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pycharm

Expert PyCharm assistance covering Python interpreters, virtualenv/Poetry/Conda management, scientific notebooks, remote debugging, and database integrations. Use when configuring PyCharm interpreters, setting up pytest test configurations, tuning IDE performance, or debugging Python applications.

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تعليمات المصدر · معاينة للقراءة فقط
name
pycharm
description
Expert PyCharm assistance covering Python interpreters, virtualenv/Poetry/Conda management, scientific notebooks, remote debugging, and database integrations. Use when configuring PyCharm interpreters, setting up pytest test configurations, tuning IDE performance, or debugging Python applications.
# PyCharm PyCharm is the best IDE for serious Python development. It excels in **Django** support, **Data Science** (Jupyter), and virtualenv management. ## When to Use - **Professional Python Development**: Full-stack Django, FastAPI, Flask, and scientific computing with PyTorch and NumPy. - **Interactive Visual Debugging**: Inspecting threads, evaluation expressions, and profiling CPU/Memory bottlenecks. - **Remote Interpreter & Docker Execution**: Running and debugging Python processes inside Docker containers and remote SSH servers. - **Database & Data Science Tooling**: Exploring relational databases, Jupyter notebooks, and Pandas dataframes interactively. ## Quick Start ### 1. Launch via Terminal ```bash # Open directory in PyCharm pycharm . ``` ### 2. Configure Poetry / Virtualenv Interpreter ```bash # Ensure local virtualenv exists poetry install poetry env info --path # Point PyCharm to .venv/bin/python ``` ## Core Concepts ### Configuring Poetry / Conda Remote Virtual Environments Setting up modern Python packaging and interpreter synchronization via `pyproject.toml`: ```toml [tool.poetry] name = "enterprise-api" version = "0.1.0" description = "FastAPI Enterprise Service" authors = ["Engineering Team <eng@example.com>"] [tool.poetry.dependencies] python = "^3.12" fastapi = "^0.111.0" uvicorn = {extras = ["standard"], version = "^0.30.0"} pydantic = "^2.7.0" sqlalchemy = "^2.0.30" [tool.poetry.group.dev.dependencies] pytest = "^8.2.0" ruff = "^0.4.0" mypy = "^1.10.0" ``` Configure PyCharm Interpreter: 1. Open **Settings -> Project -> Python Interpreter**. 2. Click **Add Interpreter -> Add Local Interpreter...** -> Select **Poetry Environment**. 3. Select Python 3.12 executable path. ### Programmatic PyCharm Remote Debugger Setup Connecting headless Python scripts or Docker containers to PyCharm's visual debugger: ```python # Install pydevd-pycharm in container: pip install pydevd-pycharm~=241.14494.241 import pydevd_pycharm def enable_remote_debugging(): try: pydevd_pycharm.settrace( 'host.docker.internal', port=5678, stdoutToServer=True, stderrToServer=True, suspend=False ) print("Connected to PyCharm Remote Debugger!") except ConnectionRefusedError: print("PyCharm Debugger not listening. Continuing normal execution.") ``` ### Fast Code Quality Inspections with Ruff Integration Configuring Ruff as the primary linter and formatter inside PyCharm: ```json // External Tools or Ruff Plugin configuration // Settings -> Tools -> File Watchers -> Add Ruff { "program": "$ProjectFileDir$/.venv/bin/ruff", "arguments": "check --fix $FilePath$", "workingDir": "$ProjectFileDir$" } ``` ## Common Patterns ### Remote Docker Compose Interpreter **Problem**: Application dependencies require system libraries or services running inside Docker. **Solution**: Configure Docker Compose as remote interpreter in PyCharm Professional. ```yaml # docker-compose.yml services: app: build: . volumes: - .:/app environment: - PYTHONUNBUFFERED=1 ports: - "8000:8000" ``` ### Automated File Watcher Configuration **Problem**: Automatically format and lint Python files on save with Ruff. **Solution**: Define File Watcher configuration in PyCharm. ```bash # Arguments for Ruff File Watcher format --stdin-filename $FilePath$ - ``` ## Best Practices **Do**: - Configure **Ruff** plugin or file watcher for sub-second linting and auto-formatting. - Use PyCharm's built-in **HTTP Client** (`.http` files) with environment support for testing REST APIs. - Set up Docker Compose interpreters to maintain identical runtime environments across teams. - Use **Python Profiler** (`Run -> Profile 'App'`) to locate algorithmic CPU and memory bottlenecks. **Don't**: - Commit `.idea/` directory without adding `.idea/workspace.xml` to `.gitignore`. - Leave PyCharm indexing large data directories; right-click data folders -> **Mark Directory as -> Excluded**. - Run untrusted Jupyter notebooks with elevated host permissions. ## Troubleshooting | Error | Cause | Solution | | ------------------------------------------------- | --------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------- | | `No module named '...'` inside PyCharm terminal | PyCharm internal terminal opened outside the project virtualenv | Verify `Settings > Tools > Terminal > Activate virtualenv` is checked; manually run `source .venv/bin/activate`. | | Unresolved reference inspection on local packages | Root source directories not marked as Sources Root | Right-click `src` or root folder > **Mark Directory as > Sources Root**. | | PyCharm sluggish during large indexing | Indexing `.venv`, `node_modules`, or data directories | Right-click cache or data directory > **Mark Directory as > Excluded**. | ## References - [PyCharm Documentation](https://www.jetbrains.com/pycharm/documentation/)
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