As I started my journey into AI and Data Science, I quickly realized that managing Python environments can be a total headache. Between broken dependencies and 'it works on my machine' errors, I was spending more time troubleshooting my setup than actually writing code.

I wanted a way to streamline my workflow and keep my machine clean, so I moved to a Miniconda + Homebrew setup on my Mac. To save myself (and hopefully you) from digging through endless documentation, I condensed the 'essential' commands into a single, high-density A4 infographic.

This cheatsheet skips the legacy bloat and focuses on a production-first workflow:

Clean Installation: Setting up via Homebrew for macOS.
Environment Hygiene: Creating, cloning, and exporting environments so your projects stay isolated.
Jupyter Integration: Properly registering kernels so your notebooks actually see your installed packages.
The Workflow: A step-by-step checklist for starting any new project the right way.

Whether you are a fellow student trying to organize your labs or a developer looking for a 'no-nonsense' reference, I hope this helps you spend less time in the terminal and more time building."

🐍 Miniconda Cheatsheet

📦 Installation (macOS via Homebrew)

# Install Miniconda
brew install miniconda

# Init conda for zsh
conda init zsh

# Reload shell
source ~/.zshrc

# Verify
conda --version

🌍 Environment Management

# List all environments
conda env list

# Create a new environment
conda create --name myenv python=3.11

# Activate an environment
conda activate myenv

# Deactivate current environment
conda deactivate

# Delete an environment
conda env remove --name myenv

# Clone an environment
conda create --name newenv --clone myenv

# Export environment to file (for sharing/backup)
conda env export > environment.yml

# Recreate environment from file
conda env create -f environment.yml

# Show info about current environment
conda info

📚 Package Management

# Install a package
conda install numpy

# Install a specific version
conda install numpy=1.26

# Install multiple packages at once
conda install numpy pandas matplotlib scikit-learn

# Install from conda-forge channel (wider package selection)
conda install -c conda-forge jupyterlab

# Install with pip — ONLY when package is not available on conda or conda-forge
# Always check first: conda search -c conda-forge packagename
pip install somepackage

# Update a package
conda update numpy

# Update all packages in active environment
conda update --all

# Remove a package
conda remove numpy

# List installed packages in active environment
conda list

# Search for a package
conda search numpy

🚀 JupyterLab

# Install JupyterLab
conda install -c conda-forge jupyterlab

# Launch JupyterLab
jupyter-lab

# Launch from a specific folder
jupyter-lab --notebook-dir=~/projects

🔌 Kernel Management

# List available kernels
jupyter kernelspec list

# Register current env as a Jupyter kernel
conda install -c conda-forge ipykernel
python -m ipykernel install --user --name myenv --display-name "Python (myenv)"

# Remove a kernel
jupyter kernelspec remove myenv

🔧 Conda Maintenance

# Update conda itself
conda update conda

# Clean unused packages and cache (frees disk space)
conda clean --all

# Show conda configuration
conda config --show

# Add conda-forge as default channel
conda config --add channels conda-forge
conda config --set channel_priority strict

💡 Typical Project Workflow

# 1. Create and activate a fresh environment
conda create --name myproject python=3.11
conda activate myproject

# 2. Install packages (always prefer conda over pip inside conda envs)
conda install -c conda-forge jupyterlab numpy pandas matplotlib scikit-learn ipykernel

# 3. Register as a Jupyter kernel
python -m ipykernel install --user --name myproject --display-name "Python (myproject)"

# 4. Launch JupyterLab
jupyter-lab

# 5. When done, deactivate
conda deactivate

# 6. Export environment for reproducibility
conda env export > environment.yml

🗂️ Quick Reference

Task	Command
Create env	`conda create --name myenv python=3.11`
Activate env	`conda activate myenv`
Deactivate env	`conda deactivate`
Delete env	`conda env remove --name myenv`
Install package	`conda install numpy`
Remove package	`conda remove numpy`
List packages	`conda list`
List envs	`conda env list`
Launch JupyterLab	`jupyter-lab`
Export env	`conda env export > environment.yml`
Update conda	`conda update conda`
Clean cache	`conda clean --all`