Package Manager Comparison¶

Choosing the right package manager depends on your use case. This page compares the major options.

Overview¶

Feature Comparison¶

Package Sources¶

When to Use Each¶

Use pip when:¶

• ✅ Working on general Python projects
• ✅ Package is only on PyPI
• ✅ Simple dependency requirements
• ✅ Inside virtual environments

bash python -m venv myenv source myenv/bin/activate pip install requests flask pandas

Use conda/mamba when:¶

• ✅ Data science / ML projects
• ✅ Need non-Python dependencies (C libraries, CUDA)
• ✅ Cross-platform binary packages
• ✅ Reproducible scientific environments

bash mamba create -n ml python=3.11 numpy pandas scikit-learn pytorch mamba activate ml

Use Homebrew when:¶

• ✅ Installing Python interpreter itself
• ✅ System tools (git, databases, CLI tools)
• ✅ macOS development setup
• ✅ GUI applications

bash brew install python git postgresql brew install --cask visual-studio-code

Installation Commands¶

Environment Management¶

Recommended Workflows¶

Web Development¶

```bash

Use pip + venv¶

python -m venv venv source venv/bin/activate pip install django flask fastapi ```

Data Science / ML¶

```bash

Use Mambaforge (mamba + conda-forge)¶

mamba create -n ds python=3.11 mamba activate ds mamba install numpy pandas scikit-learn matplotlib jupyter ```

Deep Learning (GPU)¶

```bash

Use conda/mamba for CUDA dependencies¶

mamba create -n dl python=3.11 mamba activate dl mamba install pytorch torchvision pytorch-cuda=12.1 -c pytorch -c nvidia ```

macOS Setup¶

```bash

System tools with Homebrew¶

brew install python git node postgresql

Python packages with pip¶

python3 -m venv myproject source myproject/bin/activate pip install -r requirements.txt ```

Mixing Package Managers¶

pip inside conda ✅¶

bash conda activate myenv conda install numpy pandas # conda packages first pip install some-pypi-only # pip for PyPI-only packages

Don't: Homebrew Python + conda ❌¶

```bash

Avoid mixing Homebrew Python with conda environments¶

Pick one:¶

- Homebrew Python + pip/venv¶

- Miniforge/Mambaforge + conda/mamba¶

```

Commercial / Enterprise Use¶

For commercial projects: Use Miniforge or Mambaforge with conda-forge channel.

Decision Flowchart¶

Start │ ├─ Need non-Python deps (CUDA, C libs)? │ │ │ ├─ Yes → Use conda/mamba (Mambaforge) │ │ │ └─ No → Continue │ ├─ Data science / ML project? │ │ │ ├─ Yes → Use conda/mamba (Mambaforge) │ │ │ └─ No → Continue │ ├─ Simple Python project? │ │ │ └─ Yes → Use pip + venv │ └─ Installing system tools? │ └─ Yes → Use Homebrew (macOS/Linux)

Summary Recommendations¶

Key Takeaways¶

• pip: Default for Python packages, use with venv
• conda: Good for data science, includes non-Python deps
• mamba: Fast conda replacement, same commands
• Miniforge/Mambaforge: Free for commercial use, uses conda-forge
• Homebrew: System packages on macOS/Linux
• Don't mix Homebrew Python with conda
• For commercial projects, avoid Anaconda defaults channel

Exercises¶

Exercise 1. For each scenario, recommend the best package manager and explain why: (a) a beginner starting a Django web project, (b) a data scientist needing GPU-accelerated TensorFlow, (c) a DevOps engineer setting up a CI/CD pipeline.

Exercise 2. Compare pip install numpy vs conda install numpy vs mamba install numpy in terms of: what gets installed, where it comes from, how dependencies are resolved, and approximate installation speed.

Exercise 3. Write the commands to set up a complete development environment using pip + venv: (a) create a virtual environment, (b) activate it, (c) install packages from a requirements.txt, (d) freeze the current packages, and (e) deactivate the environment.