Section 1.1: Getting Started with Python

This lesson is part of the "🐍 Python Masterclass: Unlock the Code to Your Future!" series. By the end of this lesson, you will have a fully operational Python environment on your computer.

Section 1.2: Downloading Python

To begin coding in Python, you must first install it on your computer. Follow these steps:

1. Visit the official Python website.
2. Click the "Download Python" button. The site will automatically detect the latest version suitable for your operating system.

Section 1.3: Installation Steps

Once you've downloaded Python, it's time to install it based on your operating system.

Section 1.4: Verifying Your Installation

After installation, you can confirm that Python was installed correctly. Open your command prompt (Windows) or terminal (macOS, Linux) and enter:

python3 --version

You should see the version number of Python you installed. If you encounter an error, you may need to reinstall.

Section 1.5: Setting Up the Python Interpreter

The Python interpreter allows you to execute your Python code. You can access it using the Python Shell.

• On Windows: Open the Command Prompt and type python or py.
• On macOS and Linux: Open a terminal and type python3.

You should see a prompt indicating that the interpreter is ready for your commands.

Section 1.6: Running Your First Program

Now that you have installed Python and set up the interpreter, let's run your first program. At the Python prompt, type:

print("Hello, World!")

Congratulations! You've just executed your first Python program.

Section 1.7: Exiting the Python Shell

To leave the Python Shell and return to the command line, type exit() and hit Enter, or use the keyboard shortcut Ctrl + Z (Windows) or Ctrl + D (macOS, Linux).

Section 1.8: Setting Up a Code Editor

While the Python Shell is useful for short snippets of code, for larger projects you'll need a code editor or Integrated Development Environment (IDE).

Two excellent choices for Python development are Visual Studio Code and PyCharm, both of which come with comprehensive documentation to assist you in getting started.

Section 2.1: Setting Environment Variables

After installing Python, it's beneficial to configure environment variables. These variables help customize how your operating system behaves and manage application execution.

For Python, setting environment variables allows your system to efficiently locate Python executables and scripts.

• Windows: Navigate to 'System Properties' > 'Advanced' > 'Environment Variables.' Click 'New' under 'System variables.' For 'Variable name,' enter 'PYTHONPATH,' and for 'Variable value,' input the path to your Python installation (e.g., C:Python39).
• macOS and Linux: Open a terminal and edit the .bash_profile (macOS) or .bashrc (Linux) file in a text editor (e.g., nano ~/.bash_profile). Add the line:

export PYTHONPATH="/path/to/your/python"

Be sure to replace /path/to/your/python with the actual installation path.

Section 2.2: Configuring the Python Path

Setting the Python path is vital for running Python from any command prompt. Typically, the option to "Add Python to PATH" during installation manages this. However, verifying or adjusting it manually ensures that you can run Python scripts seamlessly.

To confirm, open a command prompt or terminal and type python (or python3 on macOS and Linux). If the interpreter launches, your path is correctly set.

Section 2.3: Selecting an Integrated Development Environment (IDE)

An IDE offers a comprehensive suite of tools that can greatly enhance your development experience. When choosing a Python IDE, consider your specific needs and project requirements. Some popular options include:

• PyCharm: A robust IDE for Python supporting web development and data science, complete with a smart code editor and debugger.
• Thonny: Designed for beginners, Thonny focuses on teaching Python programming with a straightforward interface.
• Spyder: An advanced IDE suitable for data science and scientific computing, featuring sophisticated editing and debugging capabilities.
• Jupyter Notebooks: A versatile IDE for live code, equations, and visualizations, widely used in data analysis and machine learning.

When selecting an IDE, ensure it matches your project goals and personal preferences. Many IDEs also offer plugins and extensions to enhance functionality.