Python Packaging

Mentor's Note: Packaging is how Python projects travel. A well-packaged project can be installed by anyone with a single command — pip install your_project. 📦

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The Scenario: The Shipping Warehouse

Imagine you run a shipping warehouse.

• Without Packaging: You hand-deliver each product individually. Chaos. Nothing is tracked. Dependencies get lost.
• With Packaging: Every product is boxed 📦, labeled 🏷️, and logged 📋. You know exactly what's in every shipment and where it goes.
• The Result: Clean, repeatable, shippable deliveries — every time. ✅

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What Is pip?

pip is Python's package installer. It downloads and installs packages from the Python Package Index (PyPI) — the official repository of Python libraries.

# Install a package
pip install requests

# Install a specific version
pip install pandas==2.0.0

# Install with minimum version
pip install "flask>=2.3.0"

# Install multiple packages
pip install requests pandas numpy flask

# List installed packages
pip list

# Show package info
pip show requests

# Uninstall a package
pip uninstall requests

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Virtual Environments

A virtual environment is an isolated Python environment for each project. Packages installed in one venv do not affect others.

Why Use Virtual Environments?

Problem	Solution
Project A needs Django 3.2, Project B needs Django 5.0	Separate venvs — no conflict
You install something experimental and break system Python	The venv is just a folder — delete it and start fresh
Your teammate gets different versions than you	Share requirements.txt for exact reproducibility

Creating and Using a Virtual Environment

# Create a virtual environment
python -m venv venv

# Activate it:
# On Windows (Command Prompt)
venv\Scripts\activate

# On Windows (PowerShell)
venv\Scripts\Activate.ps1

# On macOS / Linux
source venv/bin/activate

# Your prompt now shows (venv) — everything is isolated
# Install packages inside the venv
pip install requests pandas

# When done, deactivate
deactivate

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requirements.txt

A requirements.txt file lists all the packages your project needs.

Creating requirements.txt

# Freeze current environment into a file
pip freeze > requirements.txt

Example requirements.txt

requests==2.31.0
pandas>=2.0.0,<3.0.0
numpy>=1.24.0
flask>=2.3.0
pytest>=7.4.0

Installing from requirements.txt

# Setup
python -m venv venv
source venv/bin/activate

# Install everything at once
pip install -r requirements.txt

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Common Useful Packages

Package	Purpose	Install
requests	HTTP requests	pip install requests
pandas	Data analysis, CSV/Excel	pip install pandas
numpy	Numerical computing, arrays	pip install numpy
flask	Lightweight web framework	pip install flask
django	Full-featured web framework	pip install django
pytest	Testing framework	pip install pytest
beautifulsoup4	HTML/XML parsing	pip install beautifulsoup4
matplotlib	Charts and plots	pip install matplotlib
pillow	Image processing	pip install pillow

# Quick examples of each:
import requests          # requests.get("https://api.github.com")
import pandas as pd      # df = pd.read_csv("data.csv")
import numpy as np       # arr = np.array([1, 2, 3])
from flask import Flask  # app = Flask(__name__)
import pytest            # pytest.main()
from bs4 import BeautifulSoup  # soup = BeautifulSoup(html, "html.parser")
import matplotlib.pyplot as plt  # plt.plot([1, 2, 3])
from PIL import Image    # img = Image.open("photo.jpg")

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Creating Your Own Package

Project Structure

my_package/
├── pyproject.toml          # Project metadata and dependencies
├── README.md               # Documentation
├── src/
│   └── my_package/         # Your actual code
│       ├── __init__.py
│       ├── module1.py
│       └── module2.py
└── tests/
    ├── __init__.py
    └── test_module1.py

pyproject.toml (Modern Standard)

[build-system]
requires = ["setuptools>=61.0"]
build-backend = "setuptools.backends._legacy:_Backend"

[project]
name = "my-package"
version = "0.1.0"
description = "A useful description of my package"
readme = "README.md"
license = {text = "MIT"}
authors = [
    {name = "Your Name", email = "[email protected]"}
]
requires-python = ">=3.8"
dependencies = [
    "requests>=2.28.0",
    "click>=8.0.0",
]

[project.urls]
Homepage = "https://github.com/yourname/my-package"

setup.py (Legacy — Still Common)

from setuptools import setup, find_packages

setup(
    name="my-package",
    version="0.1.0",
    packages=find_packages(where="src"),
    package_dir={"": "src"},
    install_requires=[
        "requests>=2.28.0",
        "click>=8.0.0",
    ],
    entry_points={
        "console_scripts": [
            "my-cli=my_package.cli:main",
        ],
    },
)

Installing Your Package Locally

# Install in development mode — changes take effect immediately
pip install -e .

# Install normally
pip install .

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Publishing to PyPI

A conceptual overview of sharing your package with the world.

# 1. Install build tools
pip install build twine

# 2. Build distribution files
python -m build

# 3. Upload to Test PyPI first
twine upload --repository testpypi dist/*

# 4. Upload to real PyPI
twine upload dist/*

What happens:

1. python -m build creates .tar.gz (source) and .whl (wheel) files in dist/
2. twine uploads these files to PyPI
3. Anyone can now run pip install my-package

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Visual Logic: Packaging Workflow

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Sample Dry Run

Scenario: Setting up a new project

Step	Command	Result
1	mkdir my_project && cd my_project	Project directory created
2	python -m venv venv	venv/ folder appears
3	source venv/bin/activate	Prompt shows (venv)
4	pip install requests pandas	Packages installed in venv
5	pip freeze > requirements.txt	requirements.txt created
6	pip install -r requirements.txt	Reproduces exact environment
7	deactivate	Back to system Python

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Pro Tips

• Always use a virtual environment — never install packages globally.
• Add venv/ and __pycache__/ to your .gitignore.
• Pin exact versions in requirements.txt for deployment; use loose ranges for libraries.
• Use pip install -e . during development so imports always reflect your latest code.
• Python 3.12+ has faster venv creation and better dependency resolution.

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Interview Tip

"Interviewers ask: 'What's the difference between a virtual environment and a container (Docker)?' A venv isolates Python packages only; Docker isolates the entire OS, including system dependencies, Python version, and network. Use venv for development, Docker for deployment."

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