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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11There is no universal winner. Choose VS Code if you want a flexible, lightweight editor and do not mind installing extensions and managing your Python interpreter separately. Choose PyCharm if you want a dedicated Python IDE with an integrated workflow and its free core features cover your work. Pay for PyCharm Pro only when a specific advanced feature justifies the subscription.
VS Code and PyCharm are different kinds of tools
VS Code is a general-purpose editor. Microsoft describes the Python setup as three separate pieces: VS Code provides the editor, the Python extension adds Python support, and a separately installed Python interpreter runs your code. The extension supplies IntelliSense, linting, debugging, testing and interpreter switching (Microsoft’s Python documentation).
PyCharm is a cross-platform Python IDE for Windows, macOS and Linux. JetBrains’ current unified product keeps core functionality free, including Jupyter support, while Pro adds advanced features. A new installation includes a 30-day Pro trial; after it ends, you can continue with the free core or subscribe to Pro (PyCharm Quick Start Guide).
Feature comparison
| Area | VS Code | PyCharm | What it means |
|---|---|---|---|
| Product model | General editor extended with Python extensions | Dedicated Python IDE | VS Code is assembled to your preferences; PyCharm is more opinionated out of the box. |
| Initial setup | Install VS Code, Python extension(s), and Python separately | Install PyCharm; core is free, Pro is optional | Neither choice removes the need to understand Python environments. |
| Environments | Environment creation, switching and package management documented for venv, uv, conda, pyenv, poetry and pipenv |
Environment behavior varies by project and version; no directly comparable matrix is established here | Test your team’s preferred manager before standardizing. |
| Debugging | Python Debugger supports breakpoints, inspection, scripts, web apps and remote processes | Breakpoints, stepping, inspection and debugging of running programs | Both cover normal application debugging. |
| Testing | Discovery, running, coverage and debugging for unittest and pytest |
Debugger and failed-test behavior are documented; a directly equivalent inventory is not established | VS Code’s documented test UI may matter if test workflow is central. |
| Notebooks | Jupyter notebooks, interactive cells, variable inspection, remote servers and notebook debugging | Jupyter support is part of the free core | Compare the notebook workflow with your actual kernels and extensions. |
| Cost | Editor plus separately installed components; complete current licensing should be checked | Free core, optional Pro after a 30-day trial | Do not assume every PyCharm feature is free or every VS Code extension has identical licensing. |
Setting up Python in VS Code
- Install Python from your operating system’s approved distribution, then install VS Code.
- Open Extensions and install Microsoft’s Python extension. The Python Debugger is installed automatically with it (debugging documentation).
- Open your project folder. Use the Command Palette command Python: Select Interpreter and choose the environment that should run the project.
- Create or activate an environment using your normal tool, such as
python -m venv .venv, then install project dependencies. The Python Environments extension documents creation, deletion, switching and package management acrossvenv,uv,conda,pyenv,poetryandpipenv(environment documentation). - Open a
.pyfile and confirm that the status bar shows the intended interpreter. Run a small import or your test suite before debugging.
VS Code environment limitations
Pylance uses one interpreter per workspace. If a monorepo needs different interpreters, split the work into appropriately configured workspaces or understand which interpreter each folder is using. Notebook environment discovery follows a separate API from the main Python extension, so the interpreter selected for a code file is not a guarantee that a notebook uses the same kernel.
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Setting up Python in PyCharm
- Install PyCharm for Windows, macOS or Linux using JetBrains’ installation guide.
- Create a project or open an existing one. Select the project interpreter when prompted, or open the project settings and choose the environment your code should use.
- Install dependencies into that interpreter and run a file from the editor. Keep the project interpreter consistent with the environment used by your shell, CI job and deployment.
- Open a notebook in the project to use the included Jupyter support. Confirm the selected kernel has the packages your notebook imports.
PyCharm’s unified installation provides a 30-day Pro trial. JetBrains states that core functionality remains free after the trial, while advanced functionality requires Pro. Exact Pro features and regional prices change, so check JetBrains’ current pricing page before purchasing.
Debugging: choose the workflow you will use
VS Code
Install the Python extension, set the interpreter, add a breakpoint by clicking beside a line number, and start the debugger from Run and Debug. The Python Debugger supports variable inspection, scripts, web applications and remote processes. It uses the workspace’s selected interpreter by default, so a wrong interpreter is the first thing to check when imports or breakpoints behave unexpectedly (Microsoft’s debugger guide).
PyCharm
Choose Debug for a run configuration, place breakpoints, then step through execution and inspect variables. JetBrains documents debugging a running Python program and settings for failed tests (PyCharm debugger; debugging code).
Neither official documentation establishes that one debugger is faster or universally easier. The practical choice is the interface your team can reproduce in launch configurations, remote sessions and CI investigations.
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Testing in VS Code
Configure the testing interface from the beaker icon, select pytest or unittest, and set the test folder or discovery pattern. VS Code documents discovery, execution, coverage and debugging for both frameworks (Python testing documentation). If no tests appear, verify the selected interpreter, framework installation and discovery settings.
Notebooks in VS Code
VS Code supports native Jupyter notebooks, Python files with Jupyter-like cells, variable inspection, remote Jupyter servers and notebook debugging. The environment must have the jupyter package installed (Jupyter support documentation).
Notebooks in PyCharm
Jupyter support is included in PyCharm’s free core according to JetBrains. Your project still needs a correctly configured kernel and dependencies. If notebook work is your main task, open representative notebooks in both products and check kernel selection, plots, debugging and collaboration requirements rather than assuming feature parity.
Which one should you choose?
Choose VS Code when
- You want one customizable editor for Python plus other languages, configuration formats and infrastructure files.
- You are comfortable selecting extensions and explicitly managing interpreters.
- Your workflow depends on documented
pytest/unittestintegration, remote debugging or several environment managers. - You prefer to keep the editor and language tooling as separate, replaceable components.
Choose PyCharm when
- You want a Python-focused IDE rather than assembling a toolchain.
- The free core, including Jupyter support, covers your projects.
- A specific Pro capability saves enough time to justify its subscription.
- Your team already uses PyCharm run configurations, inspections and debugger conventions.
For teams and classrooms
Standardize the tool that matches existing project instructions, environment files, test commands and support expertise. A mixed team can work, but document interpreter selection, formatting, test invocation and debugger launch steps so the editor does not become a hidden source of differences. The available official documentation does not provide a controlled head-to-head productivity or performance result, so claims that one IDE is universally superior are not evidence-based.
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Troubleshooting common problems
“Python is not found” or imports fail
In VS Code, run Python: Select Interpreter and verify the status bar. In PyCharm, check the project interpreter. Then install the missing package into that exact interpreter, not merely into a different shell environment.
Breakpoints are ignored
Confirm you started a debug session rather than a normal run, the file being executed is the file you edited, and the selected interpreter contains the application dependencies. For web or remote processes, verify the documented launch or attach configuration.
Tests are missing in VS Code
Install the chosen framework in the selected environment, enable the framework in testing settings, set the correct project root and run discovery again. A mismatched interpreter is the common cause.
A notebook uses the wrong packages
Install Jupyter and your dependencies in the kernel’s environment, then select that kernel explicitly. VS Code’s notebook discovery is separate from the main Python extension, so check it independently.
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The unified installation’s Pro trial lasts 30 days. Afterward, core features remain available; advanced features require an active Pro subscription.
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Frequently Asked Questions
Can I use both VS Code and PyCharm on one machine?
Yes. Keep each project’s interpreter and run commands explicit; the applications can use the same virtual environments without sharing editor settings.
Is PyCharm’s free edition still separate from Professional?
JetBrains combined Community and Professional starting with PyCharm 2025.1. The unified installation provides free core features and a 30-day Pro trial.
Does VS Code include Python itself?
No. Microsoft documents VS Code, the Python extension and the Python interpreter as separate components.
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