Tag Archives: python-packaging

Programming, Tech Journey

Is uv the Standard Way to Create Python Project Structure?

Python developers often ask whether there is a standard way to create a project structure—and whether newer tools like uv are replacing older methods. The short answer is: there is no single official standard, but there are widely adopted practices.

This article explains where uv fits today, what is considered standard in practice, and what other methods are still mostly used.

Is There an Official Standard for Python Project Structure?

No.
Python does not mandate a single command or tool to create project structure.

What is standardized:

  • pyproject.toml (PEP 517, PEP 518, PEP 621)
  • How tools read project metadata
  • How builds and dependencies are declared

What is not standardized:

  • The command used to create the project
  • The tool used to manage environments

This is documented in Python Enhancement Proposals and PyPA guidance.

What Is uv?

uv is a modern Python package and environment manager developed by Astral (the team behind Ruff).

Key, verifiable facts:

  • uv is written in Rust
  • Distributed primarily as a standalone binary
  • Focused on speed and simplicity
  • Uses pyproject.toml as the core configuration
  • Introduced project initialization via:
uv init

What Does uv init Do?

When you run:

uv init

It:

  • Creates a pyproject.toml
  • Sets up project metadata (PEP 621 compliant)
  • Prepares the project for dependency management with uv

Important clarification:

  • uv init does not invent a new structure
  • It follows modern Python packaging standards
  • It is intentionally minimal

Is uv the Standard Way Today?

No — not yet.

Factually accurate status (as of 2025):

  • uv adoption is growing rapidly
  • It is not the most widely used method
  • It has not replaced pip-based or Poetry-based workflows in most codebases

This is observable from:

  • Existing enterprise Python projects
  • Long-standing tooling documentation
  • Community adoption patterns

Most Commonly Used Methods to Create Python Project Structure

1. pip + venv (Most Common Overall)

This remains the de facto standard, especially in:

  • Enterprise projects
  • Older codebases
  • Official Python tutorials

Typical flow:

python -m venv .venv
source .venv/bin/activate
pip install requests

Structure is usually created manually.

Why it’s still dominant:

  • Included with Python
  • Stable
  • Well-understood

2. Poetry (poetry new, poetry init)

Poetry is one of the most popular structured project tools.

poetry new my_project

Creates:

  • pyproject.toml
  • Standardized folder layout
  • Dependency management

Facts:

  • Widely used in modern open-source projects
  • Strong community adoption
  • Slower than uv, but very mature

3. Setuptools + Manual Structure

Common in:

  • Python libraries
  • Long-lived projects

Characteristics:

  • pyproject.toml or setup.cfg
  • Structure created manually
  • Still compliant with modern standards

This method remains fully supported by PyPA.

4. Framework-Specific Generators

Used when applicable:

  • Django → django-admin startproject
  • Cookiecutter templates
  • Framework-provided scaffolding

These are context-specific, not general standards.

5. uv (uv init)

Current position:

  • Modern
  • Fast
  • Minimal
  • Increasing adoption

But:

  • Not yet dominant
  • Not mandated by any authority
  • Still new compared to pip and Poetry

Factually accurate description:

uv aligns with modern standards but is not itself the standard.

Comparison Summary

MethodWidely UsedStructuredModernSpeed
pip + venv✅ Yes❌ Manual⚠❌
Poetry✅ Yes✅ Yes✅❌
Setuptools✅ Yes⚠⚠❌
Framework tools⚠ Depends✅✅⚠
uv⚠ Growing✅✅✅

When uv Is a Good Choice

Use uv if:

  • You are starting a new project
  • You want speed
  • You want minimal configuration
  • You want to follow modern packaging standards

Avoid (for now) if:

  • Your organization mandates Poetry or pip
  • You maintain legacy Python projects
  • You need long-established tooling guarantees

Key Takeaway

uv does not replace the Python standard — it implements it efficiently.

There is no single official way to create Python project structure.
uv is a strong modern option, but pip-based workflows and Poetry remain more widely used today.

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Programming, Tech Journey

When (and When Not) to Use pip install in Python Projects

Python’s pip is one of the most widely used tools in the ecosystem—but it is also one of the most misused. Many Python environment issues, broken systems, and dependency conflicts happen because pip install is used in the wrong place or for the wrong purpose.

This article explains, based on documented best practices, when you should use pip install and when you should not.

What pip install Is Actually Designed For

pip is the official Python package installer.
Its primary purpose is:

Installing Python libraries into an isolated Python environment (usually a virtual environment).

This design goal is consistent across:

  • Python documentation
  • PyPA recommendations
  • Linux distribution guidance
  • Tooling such as venv, pipx, poetry, and uv

✅ When You Should Use pip install

1. Inside a Virtual Environment (Recommended Use)

This is the correct and most common use of pip.

Example:

python -m venv .venv
source .venv/bin/activate
pip install requests numpy pandas

Why this is safe:

  • Dependencies are isolated
  • No system Python is modified
  • No conflict with OS package manager

This approach is explicitly recommended by Python documentation.

2. Installing Project-Specific Dependencies

Libraries such as:

  • requests
  • numpy
  • pandas
  • fastapi
  • django

are designed to be installed per project, not globally.

Best practice:

  • Use pip install inside a virtual environment
  • Or use modern tools (uv, poetry) that still rely on pip-compatible behavior internally

3. Temporary or Disposable Environments

Examples:

  • CI pipelines
  • Test containers
  • Short-lived development environments

In these cases, using pip install directly is acceptable because:

  • The environment is disposable
  • Long-term system stability is not a concern

❌ When You Should Not Use pip install

1. Installing System-Level Tools with pip

pip is not a system package manager.

Installing global tools like the following using system-level pip is documented as unsafe:

Tool TypeExamples
Environment managersuv, poetry, pipenv, virtualenv
Build & packaging toolssetuptools, wheel, twine
Code formatters / lintersblack, ruff, flake8, isort
Test runnerspytest
CLI utilitiesawscli, ansible, httpie

Why this is discouraged:

  • Can conflict with OS package managers (apt, dnf, brew)
  • Can break system Python
  • Can create PATH conflicts
  • Can fail silently after Python upgrades

This is explicitly warned against in Linux distro documentation and PyPA guidance.

2. Installing Tools That Manage Python Itself

Installing tools that control environments or dependencies via pip creates fragile or circular setups.

Examples:

  • uv
  • poetry
  • pipenv

These tools are:

  • Designed to be standalone
  • Officially distributed via installers or binaries
  • Not intended to depend on a single Python installation

Even when installed outside a virtual environment, using pip for these tools is not the preferred or documented method.

3. Installing Packages into System Python

Examples of unsafe commands:

sudo pip install <package>
pip install <package>  # against system Python

Risks (documented by OS vendors):

  • Can break OS utilities written in Python
  • Can break apt, dnf, or yum
  • Can cause OS-level instability

Linux distributions explicitly warn not to use pip against system Python.

4. Installing OS-Critical Python Packages

Never install the following with pip:

  • python-apt
  • dnf Python libraries
  • yum Python libraries

These are managed exclusively by the OS package manager.

5. Installing Global CLI Tools That Should Be Isolated

Many Python CLI tools install executables into your PATH.

If installed with pip globally, they can:

  • Shadow OS binaries
  • Cause unexpected version usage
  • Break scripts after upgrades

Documented safer alternatives:

  • pipx
  • Standalone binaries
  • OS package managers

Recommended Alternatives (Fact-Based)

Use CaseRecommended Tool
Project dependenciespip (inside venv)
Global Python CLI toolspipx
Environment & dependency managementuv, poetry
OS-level toolsOS package manager
CI / serversPrebuilt binaries or containers

Example:

pipx install black
pipx install poetry

A Simple Rule That Works

If it’s a library used by your code, use pip inside a virtual environment.
If it’s a tool you run from the terminal, don’t install it with system-level pip.

This rule aligns with:

  • PyPA guidance
  • Linux and macOS documentation
  • Modern Python tooling practices

Final Thoughts

pip install is not bad—it is precise.
Problems arise when it is used outside its intended scope.

Understanding where pip belongs is one of the most important skills for maintaining a clean, stable Python development environment.

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