How to Run a Python Project: Step-by-Step Guide, Methods & Best Practices (2025)

Did you know? In 2025, Python powers over 28% of global software projects due to its simplicity, vast standard libraries, and strong support for frameworks like Django and Flask

Running a Python project involves more than just writing code. It requires setting up the right environment, managing dependencies, and following best practices to ensure your project runs smoothly and is easy to maintain. Whether working on a simple script or a complex application, knowing how to run a Python project step by step can save you time and headaches.

This blog will teach you the essential methods and best practices for running Python projects in 2025.

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Prerequisites Before Running a Python Project

Before diving into running a Python project, having the right tools and basic skills is essential. These prerequisites are the foundation to ensure your project runs smoothly and avoids common headaches. Without them, you might face errors, compatibility issues, or struggle to manage your code efficiently. 

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Let’s walk through what you need to get started.

Installing Python (Latest Stable Version)

First and foremost, you need to have Python installed on your system. Using the latest stable version is important because it ensures access to the newest features, security updates, and performance improvements. Older versions might lack support for specific libraries or introduce compatibility problems.

You can download Python directly from the official Python website. When installing, make sure to check the option to add Python to your system PATH. This lets you easily run Python commands from your terminal or command prompt.

Once installed, you can verify your Python version by opening your terminal and typing:

python3 --version

Setting Up a Code Editor or IDE

You need a reliable development environment to write and run Python code effectively. Choosing the right code editor or Integrated Development Environment (IDE) directly impacts your productivity, code quality, and ease of debugging. A good environment helps you stay focused on coding rather than getting stuck on tooling issues.

While beginners can start with lightweight editors, more advanced projects benefit from the power of full-featured IDEs that offer integrated support for project management, testing, and debugging.

Recommended Editors and IDEs

• Visual Studio Code (VS Code)
  A free, lightweight, and highly customizable editor. Ideal for beginners and pros as well.
  • Install the Python extension from the Extensions Marketplace.
  • Offers syntax highlighting, code suggestions, and intelligent debugging
  • Built-in terminal for running scripts directly inside the editor
  • Supports virtual environments and pip integration
• PyCharm 
  A professional IDE designed specifically for Python development.
  • Excellent for large-scale projects and complex codebases
  • Built-in tools for testing, version control, and refactoring
  • Strong support for web frameworks like Django and Flask
  • Comes in Community (free) and Professional (paid) editions
• Other Editors (Optional)
  • Sublime Text – Lightweight, fast, and supports Python with plugins.
  • Atom – Open-source and customizable, with Python packages available
  • Thonny – Great for absolute beginners with a simple UI and built-in Python shell

Here’s a quick comparison of some of the most popular Python IDEs to help you make an informed decision:

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Also Read: Top Python IDEs: Choosing the Best IDE for Your Python Development Needs

Now that you’ve set up your environment and tools, it’s time to understand how a typical Python project is organized behind the scenes. Let’s explore the file structure that holds everything together.

Understanding Python File  Structure

Writing all your code in a single .py file is easy when starting a Python project. But as your project grows slightly, this quickly becomes messy and hard to manage.

That’s where understanding Python’s typical file and folder structure becomes essential.

Having a clean and logical project layout helps you:

• Keep your code organized and easy to navigate
• Reuse code across files using imports
• Simplify debugging and testing
• Collaborate more easily with other developers
• Follow industry best practices and expectations

Think of it this way: your code is a product, and its structure is your packaging. A well-organized structure makes your project look professional, understandable, and function reliably.

Basic Structure of a Python Project

Here’s what a typical Python project directory looks like:

Let’s break this down step-by-step:

Root-Level Files

When you look at a Python project folder for the first time, the files you see immediately inside the main project directory are called root-level files. These files sit at the “top” of your project’s folder hierarchy, not inside any subfolders.

• main.py: This is the entry point of your program, where your app starts running. You can rename it to something more specific (e.g., app.py) based on your use case.
• README.md: A markdown file where you describe what your project does, how to install/run it, and any relevant notes for users or contributors.
• .gitignore: A file used with Git to tell version control which files/folders to ignore (e.g., venv, __pycache__, etc.).
• requirements.txt:  This file lists all the external Python packages on which your project depends. You can generate it using:

pip freeze > requirements.txt

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src/ – Source Code Folder

Instead of scattering Python files at the top level, it is best practice to place all your main code inside a src or my_project folder. This helps separate logic from configuration or documentation.

• __init__.py: This file makes the folder a Python package. It tells Python you can import from this folder even if it's empty.
• module_one.py, module_two.py: These are where your actual code functions, classes, or APIs live. You can structure them by functionality (e.g., utils.py, auth.py, db.py, etc.).

tests/ – Testing Code

Keeping your test scripts in a separate folder makes it easier to run automated tests using tools like pytest.

• Include test files that mirror your source files: If you have module_one.py, you should have a test_module_one.py.

venv/ – Virtual Environment

This folder contains your project's isolated Python environment. You shouldn’t modify anything inside it manually.

• Keeps your dependencies separate from the global Python install
• Allows others to recreate your environment exactly using requirements.txt

You can create a virtual environment with:

python -m venv venv

This command creates a folder named venv in your project directory.

You can activate the environment with this command on Windows:

\venv\Scripts\activate

And on macOS/Linux:

source venv/bin/activate

Install packages inside the activated environment:

pip install package_name

What Is a Virtual Environment in Python?

A virtual environment is like a self-contained bubble for your Python project. It isolates the specific Python interpreter and all the packages your project needs, separate from the rest of your computer’s system.

Why does this matter?

Imagine you’re working on multiple Python projects. Each might require different versions of libraries or even different versions of Python itself. If all projects share the same global Python project setup, this can cause conflicts and “dependency hell” where one project’s requirements break another’s.

With a virtual environment, you create a dedicated space just for your current project:

• You can install the exact packages and versions needed without affecting other projects.
• It keeps your global Python installation clean and unaffected by project-specific libraries.
• When you share your project, others can recreate this environment on their machine to avoid compatibility issues.

Also Read: Career Opportunities in Python: Everything You Need To Know [2025]

With a solid grasp of your project’s file and folder layout, you are ready to learn how to run your Python project correctly.

 How to Run a Python Project? Different methods

Before diving into advanced tools and configurations, let’s keep things simple. To run a Python project, you must execute your .py script — your Python file. This might contain a simple print statement or the entry point of an entire app.

Running Python code in IDE is all about executing it in the right environment, whether it's a terminal, a code editor, or a full-fledged IDE. Knowing how to run your script from different places helps you work more flexibly and efficiently, especially when switching between tools or debugging.

Let’s walk through how you can run a Python script step by step in various environments you might use.

1. Using the Command Line (Terminal or CMD)

This is the most direct and platform-independent way to run Python code. Every Python developer should be comfortable running scripts from the command line, as it gives you full control.

Why it’s useful: It works in any system, doesn’t require an IDE, and is great for automation or testing.

Steps:

1. Open your terminal (Command Prompt on Windows, Terminal on macOS/Linux).

Navigate to the directory where your .py file is located:

cd path/to/your/project

Run Python script:

python filename.py

Make sure your virtual environment is activated if you’re using one.

Also Read: Top 36+ Python Projects for Beginners and Students to Explore in 2025

2.  Using Python IDLE.

Python IDLE is the built-in editor that comes with Python. It’s great if you’re just starting and don’t want to install additional tools.

Why it’s useful: Lightweight, beginner-friendly, and requires no python project setup.

Steps:

1. Open IDLE (search "IDLE" from your start menu or applications).
2. Open your script: File > Open > select your .py file.
3. Run it: Run > Run Module or just press F5.

This opens a shell window where the output is displayed.

3. Using VS Code

VS Code is a popular code editor with great Python support through extensions.

Why it’s useful: It's lightweight yet powerful, great for real projects, debugging, Git integration, and customization.

Steps:

1. Install the Python extension from the Extensions tab.
2. Open your project folder in VS Code.
3. Ensure the correct Python interpreter or virtual environment is selected (bottom-left bar or Ctrl+Shift+P > Python: Select Interpreter).
4. Open your .py file.
5. Click the Run play button in the top-right corner, or press Ctrl+F5.

VS Code has a built-in terminal, so you can also run the script manually via python filename.py.

4. Using PyCharm

PyCharm is a full IDE explicitly built for Python. It’s ideal for large projects and professional workflows.

Why it’s useful: Great for complex applications, web development, and test-driven development.

Steps:

1. Open or create a new project in PyCharm.
2. Add your .py script or write your code in the editor.
3. Right-click on the script and choose Run filename.
4. Output appears in the Run console at the bottom.

You can also configure run/debug configurations for custom scripts or arguments.

Now that you know how to run your Python project let’s visualize the different methods available so you can pick the one that fits your workflow best.

Visual Guide: Different Ways to Run a Python Project

When you’re switching between environments or explaining this to others, a visual summary acts like a cheat sheet. It also helps you troubleshoot faster when something doesn’t run as expected, because you know the context and process for each method.

Here’s a simplified breakdown of how to run Python script in various environments. Use this as a quick-reference visual guide as you work.

• Open your preferred environment (Terminal, IDLE, VS Code, or PyCharm)
• Navigate to or open your project folder.
• Locate your main Python script (main.py, app.py, or similar)
• If needed, activate your virtual environment.
• Run the script using the method suited to the environment:
  • Terminal: python filename.py
  • IDLE: Press F5 or Run the Run Module
  • VS Code: Click the play button or press Ctrl+F5
  • PyCharm: Right-click file → Run
• View output in the respective terminal or console
• Fix any errors, re-run as needed.

Also Read: 12 Amazing Real-World Applications of Python

Now that you understand how to run your Python project, let’s explore the best practices for keeping your code clean, efficient, and maintainable.

Best Practices When Running a Python Project

Running a Python script is easy, but running a Python project correctly means being consistent, organized, and professional. Whether building a small script or a large application, following best practices will save you headaches later. It will make your code easier to scale, debug, and share.

Here are the best practices you should stick to:

 1. Organize Your Project Structure

• Keep related files grouped in folders (src, tests, data, etc.).
• Place your main script at the root or in a clearly labeled entry point like main.py or app.py.
• Avoid dumping all files into one folder, as structure reflects clarity.

 2. Use a Virtual Environment

• Always create a virtual environment using python -m venv venv.
• This keeps dependencies isolated and avoids conflicts with other projects.
• Activate the environment before installing packages or running your project.

 3. Track Dependencies with requirements.txt

After installing packages, freeze them with:

pip freeze > requirements.txt

This makes it easy for others and your future self to recreate the environment using:

pip install -r requirements.txt

4. Use .gitignore to Clean Your Repo

• Ignore unnecessary files like __pycache__, .env, venv/, and .DS_Store.
• Use a .gitignore file to keep your version control clean and relevant.

5. Keep Your Code Clean and Modular

• Split code into modules and functions. Avoid long monolithic scripts.
• Follow PEP 8 (Python style guide) for clean formatting and naming.
• Use meaningful variable names and add comments where logic isn’t apparent.

6. Test Your Code

• Add unit tests as your project grows.
• Use unittest, pytest, or other frameworks to check your code’s logic.
• Test edge cases and possible failure points.

 7. Use Debugging Tools

• Leverage built-in debuggers in VS Code or PyCharm.
• Use print() for quick debugging, but remove or comment it out before production.
• For complex issues, tools like pdb or breakpoints inside IDEs work best.

8. Include a README File

• Write a simple README.md to explain the project, how to install dependencies, and how to run it.
• This helps collaborators, recruiters, or your future self understand and use the project efficiently.

 9. Automate Repetitive Tasks

• Use scripts or makefiles to automate python project setup, tests, or app running.
• This speeds up development and ensures consistency across teams.

10. Never Hardcode Secrets

• Use environment variables or .env files to store sensitive data like API keys or passwords.
• Load them using libraries like python-dotenv.

Even when you follow best practices, you might still face errors. Let’s look at common issues and how you can troubleshoot them effectively.

Troubleshooting Common Errors When Running Python Code

Even with everything set up, running into a few errors is normal, especially when you’re starting or switching environments. The key is knowing what these errors usually mean and how to fix them quickly.

Here are some common issues you might encounter when running a Python project, along with what they usually indicate:

1. 'python' is not recognized as an internal or external command

• What it means: Python isn’t added to your system’s PATH.
• How to fix it:
  • Reinstall Python and check the box that says “Add Python to PATH”.
  • Or manually add the Python installation path to your system environment variables.

2. ModuleNotFoundError: No module named 'xyz'

• What it means: You’re trying to import a package that isn’t installed in your environment.
• How to fix it:
  • Activate your virtual environment.
  • Run pip install xyz to install the missing package.

3. IndentationError: unexpected indent

• What it means: Python relies on indentation instead of braces. Even one wrong space or tab can break it.
• How to fix it:
  • Make sure you're using either spaces or tabs, not both.
  • Stick to 4 spaces per indent level and keep it consistent throughout the file.

4. SyntaxError: invalid syntax

• What it means: You’ve typed something Python doesn’t understand.
• How to fix it:
  • Check for missing colons :, parentheses (), or quotation marks.
  • The error line will usually point right before the actual problem.

5. TypeError or ValueError

• What it means: You’re passing the wrong value type to a function or operation.
• How to fix it:
  • Review the error message, which tells you which function and what type it expected.
  • Convert or validate your data before passing it (e.g., use int(), str(), etc.).

6. PermissionError: [Errno 13]

• What it means: Python is trying to access a file or directory it doesn’t have permission.
• How to fix it:
  • Run your script as an administrator (on Windows) or use sudo (on macOS/Linux).
  • Ensure the file path exists and the file isn’t open elsewhere.

7. FileNotFoundError

• What it means: Python can't locate the file you’re trying to open or read.
• How to fix it:
  • Double-check your file paths and make sure they’re relative to the script.
  • Use os.path or Pathlib to handle file paths more reliably across OSs.

Wrapping Up!

Running a Python project involves setting up the right environment, understanding the file structure, and using effective methods to execute your code seamlessly. Following best practices promotes clean organization, scalability, and maintainability. Mastering these steps helps you overcome common roadblocks, making your Python projects easier to run and debug.

If you're looking to strengthen your Python skills further and bridge any gaps in your knowledge, upGrad’s Software Development courses offer hands-on experience, expert mentorship, and 1:1 guidance. 

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