10 Best Python IDE & Code Editors [2024 Update]

In this article, I cover the 10 best Python IDE and code editors available in 2024.

In the ever-evolving world of Python, it’s no understatement to say that the best Python IDE can have a huge impact on your coding experience.

Whether you're experienced in development or a total beginner, the best Python IDE can boost your productivity, streamline your workflow, and make your coding more enjoyable.

What’s not to like?

But where do you start? Well, don’t worry, that’s why I’ve written this article.

Whether you want to prioritize a seamless debugging experience, advanced code completion, or extensive plugin support, I’ll cover Python IDE options to meet your needs.

What Are IDEs & Code Editors?

Whether you’re a college freshman taking your first Python course, or a Staff Software Engineer, you need an IDE or a code editor to efficiently edit, read, and debug code.

But what is an IDE?

Great question. An Integrated Development Environment is a software package that facilitates code writing, testing, and debugging, all within a single application. For Python developers, an IDE is a crucial tool to streamline the coding process and it's where you build your Python projects!

To my mind, the most important thing to know about an IDE is that it increases your productivity by combining common developer tools such as software editing, building, testing, debugging, and packaging in one easy-to-use graphical user interface (GUI).

And that's not to mention other helpful features, like code refactoring, code search, code auto-completion, and continuous integration/continuous deployment (CI/CD).

Moving on to the IDE's cousin, the code editor.

Sometimes mistaken for IDEs, the main difference is that IDEs provide more powerful tools to simplify the coding process. That said, popular code editors like Visual Studio Code have become so fully featured that it's hard to differentiate between the two!

So, to keep things simple, I'll be including both types of coding environments in this article.

The Role of AI in Modern IDEs

You've likely noticed that AI is becoming increasingly prevalent in the development world, whether that's Python development or any other programming language.

The best proof of this is the increasing inclusion and support for AI coding assistants in modern IDEs. This allows you to use the AI assistant as a pair programmer, whether that's with AI tools like GitHub Copilot, Amazon CodeWhisperer, or TabNine.

If you're not yet familiar with these tools, they are insanely powerful, with the ability to auto-complete code or even generate entire functions based on natural language comments.

But why am I bringing this up?

Simple, when it comes to selecting your preferred Python IDE, I'd highly encourage you to consider not just your current needs but also future trends, and this means choosing an IDE that supports AI features that can set you up for long-term success.

Many of the options in my list offer this, with my top two picks, VSCode and PyCharm, both offering support for a range of AI assistants.

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The 10 Best Python IDE & Code Editors In 2024

To help you choose the best Python IDE or code editor, I’ve extensively researched the ten top Python IDEs and Python code editors based on popularity, functionality, number of users, and user feedback.

I’ve also included IDEs and editors in this list that cater to beginners and professional developers who are chasing Python certifications.

Plus, to make sure each IDE or editor was up to the task, I've taken the time to put them through their paces.

This included installing each on a Windows, macOS, and Linux platform and running a range of Python scripts from small individual files to multi-folder packages.

This was ideal for confirming that each IDE on our list is up to the task — whether that’s assisting beginner developers or allowing professionals to level up their day-to-day coding.

1. PyCharm

Securing the top spot on my list of Python IDEs is JetBrains' PyCharm. In my opinion, it's perhaps the very best Python-specific IDE available right now, which is also why I chose it as a default for my own Python course, Python with Dr. Johns!

PyCharm comes in three versions: The Apache-licensed Community version, the Educational (Edu) version, and the Professional version. The first two versions are open source and free of charge, but you’ll have to pay for the Professional version.

If you've never used it, trust me when I say its user-friendly interface is a big plus. But what makes PyCharm really stand out is its deep understanding of Python's nuances, ensuring a more intuitive coding experience.

PyCharm's debugging capabilities are also excellent, as the debugger offers local and remote debugging functionalities. This really simplifies the bug identification and fixing process, making it less of a hassle and more of a streamlined experience.

Another plus point for PyCharm is its intelligent code completion feature. This is not just about saving time with auto-completion or bracket matching; it’s as if the IDE comprehends your code, offering relevant suggestions and streamlining your coding workflow, making it faster and more efficient.

And that's not even mentioning that you can now take advantage of their new AI coding assistant extension with the paid version.

PyCharm also offers seamless integration with tools like Docker and version control systems like Git. This makes it incredibly convenient for modern development, especially when working with complex Python projects involving different technologies.

PyCharm's prowess isn't limited to pure Python development. It also offers excellent support for web development frameworks like Django, Flask, and web2py, making it a versatile tool for full-stack developers.

Plus, its integration with scientific tools like Jupyter Notebook and Anaconda further extends its usefulness, particularly for data scientists and researchers.

Overall, this Python IDE’s comprehensive suite of tools covers the entire software development lifecycle. From efficient code editing to robust version control, from project management to test automation, PyCharm offers an integrated, cohesive development environment.

Features:

• Advanced Debugging Tools: Offers robust local and remote debugging capabilities, crucial for Python projects of all sizes.
• Intelligent Code Completion: Enhances coding efficiency with smart code suggestions, completion, and navigation.
• Integration with Containers and Version Control Systems: Facilitates modern development practices and team collaboration.
• Support for Web Development Frameworks: Ideal for full-stack development with Django, Flask, and other popular frameworks.
• Scientific Tools Integration: Supports Jupyter Notebook and Anaconda, beneficial for data science and research projects.
• Comprehensive Development Lifecycle Tools: Integrates code editing, version control, project management, and testing.
• Editions for Different Needs: Offers a free Community version with more advanced features in the Professional edition.

2. Visual Studio Code (VSCode)

Landing the second spot on my list of Python IDEs is Microsoft's Visual Studio Code (VSCode). This lightweight yet powerful code editor is incredibly popular among developers, with more than 70% choosing it as their go-to coding environment.

It's also my personal favorite IDE, as not only is it well suited to coding in Python, but it's also versatile enough to let me code in a range of languages for various projects.

One of the things I like the most about VS Code is its extensibility. The Python extension, for instance, transforms VSCode into a fully-fledged Python IDE. This VSCode extension not only supports Python coding but also enhances the experience with features like linting, debugging, and unit testing.

The debugging experience in VSCode is also very streamlined, with the integrated debugger supporting both local and remote debugging, which is crucial for a variety of Python projects. It's also very intuitive, which makes the often-dreaded task of debugging surprisingly manageable.

Another stand-out for me with VSCode is its intelligent code completion, with the IntelliSense. feature offering relevant suggestions and making coding a smoother and more efficient process. You can even install extensions for popular AI coding assistants like GitHub Copilot and Amazon CodeWhisperer.

VSCode's seamless integration with Git is another big plus in my book. This makes managing version control a breeze, especially for collaborative projects. It's not just about committing and merging; the integrated experience makes it easier to track changes and collaborate with others.

Moreover, VSCode's support for a myriad of languages and frameworks, including Python web development frameworks like Django and Flask, makes it a versatile choice for developers. Whether you're working on a web application, a script, or diving into machine learning, VSCode adapts to your needs.

The customization potential of VSCode is another aspect I find invaluable. With a vast marketplace of extensions and VSCode themes, you can tailor the environment to your specific requirements, making it as simple or as powerful as you need.

Features:

• Extensible with Python Extension: Transforms VSCode into a Python IDE with features like linting, debugging, and testing.
• Advanced Debugging Capabilities: Offers intuitive local and remote debugging, crucial for diverse Python projects.
• IntelliSense Code Completion: Enhances coding efficiency with context-aware code suggestions and completions.
• Git Integration: Streamlines version control processes, ideal for individual and collaborative coding.
• Support for Multiple Languages and Frameworks: Adapts to various development needs, including web development with Django and Flask.
• Customizable Environment: A vast marketplace of extensions allows for personalization to suit different development styles.
• Lightweight and Efficient: Provides a powerful coding experience without the heaviness of traditional IDEs.

3. Jupyter Notebooks

Next up, we have Jupyter Notebooks, which, if you're not familiar with it, offers a really unique and interactive coding experience that is really great for data analysis, scientific computing, and educational purposes.

In fact, if you've spent any time learning Python for data science, you've probably noticed that Jupyter is the go-to coding environment for many data science courses and tutorials.

The reason is really simple: versatility and ease of use.

For me, what I enjoy the most about Jupyter is the way it blends code, text, and visualizations into a single, cohesive document. This is invaluable for data exploration and analysis, as it allows me to run code, see the results, and annotate them with markdown in one place.

The interactive nature of Jupyter is another major plus point for me, as it means I can execute small blocks of code independently, which is perfect for testing hypotheses, experimenting with data, and building complex analyses incrementally.

This level of interactivity is something that traditional Python IDEs can't quite replicate. Trust me, try it out, and you'll see!

In fact, I'd go so far as to say, that if you're interested in data, head straight to Jupyter, as it's a free and open-source IDE that you can use as a web-based IDE for creating your Jupyter documents.

It's also useful to point out that Jupyter utilizes the IPython kernel as its backend for executing Python code. This also means that it extends IPython's interactive features, like magic commands and widgets. If you're an IPython aficionado, this will be music to your ears!

Of course, in this article, we're focusing on Python, but Jupyter Notebooks also support various programming languages, including R (makes sense!), Julia, and even JavaScript, among others. This is a nice bonus level of versatility that makes it handy when working on projects that require cross-language integration.

Another aspect where Jupyter excels is in its visualization capabilities. I love being able to seamlessly integrate charts, graphs, and other visual aids alongside code and text, especially when I want to analyze and present data in a clear and understandable manner to members of my team.

Jupyter also integrates nicely with big data tools and frameworks, like Apache Spark, which makes it well-suited to working on complex data processing tasks, while also  broadening its applicability in data science.

Overall, it's fair to say that Jupyter Notebooks is unparalleled for educational purposes, thanks to a format that's perfect for creating and sharing documents that combine live code with explanatory text.

Features:

• Integrated Code, Text, and Visualizations: Comprehensive and interactive coding and data analysis experience.
• Interactive Execution: Independent execution of code blocks, ideal for incremental development and testing.
• Multi-Language Support: Accommodates various languages, though predominantly used for Python.
• Powerful Data Visualization Tools: Integrates a wide range of visualization libraries for enhanced data presentation.
• Compatibility with Big Data Tools: Works seamlessly with frameworks like Apache Spark for complex data processing tasks.
• Ideal for Education: Well-suited for creating instructional materials that combine live code, explanations, and visual aids.
• Shareable and Exportable: Notebooks can be easily shared and exported in various formats, facilitating collaboration and dissemination.

4. Pydev (for Eclipse)

Now, let's talk about PyDev, which allows you to use Eclipse as a fully-fledged Python IDE. Sure, you probably think of Eclipse as a Java IDE, and that's fair, but thanks to PyDev, developers who are already familiar with Eclipse can take advantage of Python-specific features within Eclipse's robust coding environment.

What I particularly like about PyDev is how it seamlessly integrates into the Eclipse ecosystem. This means you get the best of both worlds: Eclipse's powerful features and PyDev's Python-focused functionality.

It's also an ideal setup for anyone who's already comfortable with Eclipse and is looking to extend its capabilities to Python development.

For me, one of the standout features of PyDev is its powerful code analysis capabilities, with real-time syntax highlighting, error checking, and code completion. Of course, this seems fairly standard for a modern IDE, but again, if you like to stay in a single coding environment, having this available in Eclipse for Python is awesome!

Another aspect where PyDev shines is its debugging tool, which is comprehensive with features like remote debugging and an interactive console. This makes it easy to diagnose and resolve issues, which is essential in any development process.

PyDev also excels in supporting web development frameworks like Django and Flask. This makes it a versatile tool for Python developers, whether you're building web applications or working on more traditional Python projects.

The integration with Jython and IronPython is another unique feature of PyDev. This allows for Python scripting on the Java and .NET platforms, respectively, opening up a range of possibilities for cross-platform development.

In terms of customization and flexibility, PyDev has a plethora of plugins available in the Eclipse marketplace. This adaptability makes it suitable for a variety of development needs and workflows.

Features:

• Seamless Eclipse Integration: Combines Eclipse's comprehensive features with Python-specific functionality.
• Advanced Code Analysis: Real-time syntax highlighting, error checking, and code completion.
• Comprehensive Debugging Tool: Features like remote debugging and an interactive console.
• Support for Web Development Frameworks: Facilitates development with Django, Flask, and other Python web frameworks.
• Jython and IronPython Integration: Enables Python scripting on Java and .NET platforms for cross-platform development.
• Customizable and Flexible: Wide range of plugins and extensions through the Eclipse marketplace.

5. IDLE

IDLE, otherwise known as 'Integrated Development and Learning Environment', is a handy coding environment that comes bundled with Python. If you're completely new to coding, this is always a great place to start, as it's literally bundled with your Python install.

In fact, I'd say it's one of my go-to choices for beginners and those who prefer a straightforward, no-frills coding environment.

What I really appreciate about IDLE is its user-friendly interface. It's a stark contrast to more complex IDEs, making it perfect for those just starting out with Python or for anyone who values simplicity. It's because of this accessibility that I often recommend IDLE to newcomers to Python programming.

Another aspect of IDLE that stands out to me is its interactive Python shell. This is great for experimenting with Python code in real time, which is invaluable for learning and understanding Python concepts. It’s not just about running scripts; it’s about immediate feedback and experimentation.

IDLE also includes a simple but effective code editor. This has syntax highlighting and basic auto-completion, which are essential features that help in writing clean and error-free code. While it might not have the advanced features of more robust IDEs, its simplicity is its strength, especially for educational purposes.

One of the benefits of using IDLE is that it's lightweight and doesn't require a heavy setup. It runs well on almost any system, making it accessible to a wide range of users, regardless of their hardware capabilities.

IDLE also integrates seamlessly with Python, as it is part of the standard Python distribution. This means there are no additional installations or configurations needed to start coding in Python.

Features:

• User-Friendly Interface: Ideal for beginners and those who prefer a simple, straightforward coding environment.
• Interactive Python Shell: Real-time code experimentation, perfect for learning and quick testing.
• Basic Code Editor: Syntax highlighting and basic auto-completion, suitable for writing clean code.
• Lightweight and Accessible: Runs smoothly on various systems without the need for complex setups.
• Seamless Python Integration: Part of the standard Python distribution, ensuring easy access and use.

6. Sublime Text

Sublime Text is next up on my list. With a long-standing reputation as a versatile and efficient code editor in various languages, it's also excellent for Python development.

To my mind, one of the best things about Sublime Text is its lightning-fast performance and clean, distraction-free interface. If you like a sleek coding environment or you prioritize speed and efficiency, this is a great option.

Another standout feature for me is Sublime Text's Goto Anything functionality. This powerful feature allows quick navigation to files, symbols, or lines with just a few keystrokes, significantly speeding up the workflow. It really exemplifies this code editor's commitment to efficiency and time-saving.

Sublime Text's extensive customizability is another aspect I like, with a rich ecosystem of plugins and themes that you can use to tailor the editor to your specific needs, whether you're working on a small script or a large Python project.

The Package Control ecosystem is particularly noteworthy, offering a vast array of extensions to enhance functionality.

I also find that this code editor excels when handling multiple projects simultaneously, making it very easy to manage and switch between different project workspaces. If you're like me and you're working on multiple projects at once, this is great for juggling tasks.

In terms of Python-specific features, while Sublime Text isn't an IDE, it can be heavily customized for Python development with Python-related plugins that turn it into a powerful environment for writing Python code, complete with syntax highlighting, code completion, and linting.

Plus, Sublime Text's multi-line editing and selection capabilities are terrific for making complex edits across multiple lines simultaneously, which is a massive time-saver for refactoring code or making repetitive changes.

Features:

• Fast Performance and Distraction-Free Interface: Highly efficient coding experience with a focus on productivity.
• "Goto Anything" Functionality: Quick navigation and file access, enhancing workflow efficiency.
• Extensive Customizability: Rich selection of plugins and themes allows for personalization and enhanced functionality.
• Multi-Project Management: Seamlessly handles multiple projects, ideal for developers with diverse workloads.
• Customizable for Python Development: Tailor it with Python plugins for syntax highlighting, code completion, and more.
• Multi-Line Editing and Selection: Powerful tools for making complex, simultaneous edits across multiple lines.

7. Spyder

Taking the seventh spot on my list is Spyder. And while it may not have the same reputation as something like PyCharm or VSCode, it's an excellent choice for scientific computing and data analysis tasks.

I guess the clue is in the name, as Spyder is an acronym of sorts for The Scientific Python Development Environment. But I guess Spyder is catchier!

It's no surprise then that what sets Spyder apart is its integration with scientific Python libraries like NumPy, SciPy, Matplotlib, and Pandas, which makes it incredibly powerful for scientific computing.

In the end, when it comes to Python for data-driven roles, It’s not just about writing code, but it’s about having the right tools at your fingertips for complex data analysis and visualization tasks.

One of the things I like most about Spyder is its variable explorer, as this allows me to interactively inspect and manage the variables in my code, including complex data structures.

I love this level of interactivity for data exploration and debugging, and it really makes complex data more manageable and understandable.

Spyder also offers an excellent debugging experience. The interactive debugger is easy to use and integrates well with the rest of the IDE, allowing for efficient tracking down and fixing of issues in code. It's one of those features that saves a lot of time and frustration in the development process.

The IPython console in Spyder is another highlight for me. It provides a robust and interactive environment for testing and running Python code. The ability to execute code line-by-line and see immediate results enhances the overall coding and data analysis experience.

Continuing with that theme, you also get integration with Jupyter Notebooks! Yes, you can open and edit notebooks directly in Spyder. This is a shrewd move and, in my opinion, a feature that significantly broadens its appeal, as you get the robustness of an IDE and the interactivity of Jupyter Notebooks.

It's also nice that Spyder's layout is customizable, so you can arrange the panels and tools according to your workflow, making it a flexible environment that adapts to your needs, whether you’re working on data analysis, machine learning models, or other scientific computing tasks.

Features:

• Integration with Scientific Python Packages: Direct support for packages like NumPy, SciPy, Matplotlib, and Pandas.
• Variable Explorer: Interactively inspect and manage variables, which is crucial for data analysis and debugging.
• Advanced Debugging Tools: Intuitive and integrated debugging environment for efficient problem-solving.
• IPython Console: Powerful environment for interactive execution and testing of Python code.
• Customizable Layout: Personalization of the working environment to suit various scientific computing needs.
• Jupyter Notebook Integration: Open and edit Jupyter Notebooks within the IDE.

8. GNU Emacs

GNU Emacs holds a special place in my heart when it comes to programming tools. Perhaps I'm biased as a long-time Linux user, but still, hear me out! And let's try to avoid a flame war if you prefer Vim!

Overall, Emacs is renowned for its versatility, especially when configured for Python development. But in my experience, what truly sets Emacs apart is its extensibility.

This means it's not just a code editor but a platform where you can build your ideal development environment. This is especially true for Python, where Emacs can be transformed into a fully-featured Python IDE with the right extensions, like Elpy or Python-mode.

Another feature of Emacs that I really love is its powerful text editing capabilities and, of course, its rich set of key bindings.

This means that Emacs has an extensive set of built-in commands and shortcuts that make coding and text manipulation incredibly efficient. The ability to edit at lightning speed, with keyboard shortcuts for almost every action, significantly enhances productivity.

Just be prepared to develop an "Emacs Pinky" due to the heavy use of the Control key (Ctrl), which you'll tend to press with your little finger (pinky).

It's also nice that Emacs offers integration with version control systems like Git, debuggers, and other programming tools, creating a cohesive development environment. This makes it a one-stop solution for many developers who prefer a unified workspace.

The Emacs ecosystem is also rich with packages and extensions, allowing you to extensively customize it to suit your programming needs or style.

When it comes to Python developers, check out packages like Elpy to get IDE-like features like code completion, syntax checking, and an interactive Python shell.

Of course, Emacs also has support for multiple programming languages, so you can efficiently work on projects in almost any programming language, making it a versatile tool for polyglot programmers.

Features:

• Unparalleled Extensibility: Can be customized into a full-fledged Python IDE with the right extensions.
• Powerful Text Editing Capabilities: Offers a vast array of built-in commands and shortcuts for efficient coding.
• Comprehensive Tool Integration: Seamlessly integrates with version control systems, debuggers, and other tools.
• Rich Ecosystem of Packages: A wide range of extensions available for customization to various programming needs.
• Support for Multiple Programming Languages: Suitable for working on projects in virtually any programming language.

9. Wing

Wing is another lesser-known Python IDE that is highly efficient and feature-rich, making it a good option for a wide range of Python developers, from those working on small scripts to large-scale applications.

What impressed me about Wing is its exceptional debugger, which is both robust and versatile. Plus, with features like remote debugging, not to mention multi-threaded and multi-process debugging, you can get a comprehensive understanding and control over your application's execution.

Wing also offers support for test-driven development (TDD), with integration for Python's unittest, doctest, nose, pytest, and Django testing frameworks. This is really cool, and it makes it a lot easier to maintain high code quality and reliability.

For web development, Wing also provides excellent support for frameworks like Django, Flask, and Pyramid, among others, making it a versatile tool for both backend and frontend Python development.

Another aspect of Wing that I like is the intelligent editor, with features like auto-completion, error checking, and refactoring tools that greatly enhance coding efficiency.

Overall, Wing was designed with Python in mind, which means that it understands Python constructs and idioms exceptionally well, facilitating a more natural and productive coding experience.

This Python IDE also excels in customizability, whether that's layout, key bindings, or toolsets. I think this level of flexibility can be particularly appealing to developers who are used to a certain way of working.

Features:

• Advanced Debugging Tools: Robust capabilities like remote, multi-threaded, and multi-process debugging.
• Intelligent Code Editor: Enhances productivity with features like auto-completion, error checking, and refactoring.
• Deep Python Integration: Tailored specifically for Python, facilitating a natural and efficient coding experience.
• Highly Customizable: Allows personalization of layout, key bindings, and toolsets to fit individual preferences.
• Support for Test-Driven Development: Integrates with multiple Python testing frameworks to ensure code quality.
• Web Development Support: Provides tools and features for popular frameworks like Django, Flask, and Pyramid.

10. Thonny

Rounding out my list of Python IDEs is Thonny, which if you didn't know, is an exceptionally user-friendly and straightforward IDE designed for beginners in Python programming.

What I find most appealing about Thonny is its simplicity and focus on providing a gentle introduction to Python. The IDE removes many of the complexities and potential obstacles that can overwhelm beginners. This makes it an ideal starting point for those who are just embarking on their programming journey.

One of Thonny's standout features is its built-in Python interpreter, which simplifies the process of writing and running Python scripts. It's designed to be intuitive, displaying variable values and providing a clear view of how Python executes code.

This level of transparency is invaluable for understanding the basics of programming and Python syntax.

The debugger in Thonny is another aspect that I particularly appreciate. It's tailored for beginners, offering a step-by-step code execution feature that allows users to observe how their code runs and where errors might occur. This is crucial for learning how to debug programs effectively.

Thonny's interface is clean and uncluttered, which I think is perfect for beginners as it lets them keep the focus on learning. Yes, the temptation is there to use something like PyCharm, but Thonny doesn't overwhelm the true beginner with too many tools or options, offering just what is needed for a beginner to start coding in Python.

This IDE also includes features like syntax highlighting and auto-completion, which, while standard in more advanced IDEs are implemented in Thonny in a way that supports the learning process. This is great for helping beginners write readable and error-free code.

Another advantage of Thonny is that it comes with everything needed to start Python programming, including its own version of Python. This means there's no need for a separate Python installation, simplifying the setup process.

Features:

• Beginner-Friendly Interface: Provides a simple and intuitive environment, ideal for newcomers to Python.
• Built-in Python Interpreter: Offers an easy way to write and run Python scripts with clear execution visualization.
• Beginner-Focused Debugger: Includes step-by-step code execution to help users understand how their code works and identify errors.
• Clean and Uncluttered Interface: Helps maintain focus on learning without overwhelming beginners.
• Syntax Highlighting and Auto-Completion: Supports beginners in writing readable and error-free code.
• Self-Contained Setup: Comes with its own Python installation, making it easy to get started without additional setups.

How To Find The Best Python IDE in 2024?

Great question! And trust me, this can be tough!

Sure everyone raves about VSCode, given its popularity among developers, but what is the best Python IDE or code editor in 2024?

Well, don't worry, because I want to help!

That's why I've extensively researched the best Python coding environments based on popularity, functionality, user interface, number of users, and user feedback.

I’ve also included IDEs and code editors in this list that cater to beginners and professional developers, so whatever your skill level, there's something for you.

Plus, to make sure each IDE or editor was up to the task, I've taken the time to put them through their paces for a wide range of Python projects.

This included installing each on a Windows, macOS, and Linux platform and running a range of file types, from small individual files to multi-folder projects.

This was ideal for confirming that each IDE on our list is up to the task — whether that’s assisting beginner developers or allowing professionals to level up their day-to-day coding.

Wrapping Up

So there you have it! The 10 best Python IDE and code editors available in 2024, including Python IDE options for experienced developers and total beginners.

Whether you’re looking for a professional Python IDE, lightweight code editor, or simple Python IDE to help you learn, there’s something for you on our list.

Remember, there is no one-size-fits-all answer when it comes to choosing the best Python IDE.

The right choice depends on your specific projects, personal workflow, and even company policies.

My advice is to try out a few different IDEs to see which one aligns best with your needs. Keep an eye on the evolving landscape of AI tools and ensure that your IDE can integrate these advancements.

What do you think? What's your favorite Python IDE? Let us know in the comments, and as always, happy coding!

Enjoyed learning about these Python IDEs and ready to dive deeper into Python? Check out:

Our Python Masterclass - Python with Dr. Johns

Frequently Asked Questions

1. What Is the Best IDE for Python?

The best Python IDE is that which enhances your coding experience and productivity by the greatest margin. If you’re a beginner, then it may be good to start with IDLE, Thonny, or Wing. If you’re an experienced developer, you can choose any of the IDEs we’ve covered, but the most popular choices tend to be Pycharm for an IDE and VSCode for a code editor.

2. Which is the Best Python IDE for Beginners?

Several Python IDEs have been designed with beginners in mind. These let you build a comfort level with the environment as you become familiar with each feature. IDLE is a good choice as it’s installed with Python. Thonny and Wing are also excellent choices, as they offer simple UIs that were designed for beginner developers. Pycharm’s free tier is also one of the best Python environments for beginners.

3. What Is the Official IDE for Python?

There is no official IDE for Python per se. IDLE is bundled with Python, so it could be viewed as the official IDE, but the Python developer community is open-minded and collaborative, meaning that there’s extensive user support and feedback for each of the Python IDEs we’ve covered. 

4. Which Python IDE Should I Use for Machine Learning?

Jupyter is a popular choice for machine learning developers as it offers ease of use with running simulations and sharing results with others. Similarly, Spyder is popular with the data science community due to its extensive scientific computing features and library integration. However, any of the IDEs we’ve covered can be used for machine learning if you’ve installed the necessary libraries.