How to Run Python in VSCode Smoothly

Running Python code in Visual Studio Code (VSCode) can elevate your development experience, offering an efficient and feature-rich environment for coding, debugging, and more. If you’re diving into Python programming, understanding how to run Python in VSCode is essential.

This article will walk you through the setup and execution process, leveraging tools like VSCode extensions, the integrated terminal, and Python debugging features.

You’ll learn how to install the Python interpreter, configure VSCode to recognize your Python environment, and run your scripts seamlessly. With insights on using Python packages, configuring virtual environments, and enabling IntelliSense, you’ll be equipped to handle complex projects effortlessly.

By the end of this guide, you’ll be adept at setting up a Python development environment in VSCode, ensuring optimal user experience and efficient code execution. Let’s jump into the step-by-step process without the fluff and master how to run Python in VSCode like a pro.

How to Run Python in VSCode Smoothly: Quick Workflow

Setting Up Your Environment

1. Install Visual Studio Code:

• Download and install VSCode from the official website. Follow the installation prompts for your operating system.

2. Install Python:

• Download the latest version of Python from the official Python website. During installation, ensure you check the option to “Add Python to PATH” to make it accessible from the command line.

3. Install the Python Extension:

• Open VSCode, go to the Extensions view (Ctrl+Shift+X), and search for “Python”. Install the extension provided by Microsoft, which adds support for Python development including IntelliSense and debugging features.

Creating and Running Python Files

4. Create a New Python File:

• In VSCode, create a new file with a .py extension (e.g., hello.py). This tells VSCode to treat it as a Python script.

5. Write Your Code:

• Enter your Python code in the file. For example:
  python

  msg = "Hello, World!"
print(msg)


6. Running Your Code:

• You can run your Python script in several ways:
  • Run Button: Click the green play button in the top right corner of the editor.
  • Integrated Terminal: Right-click in the editor and select Run Python File in Terminal, or press Ctrl+F5 to run without debugging.
  • Keyboard Shortcuts: Use F5 to start debugging, or create custom shortcuts as needed.

Debugging Your Code

7. Set Up Debugging:

• To debug your code, set breakpoints by clicking in the gutter next to the line numbers. Start debugging by pressing F5, which will open a configuration menu if it’s your first time setting it up.

8. Configure Debug Settings:

• You may need to create or edit a launch.json file for more complex debugging setups. This file allows you to specify how your scripts should run during debugging.

Additional Tips

• Use Virtual Environments: For managing dependencies and different project requirements, consider using virtual environments (like venv or conda). This keeps your projects isolated and manageable.
• Explore Integrated Features: Utilize built-in features like IntelliSense for code completion and linting tools for error checking to enhance your coding experience.
• Testing Support: Leverage the testing capabilities of the Python extension to run and debug tests within your projects easily.

By following these steps, you can create a robust environment for developing Python applications in VSCode, ensuring a smooth coding experience.

Setting Up Your Python Environment

Installing Python

Downloading Python from Python.org

To get Python running on your machine, head over to Python.org. Here, you’ll find the latest stable release. Look for the “Download Python” button.

• Click it.
• Save the installer to your local machine.
• Run the downloaded file.

During installation, ensure the “Add Python to PATH” option is checked. This step is crucial, as it allows you to use Python from the command line.

Alternative Installation Methods

For different setups, various methods exist:

• Windows Store: Search for Python in the Microsoft Store if you’re on Windows.
• Anaconda Distribution: Best for data science enthusiasts, Anaconda offers a suite of tools, including Python pre-installed.
• CLI-based Installers: Useful for Linux and macOS users. Examples:
  • Homebrew:

    brew install python
    

  • Winget:

    winget install Python.Python.3
    

  • Chocolatey:

    choco install python
    

  • apt-get (Debian/Ubuntu-based systems):

    sudo apt-get install python3
    

Installing Visual Studio Code

Accessing the Official VSCode Download

Navigate to the Visual Studio Code website. Download the version suitable for your operating system.

Installation Options for Different Platforms

• Windows:
  • Run the installer.
  • Follow the on-screen instructions.
• macOS:
  • Drag the downloaded .app file to the Applications folder.
• Linux:
  • Debian/Ubuntu:

    sudo apt install ./<file>.deb
    

  • Fedora:

    sudo rpm -i <file>.rpm
    

Setting Up the Python Extension in VSCode

Locating and Installing the Python Extension

Open VSCode. Head to the Extensions view by clicking the Extensions icon in the Activity Bar on the side of the window.

Search for the official Python extension provided by Microsoft and click install. This extension is vital for various functionalities like linting, debugging, and code navigation.

Introduction to Key Functionalities

• Linting: Automatic code quality checks ensuring best practices.
• Debugging: Run your Python code with breakpoints, step through code, and inspect variables in real-time.
• Code Navigation: Efficiently navigate through your codebase, utilizing features like IntelliSense for auto-completion.
  

  Creating and Managing Python Projects in VSCode

Starting a New Project

Setting Up a Workspace and Project Folder

Kick off your Python journey by setting up a workspace. This is your project’s central hub in Visual Studio Code (VSCode). First, you’ll need a project folder. Simply create a new directory on your machine—call it whatever fits your project.

Naming and Organizing Project Files

Next, organize this folder efficiently. Structure matters. Create subdirectories for modules, scripts, tests, and more. Use clear and consistent naming conventions to avoid chaos later on.

Configuring the Python Interpreter

Selecting an Interpreter Based on Project Requirements

Visual Studio Code requires knowing which Python interpreter to use. By default, it might pick one, but you know your project’s needs better. Check the requirements and select the interpreter accordingly.

Steps to Set and Switch Interpreters in VSCode

To set the interpreter:

• Open the command palette (Ctrl+Shift+P)
• Type Python: Select Interpreter
• Choose the appropriate version

Switching is just as straightforward. This flexibility is vital, especially when dealing with multiple projects.

Best Practices for Managing Multiple Python Versions

Keep your projects isolated. Use virtual environments (venv, Conda). Never mix different versions—avoids a lot of headaches. Stick to this practice to keep dependencies in order and your project stable.

Working with Virtual Environments

Creating Virtual Environments (venv, Conda)

Virtual environments are crucial. Here’s how to create them:

• venv:

  python -m venv myenv
  

• Conda:

  conda create --name myenv python=3.x
  

Activating and Deactivating Virtual Environments

Once created, activate them:

• venv (Windows):

  myenv\Scripts\activate
  

• venv (macOS/Linux):

  source myenv/bin/activate
  

• Conda:

  conda activate myenv
  

Deactivate with a simple deactivate or conda deactivate.

Using Virtual Environments with VSCode’s Command Palette

To integrate virtual environments with VSCode, use the command palette:

• Open the command palette (Ctrl+Shift+P)
• Type Python: Select Interpreter
• Choose your environment
  

  Writing and Running Python Code in VSCode

Creating Python Files

Naming Conventions and File Organization

First off, create a Python file. Files should have the .py extension. Use clear and descriptive names. Avoid complex names. Stick to lowercase letters, numbers, and underscores (_).

Example:

• data_analysis.py
• main_script.py
• config_manager.py

Project file organization must be logical. Group related files together in directories.

Example Directory Structure:

my_project/
│
├── src/
│   ├── main_script.py
│   ├── helper_functions.py
│
├── tests/
│   ├── test_main.py
│
└── config/
    ├── settings.py

Setting Up .py Files in the Project Structure

Inside your project, create the .py files. Ensure each file serves a clear purpose: main logic, helper functions, configuration settings, etc. This enhances code readability and organization.

Running Python Scripts

Running Scripts Using the Editor’s Play Button

In VSCode, open your Python file. At the top right, see a small Play button (▶️). Clicking this runs your script. Simple, straightforward.

Running Python Code from the Integrated Terminal

Alternatively, run it from the Integrated Terminal.

Here’s how:

• Open Terminal (Ctrl+`).
• Navigate to the directory containing your Python script.
• Type python script_name.py and hit Enter.

Example:

python main_script.py

Using Command Palette to Execute Code Segments

The Command Palette is a powerful tool. Access it (Ctrl+Shift+P). Type “Run Python File in Terminal”. It executes the whole file.

Running Code Selections and Lines

Running Selected Code and Lines in the Terminal

Select the desired code snippet in the editor. Right-click and choose “Run Selection/Line in Python Terminal”. This is handy for testing specific parts of your code.

Benefits of REPL (Read-Eval-Print Loop) for Code Testing

REPL isn’t just a loop. It’s your interactive playground. Run small snippets of code to see immediate outputs. Open the Integrated Terminal and type python. You’ll enter REPL mode.

Example:

>>> print("Hello, World!")
Hello, World!

Debugging Python Code in VSCode

Debugging Basics

Initializing the Debugger

Start with initializing the debugger. Open your Python file. Move to the Run and Debug view (left sidebar). Hit “Run and Debug”. If this is your first time, VSCode will prompt to select an environment. Choose “Python File”.

Choosing Debug Configurations for Different Needs

Different needs, different configurations—be it a simple Python File, a complex Flask application, or a multifaceted Django project.

• Python File: Straightforward, no frills.
• Flask: Needs specific debug configurations. Add launch.json with the Flask template.
• Django: Similar approach. Ensure launch.json is configured with Django settings.

Setting Breakpoints and Running Debug Sessions

Adding and Managing Breakpoints

Breakpoints are vital. Click on the margin next to the line number. Red dot appears—that’s your breakpoint.

Manage them:

• Add: Click the margin
• Remove: Click again
• Disable/Enable: Right-click on the breakpoint

Debug Toolbar Commands

When debugging, the toolbar appears. Use these commands:

• Continue (F5) – Resumes your script until the next breakpoint.
• Step Over (F10) – Moves to the next line without diving into functions.
• Step Into (F11) – Steps into the function call.
• Step Out (Shift+F11) – Completes the function and moves back to the calling script.

Monitoring Variables in the Debug Console

Keep an eye on variables:

• The Debug Console shows their state.
• Evaluate expressions in real-time.
• Add variables to Watch for closer inspection.

Inspecting and Modifying Variables During Debugging

Viewing Variables and Expressions in Real Time

Real-time inspection is critical. Hover over variables to see their current value. Watch them change as you step through code.

Using Hover Over and Pop-Up Inspections

Hover over variables for pop-up inspections. These pop-ups show values and expressions.

Debug Console for Testing Code Changes During Debugging

Lastly, make use of the Debug Console. Modify variables, test small changes, execute expressions. This console is your interactive playground during a debugging session, enhancing how to run Python in VSCode effectively.

Customizing VSCode for Enhanced Python Development

Essential Python Extensions

Python Extension Pack (Pylance, Jupyter, isort)

First, hitting the essentials: the Python Extension Pack.

• Pylance: Your go-to for enhanced language support. It offers blazing-fast IntelliSense.
• Jupyter: Integrated notebook support within VSCode. Super handy for data science tasks.
• isort: Automatic import sorting—keeps your code neat and tidy without the hassle.

Additional Productivity Extensions

Now, let’s sprinkle in some extra productivity:

• Indent Rainbow: Different colored indentations reduce visual clutter, making nested code blocks stand out.
• Python Indent: Improves VSCode’s indentation practices for Python.
• Jupyter Notebook Renderers: Better notebook display and rendering.

Configuring Linting and Formatting

Selecting and Enabling Linters (Pylint, Flake8)

Next, ensure your code adheres to best practices with linters:

• Pylint: Popular choice. Extensive error checking.
• Flake8: Another solid option. Lightweight and customizable.

Enable these through settings:

Example:

"python.linting.pylintEnabled": true,
"python.linting.flake8Enabled": false

Configuring Code Formatting Options (Black, autopep8, yapf)

Formatting keeps your code pristine. Pick a formatter:

• Black: Uncompromising code formatting.
• autopep8: Adjusts code to align with PEP 8 guidelines.
• yapf: Configurable Python formatter.

Configure via settings:

Example:

"python.formatting.provider": "black"

Enabling Format on Save and Other Custom Formatting Settings

Enable format on save to keep things consistent automatically:

"editor.formatOnSave": true,

Adjust other custom settings as needed.

Custom Keyboard Shortcuts and the Command Palette

Setting Up Custom Shortcuts for Frequent Commands

Boost your productivity with custom shortcuts. Open Keyboard Shortcuts (Ctrl+K, Ctrl+S).

• Assign frequently used commands to specific keys.

Example: Running the current Python file.

{
  "key": "ctrl+shift+r",
  "command": "python.execInTerminal"
}

Using the Command Palette for Quick Access to Functions

The Command Palette (Ctrl+Shift+P) grants rapid access to all VSCode functions. From opening a new terminal to selecting an interpreter, this tool is indispensable.

Advanced Features for Data Science and Notebook Integration

Jupyter Notebooks in VSCode

Setting Up and Installing Jupyter Extensions

First up, Jupyter Extensions. Essential for anyone diving into data science with Python. Head to the Extensions view in VSCode. Search for “Jupyter” and install it.

Boom! Instant capability to handle notebooks right there in your IDE.

Creating and Running Jupyter Notebooks in VSCode

Now, time to create. New file, save it as .ipynb. Magic happens. The Jupyter interface appears embedded within VSCode. Write and execute your cells directly.

Quickly visualize data, manipulate it, all within a few clicks.

Running Cells and Visualizing Outputs

Running cells? Easy. Click the play button next to each cell. Outputs, whether it’s a plot or data frame, render right there. Need to adjust? Edit the cell and re-run. Immediate feedback loop.

Managing and Switching Jupyter Kernels

Selecting Python or R Kernels for Notebooks

Switch kernels as needed. Working in Python today? R tomorrow? Click on the kernel name in the top-right corner of your notebook. Select your desired kernel from the list.

Managing Kernel Dependencies and Environments

Each kernel has dependencies. Manage them well. Use Conda environments or virtual environments. Ensure the correct packages are installed:

Example for Conda:

conda install numpy pandas matplotlib

DataCamp’s DataLab and Browser-Based Notebooks

Overview of DataLab for Browser-Based Coding

Browser-based coding? Enter DataCamp’s DataLab. No local setup required. Access all your notebooks straight from the web. Perfect for collaborative projects or when switching between devices.

Benefits of Cloud-Based Notebooks for Data Science Projects

Cloud-based has perks. Accessibility from anywhere. Resources aren’t tied to your machine—scale as needed. Perfect for extensive data computations. Combine this with how to run Python in VSCode and you’ve got a robust setup for tackling any data project.

Testing and Version Control in VSCode

Setting Up Python Unit Testing

Selecting a Testing Framework (unittest, pytest)

Testing isn’t just a buzzword—it’s essential. I use unittest or pytest for keeping my code clean. Want simplicity? Go for unittest. Need more power and flexibility? Pytest is your buddy.

Example:

For unittest:

import unittest

class TestMyCode(unittest.TestCase):
    def test_addition(self):
        self.assertEqual(add(1, 2), 3)

if __name__ == '__main__':
    unittest.main()

For pytest:

def test_addition():
    assert add(1, 2) == 3

Configuring and Running Tests in the Testing Panel

Pop open the Testing Panel. Configure tests using settings.json:

"python.testing.unittestEnabled": true,
"python.testing.pytestEnabled": false,
"python.testing.unittestArgs": [
    "-v",
    "-s"
]

Switch to the Tests section in the sidebar. Hit the play button. Watch your tests run and see those green checkmarks (or red crosses, don’t shoot the messenger).

Reviewing and Interpreting Test Results

Green means good; red means… not so good. Expand each test result for details. Fix issues as they pop up—don’t leave them for future-you. Future-you will thank present-you.

Integrating Git and GitHub

Enabling Git Integration in VSCode

Version control = sanity. Enable Git in VSCode. Head to Settings:

"git.enabled": true

Watch as the Source Control icon awakens in the Activity Bar. Click it and you’re ready to roll.

Initializing Repositories, Committing, and Staging Changes

First things first, initialize the repo:

git init

Stage changes with the plus icon. Write a clear commit message. Click the checkmark to commit. Cha-ching, history saved.

git add .
git commit -m "Initial commit"

Pushing Code to GitHub and Managing Remote Repositories

Connect to GitHub. In the terminal, link your repo:

git remote add origin https://github.com/yourusername/your-repo.git
git push -u origin master

Done—code is live. Manage remotes via the command palette or terminal for seamless updates.

Productivity Tips and Tricks for Efficient Python Development

Utilizing the Command Line Interface (CLI)

Running Python Code from the Terminal

The Terminal—your power tool. Open it up (Ctrl+`). Navigate to your project directory. Type:

python script_name.py

Hit Enter. Watch your code execute. Direct, no fluff. Perfect for quick tests or running scripts on the fly.

Shortcut Commands for Code Execution and Git Management

Why click around when you can type? Master a few shortcut commands:

• Running code:

  python script_name.py
  

• Git magic:

  git add .
  git commit -m "Your message"
  git push origin master
  

Efficiency levels up with these shortcuts. Direct control. No nonsense.

Multi-Cursor Selection and Editing Tools

Using Multi-Cursor Editing for Efficiency

Visual Studio Code’s hidden gem—Multi-Cursor Editing. Activate it with Alt+Click (Option+Click on macOS). Add multiple cursors across your text.

Edit multiple lines simultaneously. Imagine renaming variables, copying lines, or aligning code blocks—all in one go.

Symbol Renaming, Code Snippets, and Bulk Modifications

Symbol renaming? Easy. Right-click on a symbol, select Rename Symbol (F2). Change once, update everywhere.

Code Snippets—create them, save time. Type a snippet prefix, press Tab, and voila! Your chunk of code appears.

Example Snippet Configuration:

{
  "Print to Console": {
    "prefix": "print",
    "body": ["print('$1')", "$2"],
    "description": "Prints text"
  }
}

Bulk modifications—use Find and Replace (Ctrl+H). Change multiple occurrences in one sweep.

Leveraging GitHub Copilot for AI-Assisted Coding

Introduction to GitHub Copilot

Meet your new pair programmer: GitHub Copilot. This AI assistant suggests code snippets, logic, and even entire functions. It learns from your style. Integrates smoothly in Visual Studio Code.

Examples of Copilot-Driven Code Suggestions and Enhancements

Start typing a function, and watch Copilot suggest:

def calculate_area(radius):
    return 3.14 * radius * radius

Fine-tune it. Need a more complex logic block? Copilot’s got options:

def fetch_data(api_url):
    import requests
    response = requests.get(api_url)
    return response.json() if response.status_code == 200 else None

Effortlessly accelerate your coding. Inject efficiency by leveraging Copilot’s suggestions.

FAQ on How To Run Python In VSCode

How do I install Python in VSCode?

First, download and install the latest Python version from the official site. Then, open VSCode. Navigate to the Extensions view by clicking on the Extensions icon. Search for the “Python” extension by Microsoft, and hit “Install.” You’ll now be able to run Python code within VSCode.

How do I open a terminal in VSCode?

Open VSCode and click on View in the top menu. Select Terminal from the dropdown. Alternatively, use the shortcut Ctrl + Shift + ~ on Windows or Cmd + Shift + ~ on Mac. This terminal allows you to run Python scripts and other commands directly within VSCode.

How do I run a Python script in VSCode?

To run a Python script, open your .py file in the editor. Right-click anywhere in the editor and select Run Python File in Terminal. The script will execute, and you’ll see the output in the integrated terminal at the bottom of the screen.

How can I set the Python interpreter in VSCode?

Click on the interpreter indication in the bottom-left corner of VSCode, or press Ctrl + Shift + P and type Python: Select Interpreter. Choose the interpreter path that matches your version of Python. This ensures that VSCode uses the correct interpreter to run your scripts.

How do I configure a virtual environment in VSCode?

Open the terminal, navigate to your project directory, and run python -m venv env to create a virtual environment. Activate it using env\Scripts\activate on Windows or source env/bin/activate on Mac/Linux. Select the virtual environment as your interpreter in VSCode.

How can I debug Python code in VSCode?

Open your Python file and click on the left margin to set breakpoints. Press F5 to start debugging, or select Run > Start Debugging. You’ll see options to step through the code, view variables, and inspect call stacks within the debug tab.

What is IntelliSense, and how do I use it for Python in VSCode?

IntelliSense provides code completions and parameter info as you type. With the Python extension installed, start typing any Python module and watch for suggestions. Use Ctrl + Space to trigger IntelliSense manually. This speeds up coding and reduces errors.

How can I configure linting for Python in VSCode?

Install a linter like pylint using pip install pylint. Inside VSCode, press Ctrl + , to open settings and search for linting. Enable Python linting and set pylint as the linter. Linting errors and warnings will appear in the Problems tab and the editor.

How do I format Python code in VSCode?

Install black by running pip install black. In VSCode, open settings and search for format on save. Enable it. Now, whenever you save your Python file, black will automatically format it according to PEP 8 standards, ensuring consistent code style.

How can I load Jupyter Notebooks in VSCode?

Install the Jupyter extension from the Extensions view. Open the command palette with Ctrl + Shift + P and type Jupyter: Create New Blank Notebook.

You can also open existing .ipynb files. This provides a seamless environment for data analysis and machine learning projects.

Conclusion

Learning how to run Python in VSCode empowers you to leverage a robust coding environment, enhancing your productivity and coding efficiency. VSCode, with its multitude of features like IntelliSense, debugging tools, and support for virtual environments, is an exceptional choice for Python developers.

By following the steps outlined in this guide, you’ve learned to:

• Install and configure the Python extension and interpreter.
• Run Python scripts seamlessly.
• Set up and activate virtual environments.
• Use Jupyter Notebooks within VSCode.
• Enable linting and automatic code formatting for improved code quality.

Implementing these practices will ensure a smooth development experience. From writing simple scripts to managing complex projects, VSCode provides the necessary tools to make Python coding straightforward and efficient. Remember, the goal is to streamline your workflow and reduce friction in coding, thus making development enjoyable. Now you’re all set to boost your productivity and harness the full potential of Python in VSCode.

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Bogdan Sandu

Bogdan is a seasoned web designer and tech strategist, with a keen eye on emerging industry trends.

With over a decade in the tech field, Bogdan blends technical expertise with insights on business innovation in technology.

A regular contributor to TMS Outsource's blog, where you'll find sharp analyses on software development, tech business strategies, and global tech dynamics.

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