The Best IDEs and Code Editors for Python (Guide)

IDLE

IDLE is the standard Python IDE and ships with the official Python installer on Windows and macOS. You can launch it from the Start menu on Windows and from the Launchpad on macOS.

On Linux, you can install IDLE separately with the sudo apt install idle command on Ubuntu or Debian. Then run idle in the terminal to start the Python shell with a code editor attached. There’s nothing to configure and no account to create.

Here’s where to read the docs, grab the installer, and go deeper with Real Python’s IDLE resources:

The editor includes syntax highlighting, a basic debugger, and an interactive REPL that lets you test code ideas immediately. The interface is minimal by design. You get the essentials and nothing more.

IDLE is a practical choice for working through the Python documentation, experimenting with short scripts, or teaching a single concept in a classroom setting where you can’t rely on students having anything else installed.

Here’s IDLE’s main interface, with its REPL and editor window side by side:

Best for: Quick experiments, learning the language, and situations where you need Python’s built-in tools with no extra installation steps.

Thonny

Thonny is a beginner-friendly IDE that bundles its own Python interpreter. This means you can install Thonny and start writing Python code without first setting up Python separately. That removes one of the most common early stumbling blocks.

Thonny’s documentation, its download page, and a full Real Python walkthrough are all here:

Thonny has a visual debugger that lets you step through code. It highlights the specific subexpression being evaluated, not just the whole line. With this tool, you can watch a list comprehension evaluate element by element or see exactly when a function call returns.

Thonny also includes a basic package manager that wraps pip, and a variable inspector. These tools spare you the command line while you’re starting out, so you can install a package or inspect a variable without leaving the editor.

Here’s Thonny’s main interface, showing the editor and the integrated REPL:

Best for: Python learners who want a self-contained environment with visual debugging that shows exactly how Python executes code.

Visual Studio Code

Visual Studio Code (VS Code for short) is an open-source code editor from Microsoft that runs on Linux, macOS, and Windows. Out of the box, it’s a general-purpose editor. With the Python extension and some tweaks, it becomes a full-featured Python IDE.

From the download and Python extension to Real Python’s in-depth guides, here’s everything you need:

Install the extension, point VS Code at your Python interpreter, and you get autocomplete, inline linting, a test runner for pytest and unittest, integrated Jupyter Notebook support, and a debugger with a visual interface.

The extension marketplace is VS Code’s biggest strength. If you need Docker integration, remote SSH development, or a database explorer, you’ll find an extension for each. The same ecosystem that makes VS Code versatile also means a fresh install requires more configuration than Thonny does, but that setup work is well documented and widely shared.

VS Code’s AI integration centers on GitHub Copilot, a free extension that adds inline completions and a chat sidebar, with a paid plan available for heavier use. Copilot makes VS Code a natural stepping stone to the AI-first editors that follow.

Here’s what VS Code’s main interface looks like:

Best for: Everyday Python development across web, CLI, automation, and data science tasks. The right choice if you want a single editor that handles almost any project type through extensions.

PyCharm

PyCharm is a dedicated Python IDE from JetBrains. The core IDE is free and includes Jupyter Notebook support. PyCharm also has an optional Pro subscription that adds support for web frameworks, database tools, and remote development features.

You’ll find PyCharm’s docs, download, and a complete guide below:

Unlike VS Code, which requires additional setup, PyCharm works well for Python out of the box, with no extensions to install. Its refactoring tools understand Python’s semantics and allow you to rename a method and have every reference updated automatically, including ones in string-based template files.

The Django and Flask support in the Pro subscription extends that semantic understanding to template tags, URL patterns, and object-relational mapping (ORM) queries. The integrated database browser lets you inspect and edit records alongside your code.

PyCharm has added JetBrains AI Assistant in recent versions, bringing inline completions and a chat interface to the IDE. The AI integration is optional. It includes a free tier, and you can either subscribe to a paid plan or bring your own API key to connect the models you prefer.

PyCharm is heavier than VS Code. This means it takes longer to start, uses more memory, and indexes your codebase aggressively on first open. On modern machines, that isn’t a blocker, but it’s worth knowing if your hardware is older.

Here’s PyCharm’s interface:

Best for: Large, complex Python projects, especially Django or Flask applications, where deep IDE intelligence and minimal extension management are worth the resource cost.

Cursor

Cursor is derived from VS Code, so when you first launch it, you can import your existing extensions, keybindings, and settings with a single click. On top of that familiar base, Cursor adds deeper AI capabilities.

Cursor’s documentation, its download page, and a Real Python video course are all here:

Cursor’s centerpiece is Agent mode, which can plan a task, edit across multiple files, run commands, and iterate until the change is done, all while you watch. A separate Plan Mode lets you review and refine that plan before Cursor touches any code.

For smaller edits, press Ctrl+K to open Inline Edit, where you describe a change in plain language, and Cursor rewrites the selected code. The chat sidebar supports @codebase references so the model can reason about your whole project. You can choose which model powers all of this, with options from OpenAI, Anthropic, and Google.

The editor itself is free to download and use. However, the free tier includes only a limited number of agent requests, so regular agent work pushes you onto a paid plan fairly quickly.

Here’s Cursor’s interface, with the AI chat panel open alongside the editor:

Best for: Developers who want a VS Code-style editor with a mature coding agent, plus a modal workflow that separates planning from execution.

Kiro

Kiro takes a spec-driven approach to AI-assisted development. Rather than jumping straight to code generation from a natural-language prompt, Kiro first converts your description into structured requirements using the Easy Approach to Requirements Syntax (EARS). Then, it generates a design document and implementation task list before writing any code.

The downside is that this process takes longer than writing a quick prompt. For small changes, like a one-line fix, you can use Kiro’s freeform chat instead.

Here’s where to read Kiro’s docs and download it:

Kiro also includes Agent Hooks, which run background tasks like updating documentation or generating tests when a workflow event fires, such as saving a file or completing an agent turn.

Built by AWS and powered by a choice of models, including Anthropic’s Claude family, OpenAI’s GPT models, and open-weight options, Kiro supports native Model Context Protocol (MCP) integration for connecting external tools and data sources. The editor is free, and you pay only for AI usage. Credits cover your prompts, spec refinement, and task execution, with a limited monthly allowance on the free tier and paid plans starting at $20 per month.

Like Cursor, Kiro is derived from VS Code, so its interface may look familiar:

Best for: Developers and teams who want AI-assisted development built around structured requirements rather than freeform chat, and who value auditability in the AI’s decisions.

Zed

Zed is written in Rust for speed, and it uses multiple CPU cores and even your GPU. It provides native Language Server Protocol (LSP) support for many programming languages.

Zed is the borderline case in this category, because speed rather than AI came first in its design. Even so, agentic editing is native to the editor rather than something you add through an extension.

Download Zed and dive into its documentation below:

Collaborative editing is built into the core, so you can share a session with a teammate as easily as you open a file.

The AI features include agentic editing with multi-step changes, inline assistance with context-aware suggestions in the editor, and edit prediction with real-time completions. Zed supports configurable model backends, so you can connect it to different AI providers.

Zed’s free tier gives you the full editor, unlimited use of your own API keys or an external agent, and a monthly allowance of two thousand accepted edit predictions. The hosted models require a paid plan, which starts at $10 per month.

Because Zed is relatively new compared to VS Code, its extension ecosystem is smaller, and some Python-specific integrations are less polished. If raw speed and low overhead matter more than a larger extension ecosystem, Zed is worth trying.

Here’s Zed’s interface with the integrated terminal and the AI chat panel:

Best for: Developers who want the fastest possible local editor with native AI features and built-in real-time collaboration.

JupyterLab and Jupyter Notebook

Jupyter’s cell-based approach is the standard for interactive Python development in data science. Each cell contains code, Markdown, or rich output, such as plots, tables, and HTML. You can run cells in any order, which speeds up exploration.

This flexibility has a downside. When you run cells out of order, you can end up with results that you can’t reproduce from a fresh restart.

You can install Jupyter and get up to speed with the resources below:

JupyterLab is the current-generation interface. It offers a full IDE-like layout that includes a file browser, multiple notebook tabs, a terminal, and a text editor. The classic Jupyter Notebook interface is simpler and still widely used. Both tools run in the browser and connect to a local or remote kernel.

You can significantly extend both interfaces through JupyterLab’s extension API.

Here’s the JupyterLab interface showing the file browser and the launcher:

Best for: Data exploration, visualization, reproducible research, and teaching. The right environment when your output is as important as your code.

Google Colab

Google Colab is an online Jupyter notebook environment that runs entirely in your browser and is backed by Google’s cloud. The most compelling feature for machine learning workflows is its free access to GPU and TPU hardware. You can use this feature to train a model without any local setup.

You can learn more about Colab in the official documentation:

Sharing is built in through Google Drive. You send a link, and your collaborators open the same notebook in their browser. Colab notebooks integrate seamlessly with other Google services.

The main limitations are session timeouts and the lack of a persistent local file system. Idle sessions typically disconnect after around ninety minutes. Google doesn’t officially guarantee that limit, but it’s a widely observed window for the free tier. Files saved inside a session are lost when the runtime ends unless you mount a Drive folder or save to an external location. Colab Pro extends runtime limits and provides access to faster GPUs.

Here’s Google Colab in the browser:

Best for: Machine learning experimentation, sharing interactive notebooks without asking recipients to install anything, and situations where you need free GPU access.

marimo

marimo is an open-source reactive notebook for Python. Unlike a Jupyter notebook, a marimo notebook is stored as a plain Python file, and its cells form a dependency graph.

When you change one cell, marimo automatically reruns the cells that depend on it, so the code, outputs, and program state never drift out of sync. That design removes the hidden-state and out-of-order-execution problems that come from running Jupyter cells in an arbitrary order.

This automatic rerunning can slow you down when a cell is expensive to run. You can switch to lazy execution, but then you have to track which cells are stale yourself.

You can install marimo and explore the full tutorial and video course through these links:

Because marimo notebooks are pure Python, they play well with version control, and you can run them as scripts or serve them as interactive web apps without converting them first.

Here’s the marimo interface, with the file browser open beside a new notebook cell:

Best for: Data scientists who want notebook-style exploration without Jupyter’s hidden-state pitfalls, and who value notebooks that double as scripts and shareable web apps.

Spyder

Spyder is an open-source IDE designed specifically for scientific Python work. It comes bundled with Anaconda, so many data scientists already have it.

Spyder’s documentation, its download page, and a Real Python data science tutorial are all here:

The interface will feel familiar if you’ve worked with MATLAB or RStudio. It consists of a code editor on the left, an IPython console at the bottom, and a variable explorer and documentation browser on the right.

The variable explorer is Spyder’s standout feature. You can inspect NumPy arrays, pandas DataFrames, and Python objects as interactive tables without calling print(). The documentation browser fetches docstrings on demand and renders them inline.

The trade-off is that Spyder is a weaker fit for general software development, and its plugin ecosystem is far smaller than VS Code’s.

Here’s the Spyder interface:

Best for: Scientific Python users coming from MATLAB or R who want a familiar IDE experience and tight integration with NumPy, SciPy, and pandas.

Positron

Positron comes from Posit, the company behind RStudio. The editor targets data scientists who work in both Python and R. Because it’s built on VS Code, you get data science tooling inside a modern, extensible code editor instead of a single-purpose GUI like RStudio.

You can download Positron and explore its documentation here:

Positron’s main draw is support for both languages in one environment. You can switch between Python and R kernels without leaving the editor. It also includes AI assistance and a variable inspector similar to Spyder’s.

Note that Positron gets its extensions from Posit Public Package Manager rather than Microsoft’s marketplace, so some VS Code extensions are missing or out of date.

Here’s the Positron interface:

Best for: Data scientists who work in both Python and R, or who are transitioning from an R or RStudio background and want to bring familiar habits into a Python-capable environment.

Neovim

Neovim is a modernized fork of Vim with a focus on extensibility through Lua, built-in LSP client support, and an active plugin ecosystem.

Here’s where to get Neovim and its docs, plus Real Python’s guide to Vim, which covers a setup you can adapt for Neovim:

For Python development, the standard setup combines a few key components:

• Native LSP, built into Neovim since version 0.5 and configurable without plugins since version 0.11, or nvim-lspconfig for connecting to Pyright or the Ruff server to get completions and diagnostics
• nvim-treesitter for managing syntax parsers and queries
• conform.nvim for integrating formatters like black and ruff format
• lazy.nvim as a plugin manager for loading and updating everything above

Modal editing—where Normal, Insert, and Visual modes each have distinct behaviors—requires considerable investment to internalize. Once the modal workflow becomes second nature, navigation and editing turn into keyboard-driven operations that feel faster than reaching for a mouse.

Here’s Neovim’s initial screen in the terminal:

Best for: Experienced developers who live in the terminal, value a fully keyboard-driven workflow, and are prepared to invest time in building and maintaining a configuration.

Emacs

Emacs describes itself as an “advanced, extensible, customizable, self-documenting editor,” but that undersells its scope. It’s closer to a programmable environment that happens to edit text and code. Most of Emacs, including the editing commands themselves, is written in Emacs Lisp and can be changed at runtime.

Emacs’s documentation, its download page, and a Real Python tutorial are all here:

For Python development, the main building blocks are:

• python.el (built in) for syntax highlighting and indentation
• lsp-mode or eglot (built in since Emacs 29) for connecting to an LSP server
• pet.el or envrc.el with direnv for detecting and activating the right virtual environment automatically—the older pyvenv and virtualenvwrapper.el still work but are less actively maintained

If configuring Emacs from scratch sounds daunting, Spacemacs and Doom Emacs are community-maintained configurations that give you a complete, opinionated setup. Doom Emacs is particularly popular for its startup speed and clean defaults.

Here’s Emacs configured for Python, with lsp-mode providing inline completions and diagnostics:

Best for: Developers who want maximum extensibility and are willing to treat their editor configuration as a long-term personal project.

GitHub Codespaces

GitHub Codespaces runs VS Code—either in the browser or in your local VS Code installation—backed by a cloud-hosted development container. When you open a Codespace from a GitHub repository, you get a complete development environment.

The official documentation walks you through getting started:

You can run and test code within minutes, without the usual setup work. The tight integration also means pull requests, issues, and GitHub Actions are one click away.

GitHub offers a free monthly quota of Codespace compute hours. Heavier use is billed by the hour. Cost aside, everything runs remotely, so a Codespace is only as good as your network connection, and it isn’t an option when you’re offline.

Here’s GitHub Codespaces running VS Code in the browser:

Best for: Contributors to open-source projects on GitHub, teams that want a reproducible dev environment without managing local setup, and situations where you need VS Code’s full feature set without a local machine.

Real Python Exercises

If you’re learning Python here at Real Python, then our coding exercises provide a browser-based interactive environment that lets you practice directly on the website. You don’t need to install anything, and there’s no context switch away from the tutorial or video course you’re working through.

Each exercise presents a clearly scoped task with step-by-step instructions, starter code, and a set of automated tests. You write your solution in an in-browser editor, then run the tests to get immediate feedback.

If you get stuck, unlockable hints guide your thinking without giving away the answer. Once you submit a passing solution, the reference implementation and notes are revealed, explaining the reasoning behind the approach and common alternatives.

Exercises come in three difficulty levels: Basics, Intermediate, and Advanced.

The exercises are embedded directly in tutorials and video courses so you can practice the exact concept you just learned. You can also search for coding exercises and tackle them as standalone challenges.

Here’s the Real Python Exercises interface, with the in-browser code editor and automated test results:

Best for: Learners who use Real Python as their main platform and want to move from reading to doing without leaving the page. You’ll get immediate automated feedback and structured hints to guide your practice.