Brief Summary
This video provides a comprehensive guide to learning Python for AI development, starting from basic setup to building practical AI applications. It emphasises a professional coding environment, core Python concepts, and real-world applications. Key points include:
- Setting up a professional Python development environment.
- Understanding Python syntax, data types, and control flow.
- Working with external tools, APIs, and data.
- Implementing object-oriented programming (OOP) with classes.
- Utilising Git and GitHub for version control.
- Managing environment variables and secrets.
- Using Ruff for code formatting and Uv for package management.
Introduction: Learn Python for AI
The video introduces a Python programming course tailored for building AI applications, designed for both beginners and those with some coding experience. It promises to teach core concepts efficiently, focusing on practical, battle-tested knowledge relevant to the AI field. The course aims to equip learners with in-demand skills for AI app development and data science, distinguishing itself by prioritising essential knowledge and a professional coding setup.
Course Overview & Structure
The course handbook, available via a link in the description, serves as a central resource for information and code snippets. The course prioritises setting up a professional work environment, including tools, package management, and project creation, to mirror real-world coding practices. The Python basics are covered, but even experienced coders are encouraged to follow along to ensure their setup matches the course's environment.
Installing Python
The initial step involves installing Python on the user's machine, with separate instructions provided for Windows and Mac. For Windows users, the video directs them to python.org/downloads, emphasising the importance of adding Python to the system path during installation. Mac users are instructed to check for pre-existing Python installations via the terminal, and if necessary, download and install Python from the official website.
Installing Python on Windows
To install Python on Windows, navigate to python.org/downloads and download the latest version of Python. During the installation process, it's crucial to check the box that says "Add Python to Path". This ensures that Python commands can be run from the command line. Follow the rest of the installation steps without customising unless you know what you are doing.
Installing Python on Mac
To install Python on Mac, first check if Python is already installed by opening the terminal (via Spotlight using command + spacebar) and typing python3 --version. If a version number appears, Python is installed. If not, download the latest version from python.org/downloads and install it like any other application.
Installing VS Code
The next step is to install Visual Studio Code (VS Code), a free and extensible code editor suitable for Python development. Users are directed to the VS Code website to download the appropriate version for their operating system (Windows or Mac OS). The installation process is similar to other applications, with a reminder to add VS Code to the path on Windows systems.
Setting Up VS Code (Extensions)
To enhance VS Code for Python development, the Python extension from Microsoft should be installed via the extensions tab in VS Code. Additionally, a setting is changed to ensure Python files execute from their directory. The Pylance and Jupyter extensions are also recommended for installation to improve code recognition and enable the use of Jupyter notebooks.
Customizing VS Code
Customising VS Code is optional but allows for a more personalised coding environment. The Atom One Dark theme is recommended for its aesthetics, and can be installed via the extensions tab. Themes change the colouring and fonts of the code editor.
Creating Your First Project
Creating a project involves setting up a dedicated folder on the computer to store all Python projects. A subfolder is created for the specific course, named "Python-for-AI" using kebab case (all lowercase with dashes). This folder is then opened in VS Code via the explorer tab.
Creating a VS Code Workspace
To ensure project settings are saved and easily accessible, a workspace is created in VS Code. This is done by navigating to File > Save Workspace As, and saving the workspace file (with the .code-workspace extension) in the project folder. This allows for easy reopening of the project with all settings intact.
Your First Python File (hello.py)
A new Python file named "hello.py" is created within the project folder. The ".py" extension is crucial for VS Code to recognise the file as a Python file. A simple print statement (print("Hello, World!")) is added to the file as a basic test.
Running Python Code
Before running the Python file, the correct Python installation needs to be selected in VS Code. This is done by checking the bottom screen for the Python version and selecting the appropriate one. The Python file is then executed by clicking the play button in the top right corner, which opens the terminal window within VS Code and runs the code.
Exercise & Recap
As an exercise, users are encouraged to experiment with different print statements to get a feel for writing code and sending it to the terminal. A quick setting adjustment is recommended to increase the tree indentation in the file explorer for better visual organisation. The chapter recaps the installation of Python, VS Code, and the creation of the first Python file, as well as the understanding of how to run it.
Course Resources & Community
The video highlights the availability of course resources, including templates, files, and cheat sheets, accessible via a resource hub. Exclusive community access to the Data Lumina Academy is also offered, providing bonus lessons, advanced tutorials, and follow-up courses. Access to these resources is free, requiring only an email address.
Understanding Python Environments
The importance of Python environments is explained, emphasising the need for isolated copies of Python installations for different projects to avoid dependency conflicts. Virtual environments ensure that each project has its own set of packages and versions, preventing issues when projects require different versions of the same package.
Understanding Python Packages & Pip
Python packages are introduced as pre-written code collections that solve specific problems, saving developers from starting from scratch. Pip is the tool used to install and manage these packages, pulling them from the Python Package Index (PyPI) and installing them into the project's virtual environment.
Creating Virtual Environments (venv)
Two methods for creating virtual environments are demonstrated: a simple method using the command pallet within VS Code, and a terminal-based method using the python3 -m venv command. The command pallet method involves opening the command pallet (cmd/ctrl + shift + P), typing "Python: Create Environment", selecting "venv", and choosing a Python version. The terminal method involves using the command python3 -m venv <environment_name>.
A Note on Anaconda
Anaconda is mentioned as an alternative way of managing Python distributions and virtual environments, particularly popular in the data science world. However, the course suggests that it is not recommended, and that UV is a better way to manage Python environments and packages.
Installing Python Packages (pip install)
To install Python packages, the pip install command is used in the terminal, followed by the package name (e.g., pip install requests). It's important to ensure that the terminal is active within the project's virtual environment to install the package correctly. The video also shows what happens behind the scenes when you install a package.
Using Python Packages (Import)
To use an installed package, an import statement is added to the Python file (e.g., import requests). A code snippet is provided to import the requests library and download the content of a webpage as a test. Reopening VS Code can resolve any import errors.
Interactive Python with Jupyter
Interactive Python with Jupyter is introduced as a more efficient way to work with Python, using the interactive window. This requires installing the ipykernel package (pip install ipykernel) and changing a VS Code setting (Jupyter: Interactive Window Text Editor Execution). With this setup, code can be executed line by line using shift + enter, with the output displayed in a clean window to the right.
Full Setup Recap & Exercise
The getting started section is recapped, including installing Python, creating a Python file, selecting the interpreter, and running the file. As an exercise, users are instructed to create a new project from scratch, setting up a file, adding Python code, using a virtual environment, and running the code in both the terminal and the Jupyter interactive session.
What is Programming?
Programming is defined as writing instructions for computers, which are very literal and require specific commands to follow the exact order of operations. Python is one of many programming languages used to accomplish this. The goal is to combine basic building blocks to create AI chatbots, AI agents or simple scripts.
Understanding Python Syntax & PEP8
Python syntax refers to the rules for properly writing Python code. Indentation, using four spaces to separate blocks of code, is a unique and important aspect of Python syntax. PEP 8, the official style guide for Python code, covers guidelines for indentation, spaces, line length, and naming conventions to make code more readable.
Understanding & Debugging Errors
Errors are a normal part of programming. Reading Python errors involves identifying where the error happened, what went wrong, and the arrow pointing to the exact spot. AI assistants like ChatGPT can be used to explain error messages and provide solutions.
Variables
Variables are used to store and name data, allowing it to be referenced and manipulated throughout a program. Variable names must follow certain rules: they cannot start with a number, use a hyphen, or use a space. Python naming conventions recommend using lowercase letters with underscores between words (snake case).
Comments
Comments are human-readable explanations in code that Python ignores. They are created using a hashtag (#) for single-line comments or triple quotes (""") for multi-line comments. Comments should explain the "why" behind the code, not the "what".
Data Types Introduction
Data types are the different kinds of information that can be stored in Python, including numbers, strings, and booleans.
Numbers (Integers & Floats)
Numbers in Python come in two types: integers (whole numbers without decimals) and floats (numbers with decimal points). Basic math operations (addition, subtraction, multiplication, division, powers) can be performed on numbers.
Strings
Strings are text enclosed in quotes (single or double). Strings can be manipulated using operations like concatenation (joining strings with the + sign) and repetition (multiplying a string by a number). The len() function can be used to check the length of a string.
String Formatting (F strings)
F-strings are used to dynamically insert variables into strings. They are created by placing an "f" before the string and using curly brackets {} to enclose the variable names.
String Methods
String methods are functions that can be applied to strings to perform various operations, such as converting to lowercase (.lower()), converting to uppercase (.upper()), and capitalising the first letter of each word (.title()). Other methods include startswith(), endswith(), find(), count(), and replace().
Booleans
Booleans are true or false values used to make decisions in programming. They can be directly assigned or result from comparisons. Comparison operators (==, !=, >, <, >=, <=) are used to create boolean values based on conditions.
Operators (Arithmetic, Comparison, Logical)
Arithmetic operators (+, -, *, /, **) perform math calculations. Comparison operators (==, !=, >, <, >=, <=) compare values and return boolean results. Logical operators (and, or, not) combine boolean values and conditions. Truth tables are used to understand how logical operators work.
Shortcut Assignments (+=)
Shortcut assignments (e.g., +=) provide a concise way to modify a variable's value. For example, score += 5 is equivalent to score = score + 5.
Control Flow Introduction
Control flow refers to the order in which code is executed. Conditional statements (if, elif, else) and loops (for loops) are used to control the flow of execution based on conditions and to repeat actions.
Conditional Statements (if, elif, else)
Conditional statements (if, elif, else) allow programs to make decisions based on conditions. The if statement executes a block of code if a condition is true. The elif statement checks additional conditions if the previous if or elif conditions are false. The else statement executes a block of code if none of the previous conditions are true.
Loops (For Loops & range())
For loops are used to repeat a block of code a specific number of times or for each item in a sequence. The range() function generates a sequence of numbers that can be used in a for loop. Python uses zero-based indexing, meaning it starts counting from zero.
Data Structures Introduction
Data structures are used to store multiple values together. Common data structures in Python include lists, dictionaries, tuples, and sets.
Lists
Lists are ordered collections of items, created using square brackets []. They can contain items of different data types. Items in a list can be accessed by their index, starting from zero. Lists can be modified by adding, removing, or changing items.
Dictionaries
Dictionaries are collections of key-value pairs, created using curly brackets {}. Each key is associated with a value. Values can be accessed by their keys. Dictionaries are mutable, meaning items can be added, removed, or changed.
Tuples
Tuples are ordered, immutable collections of items, created using parentheses (). Once created, tuples cannot be modified.
Sets
Sets are unordered collections of unique items, created using curly brackets {} or the set() function. Sets automatically remove duplicate values.
Functions (Defining & Calling)
Functions are reusable blocks of code defined using the def keyword. They can take input parameters and perform specific actions. Functions are called by their name followed by parentheses.
Function Parameters & Arguments
Parameters are variables listed inside the parentheses in a function definition. Arguments are the actual values passed to the function when it is called. Functions can have multiple parameters, and default values can be assigned to parameters.
Global vs Local Variable Scope
Variables defined inside a function have local scope, meaning they are only accessible within that function. Variables defined outside of any function have global scope, meaning they are accessible throughout the program. Local variables take precedence over global variables within a function.
Returning Values from Functions
Functions can return values using the return keyword. The returned value can then be stored in a variable or used in another expression. Functions can return multiple values by separating them with commas.
External Tools (Modules, Packages)
Python's power comes from its extensive ecosystem of external tools, including modules and packages. Modules are single Python files, while packages are folders containing multiple modules. These tools provide pre-written code that solves specific problems.
Importing Modules & Built ins
Modules and packages are accessed using the import statement. Built-in modules come with Python and do not require installation, while external packages need to be installed using pip. Specific functions from a module can be imported using the from module import function syntax.
Import Methods Summary
Different methods for importing modules are summarised, including importing the entire module (import module), importing specific functions (from module import function), and importing with an alias (import module as alias).
Installing Packages & requirements.txt
Python packages are installed using pip (pip install package_name). Specific versions of packages can be installed using pip install package_name==version_number. A requirements.txt file lists all the packages and versions required for a project, allowing for easy installation of dependencies using pip install -r requirements.txt.
Working with APIs (Requests Example)
Working with APIs (Application Programming Interfaces) is a common task in AI development. The requests library is used to send HTTP requests to APIs and retrieve data. The video demonstrates how to use the requests library to interact with a free weather API, constructing the API URL with latitude and longitude parameters, sending a request, and extracting the data from the JSON response.
Working with Data Example (Pandas & Matplotlib)
The video demonstrates how to use the pandas library to manipulate data and the matplotlib library to visualise it. It shows how to load data from an API response into a pandas DataFrame, perform calculations, and create a plot of the data.
Reading & Saving Data Files
Pandas can be used to read data from various file formats, including CSV, JSON, and Excel. It can also be used to save data to these formats. The video demonstrates how to save a DataFrame to a CSV file using the to_csv() method.
Practical Python Introduction
The video transitions to practical Python, focusing on building real programs with a proper structure. This involves organising code into reusable functions and across multiple files.
Project Structure & Organization
A well-organised project structure is crucial for maintainability and scalability. The video recommends creating separate folders for data (input files) and output (results, figures). A main script is kept at the top level, and code is split into reusable functions.
Understanding File Paths
Understanding file paths is essential for accessing data and other files within a project. Relative paths are used to specify the location of a file relative to the current working directory. The video demonstrates how to use relative paths to access a CSV file in a data folder.
Working with Different File Types
Python can work with various file types, including CSV, JSON, XML, text files, and parquet files. The video demonstrates how to use pandas to read and write CSV, JSON, and Excel files.
Organizing Code into Modules
To make code more modular and reusable, it can be organised into separate files called modules. Functions can be defined in one module and then imported into another module using the from module import function syntax.
Error Handling (Try/Except)
Error handling is crucial for making programs more robust. The try and except blocks are used to handle potential errors gracefully. The code that might raise an error is placed in the try block, and the code that should be executed if an error occurs is placed in the except block.
Introduction to Classes (OOP)
Classes are introduced as a way to organise code by grouping related data (attributes) and functions (methods) together. This is known as object-oriented programming (OOP).
Creating Your First Class (init, self)
To create a class, the class keyword is used, followed by the class name. The __init__ method is a special method called the constructor, which is used to initialise the object's attributes. The self parameter refers to the current object and is used to access its attributes and methods.
Class Attributes vs Instances
Class attributes are shared by all instances of the class, while instance attributes are unique to each instance. The self keyword is used to access instance attributes.
Class Methods
Methods are functions defined inside a class. They always have self as the first parameter. Methods can be used to perform actions on the object's attributes.
Class Inheritance
Class inheritance allows a new class (child class) to inherit attributes and methods from an existing class (parent class). This promotes code reuse and modularity.
When to Use Classes vs Functions
Classes are best used when you want to keep track of a state between operations, group related data and functions together, create multiple instances with similar behaviour, or model real-world objects or concepts. Functions are best used for simple transformations, stateless operations, one-off calculations, or small scripts.
Introduction to Git & GitHub
Git is a version control system that tracks changes to code and allows you to go back to previous versions. GitHub is a web-based platform for hosting and collaborating on Git repositories.
Git Fundamentals
Key Git concepts include: a repository (a project tracked by Git), staging (choosing which changes to save), commit (creating a snapshot of the code), pushing (uploading changes to GitHub), pulling (downloading changes from GitHub), and cloning (downloading a repository from GitHub).
Installing Git
Git can be installed on Windows, Mac, and Linux. The video provides links to the official Git website and instructions for installing Git using package managers like Homebrew on Mac.
Basic Git Workflow
The basic Git workflow involves initialising a repository (git init), staging changes (git add .), committing changes (git commit -m "message"), and pushing changes to GitHub (git push origin main).
GitHub Account Setup & Authentication
To use GitHub, you need to create an account on github.com. Authentication is set up using the GitHub CLI, which involves logging in via the terminal and authenticating through a web browser.
Cloning GitHub Repositories
To download a repository from GitHub, use the git clone command followed by the repository URL. The URL can be copied from the GitHub website by clicking on the "Code" button.
Creating Repositories & .gitignore
To create a new repository on GitHub, click on the "New" button on the GitHub website. A .gitignore file is used to specify files and folders that should not be tracked by Git, such as virtual environments and temporary files. A comprehensive .gitignore file can be downloaded from the resource hub.
Using Git with VS Code UI
VS Code provides a user interface for Git, making it easier to manage changes and commit them. The source control menu in VS Code displays the changes, allows you to stage them, and provides a text box for entering commit messages.
Environment Variables & Secrets (.env)
Environment variables are used to store sensitive information, such as API keys and database URLs, outside of the code. This prevents them from being accidentally committed to version control.
Using python dotenv Package
The python-dotenv package is used to load environment variables from a .env file. The .env file is placed in the root of the project and contains key-value pairs for the environment variables. The load_dotenv() function is called to load the variables into the environment.
Introduction to Ruff (Linter & Formatter)
Ruff is a tool used to format and lint Python code, ensuring it adheres to style guidelines and best practices. It provides three tools in one: linting, formatting, and import sorting.
Setting Up Ruff in VS Code
Ruff is set up in VS Code by installing the Ruff extension from Astral. The "format on save" setting is enabled, and the default formatter is set to Ruff.
Ruff in Action
Ruff automatically formats code on save, ensuring it adheres to style guidelines. It also identifies potential errors and unused imports. The "Fix All Auto-Fixable Problems" command can be used to automatically correct many of these issues.
Introduction to Uv (Modern Package Manager)
Uv is introduced as a modern Python tool that can replace pip for managing packages and environments.
Installing Uv
Uv is installed using a curl command on Mac or Linux, or via PowerShell on Windows.
Using Uv (uv init, add, sync)
Uv can be used to initialise a new project (uv init), add packages (uv add package_name), and synchronise the environment (uv sync).
Complete Python Project Workflow Exercise
A complete workflow exercise is presented, guiding users through the process of creating a new project, installing packages, managing the virtual environment, and pushing the project to a GitHub repository.
Course Wrap up & What's Next
The course is wrapped up, recapping the skills learned and encouraging users to leave a comment, like the video, and subscribe to the channel. A follow-up course on building AI agents is recommended, accessible via the Data Lumina Academy.
