Python Setup & Workflow (Windows • macOS • Linux)¶

What you'll learn: - Install Python on Windows, macOS, and Linux - Use conda/mamba and pip effectively - Create & activate virtual environments (conda, venv, virtualenv, pyenv) - Install & use Jupyter Notebooks/Lab and VS Code - Work efficiently in Google Colab (GPU/TPU, Drive, packages) - Troubleshoot common setup issues & use cheat-sheets

💡 Tip: Run this notebook cell-by-cell on your machine after installing Python, or open it in Google Colab to follow along.

0) Quick Start: Verify Your Current Python & Pip¶

Use this to see what's already installed.

import sys, platform, subprocess, shutil
print("Python executable:", sys.executable)
print("Python version:", sys.version)
print("Platform:", platform.platform())

def which(cmd):
    return shutil.which(cmd) or "<not found>"

print("pip:", which("pip"))
print("conda:", which("conda"))
print("mamba:", which("mamba"))
print("python:", which("python"))
print("py:", which("py"))  # Windows launcher

1) Conda Distributions: Anaconda, Miniconda, Miniforge, Mambaforge¶

Choose one of these Conda-based options:

• Anaconda (full): Large installer including hundreds of data-science packages, Anaconda Navigator GUI, and conda. Uses the defaults channel by default. Good for offline/enterprise labs.
• Miniconda (minimal Anaconda): Small installer with just conda and Python. Uses defaults by default; you add packages as needed. Lightweight and flexible.
• Miniforge (community): Minimal installer that defaults to the conda-forge channel (huge community repo with very fresh builds). Great for scientific/geo/ML stacks.

• Mambaforge: Miniforge + mamba preinstalled (a much faster solver). Recommended if you want speed and conda‑forge by default.

• Choose one approach per OS.

• Miniforge/Mambaforge is usually the smoothest because it provides prebuilt packages.

Windows¶

Option A (Recommended): Miniforge (Conda-forge)¶

1. Download Miniforge for Windows from the official repo Miniforge
2. Run the installer → check "Add Miniforge to PATH" and "Initialize for PowerShell" if prompted.
3. Open Miniforge terminal.

Verify: Command Prompt/Powershell

conda --version

python --version

conda env list

Option B: Anaconda¶

• Anaconda is a popular open-source distribution of Python and R programming languages.

• It simplifies package management and environment management.

• Designed for data science, machine learning, AI, and scientific computing.

• Comes with a collection of pre-installed libraries like NumPy, Pandas, Matplotlib, SciPy, Scikit-learn, and more.

• Includes tools like Jupyter Notebook, Spyder, and Conda for streamlined development.

Download the Installer¶

• Visit the official Anaconda Distribution page
• Download the latest Anaconda installer for Windows.

Launch the Installer¶

• Navigate to your Downloads folder.
• Double-click the downloaded installer to start the installation process.

Start the Installation¶

• Click Next on the welcome screen.

Accept the License Agreement¶

• Read the licensing terms.
• Click I Agree to proceed.
• Tip: It is recommended to select "Just Me" during installation. This ensures Anaconda is installed only for the current user.

Choose Installation Type¶

• Click Next to confirm your installation type.

Select Installation Folder¶

• Choose a destination folder for Anaconda.
• Click Next to proceed.

Important: - Avoid installing Anaconda in a directory path that contains spaces or Unicode characters. - Do not install as Administrator unless admin privileges are necessary. - Do not add Anaconda to the PATH environment variable, as this may cause conflicts with other software.

Install Anaconda¶

• Click Install and wait for the installation to complete.
• Click Next to proceed.

Finish Installation¶

• After a successful installation, you will see the “Thanks for installing Anaconda” dialog box.

Additional Resources¶

• For more detailed information, refer to the Anaconda Installation Documentation.

Option C: Python.org Installer¶

1. Download the latest Windows installer from python.org.
2. During setup, check "Add Python to PATH".
3. Open Command Prompt/Powershell:

python --version

pip --version

Option D: Install Linux on Windows 11 with Windows Subsystem for Linux (WSL)¶

• The Windows Subsystem for Linux (WSL) lets developers use both Windows and Linux at the same time on a Windows machine.

1) Go to the search bar on your Windows Desktop and type "Windows Features" 2) Scroll down until you see "Windows Subsystem for Linux" and "Virtual Machine Platform" click the checkbox 3) Click "OK" and Click "Restart Now". when you are ready. 4) Open Command Prompt/Powershell:

wsl --status

wsl --update

ls

cd ..

cd ~

ls

cd mnt

• Install Miniforge inside WSL for conda-based workflows.

Linux (Ubuntu/Debian/Fedora)¶

Ubuntu/Debian:

sudo apt update

sudo apt install -y python3 python3-venv python3-pip

Fedora:

sudo dnf install -y python3 python3-pip python3-virtualenv

macOS¶

Option A (Recommended): Miniforge (Conda-forge)¶

1. Download arm64 (Apple Silicon) or x86_64 (Intel) installer.
2. Run the .pkg or use the .sh installer from Terminal.
3. Restart the terminal.

conda --version

python --version

Option B: Homebrew + (pyenv or CPython)¶

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

brew install python

Or use pyenv:

brew install pyenv

pyenv install 3.12.6

Verify installation:

python3 --version

pip3 --version

⚠️ Avoid relying on the macOS system Python for development.

2) Package Managers: conda vs pip¶

Conda/mamba manage Python and non-Python deps (e.g., gdal, geos, cuda). Pip installs Python packages from PyPI.

Conda / Mamba Basics¶

Set up your shell (run once):

conda init

Configure channels:

conda config --add channels conda-forge

conda config --set channel_priority strict

Create a new environment:

conda create -n ds-312 python=3.12

Activate the environment:

conda activate ds-312

Install packages:

conda install numpy pandas jupyterlab

List installed packages:

conda list

Export environment to file:

conda env export > environment.yml

Create environment from file:

conda env create -f environment.yml

Remove an environment:

conda remove --name ds-312 --all

Deactivate:

conda deactivate

Pip Basics¶

Upgrade pip:

python -m pip install --upgrade pip

Install packages:

pip install numpy pandas jupyterlab

Export requirements to file:

pip freeze > requirements.txt

Install from requirements file:

pip install -r requirements.txt

3) Create & Activate Virtual Environments¶

• When installing Python on your machine, you have the option to install it system-wide or in a virtual environment.

• Installing Python system-wide may seem like the most straightforward approach, it can lead to several problems such as dependency conflicts and version conflicts.

• Installing Python in a virtual environment provides several benefits such as Isolation, Dependency management, and Reproducibility.

• A virtual environment is an isolated Python environment that allows you to manage dependencies for different projects without conflicts.

• Below are step-by-step guides for creating virtual environments using Conda and pip.

A) Conda¶

Create environment:

conda create -n myenv python=3.11

Activate environment:

conda activate myenv

Install packages:

conda install numpy

Deactivate:

conda deactivate

Python Environment management with pip¶

• Python Default Package Manager: pip is the standard/default package manager for Python.

• Lightweight: Designed for Python-only projects and lightweight environments.

• Virtual Environment Support: Works with venv or virtualenv to create isolated environments.

• Ease of Use: Simple syntax for installing and managing packages.

• Community Driven: Extensive library ecosystem supported by the Python community.

• Customizable Requirements: Supports dependency management with requirements.txt files.

B) Built-in venv (works everywhere)¶

Create virtual environment:

python -m venv .venv

Windows (PowerShell) - Activate:

.venv\Scripts\Activate.ps1

Windows Command Prompt - Activate:

.venv\Scripts\activate

macOS/Linux (bash) - Activate:

source .venv/bin/activate

Upgrade pip:

python -m pip install --upgrade pip

Install packages:

pip install numpy

Deactivate:

deactivate

3) Interactive Development Environments¶

Jupyter Notebooks¶

Jupyter Notebooks are a versatile tool for professionals, researchers, educators, and learners, providing an interactive and collaborative environment for coding, documentation, and visualization.

1. Interactive Computing

   • Real-Time Execution: Write, execute, and modify code interactively in real-time.
   • Experimentation: Ideal for experimenting with algorithms, models, and data.
   • Visualization: Allows seamless integration of code and visual outputs for data exploration.

2. Documentation

   • Comprehensive Notes: Combine code, text (using Markdown), images, and equations in one place.
   • Collaboration Ready: Simplifies sharing results and collaborating with others.

3. Reproducibility:

   • Self-Contained Workflows: Combine code, data, and outputs in one notebook.
   • Ease of Sharing: Facilitates sharing and verification of results.
   • Research Expansion: Enables others to build on your work effortlessly.

4. Flexibility:

   • Multi-Language Support: Supports multiple languages like Python, R, Julia, and more.

5. Data Visualization:

   • Visual Insights: Supports libraries like Matplotlib, Seaborn, Plotly, and more.
   • Communicate Results: Create professional charts, graphs, and interactive visualizations.

6. Education:

   • Learning Platform: Widely used for teaching programming, data analysis, and machine learning.
   • Interactive Learning: Provides hands-on experiences to engage students and reinforce concepts.
   • Instructor-Friendly: Allows instructors to create interactive tutorials and demonstrations.

Jupyter: Notebooks & JupyterLab¶

Install and launch inside your target environment.

Install¶

Using Conda:

conda install jupyterlab ipykernel

Using Pip:

pip install jupyterlab ipykernel

Launch¶

Launch JupyterLab:

jupyter lab

Or launch classic Jupyter Notebook:

jupyter notebook

4) Integrated Development Environment (IDE)¶

• Integrated Development Environment (IDE) is a software application that provides comprehensive tools for coding, debugging, and managing Python projects.

• It streamlines development by integrating features like syntax highlighting, code completion, debugging, and version control.

Key Features to Look for in an IDE¶

• Code Completion: Auto-suggests methods, variables, and functions.

• Debugging Tools: Step through your code to identify and fix issues.

• Integrated Terminal: Run Python scripts directly from the IDE.

• Version Control: Built-in support for Git to manage project versions.

• Plugin Support: Extend functionality with custom plugins or extensions.

Types of IDEs for Python¶

These IDEs support multiple programming languages, including Python.

• PyCharm: A feature-rich IDE with smart code completion, debugging, and built-in testing tools. Ideal for both beginners and advanced users.

• Visual Studio Code (VS Code): A lightweight, highly customizable IDE with Python extensions for code linting, debugging, and virtual environment support.

• Spyder: Popular among data scientists for its integration with scientific libraries like NumPy, Pandas, and Matplotlib. It is also a Python specific IDE.

VS Code (IDE)¶

1. Install Visual Studio Code.
2. Install the Python extension (and Jupyter extension if prompted).
3. Select Interpreter: Ctrl/Cmd+Shift+P → Python: Select Interpreter → pick your env.
4. Open a folder → VS Code terminal will use your shell; activate env as usual.
5. Open or create .ipynb files and run cells in-place.

6) Google Colab¶

Start with Colab¶

• Go to https://colab.research.google.com

• New Notebook or Upload your .ipynb

• Runtime → Change runtime type → Select GPU or TPU if needed

• Cloud-Based: No need for local installations; everything runs in the cloud.

• Free GPUs and TPUs: Access hardware accelerators for machine learning and deep learning tasks.

• Interactive Development: Combine code, text (Markdown), and outputs in a single document.

• Integration with Google Drive: Automatically save and access notebooks from your Google Drive.

• Collaboration: Share notebooks and collaborate with others in real-time.

• Rich Visualization: Supports libraries like Matplotlib, Seaborn, and Plotly for creating visualizations.

• Easy Library Installation: Install libraries directly with pip or apt.

1) Access Google Colab - Go to Google Drive. - Sign in with your Google account. If you don’t have an account, create one first.

2) Create a New Folder - In Google Drive, click the New button on the left sidebar. - Select New Folder and give it a meaningful name (e.g., Colab-Projects). - Click Create to make the folder.

3) Create a New Google Colab File - Navigate to the newly created folder in Google Drive. - Right-click inside the folder and select More > Google Colaboratory. - A new .ipynb file will open in a new browser tab.

4) Mount Google Drive in Colab - To access files in your Google Drive from the Colab notebook, mount the drive:

from google.colab import drive

drive.mount('/content/drive')

### Save Notebook to Drive
- File → Save a copy in Drive
- Or `File → Download` as `.ipynb` to your machine

Replace 'Colab-Projects' with your folder name¶

import os

os.chdir('/content/drive/MyDrive/Colab-Projects')

Unmount Drive (Optional)¶

drive.flush_and_unmount()

1) Configure the Runtime - Click on Runtime > Change Runtime Type. Choose: - Hardware Accelerator: - None for CPU-only tasks. - GPU for accelerated computations (e.g., TensorFlow, PyTorch). - TPU for advanced deep learning workloads. - Runtime Type: Choose Python 3.

📝 Test Your Knowledge¶