Choosing the right development environment can make a surprisingly big difference when working with Python, data science, machine learning, and Jupyter Notebooks.

There are now many good options available. Some are designed specifically around notebooks, while others are general-purpose development environments that happen to have excellent Jupyter support.

In this article, I’ll compare six popular options:

• JupyterLab
• JupyterLab Desktop
• Visual Studio Code
• PyCharm
• Google Colab
• Anaconda

The goal is not to find one IDE that is objectively “the best.” Instead, it is to understand what each tool is good at and which one makes the most sense for your workflow.

My personal favourite: JupyterLab Desktop.

It gives me the notebook-first experience I want while still feeling like a proper desktop application.

Quick comparison

IDE / Environment	Best for	Runs locally?	Cloud-based?	Jupyter support	Ease of setup	My rating
JupyterLab	Notebook-first data science	✅	❌*	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	9/10
JupyterLab Desktop	Local notebook workflows	✅	❌*	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	9.5/10
VS Code	Python + notebooks + software development	✅	❌	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	9/10
PyCharm	Large Python projects	✅	❌	⭐⭐⭐⭐	⭐⭐⭐⭐	8.5/10
Google Colab	Quick experiments and cloud computing	❌	✅	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	8.5/10
Anaconda / Navigator	Beginners and Python environments	✅	❌	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	8/10

• JupyterLab can also be deployed remotely, but the standard installation is local/server-based.

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1. JupyterLab

JupyterLab is probably the most natural choice if your work revolves around Jupyter Notebooks.

Unlike a traditional code editor, JupyterLab is designed around interactive computing. You can work with notebooks, Python files, Markdown documents, terminals, data files, and other resources inside the same workspace.

The interface is also highly customizable, with an extension system that lets you add functionality to your environment. ReviewNB describes JupyterLab as the most notebook-native option among the tools it compares, which is one of its biggest advantages. (ReviewNB Blog)

Pros

• Excellent Jupyter Notebook experience
• Open-source
• Free
• Designed specifically for interactive computing
• Great for data analysis and exploration
• Highly extensible
• Supports notebooks, code, Markdown, terminals, and more

Cons

• Less focused on traditional software development than VS Code or PyCharm
• Can require some configuration for more advanced workflows
• The browser-based interface may not appeal to everyone

Best for

JupyterLab is a great choice for:

• Data scientists
• Researchers
• Students
• Machine learning practitioners
• Exploratory data analysis
• People who spend most of their time working in .ipynb files

Verdict: If Jupyter Notebooks are the center of your workflow, JupyterLab is one of the safest choices.

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2. JupyterLab Desktop

This is my personal favourite.

JupyterLab Desktop takes the JupyterLab experience and packages it as a desktop application.

Instead of opening JupyterLab through a browser and managing the server yourself, the desktop application handles much of that process for you. The project documentation describes it as a cross-platform desktop application that launches local JupyterLab sessions and can manage different Python environments. (GitHub)

For someone who likes JupyterLab but prefers a traditional desktop application, this is a very appealing combination.

Why I like it

The biggest advantage is simplicity.

You get the familiar JupyterLab interface, but it feels more like a normal application. You can open notebooks directly, work with local files, manage different environments, and keep your projects organized without having to think too much about the underlying Jupyter server.

It is also possible to connect the application to an existing local or remote JupyterLab server. (GitHub)

Pros

• Excellent notebook experience
• Desktop application
• Easy access to local files
• Works across Windows, macOS, and Linux
• Can work with different Python environments
• Familiar JupyterLab interface
• Good balance between simplicity and flexibility

Cons

• It is more notebook-focused than a traditional software-development IDE
• The project has had periods of limited maintenance
• You should pay attention to the current maintenance/security status before using it for sensitive or production work

One important current caveat: the GitHub repository previously stated that JupyterLab Desktop was not actively maintained as of August 2025. However, the project has since seen a revival effort, including a new v4.6.2-1 release in July 2026 with security patches. (GitHub)

That makes the current situation quite different from the older information you may find online.

Best for

I would recommend JupyterLab Desktop if you:

• Prefer local development
• Primarily work with notebooks
• Want a desktop application
• Don't need all the features of a huge software-development IDE
• Like the Jupyter ecosystem

Verdict: My personal favourite. It gives me the Jupyter experience I want without making the workflow feel unnecessarily complicated.

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3. Visual Studio Code

Visual Studio Code is probably the most versatile option on this list.

VS Code isn't specifically a Jupyter IDE. Instead, it is a general-purpose code editor that becomes a powerful Python and Jupyter environment through extensions.

Microsoft's Python documentation describes the Python extension as providing support for editing, running, debugging, and testing Python code, while the Jupyter functionality allows users to work directly with .ipynb files. (Visual Studio Code)

This makes VS Code particularly attractive if you frequently move between notebooks and traditional Python source code.

Pros

• Free
• Excellent Python support
• Excellent Jupyter support
• Built-in Git integration
• Huge extension ecosystem
• Supports many programming languages
• Great debugging tools
• Excellent for larger software projects

ReviewNB also highlights features such as notebook table of contents, notebook diffing, remote kernels, notebook search, and a data viewer. (ReviewNB Blog)

Cons

• Requires extensions and configuration
• Can feel overwhelming for beginners
• The experience is more code-first than notebook-first
• It can take time to customize

Best for

VS Code is ideal if you:

• Work with both .py and .ipynb files
• Build applications as well as analyze data
• Use Git heavily
• Work across multiple programming languages
• Want one editor for almost everything

Verdict: Probably the best all-rounder.

If I were building a large Python application and occasionally using Jupyter, I'd probably choose VS Code over JupyterLab.

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4. PyCharm

PyCharm is a dedicated Python IDE from JetBrains.

Historically, one of the major differences between PyCharm and the other options was that advanced Jupyter functionality was associated with the Professional edition. That changed with the unified PyCharm product.

Today, core PyCharm functionality, including Jupyter Notebook support, is available for free, while a Pro subscription provides additional advanced features. (JetBrains)

PyCharm is especially strong when your notebook is part of a larger Python application.

Pros

• Excellent Python support
• Powerful code completion
• Advanced debugging
• Refactoring tools
• Git integration
• Database tools
• Strong support for web development
• Jupyter Notebook support
• Excellent for large Python projects

JetBrains currently positions PyCharm for Python, web development, data science, and AI/ML workflows, with support for tools such as Jupyter, Conda, PyTorch, TensorFlow, Django, Flask, and FastAPI. (JetBrains)

Cons

• Heavier than JupyterLab
• More complex interface
• Can be overkill for simple notebooks
• Some advanced functionality requires Pro

Best for

PyCharm makes sense if you:

• Are primarily a Python developer
• Build production applications
• Work on large codebases
• Need powerful refactoring and debugging
• Want Jupyter support inside a full Python IDE

Verdict: Excellent for professional Python development, but probably more IDE than you need if your main activity is exploratory notebook work.

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5. Google Colab

Google Colab takes a completely different approach.

Instead of installing Python, Jupyter, packages, and environments on your computer, you can open a notebook in your browser and start working.

This makes Colab incredibly convenient.

It integrates with Google Drive and provides access to Google's computing infrastructure. The ReviewNB comparison also highlights Colab's free GPU/TPU resources and built-in Gemini assistance. (ReviewNB Blog)

Pros

• Almost zero setup
• Runs in the browser
• Easy to share notebooks
• Google Drive integration
• Excellent for teaching and demonstrations
• Convenient for experiments
• Access to cloud compute

Cons

• Requires an internet connection
• Compute resources are not guaranteed
• Less control over the environment
• Not ideal for every production workflow
• Cloud-based rather than fully local

Best for

Google Colab is particularly good for:

• Students
• Tutorials
• Courses
• Quick experiments
• Sharing notebooks
• Machine learning experiments
• Situations where you don't have a powerful local machine

Verdict: If you want to open a notebook and start coding immediately, Colab is hard to beat.

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6. Anaconda

Anaconda is slightly different from the other options because it is not really a single IDE in the same sense as JupyterLab, VS Code, or PyCharm.

Instead, Anaconda provides a Python distribution and environment/package management ecosystem. Anaconda Navigator provides a graphical interface for managing environments, packages, and applications without needing to use the command line. (Anaconda)

This can make Python much easier to get started with, especially for people coming from a non-programming background.

Navigator can be used to manage conda environments and launch tools such as JupyterLab and other development applications. (Anaconda)

Pros

• Beginner-friendly
• Easy package management
• Easy environment management
• Excellent for data science
• Includes a large collection of Python packages
• GUI-based workflow
• Works well with JupyterLab, VS Code, and PyCharm

Cons

• Anaconda itself isn't really an IDE
• Large installation
• Can consume significant disk space
• More environment management than you may need if you're already comfortable with Python and pip/venv

Best for

Anaconda is particularly useful for:

• Beginners
• Data scientists
• Scientific computing
• Machine learning
• Users who want simple environment management
• People who don't want to spend time configuring Python dependencies manually

Verdict: Think of Anaconda as the environment and package-management layer rather than a direct replacement for JupyterLab or VS Code.

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So, which one should you choose?

There isn't one answer for everyone.

Your choice should depend on how you actually work.

Choose JupyterLab if...

Your work is primarily notebooks, data analysis, visualization, and experimentation.

Choose JupyterLab Desktop if...

You want the JupyterLab experience but prefer a local desktop application.

This is my choice.

Choose VS Code if...

You want one tool for Python, Jupyter, Git, web development, and other programming languages.

Choose PyCharm if...

You're primarily a Python developer working on larger applications and want powerful IDE features.

Choose Google Colab if...

You want to start working immediately in the browser or need convenient access to cloud computing.

Choose Anaconda if...

You want an easier way to install Python packages, manage environments, and get started with data science.

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Key takeaways

After comparing these tools, a few things stand out.

1. JupyterLab is still the natural choice for notebook-first workflows.

It is purpose-built around interactive computing and provides a clean environment for working with notebooks, code, data, and visualizations. (ReviewNB Blog)

2. VS Code is the best all-rounder.

If your work goes beyond notebooks and includes application development, Git, debugging, and multiple programming languages, VS Code is an excellent choice.

3. PyCharm is ideal for serious Python development.

Its strength is not just running notebooks but helping you build and maintain larger Python projects through code intelligence, debugging, testing, refactoring, and project tooling. (JetBrains)

4. Google Colab wins on convenience.

There's almost no setup. Open a browser, create a notebook, and start experimenting.

5. Anaconda solves a different problem.

Rather than thinking of Anaconda as an alternative to every IDE on this list, think of it as a convenient way to manage Python environments and packages.

6. JupyterLab Desktop is my favourite.

For my workflow, it hits a sweet spot between simplicity and power. I like working locally, I like the Jupyter interface, and I don't always need the complexity of a full software-development IDE.

The important caveat is that JupyterLab Desktop's maintenance history has been uneven, so I'd keep an eye on the project's current releases and security status before making it the standard tool for a team or sensitive workload. The project has received a new release with security fixes in 2026, which is encouraging. (GitHub)

Final thoughts

The good news is that you don't necessarily have to pick just one.

You can use Anaconda for managing environments, JupyterLab Desktop for notebook work, and VS Code or PyCharm for larger Python projects. Google Colab can then fill the gap when you need a quick cloud-based environment.

For someone who spends most of their time inside Jupyter Notebooks, however, I'd start with JupyterLab.

And if you prefer a desktop application, JupyterLab Desktop is the one I'd try first.

Ultimately, the best IDE is the one that gets out of your way and lets you focus on the work you're actually trying to do.