Python Installation and Environment Setup ·

Table of Contents

Method 1 – VS Code + Homebrew + `uv` #

Best for general Python, web development, and small projects. Lightweight, fast, and modern.

VS Code: Download & Install
Homebrew: Install
Then add Homebrew to your PATH:
```
echo 'eval "$(/opt/homebrew/bin/brew shellenv)"' >> /Users/<user-name>/.zprofile
eval "$(/opt/homebrew/bin/brew shellenv)"
```
- Ensure Homebrew is available in new terminal sessions
- Apply Homebrew settings immediately without restarting the terminal
(Optional) Verify installation
```
brew --version
```
Python:
```
brew install python@3.13
brew cleanup python@3.13
```
- Installed as: /opt/homebrew/bin/python3
- Remove outdated files (does NOT uninstall Python)
(Optional) Verify installation
```
python3 --version
```

uv (a fast Python package and environment manager):

curl -LsSf https://astral.sh/uv/install.sh | sh

Step 1 - Create a Python Project Environment:
```
uv init
```
This automatically:
- Creates pyproject.toml if missing.
- Creates or reuses a virtual environment (.venv).
  - If .venv exists, uses its Python interpreter.
  - Otherwise, creates one using the default python3 from PATH.
- Optionally generates uv.lock.
- Detects existing dependencies.

Step 2 - Install dependencies:

uv add regex torch torchvision PyYAML matplotlib requests tqdm notebook

Best for data science, machine learning, and scientific computing. Heavier, but handles complex libraries well.

Step 1 - Create a Python project environment and install dependencies:
```
conda env create -f environment.yaml
```
Example environment.yaml: View Example

Step 2 - Create a Python Jupyter environment for running JupyterLab:

conda create -n jupyter_env python=3.14 jupyterlab -c conda-forge

Step 3a (recommended; choose either 3a or 3b) - Enable automatic Jupyter kernel discovery:
```
conda activate jupyter_env
conda install nb_conda_kernels -c conda-forge
conda deactivate
```
nb_conda_kernels allows JupyterLab to automatically detect all Conda environments as usable kernels.

Step 3b (choose either 3a or 3b) - Configure a Jupyter kernel manually:

conda activate <env-name>
conda install ipykernel -c anaconda
ipython kernel install --user --name=<kernel-name>
conda deactivate

List all Python / Python3 executables in PATH:
```
which -a python python3
```
Show the default Python / Python3 in use:
```
which python python3
```
Run Python scripts (with uv):
```
uv run python <script-name>.py
```

Open JupyterLab:

conda activate jupyter_env
jupyter lab
conda deactivate

List all available Jupyter kernels:

conda activate jupyter_env
jupyter kernelspec list
conda deactivate

Remove a specific Jupyter kernel:

conda activate jupyter_env
jupyter kernelspec remove <kernel-name>
conda deactivate

Remove a Conda environment:

conda env remove -n <env-name>
conda clean --all

Search from the current directory downward and delete all .DS_Store files (with confirmation):
```
find . -name '.DS_Store' -type f -exec rm -i {} \;
```