16. IDEs & Development Tools#

1. IDE Built-In Features#

What is an IDE?#

• Integrated Development Environment
• All-in-one tool for writing, testing, debugging code
• Popular for Data Science: VS Code, PyCharm, JupyterLab

Standard Built-In Features:#

2. NOT Built-In — Exam Answer#

NOT Typically Built-In:#

❌ Native hardware virtualization → needs Docker, VMware, VirtualBox
❌ Automated code review and security scanning ✅ (exam answer JAN_AN Q407)
   → needs external tools: SonarQube, Snyk, GitHub Actions

• ✅ Automated code review and security scanning is NOT built-in — exam answer (JAN_AN Q407)
• ✅ Native hardware virtualization is NOT built-in — exam answer (JAN_FN Q288)
• ❌ Syntax highlighting → IS built-in
• ❌ Integrated debugger → IS built-in
• ❌ Version control integration → IS built-in
• ❌ Terminal access → IS built-in
• ❌ Code completion → IS built-in

3. External Tools for Code Review:#

SonarQube  → static code analysis
Snyk       → security vulnerability scanning
GitHub Actions → CI/CD, automated testing
Dependabot → dependency vulnerability alerts
pre-commit → git hooks for code checks

4. Popular IDEs for Data Science:#

5. Jupyter Notebooks — Best Practices#

Structure:#

Cell 1: Imports
Cell 2: Configuration (paths, constants)
Cell 3: Load data
Cell 4: Explore data
Cell 5: Clean data
Cell 6: Analyze
Cell 7: Visualize
Cell 8: Conclusions

Before Committing to Git:#

# Clear outputs before committing
jupyter nbconvert --clear-output notebook.ipynb

# OR use nbstripout
pip install nbstripout
nbstripout notebook.ipynb

Why Clear Outputs?#

• Outputs can contain sensitive data
• Large outputs bloat git repository
• Avoids merge conflicts in output cells
• Reproducibility — others should run and get same outputs

Restart and Run All:#

Kernel → Restart & Run All
→ Ensures notebook runs top to bottom without hidden state
→ Always do this before sharing/committing

VS Code — Key Features for Data Science:#

Extensions useful for TDS:
├── Python           → Python language support
├── Pylance          → Fast type checking
├── Jupyter          → Notebook support in VS Code
├── GitLens          → Enhanced git visualization
├── Docker           → Docker file support
├── REST Client      → Test APIs directly in editor
└── Rainbow CSV      → Color-coded CSV viewing

Debugger — How to Use:#

Setting breakpoints:
1. Click left of line number → red dot appears
2. Run in debug mode (F5)
3. Code pauses at breakpoint

Debug controls:
F5  → Continue (run to next breakpoint)
F10 → Step Over (next line, don't go into function)
F11 → Step Into (go inside function)
F12 → Step Out (exit current function)

Watch panel → monitor variable values
Call stack → see function call chain
Variables panel → all current variables

Terminal Access in IDE:#

# VS Code integrated terminal
Ctrl + ` (backtick) → open terminal
Can run:
  python script.py
  pip install pandas
  git add . && git commit -m "msg"
  pytest tests/
  docker build -t app .

uv — Python Package Manager (Exam-Relevant)#

What is uv?#

• Fast modern Python package and project manager
• Written in Rust → extremely fast (10-100x faster than pip)
• Replacement for: pip + venv + pyenv combined

Correct Command Sequence — Exam Answer:#

# ✅ Correct sequence (exam answer May_FN Q359)
uv init                      # Step 1: initialize project
uv python install 3.11       # Step 2: install specific Python version
uv add pandas numpy          # Step 3: add dependencies

# ❌ Wrong sequences:
uv init python 3.11 → uv add pandas         # wrong
uv create --version 3.11 → uv install       # wrong
uv new --python=3.11 → uv sync pandas       # wrong

Complete uv Reference:#

uv init                  # create new project
uv python install 3.11   # install Python version
uv add pandas            # add dependency
uv remove pandas         # remove dependency
uv sync                  # install from lockfile
uv run script.py         # run in project environment
uv venv                  # create virtual environment

uv vs Traditional:#

Virtual Environments — Overview:#

# venv (built-in Python)
python -m venv .venv           # create
source .venv/bin/activate      # activate (Mac/Linux)
.venv\Scripts\activate         # activate (Windows)
deactivate                     # deactivate

# conda
conda create -n myenv python=3.11
conda activate myenv
conda deactivate

# uv (fastest)
uv venv
source .venv/bin/activate

Why Virtual Environments?#

• Isolate project dependencies
• Avoid conflicts between projects
• Reproducible environments
• Each project has its own Python packages

pip — Package Management Reference:#

pip install pandas              # install
pip install pandas==2.1.0       # specific version
pip install -r requirements.txt # from file
pip uninstall pandas            # remove
pip list                        # list installed
pip freeze > requirements.txt   # save current env
pip show pandas                 # package info
pip install --upgrade pandas    # upgrade

Quick Reference#

IDE Built-In Features:
  ✅ Syntax highlighting
  ✅ Integrated debugger
  ✅ Version control integration
  ✅ Terminal access
  ✅ Code completion
  ✅ Linting, refactoring, search

NOT Built-In:
  ❌ Native hardware virtualization ✅ (JAN_FN exam answer)
  ❌ Automated code review + security scanning ✅ (JAN_AN exam answer)

Popular IDEs:
  VS Code      → general purpose, lightweight
  PyCharm      → deep Python support
  JupyterLab   → notebooks, data exploration

uv sequence:
  uv init → uv python install 3.11 → uv add pandas ✅

Jupyter best practices:
  ✅ Clear outputs before committing
  ✅ Restart & Run All before sharing
  ✅ Structure cells logically

Virtual environments:
  venv → built-in Python
  conda → data science focused
  uv → fastest, modern ✅