time into this year if you want to get a high-paying role in tech, or to advance your career.
This is based on various data and trends I've observed in 2023, and some of my own gut feeling.
π Chapters 00:00 Tech Skills in 2024 01:09 Generative AI 05:05 Productivity Automation (RPA) 07:27 Cybersecurity 09:16 Cloud Computing 11:57 Leadership and Soft-Skills
#pixegamiCreate a Custom AI Assistant + API in 10 Minspixegami2023-12-25 | Learn how to build your own AI assistant using OpenAI's Assistants API and how to access it via Python.
π Chapters 00:00 OpenAI Assistants API 00:58 Create an OpenAI Assistant 02:07 Adding Custom Data 04:03 Function Calling 05:58 Code Interpreter 06:57 Using an API Endpoint 10:03 Wrapping Up
#pixegami #aiBuild a Custom AI RPG Game with OpenAI GPTspixegami2023-12-18 | Learn how to build custom OpenAI GPTs by making an interactive text-based adventure game (in the style of Dungeons and Dragons).
π Chapters 00:00 Introduction 01:06 Project Demo 03:46 Creating a Custom GPT 05:40 Loading Custom Data 08:00 AI Prompt Engineering 09:41 AI Image Generation 10:51 Adding Conversation Starters 12:09 Deploy the Custom GPT 12:48 Deploy as an API
#pixegami #openaiPython Dataclasses: Heres 7 Ways It Will Improve Your Codepixegami2023-12-11 | Dataclasses in Python simplify the creation of data structures with minimal code, and gives you a lot of useful utility right out of the box.
π Chapters 00:00 Introduction 00:25 What is a Dataclass? 01:06 Create Data Structures with Less Code 02:34 Descriptive REPR 03:30 Built-in Equality Check 04:11 Fields and Default Values 06:19 Frozen Objects 07:16 Ordered Objects 08:21 Convert To and From Dict
#pixegami #pythonStreamlit: The Fastest Way To Build Python Apps?pixegami2023-11-27 | Learn how to build a Python Streamlit app in just 12 minutes!
Streamlit turns data scripts into shareable web apps in minutes. All in pure Python. No frontβend experience required.
π Chapters 00:00 Introduction 00:41 What is Streamlit? 01:49 Using Input Elements 03:22 Working with Data 04:47 Multipage Apps 06:43 Loan Repayments App 10:03 Deploying to Streamlit Cloud
#pixegami #streamlitRAG + Langchain Python Project: Easy AI/Chat For Your Docspixegami2023-11-20 | Learn how to build a "retrieval augmented generation" (RAG) app with Langchain and OpenAI in Python.
You can use this to create chat-bots for your documents, books or files. You can also use it to build rich, interactive AI applications that use your data as a source.
π Chapters 00:00 What is RAG? 01:36 Preparing the Data 05:05 Creating Chroma Database 06:36 What are Vector Embeddings? 09:38 Querying for Relevant Data 12:47 Crafting a Great Response 16:18 Wrapping Up
#pixegami #pythonLangchain: The BEST Library For Building AI Apps In Python?pixegami2023-10-23 | Learn to build LLM applications using Langchain (an AI toolkit for Python and JS).
π Chapters 00:00 Introduction to Langchain 00:52 Setting Up Langchain and OpenAI 02:36 Generate Predictions 03:40 Getting Structured Output 05:52 Sequential Chains 07:03 Building an AI Agent 10:32 Other Langchain Features
#pixegami #langchainHow to Publish a Python Package to PyPI (pip)pixegami2023-10-16 | Learn how to publish your own custom Python package to PyPI.
This tutorial covers setting up the project, configuring the setup.py file, building and testing the package, adding CLI functionality, publishing to PyPI, and installing using pip. Watch this video if you want to share your code and make it easily installable for others.
π Chapters 00:00 Introduction 00:34 Project Setup 02:06 Configure and Build Package 04:14 Local Testing 05:53 Adding a CLI Entry Point 07:36 Publish to PyPI 10:01 Wrapping Up
#pixegami #pythonAmazon Bedrock Tutorial: Generative AI on AWSpixegami2023-10-09 | Learn how to use Amazon Bedrock for generative AI (text and images) on AWS.
This tutorial will guide you through the setup process, showing you how to generate text and images via code. You'll learn how to generate text with the Anthropic Claude model, and images with the Stable Diffusion model. All you need is an AWS account and the AWS CLI installed and configured.
π Chapters 00:00 Introduction to Amazon Bedrock 01:12 Setting Up Amazon Bedrock 02:54 Text Playground 03:41 Image Playground 04:47 Generating Text with Code 07:43 Generating Images with Code 09:40 Amazon Bedrock Pricing 12:13 Amazon Bedrock vs OpenAI
#pixegami #awsPython Requests Tutorial: HTTP Requests and Web Scrapingpixegami2023-10-03 | Learn how to install and use "requests", one of Python's most popular module by downloads and adoption.
This step-by-step guide covers installation, making HTTP GET/POST requests, handling errors and timeouts, and scraping data from websites. It is important for Python developers who want to integrate their applications with external services and retrieve data from websites.
π Chapters 00:00 Introduction 00:36 GET Request 01:49 HTTP Status Codes 02:33 Request Content 03:22 POST Request 04:54 Handling Errors 07:17 Setting a Timeout 09:27 HTTP Request Headers 10:22 Web Scraping with BeautifulSoup 12:38 Requests vs urllib
#pixegami #pythonHow to Get Started with AWS β’ Crash Coursepixegami2023-09-25 | Learn how to get started with AWS in 30 minutes!
You'll learn how to create an AWS account, configure IAM users, and use the AWS CLI and SDK. We'll also look at AWS CDK and see how we can use it to manage cloud infrastructure.
π Chapters 00:00 Introduction to AWS 00:57 Example AWS Services 05:51 AWS Pricing 07:32 AWS Free Tier 09:30 4 Ways to Use AWS 10:26 Create an AWS Account 11:40 How AWS Regions Work 13:08 Creating an IAM User 16:21 Using the AWS CLI 19:11 Using the AWS SDK for Python 20:47 Using AWS CDK 25:37 Deploying a Serverless To-Do List 27:16 Reference Architecture Diagrams
#pixegami #awsPydantic Tutorial β’ Solving Pythons Biggest Problempixegami2023-09-18 | Learn how to use Pydantic in this short tutorial!
Pydantic is the most widely used data validation library for Python. It lets you structure your data, gives you type-hints and auto-complete in your IDE, and helps to serialize to and from JSON. Learn how to use it in just 10 minutes!
π Chapters 00:00 Python's Dynamic Typing Problem 02:11 How To Use Pydantic 05:04 Validating Data with Pydantic 06:36 Custom Field Validation 07:58 JSON Serialization 08:49 Pydantic vs Dataclasses
#pixegami #pythonPython FastAPI Tutorial: Build a REST API in 15 Minutespixegami2023-09-11 | Learn how to install and build your first app with FastAPI (a high-performance web framework for Python).
In this tutorial, you'll learn how to instal FastAPI, and use it to create a new app. Learn how to define routes, handle errors, use request and path parameters, validating data with Pydantic models, modelling responses, and how to use the interactive documentation.
π Chapters 00:00 Why Use FastAPI? 00:49 Install and Get Started with FastAPI 02:22 GET and POST Routes 05:27 Handling HTTP Errors 06:48 JSON Request and Path Parameters 10:38 Response Models 11:42 Interactive Documentation 13:31 FastAPI vs Flask
#pixegami #fastapiHost a Python Discord Bot on AWS Lambda (Free and Easy)pixegami2023-09-04 | Learn how to create and host a Python Discord bot on AWS Lambda.
In this tutorial, you'll learn how to create a Discord Bot using Discord's Interaction HTTP endpoints β and then host it for FREE on AWS Lambda. This is a super easy, cost-effective, and scalable way to host Discord bots.
We'll be using Flask and Python for the Discord bot, and AWS CDK to deploy our Lambda infrastructure.
00:00 Introduction 02:40 Set Up Discord Bot 04:13 Register Bot Commands 07:21 Creating Bot Endpoints 10:38 Token Validation 13:02 Create Docker Image 16:04 Deploying with AWS CDK 21:15 Wrapping UpMy MacOS Terminal Setup for HIGH Productivitypixegami2023-08-28 | This is an AWESOME set-up that helps me be a more productive developer with Python.
Starship enhances your prompt bar, Fish provides auto suggestions and syntax highlighting, and Warp offers an enhanced editing experience.
π Chapters 00:00 Custom Terminal Overview 02:15 Starship Prompt 04:30 Fish Shell 06:07 Warp TerminalHow to Run a Python Docker Image on AWS Lambdapixegami2023-08-21 | Learn how to run a Python Docker image on AWS Lambda using Lambda container images.
Using Docker containers for your Python app on AWS Lambda means you can create a special environment for your app with its own tools and settings, making sure it works everywhere, and also making it easy to include all the things (libraries and packages)your app needs to run correctly.
π Chapters 00:00 Why run Docker on AWS Lambda? 01:04 Create CDK Project 01:45 Create a Python handler app 04:07 Create Dockerfile for Lambda 06:23 Test the Docker image locally 07:11 Create AWS Docker Lambda with CDK 09:44 Deploying to AWS 11:59 Wrapping up
#pixegami #awsHow To Use GitHub Actions β’ Automate Your AWS Deploymentspixegami2023-08-07 | Learn how to automate your code deployments with GitHub actions.
In this project, I'll show you how to use GitHub actions to automate the deployment (CI/CD) of an AWS project. The project is a simple Python API hosted on AWS Lambda (managed with AWS CDK).
We'll learn how to set up a simple GitHub action workflow to deploy the project to our AWS account on each commit. We'll also look at how to add a version number that increments automatically with each commit, and how to cache workflow steps (so we don't have to do the same things over and over again).
π Chapters 00:00 Introduction 00:38 What is CICD? 01:51 Create an AWS CDK Project 05:46 Create GitHub Actions Workflow for CDK Deploy 10:18 Automate Version Bump with Git Hooks 12:42 Caching Workflow Steps 14:35 Deploy a Major Infrastructure UpdateStripe & Firebase Tutorial β’ Add Payments To Your NextJS Apppixegami2023-08-01 | Want to collect monthly payments from your app or online business?
In this project, we will implement a monthly subscription payment system for our NextJS web app. We'll be using Stripe as our payment processor, and integrate it with Google Firebase for user authentication.
00:00 Introduction 01:52 Project Architecture 04:04 Authentication With Firebase 12:27 Setting Up Stripe For Payments 21:51 Adding a Checkout Session 29:17 How To Check Subscription Status 32:18 Adding Stripe Account Management 34:47 Wrapping UpLearn Python β’ #12 Final Project β’ Build an Expense Tracking App!pixegami2023-04-07 | In the final project of this Python beginner course, we'll build a personal expense tracking app from scratch.
With this app, you'll learn how to get expense data from a user (via the terminal), save and categorize that expense into a spreadsheet. This project will take about an hour, and will test all of the Python skills you've learnt so far.
You'll learn how to plan, structure and implement a project all the way from an idea to a finished product β as well as seeing how you can solve new and unexpected problems.
π Chapters 00:00 - Introduction 02:55 - Project Specs: Expense Tracker 06:30 - Create an "Expense" Class 07:55 - Main Project Structure 13:27 - Getting User Expenses 30:58 - Saving Expenses to a File 36:14 - Summarise All Expenses 43:54 - Group Expense By Category 49:07 - Track Remaining Budget 56:46 - Wrapping UpLearn Python β’ #11 Classes β’ Create and Use Classes in Pythonpixegami2023-04-06 | Classes are like a blueprint for creating your own reusable data structures in Python.
They allow you to define your own custom data types, with properties and methods that can be accessed and manipulated from other parts of your code.
In this video, you'll learn the basics of creating and using classes in Python. We'll cover why classes are useful, how to define them, and how to create instances of classes.
This video is part of a beginner tutorial series for anyone who wants to learn Python from scratch, and get to a point where you can start coding your own projects.
π Chapters 00:00 - Introduction to Classes in Python 01:33 - How to Create a Class 04:53 - Creating an "Instance" of a Class 08:40 - Instance Methods 10:49 - Method Arguments 12:38 - Static Variables 15:30 - Static Methods 19:15 - Example Uses of Classes 20:53 - Coding Exercise: ClassesLearn Python β’ #10 User Input β’ 4 Ways To Get Input From Your Userpixegami2023-04-05 | If you want users to interact with your Python app, you'll need some way to get their input.
In this video, you'll learn 4 different ways your Python app can get input from the outside world β directly from the terminal, from a command line, from a file on your computer, and over the internet.
This video is part of a beginner tutorial series for anyone who wants to learn Python from scratch, and get to a point where you can start coding your own projects.
π Chapters 00:00 - Different Ways to Get User Input 02:24 - Using the input() Function 05:25 - Getting CLI Input 08:20 - Reading and Writing from Files 11:35 - Rest APIs (Talking To Other Apps) 14:20 - Coding Exercise: User InputLearn Python β’ #9 Functions β’ Pythons Most Important Concept?pixegami2023-04-04 | Functions are a fundamental building block of programming in Python.
They allow you to package code into a named, reusable unit that can be called from other parts of your program. In this video, you'll learn how to define a function, pass in arguments, and return values.
This video is part of a beginner tutorial series for anyone who wants to learn Python from scratch, and get to a point where you can start coding your own projects.
π Chapters 00:00 - Introduction to Functions in Python 01:01 - Creating a Function 03:28 - Input Arguments 08:57 - Multiple Arguments 10:43 - Positional Arguments 11:40 - Keyword Arguments 13:34 - Optional Arguments 15:46 - Common Mistakes With Functions 21:03 - What is "Abstraction"? 23:42 - Coding Exercise: FunctionsLearn Python β’ #8 Tuples and Sets β’ More Ways To Store Data!pixegami2023-04-03 | Tuples and Sets are two important data structures in Python that you'll definitely want to know about.
Tuples are immutable sequences of elements, typically used for grouping related data.
Sets, on the other hand, are mutable collections of unique elements. With Sets, it's easy to check if an item is inside that collection. It's also easy to do mathematical operations like "intersection" (finding common elements between two sets) and "union" (finding all elements of two combined sets).
This video is part of a beginner tutorial series for anyone who wants to learn Python from scratch, and get to a point where you can start coding your own projects.
π Chapters 00:00 - Tuples vs Lists 01:58 - Tuple Syntax in Python 03:36 - Hands-on Coding with Tuples 05:45 - Using Tuples as a Dict Key 08:02 - Sets in Python 14:28 - Coding Exercise: Tuples and SetsLearn Python β’ #7 Dictionaries β’ The Most Useful Data Structure?pixegami2023-04-02 | Dictionaries are a data structure that store key-value pairs, and is quite possibly the most useful data structure in Python.
Dictionaries allowing for fast and efficient data retrieval. We'll cover how to create and manipulate dictionaries, as well as common methods for iterating through them.
You'll also learn how you can use dictionaries to represent real objects (e.g. like a shoe), and how to turn that data into a format that you can send over the internet to other apps (JSON serialization).
This video is part of a beginner tutorial series for anyone who wants to learn Python from scratch, and get to a point where you can start coding your own projects.
π Chapters 00:00 - Dictionaries vs List 03:42 - How to Use a Dictionary 05:03 - Adding, Updating and Removing items 12:03 - Dictionary Built-In Methods 13:15 - Looping Through a Dictionary 14:58 - Dictionary as Objects 16:45 - JSON Serialization 19:12 - Coding Exercise: DictionariesLearn Python β’ #6 Loops β’ How to Repeat Code Executionpixegami2023-04-01 | Loops in Python will let you run the same code over and over again.
This is useful when working with lists, or if you have something that you want to execute for a certain number of times. In this tutorial, you'll learn why loops are such an important programming concept, and how you can use them in Python.
You'll learn how to loop through every item in a list, or loop for a fixed number of times. You'll also learn how to break early out of a loop, or how to short-circuit a loop execution to "continue" to the next iteration of a loop.
This video is part of a beginner tutorial series for anyone who wants to learn Python from scratch, and get to a point where you can start coding your own projects.
π Chapters 00:00 - Introduction to Loops in Python 00:40 - Using "for" Loops 04:00 - Using enumerate() 04:58 - Looping X Times ("for in range") 07:13 - Using "while Loops 09:00 - How to "break" a loop 10:53 - How to "continue" a loop 13:17 - List Comprehensions 15:00 - Coding Exercise: LoopsLearn Python β’ #5 Lists β’ Storing Collections of Datapixegami2023-03-31 | In Python, you can use "lists" to store collections of data.
You can add/remove items from a list, check if items are in a list, or even sort and reverse a list. The "list" (also known as an array or a vector in some other coding languages) is the first data structure you'll learn about in this seriesβyet it will remain one of the most useful and important concepts you'll rely on throughout your coding journey.
This video is part of a beginner tutorial series for anyone who wants to learn Python from scratch, and get to a point where you can start coding your own projects.
π Chapters 00:00 - Introduction to Lists 02:35 - Hands-on Coding with Lists 04:41 - 2D Lists 06:42 - Add / Remove Items from Lists 13:08 - Other Lists Methods 18:43 - Coding Exercise: ListsLearn Python β’ #4 Conditions β’ If / Else Statementspixegami2023-03-30 | Conditions (if/else statements) in Python let you control the flow of your program based on a set of rules.
By using if/else statements, you can create decision-making processes in your code that allow it to do different things depending on whether certain conditions are true or false. This is how you can create programs that can respond to user input and other dynamic situations.
This video is part of a beginner tutorial series for anyone who wants to learn Python from scratch, and get to a point where you can start coding your own projects.
π Chapters 00:00 - "if" in Python 03:05 - "else" in Python 04:43 - "elif" in Python 06:52 - Inline Boolean Expressions 09:01 - Coding Exercise: ConditionsLearn Python β’ #3 Operators β’ Add, Subtract and More...pixegami2023-03-29 | Logical and arithmetic operators in Python allows you to manipulate data and variables in your application.
You can use them to add, subtract, multiply and divide numbers. You can also use logical operators, like "and, or, not" with boolean (True/False) values to create more complex logic in your application.
This video is part of a beginner tutorial series for anyone who wants to learn Python from scratch, and get to a point where you can start coding your own projects.
π Chapters 00:00 - What are operators? 00:29 - Arithmetic Operators 04:29 - Assignment Operators 06:50 - Comparison Operators 09:25 - Logical Operators 14:48 - Coding Exercise: OperatorsLearn Python β’ #2 Variables and Data Types β’ Pythons Building Blockspixegami2023-03-28 | Variables and data-types are the foundational building blocks of your Python code.
They will let you store any type of data, and allow you to refer to it later on when you need to do something with it. Learn all about them in this quick tutorial.
This video is part of a beginner tutorial series for anyone who wants to learn Python from scratch, and get to a point where you can start coding your own projects.
π Chapters 00:00 - Using Variables in Python 05:20 - Good vs Bad Variable Names 08:48 - Data Types in Python 11:08 - Using print() in Python 14:38 - Coding Exercise: VariablesLearn Python β’ #1 Installation and Setup β’ Get Started With Python!pixegami2023-03-27 | Learn how to install and run Python (on Windows/Mac/Linux) so you can get started on your learning journey. Python is a versatile programming language, and is an ideal first language for anyone who wants to learn.
This video is part of a beginner tutorial series for anyone who wants to learn Python from scratch, and get to a point where you can start coding your own projects.
π Chapters 00:00 - Install Python 01:42 - Run the Python Interpreter 03:34 - Install VSCode 04:20 - Write and Run a Python Script 07:46 - How To Get Help