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The best free and open source Python integrated development environments in 2026 are Spyder for scientific work, Thonny for beginners, IDLE for a minimal start, JupyterLab for notebooks, Eclipse with PyDev for multi-language projects, Eric for a Python-focused desktop IDE, VSCodium for extensibility, Geany for lightweight editing, and PyCharm’s free core for polished Python tooling with important licensing limits.
Those recommendations are not interchangeable. “Free,” “open source,” and “IDE” describe different things: some tools are fully open-source applications, some combine open-source platforms and plugins, some are editors that become IDE-like after configuration, and PyCharm is a unified product with a free core and paid advanced features.
This list therefore ranks tools by workflow rather than declaring one universal winner. The strictest free-software choices are Spyder, Thonny, JupyterLab, IDLE, Eclipse with PyDev, and Eric; VSCodium and Geany are open-source-oriented editor choices, while PyCharm requires the clearest qualification.
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- Spyder is the strongest first choice for scientific Python, data analysis, engineering, and variable-oriented workflows.
- Thonny is the most approachable choice for beginners and classroom teaching, while IDLE offers the lowest setup burden when Python is already installed.
- JupyterLab is a web-based interactive development environment for notebooks, code, and data, but it is not a direct replacement for a conventional package-oriented IDE.
- Eclipse with PyDev suits large multi-language projects, whereas VSCodium and Geany require more configuration and are better described as editor-based IDE alternatives.
- PyCharm’s free core remains attractive for mainstream Python development, but the unified PyCharm product is not wholly open source and advanced features require Pro after the trial period.
What counts as a free and open source Python IDE?
A free and open source Python IDE should provide source code that readers can inspect, modify, and redistribute under its applicable license, while allowing them to use the software without paying for ordinary core functionality. The phrase does not automatically describe every no-cost editor or every product whose source repository is publicly available.
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Python development environments fall into several useful categories:
| Category | What it means | Examples in this guide |
|---|---|---|
| Fully open-source application | The application is openly developed and its source and licensing are available. | Spyder, Thonny, JupyterLab, IDLE |
| Open-source platform plus Python plugin | The host IDE and Python tooling are separate projects with their own licensing. | Eclipse plus PyDev |
| Open-source core with proprietary additions | The free core remains available, but some features or parts of the product are commercial. | PyCharm |
| Open-source editor usable as an IDE | The editor becomes an IDE through extensions, plugins, and external tools. | VSCodium, Geany |
| Free but proprietary | The software costs nothing under its terms, but users do not receive equivalent source-code rights. | Microsoft VS Code, Wing Personal |
A conventional IDE usually combines editing, code completion, execution, an interactive console, debugging, project navigation, testing, interpreter selection, version control, dependency management, and refactoring. No tool in this list handles all of those functions equally well. JupyterLab emphasizes interactive documents and kernels; IDLE intentionally stays modest; and VSCodium requires extensions for much of its Python workflow.
Which free and open source Python IDE should you choose?
| Reader or workflow | Recommended first choice | Why | Main limitation |
|---|---|---|---|
| Complete beginner | Thonny | Simple interface and approachable execution and debugging. | Advanced project features are limited. |
| Python already installed | IDLE | Provides a shell, editor, and basic debugger with minimal setup. | Weak project management and code intelligence. |
| Data analyst or scientist | Spyder | Scientific console, variable inspection, and data-oriented workflow. | Less natural for large web applications. |
| Notebook and exploratory work | JupyterLab | Interactive notebooks, kernels, rich output, and data tools. | Stateful notebooks can complicate reproducibility. |
| Large multi-language project | Eclipse with PyDev | Mature workspace and plugin model with Python support. | More setup and complexity. |
| Python-focused desktop IDE | Eric | Traditional, Python-centric all-in-one workflow. | Smaller ecosystem and mindshare. |
| Extensible modern editor | VSCodium | Flexible extensions, terminal, source control, and language tooling. | Core Python functionality depends on extensions. |
| Older or modest hardware | Geany | Lightweight editor with useful IDE-style features. | Testing, debugging, and environment features are less integrated. |
| Polished mainstream Python workflow | PyCharm free core | Strong completion, navigation, debugging, refactoring, and Git support. | The unified product includes proprietary paid features. |
How were these Python development environments compared?
The comparison considers licensing clarity, free-use conditions, Python depth, interpreter and environment selection, debugging, testing, project scale, notebook and data-science support, extensibility, setup friction, performance tendencies, accessibility controls, and maintenance confidence. The article does not assign artificial benchmark scores: no reproducible hardware-and-project test data was supplied, so performance is described qualitatively rather than with invented startup or memory figures.
Installation details and current releases change frequently. Use the official project pages linked in each review for the current installer, operating-system support, architecture requirements, and license text.
1. Is Spyder the best open source Python IDE for data science?
Spyder is the best first choice for scientific Python, data analysis, engineering, and research workflows. Spyder’s official site positions the application around scientific Python, data analysis, and engineering, while its current FAQ describes Spyder as 100% free and open source, with no paid version and permission for commercial use.
Why choose Spyder?
- Python-focused editing and execution.
- An interactive console suited to exploratory work.
- Variable and data inspection that is particularly useful with arrays and data frames.
- A workflow designed around scientific packages such as NumPy, pandas, SciPy, and visualization libraries.
- Standalone installers and multiple distribution methods, depending on the user’s platform and preferred Python distribution.
Spyder’s variable explorer is a practical advantage for analysts who need to inspect values while developing. The feature does not replace source control, tests, clear project structure, or reproducible dependency files; it complements those practices.
How does Spyder handle installation and environments?
Spyder can be installed through its official distribution methods or through a Python distribution such as conda, but the interpreter, Spyder process, scientific kernel, and installed packages must belong to compatible environments. A common failure is launching Spyder from one installation while selecting a kernel or package set from another.
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python -m pip --version
python -m pip listWho should skip Spyder?
Readers building large web applications, multi-language repositories, or heavily plugin-driven enterprise projects may find PyCharm or Eclipse with PyDev more natural. Readers who work primarily in notebooks may prefer JupyterLab, while beginners who are still learning basic control flow may find Thonny less intimidating.
2. Is Thonny the best free Python IDE for beginners?
Thonny is the best choice for a complete beginner, student, or teacher who wants a deliberately simple Python environment. Thonny reduces visual and configuration complexity, gives learners immediate feedback, and provides beginner-friendly execution and debugging without requiring the full machinery of a professional IDE.
What makes Thonny beginner-friendly?
- A comparatively uncluttered interface.
- Clear feedback when running a script.
- Debugging designed to make program execution easier to understand.
- A classroom-friendly workflow with less setup than a general-purpose editor.
- A gentler first experience than a large extension-based environment.
Thonny is a good place to learn variables, loops, functions, modules, and basic file handling. The simplicity is intentional, not evidence that Thonny is defective.
What are Thonny’s limitations?
Thonny is not the strongest choice for a large package, complex Git workflow, multi-language repository, containerized service, or extensive web-development toolchain. Advanced users may outgrow Thonny once they need sophisticated refactoring, broad test discovery, remote interpreters, or highly configurable language tooling.
Check the official Thonny project page immediately before installation for its current release, platform installers, license, and bundled-Python behavior. Those details can change between releases.
Rank #2
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3. Is IDLE the best zero-install Python IDE?
IDLE is the best minimal starting point when Python is already installed and the reader needs only an editor, shell, and basic debugger. Python’s official IDLE documentation calls IDLE the “Integrated Development and Learning Environment” and documents its Python shell, multi-window editor, syntax coloring, smart indentation, autocomplete, search, and debugger.
What does IDLE include?
- A Python interactive shell.
- A multi-window text editor.
- Syntax highlighting and indentation assistance.
- Autocomplete and search.
- Basic debugging with stepping, persistent breakpoints, and namespace inspection.
IDLE can be launched with:
python -m idlelibThe same Python documentation describes command-line options for opening an editor, running a file, and enabling the debugger. IDLE is optional in some Python distributions, so “Python is installed” does not guarantee that IDLE is present on every platform or vendor build.
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IDLE has sparse project management, basic code intelligence, a small extension ecosystem, and limited support for large collaborative applications. IDLE is appropriate for learning and quick scripts, but it should not be presented as equivalent to a modern professional IDE.
4. Is JupyterLab an open source Python IDE?
JupyterLab is the best open-source choice for notebooks, experiments, teaching, and interactive data work. Project Jupyter describes JupyterLab as a web-based interactive development environment for notebooks, code, and data, and describes Jupyter as free to use and released under a modified BSD license on its About page.
How is JupyterLab different from a desktop IDE?
JupyterLab uses documents and kernels rather than treating a folder of Python files as the only center of development. A notebook can contain code, narrative text, equations, and output in Jupyter’s open JSON-based notebook format. Multiple documents, consoles, terminals, and data views can coexist in the browser-based workspace.
JupyterLab is installed and launched with the official commands:
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jupyter labThe official installation guidance also recommends the conda-forge channel for users working with conda or mamba. The command must be run in the environment that contains the intended JupyterLab installation.
What are JupyterLab’s trade-offs?
- Notebook state can become non-linear when cells are run out of order.
- Running all cells from top to bottom may produce a different result from the original interactive session.
- The browser, server, kernel, and package environment create additional concepts for beginners.
- Large notebooks can produce awkward Git diffs.
- A notebook workspace is not automatically a complete application-development workflow.
For reliable work, restart the kernel and run every cell from top to bottom before sharing results. Record dependencies, move reusable logic into .py modules, keep hidden state to a minimum, and check the selected kernel separately from any general interpreter setting.
5. Is Eclipse with PyDev the best open source IDE for large projects?
Eclipse with PyDev is the strongest choice in this list for readers who want a conventional, extensible, multi-language IDE workspace. Eclipse supplies the host platform, while PyDev supplies Python support. The Python Wiki’s IDE catalogue lists PyDev as a free, open-source Eclipse plugin supporting Python, Jython, and IronPython.
What does PyDev add to Eclipse?
- Python editing and code completion.
- Debugging and navigation.
- Refactoring and templates.
- Code analysis.
- Unit-test integration.
- Django integration.
Eclipse’s workspace and plugin model can be valuable for organizations already using Eclipse for other languages. The same model can be excessive for a short script or a learner who has not yet developed a project structure.
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What makes Eclipse with PyDev difficult?
Python support depends on the installed PyDev plugin, the Eclipse version, the selected interpreter, and project settings. Troubleshooting may involve all four layers. Install Eclipse and PyDev using the current instructions from the relevant official projects, then explicitly configure the project interpreter rather than assuming the first system Python is correct.
6. Is Eric the best traditional Python-focused open source IDE?
Eric is the best fit for readers who specifically want a traditional, Python-oriented desktop IDE rather than a browser notebook or a configurable editor. The Python Wiki identifies Eric as a Python IDE and lists Eric among free Python development environments.
Why consider Eric?
Eric offers the shape many users expect from a desktop IDE: an editor, project workflow, debugging, and development tools in a Python-centric application. Eric is especially relevant to readers who want an all-in-one open-source desktop environment without adopting Eclipse’s general-purpose workspace.
Rank #3
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- ✔️[Ergonmic Desing] - You can freely adjust the height and angle of the laptop stand to keep it at eye level, which helps to reduce the pressure on your body while working. Whether sitting or standing, there is a comfortable angle.
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What should readers verify first?
Eric has less public mindshare than PyCharm, VS Code, or Eclipse. Check the official Eric project page for current release activity, documentation, platform packaging, and license details before choosing Eric for a long-lived team project. A smaller ecosystem does not make Eric unsuitable, but it can affect tutorials, plugins, troubleshooting, and onboarding.
7. Is VSCodium a complete open source Python IDE?
VSCodium is the best choice for readers who want an open-source-oriented, highly extensible editor that can be assembled into a Python IDE. It is more precise to call VSCodium an editor distribution with IDE capabilities than a fully integrated Python IDE out of the box.
What does VSCodium provide?
- A flexible editor and integrated terminal.
- Source-control workflow.
- Extension-based language support.
- Configurable language-server, formatter, linter, debugger, test, and notebook workflows.
- An option for readers concerned about proprietary binaries or telemetry.
The experience depends on the extensions selected. A reader may need separate Python language support, a debugger, a language server, a formatter, a linter, notebook support, and a test runner. Extension availability, licensing, update timing, and marketplace access can differ from Microsoft’s official VS Code distribution.
Use the official VSCodium project page to verify its current downloads, licensing, extension-source policy, and platform support. Do not assume that every extension available for Microsoft’s product is available under identical terms in VSCodium.
Who should skip VSCodium?
Beginners who want Python to work immediately may prefer Thonny, Spyder, or PyCharm. Readers with strict supply-chain requirements should inspect every extension and update channel rather than treating the open-source distribution as proof that all installed components are open source.
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8. Is Geany the best lightweight open source Python editor?
Geany is the best lightweight option for scripts, small projects, older laptops, and readers who prefer a fast editor with basic IDE conveniences. Geany provides syntax highlighting, navigation, project features, and build-oriented tools without the complexity of a heavyweight traditional IDE.
Where does Geany fit?
Geany is better described as a lightweight editor with IDE-style features than as a fully integrated Python environment. Python editing may be productive immediately, but debugging, refactoring, testing, and interpreter management can require plugins or external commands.
Geany is a sensible choice when low complexity matters more than integrated enterprise tooling. It is less suitable for a large Python application that needs robust test discovery, multiple interpreters, deep refactoring, remote debugging, or extensive notebook integration.
Check the official Geany website for current downloads, plugins, supported platforms, release status, and licensing before deployment. Qualitative claims about lightness should not be mistaken for a measured benchmark on every machine.
9. Is PyCharm’s free core open source?
PyCharm’s free core is the best mainstream Python experience for readers who prioritize polished tooling, but the unified PyCharm product should not be called completely open source. JetBrains changed the distribution model beginning with PyCharm 2025.1: Community and Professional became one unified product. JetBrains explains the current model in its unified PyCharm documentation.
What remains free?
JetBrains advertises PyCharm’s core functionality as free forever and provides a one-month Pro trial for new installations on its PyCharm product page. JetBrains states that core functionality, including Jupyter support, remains free after the trial, while advanced features require Pro.
PyCharm’s free core is attractive because it combines Python completion and navigation, refactoring, debugging, Git integration, project support, and Jupyter functionality in a polished workflow. PyCharm is often easier to use for a substantial Python project than an editor that requires assembling and maintaining many extensions.
What is the licensing limitation?
The unified PyCharm product contains commercial advanced functionality, so the entire product is not equivalent to Spyder, Thonny, JupyterLab, or another fully open-source application. The open-source portions remain public, but “PyCharm is open source” is too broad a description. Readers requiring strict FOSS should choose a fully open-source alternative; readers who mainly require a no-cost professional workflow may find the free core sufficient.
Recommended Free Tools
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- 【Heat Dissipation】 ：The top of the laptop stand is designed with multiple ventilation holes. The open design offers greater ventilation and more airflow to cool your laptop during operation other than it just lays flat on the table.
- 【Portable & Foldable】：The foldable design allows you to easily slip it in your backpack. Ideal for people who travel for business a lot.
- 【Broad Compatibility】：Our desktop book stand is compatible with all laptops from 10-15.6 inches, such as MacBook Air/ Pro, Google Pixelbook, Dell XPS, HP, ASUS, Lenovo ThinkPad, Acer, Chromebook and Microsoft Surface, etc.Be your ideal companion in Home, Office & Outdoor.
The old recommendation to install a standalone PyCharm Community Edition is incomplete for new users. Consult JetBrains’ current installation guide for the present distribution model.
How should you manage Python environments in any IDE?
The most important environment rule is simple: the IDE, terminal, test runner, and notebook kernel must use the same intended Python environment. An interpreter selector cannot repair a broken or incorrectly populated environment.
What are the common environment types?
- System Python: the interpreter installed or managed by the operating system or Python distributor.
venv: a project-local virtual environment created with Python’s standard tooling.- Conda or mamba: environment and package-management systems commonly used in scientific workflows.
- Poetry or Pipenv: project-management workflows that control dependencies and environments.
- Remote interpreters and containers: environments where code executes outside the local desktop installation.
A basic project-local environment can be created with:
python -m venv .venvActivation commands differ by shell and operating system. After activation, install packages with the environment’s Python, preferably using python -m pip rather than an ambiguous bare pip.
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Symptoms include ModuleNotFoundError in the IDE despite a successful terminal import, notebooks using a different package set, tests passing in one environment but failing in another, and an IDE silently selecting system Python instead of .venv.
Run these commands in the environment the project is supposed to use:
python -c "import sys; print(sys.executable)"
python -m pip --version
python -m pip listCompare the executable path and package list with the interpreter selected in Spyder, PyCharm, Eclipse, VSCodium, or the notebook kernel. Installing a package globally does not make that package available inside a project-local environment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which IDEs are strongest for debugging and testing?
Debugging quality separates a learning tool from a professional project environment. Breakpoints, step over, step into, step out, variable inspection, exception handling, test discovery, and integration with pytest or unittest matter more as projects grow.
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|---|---|---|
| Thonny | Beginner-friendly execution and debugging. | Good for learning; limited for complex projects. |
| IDLE | Basic debugger with stepping, breakpoints, and namespace inspection. | Useful for scripts, not equivalent to a modern professional debugger. |
| Spyder | Interactive scientific debugging and variable inspection. | Strong for data work; conventional application workflows may need more structure. |
| JupyterLab | Kernel-based interactive execution rather than ordinary file debugging. | Excellent for experiments; notebook state must be controlled carefully. |
| Eclipse with PyDev | Full IDE debugging and project tooling through PyDev. | Better suited to structured, multi-language work; setup is more complex. |
| Eric | Traditional Python IDE debugging. | Useful all-in-one model; verify current ecosystem and documentation. |
| VSCodium | Depends on selected debugger and language extensions. | Powerful when configured; maintenance responsibility is higher. |
| Geany | Less integrated by default; plugins or external commands may be needed. | Best for smaller projects and scripts. |
| PyCharm | Strong integrated debugging and navigation in the free core. | Advanced professional features may require Pro. |
For any tool, confirm that the test runner uses the same interpreter as the application. A green test result from one environment does not validate a different environment used by the IDE or deployment system.
How do Git and project workflows differ?
Small scripts can work well in almost any editor, but a package with tests, CI, documentation, configuration files, and multiple environments benefits from a stronger project model. Evaluate built-in or extension-based Git operations, branch and diff views, terminal access, workspace navigation, multi-root support, repository-wide search, refactoring safety, .gitignore handling, pyproject.toml support, and test discovery.
PyCharm and Eclipse with PyDev offer the most conventional project-oriented experiences in this shortlist. VSCodium can provide a highly capable workflow, but readers assemble more of the experience through extensions. Spyder, Thonny, and IDLE are excellent at their intended tasks without needing to imitate an enterprise workspace. JupyterLab is organized around interactive documents and kernels, so Git and deployment practices require extra discipline.
What should data scientists know about notebooks?
Notebook support is not the same as an interactive console, and a variable explorer is not the same as a notebook. Spyder is strongest when a scientist wants an interactive console and direct inspection of variables while working with Python files. JupyterLab is strongest when the work itself is a document combining executable code, explanation, equations, visualizations, and output.
Notebook users should restart the kernel, run cells from top to bottom, record dependencies, move reusable code into modules, avoid hidden state, and review notebook diffs before committing them. The selected notebook kernel must be checked separately from the general interpreter configured for a project.
Best Value
- ✅【Adjustable & Ergonomic】：This laptop stand can be adjusted to a comfortable height and angle according to your actual needs, letting you fix posture and reduce your neck fatigue, back pain and eye strain. Very comfortable for working in home, office and outdoor.
- ✅【Sturdy & Protective】 ：Made of sturdy metal, it can support up to 17.6 lbs (8kg) weight on top; With 2 rubber mats on the hook and anti-skid silicone pads on top & bottom, it can secure your laptop in place and maximum protect your device from scratches and sliding. Moreover, smooth edges will never hurt your hands.
- ✅【Heat Dissipation】 ：The top of the laptop stand is designed with multiple ventilation holes. The open design offers greater ventilation and more airflow to cool your laptop during operation other than it just lays flat on the table.
- ✅【Portable & Foldable】：The foldable design allows you to easily slip it in your backpack. Ideal for people who travel for business a lot.
- ✅【Broad Compatibility】：Our laptop holder is compatible with all laptops from 10-17.3 inches, such as MacBook Air/ Pro, Google Pixelbook, Dell XPS, HP, ASUS, Lenovo ThinkPad, Acer, Chromebook and Microsoft Surface, etc.Be your ideal companion in Home, Office & Outdoor.
What are the main accessibility and performance trade-offs?
Use qualitative rather than benchmark-based comparisons unless the same projects, hardware, operating systems, and configurations have been measured. IDLE and Geany generally minimize interface and extension complexity, while Eclipse and fully configured editor environments can demand more setup and project management. Spyder and JupyterLab trade simplicity for data-oriented capabilities.
Before choosing, test keyboard navigation, screen-reader behavior, font scaling, theme controls, high-resolution display support, and large-project indexing on the actual hardware. An older laptop may favor IDLE or Geany for simple scripts, while a scientific workflow may justify Spyder’s additional functionality. Accessibility quality can vary by operating system and toolkit, so a current hands-on check is more reliable than a universal ranking.
What does “open source” exclude?
An open-source application can still depend on external package indexes, cloud-hosted notebooks, extension marketplaces, AI services, telemetry infrastructure, proprietary operating-system components, or vendor-controlled update channels. Readers with strict privacy or supply-chain requirements should inspect installers, extensions, network behavior, and package sources separately.
VSCodium illustrates the distinction: an open-source-oriented distribution does not make every extension open source or guarantee identical access to Microsoft’s official extension marketplace. Similarly, Spyder’s own licensing does not automatically make every third-party distribution or bundled package channel equivalent to Spyder’s licensing.
Which free alternatives are not strict open-source choices?
Several capable tools deserve mention but should not be silently mixed into a strict free-and-open-source ranking:
- Microsoft VS Code: highly capable, but distinguish the distributed Microsoft product from the publicly available source repository and its licensing.
- Wing Personal: has a free tier but is not a strict open-source choice.
- Vim or Neovim: powerful and open source, but configuration-heavy and not a conventional IDE by default.
- Emacs: highly extensible and capable, but requires substantial setup for a complete Python workflow.
- Visual Studio Community: free under conditions and primarily Windows-oriented, making it a poor fit for a strict cross-platform FOSS shortlist.
- Anaconda Navigator: a distribution and environment-management interface, not an IDE by itself. Commercial licensing and usage terms require separate review.
Is a paid Python development tool worth considering?
Most readers do not need to buy an IDE. Paid options become relevant when a reader needs advanced web-development features, database tooling, remote development, centralized administration, managed environments, or enterprise support.
The clearest optional upgrade in this list is PyCharm Pro. Readers who like the free core can consider Pro when the advanced features justify it, but PyCharm Pro is a poor fit for readers who require every component to be open source or who want to avoid proprietary vendor lock-in. Do not publish a numeric PyCharm price without checking JetBrains’ current pricing page.
Other commercial layers include managed JupyterHub hosting, cloud notebook platforms, enterprise Python distributions, professional support, code-quality and security services, and corporate development-environment management. Those services solve team or infrastructure problems; they are not prerequisites for running Spyder, Thonny, IDLE, JupyterLab, or the other local tools listed here.
Frequently Asked Questions
What is the best free and open source Python IDE for beginners?
Thonny is the best first choice for most beginners because its interface, execution model, and debugger are intentionally simple. IDLE is an even more minimal alternative when Python is already installed and the learner needs only an editor and shell.
What is the best open source Python IDE for data science?
Spyder is the best open-source Python IDE for scientific computing and data analysis because it combines a Python-focused editor, interactive console, and variable-oriented inspection. JupyterLab is usually better when the work is primarily notebook-based.
Is PyCharm completely open source?
No. PyCharm’s current unified product has a free core and public open-source portions, but advanced features require Pro after the trial period. PyCharm should therefore be described as a free product with an open-source core or components, not as a wholly open-source IDE.
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Is JupyterLab a traditional Python IDE?
JupyterLab is a web-based interactive development environment for notebooks, code, and data, but it is not a direct replacement for a conventional package-oriented desktop IDE. Kernel selection, notebook state, execution order, and reproducibility require separate attention.
Which open source Python IDE is best for an older computer?
Geany or IDLE is usually the most sensible starting point for simple scripts on modest hardware because both avoid the complexity of a heavyweight IDE. Actual performance depends on the operating system, project size, extensions, display scaling, and installed plugins.
The Bottom Line
Choose Thonny for learning, IDLE for the simplest possible start, Spyder for scientific Python, and JupyterLab for notebooks and interactive research. Choose Eclipse with PyDev for a full multi-language workspace, Eric for a traditional Python-first desktop IDE, VSCodium for an extensible editor-based workflow, and Geany for lightweight editing. Choose PyCharm’s free core only if its polished Python experience matters more than a strict whole-product open-source requirement.
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