Python has become the most popular data science and machine learning programming language in recent years. It is easy to learn, powerful, and versatile.

But which Python platform or integrated development environment (IDE) should you use?

This blog post will discuss the 5 best Python IDE for data science. We will look at each of their features and my experience using them.

What Are The Best Python IDEs for Data Science?

Let’s jump right into each of these IDEs!

1. Spyder



Spyder is an open-source IDE best suited for data analysis and scientific computing. It comes with a powerful editor, debugging tools, a fully-featured IPython console, a variety of plugins to extend its features, and more.

The best part about Spyder is that it’s an integrated code editing environment. The user interface is also highly customizable, which is great for quickly switching projects.

Spyder is a great IDE for programming and developing applications, but it might not be the best use for data science work.

Since Spyder does not run Python code in chunks or code blocks, data scientists might find Spyder slightly inconvenient. However, data analysts who create data analysis and automation pipelines can still use it.

Spyder is commonly used by:

Key Features:

  • Highlight syntax errors and code completion
  • Debugging support with breakpoints, stepping, etc.
  • An interactive IPython console
  • Support multiple plugins to extend features such as data science libraries, debugging tools, etc.
  • Commonly used for Python software development, data analysis, and automation pipelines.

My Experience:

I have used Spyder for several of my Python projects in the past. I’ve also used Spyder in my previous workplaces as a data analyst.

It’s best suited for Python development, but as a data analyst, I found it slightly inconvenient due to the lack of support for running chunks or code blocks.

However, it works amazing when creating data-cleaning automation pipelines. The debugging tools and IPython console are useful for testing and debugging code.

The syntax highlighting is also great for quickly understanding your code, and the customizability makes it easy to switch projects.

I found the Spyder interface pretty similar to RStudio’s (GUI) graphical user interface! This is great for those who have come from a background of learning R programming first.

Overall, I’d say that this is the perfect balance between a scientific and simple environment for anyone who’s new to programming in Python.

2. PyCharm

best python ide for data science


PyCharm is a full-featured IDE Python text editor by JetBrains, a company well-known for its developer tools.

PyCharm is best suited for web and application development in Python.

However, PyCharm is slightly harder to set up compared to Spyder since it does require some knowledge of the Python interpreter.

But once you get the basics down, PyCharm is a powerful IDE for Python development.

PyCharm also has great debugging and refactoring capabilities, making it a great choice for developers.

It’s also flexible for data analysis through the use of the PyCharm Scientific Mode! It supports using code cells to divide your Python scripts to test your code.

PyCharm is best for:

  • General Python development
  • Web and application development
  • Data science using the Scientific Mode

Key Features:

  • Syntax highlighting and code completion
  • Advanced debugging capabilities with breakpoints and stepping
  • Auto code completion
  • Refactoring support to help quickly change your code structure without breaking it
  • Scientific Mode to easily run code cells, debug and visualize data
  • Supports multiple plugins for extra features

My Experience:

I’ve used PyCharm extensively during my time at university. It was great for someone that was pretty new to programming.

Although I didn’t get to try any advanced functions using PyCharm, I got to try out their Scientific Mode, which was a great help in my data science schoolwork.

The best feature I found with PyCharm was its debugging tools since the breakpoints and stepping capabilities greatly help a new Python programmer.

Overall, PyCharm is an excellent IDE for web and application development and basic data science tasks or even advanced machine learning.

It’s best suited for Python developers and data analysts who need a powerful scientific Python development environment that can store local variables for on-the-fly analysis work.

3. JupyterLab



JupyterLab is an open-source interactive data science environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text.

It’s best suited for data scientists who need to quickly create notebooks with code, visuals, and reports on the fly.

Many data scientists use JupyterLab for its shareability. For example, I can share a Jupyter Notebook with a colleague to present my code and show its respective outputs and errors.

This makes JupyterLab an excellent team collaboration IDE!

Key Features:

  • Support for over 100 programming languages, including Python
  • Interactive code cells to quickly test and debug your code
  • Rich media support with markdown, HTML, images, and videos
  • Integrated terminal for running shell commands
  • Prints output onto shareable notebooks
  • Supports extensions and notebook sharing
  • Lightweight with easy and minimal installation
  • Able to show data visualization images

My Experience:

I’ve been using JupyterLab professionally ever since 2 years ago when I first picked it up. It was really easy to install and start using right away.

The best feature that I utilize constantly is the ability to share my notebooks with friends. This makes it so much easier to present our results quickly without requiring a lot of setup or changes.

Additionally, JupyterLab’s interactive cells are a huge help for data science tasks.

However, JupyterLab is not efficient for Python programming for backend and frontend web development.

Overall, JupyterLab is best for data scientists who want an IDE with shareability and collaboration capabilities. It’s also best for quick prototyping and testing of code.

4. Thonny


Thonny is a simple, Python-specific IDE that’s best for beginners.

It was designed to give learners a simple environment where they could learn and practice Python programming without worrying about the complexity of setup and Python installation.

It also has features specifically designed for educational purposes, such as step-by-step debugging and a syntax error highlighter.

Key Features:

  • Easy setup
  • Clean and fuss-free Python coding environment
  • Syntax highlighting for easy code readability
  • Step-by-step debugging to help isolate errors and bugs

My Experience:

My first experience with Thonny was when I first picked up Python programming myself!

At the time, I had no idea about Python installations or setups. Thonny made it super easy for me to jump into coding without worrying about the technical details.

Since most beginner errors are syntax errors, I found the syntax highlighting feature particularly helpful! It made my code more readable and easier to debug.

The best part was that I could easily identify my errors with the step-by-step debugger.

Overall, Thonny is best for students and people who are just starting out in Python programming but don’t want to get bogged down in technical details. I’d recommend it as a “training wheels” environment for beginners.

5. Visual Studio Code (VSCode)


Visual Studio Code (VSCode) is a free, open-source code editor developed by Microsoft.

A common coding tool used among many software engineers, data scientists, and data analysts, VS Code is one of the most popular IDEs.

In fact, it has one of the largest collections of programming languages out there, with the support of several hundreds of them!

Additionally, you’ll be able to get more extensive languages through the VS Code Marketplace.

Key Features:

  • Fully customizable user interface
  • Powerful syntax highlighting and IntelliSense code completion
  • Integrated debugging tools for code analysis and troubleshooting, and editing source code
  • Integrated source control system with support for Git and other providers
  • Built-in terminal to run shell commands from within the editor
  • Customizable through extensions

My Experience:

I’ve only started using VS Code recently since the start of 2022, and it has been absolutely amazing.

The best features I use often are the great syntax highlighting – they’ve made it much easier for me to write code quickly and efficiently.

Additionally, with the built-in terminal, I can quickly run shell commands without switching applications or installing something extra.

I also really liked how it supports Jupyter Notebooks as well! I could run my code cells from within the VS Code environment.

The VS Code mssql extension is also great for data science since it provides a connection to Microsoft SQL Server.

The built-in Git integrations also make it easier to open my terminal to use Git.

Related Questions

What IDE do data scientists use?

Data scientists often use JupyterLab, Thonny, and Visual Studio Code (VSCode) as their IDE of choice. However, the best IDE for data science depends on the project – different IDEs offer different features and capabilities that best suit certain tasks.

Are there any free Python IDEs?

Yes! There are several free Python IDEs, such as Thonny, Visual Studio Code (VSCode), Spyder, and PyCharm Community Edition. You should be able to find one that best suits your needs without having to break the bank.

Is PyCharm good for data science?

PyCharm is a great tool for data science because it includes support for interactive data visualizations, powerful debugging tools, and built-in integration with Jupyter Notebooks.

Which Python IDE is the fastest?

Thonny is best known for its simplicity, speed, and ease of use. It also includes several features that are optimized to help you quickly write code and debug errors. Therefore, it is considered one of the fastest Python IDEs available today.

Which is the best Python IDE for beginners?

Thonny is best for beginners because it is incredibly simple and easy to use, with a clean interface that makes coding less daunting. In addition, its step-by-step debugger and syntax error highlighter makes it easier to find errors quickly.

Is VScode good for data science?

Visual Studio Code (VSCode) is a great tool for data science due to its integration support for Jupyter Notebooks, so you can quickly run code cells to run models. VS Code is also lightweight and can process data analysis tasks easily.

Which is the best Python IDE for Windows?

Visual Studio Code (VSCode) is a great IDE for Windows. Developed by Microsoft, VSCode has extensive support for Windows computers, powerful debugging tools, and a built-in Git version control system.

Which is the best Python IDE for Mac?

PyCharm is the best Python IDE for Mac users. Its open-source community and paid versions are both available on Mac devices.

Final Thoughts

That’s all on my best Python IDEs for data science and machine learning in 2023. All five of these tools have their own strengths and weaknesses, so you should be able to find the best one that best suits your needs.

Thanks for reading!