Python development looks simple from the outside. But managing real projects is rarely easy. You need to install packages, update them, avoid version conflicts, create virtual environments, and prepare your project for distribution.
Many beginners think they can handle everything with PIP and VENV. This works for small scripts, but it gets messy once your project grows.
Poetry Python solves this problem by giving you a clean workflow for managing projects from start to finish. Poetry brings structure to your project. It automates package management, independently creates virtual environments, and prepares your project for building and publishing. It replaces many scattered tools, bringing clarity and reliability. With Poetry, you focus on writing the code while it takes care of the setup.
Poetry is also very helpful for AI projects. This locks the exact version of the dependency, which avoids sudden breaks when libraries such as Transformer, Torch, or Lingchen Release updates that may change model behavior or API output.
This article explains how rhyming works, how to use it with examples, and how it compares to other alternatives. The goal is to make poetry easy to understand, even if you’re new to Python.
What we will cover
The problem poetry tries to solve
Modern Python projects require many moving parts. You install libraries Pypiupdate them over time, track versions to keep the project stable, and share those versions with your team. You also need to package your project if you want others to use it.
Traditional method of use requirements.txt And pip install Doesn’t solve everything. Dependencies may be broken when a new version is released. Two developers can use it without using different versions. You may forget which environment you used. When you want to package a project, you often need tools like setup tools and wheels.
Poetry brings all these pieces together. It uses a file, pyproject.tomlexplaining everything. It installs packages in a clean virtual environment. It locks the version to avoid surprises. And it can build and publish your package with a couple of commands.
Getting started with poetry
Getting started with poetry is easy. You install it once, and it works on any Python project. Run this command to install Rhyme on your system:
pipx install poetry
Once installed, you can start a new project using:
poetry new my_project
This creates a folder with a basic structure. It includes one pyproject.toml File This file is the heart of your project. This includes your project name, version, description, and dependencies.
(tool.poetry)
name = "myapp"
version = "0.1.0"
description = ""
authors = ("Manish Shivanandhan " )
readme = "README.md"
(tool.poetry.dependencies)
python = "^3.12"
(build-system)
requires = ("poetry-core")
build-backend = "poetry.core.masonry.api"
If you want to add a poem to an existing project, you use:
poetry init
It asks you simple questions about your project and creates a configuration file.
This command will guide you through creating your pyproject.toml config.
Package name (myapp):
Version (0.1.0):
Description ():
Author (Manish Shivanandhan , n to skip):
License ():
Compatible Python versions (^3.12):
Would you like to define your main dependencies interactively? (yes/no) (yes)
You can specify a package in the following forms:
- A single name (requests): this will search for matches on PyPI
- A name and a constraint (requests@^2.23.0)
- A git url (git+
- A git url with a revision (git+
- A file path (../my-package/my-package.whl)
- A directory (../my-package/)
- A url (https://example.com/packages/my-package-0.1.0.tar.gz)
Package to add or search for (leave blank to skip):
Would you like to define your development dependencies interactively? (yes/no) (yes)
Now you can install the package using:
poetry add
Poetry will install the package inside its virtual environment. You don’t have to run venv To manually run your Python program, you use:
poetry run python main.py
Or you can enter the environment:
poetry shell
This simple workflow becomes natural very quickly.
understanding pyproject.toml
pyproject.toml The file contains data that defines your project. Poetry populates this file when you add or remove dependencies. An example of a simple file is:
(tool.poetry)
name = "weather"
version = "0.1.0"
authors = ("Manish")
(tool.poetry.dependencies)
python = "^3.10"
requests = "^2.32.0"
(tool.poetry.group.dev.dependencies)
pytest = "^8.2.0"
This changes a single file setup.pyfor , for , for , . requirements.txtand many manual steps. Poetry acts as a manager for everything within it.
Creating a sample app
Imagine you’re building a simple weather app that calls an API. After creating a poetry project, you add the dependencies:
poetry add requests
Then you write a Python script:
import requests
def get_weather(city):
url = f"https://wttr.in/{city}?format=3"
response = requests.get(url)
print(response.text)
get_weather("London")
To run it:
poetry run python weather.py
Poetry locks versions of applications so your app works the same for everyone. If a new version is released and breaks something, you’re safe because Poetry maintains your closed version.
When you want to build your project for publication, you run:
poetry build
This command creates a file that you can upload to PYPI:
poetry publish
Simplicity like this is why Rhyme has become a favorite among developers.
Lock file
One of Poetry’s quietly powerful features is the lock file. When you add a package, Poetry writes the exact version poetry.lock. This file ensures that your project behaves the same across machines. If someone laughs at your project, all they need to do is:
poetry install
Poetry reads the lock file and installs the exact version you are using. This helps with debugging because nothing is silently changed after installation.
Poetry is not the only tool for managing Python projects. To understand why developers choose Rhyme, it helps to compare it to other popular options. Here are three alternatives and how they differ.
Poetry vs. Pipes and Virtual Environments
Pipe The default is the Python package installer, and VENV creates an isolated environment. These two tools have been used together for years. They work fine for simple scripts but require manual steps for real projects.
You create a virtual environment manually:
python -m venv env
Then you activate it, install packages, update requirements.txtand manage version conflicts. Project packaging is a separate process entirely.
Poetry automates all of this. It creates environments, tracks versions and builds packages. The workflow is cleaner and more modern. PIP and VENV feel manual compared to the automated approach to poetry.
If you just need a quick script, PIP and VENV are sufficient. But for repeatable and doable projects, poetry wins by a wide margin.
Poetry vs. Pipeino
Pippinio PIP was designed to be easy to use. It combines PIP and virtual environments into a single workflow. Many believed that Pipenov would become a primary tool, but it has struggled with performance and reliability issues.
For example, installing packages in Pipenew can be slow. Pipenew also uses a pip file instead of paperokt.toml, which makes it less aligned with modern Python standards.
A basic Pipeline V command to install applications looks like:
pipenv install requests
Poetry does the same:
poetry add requests
The biggest difference is durability. Poetry resolves dependencies faster and more reliably. This works well for large projects. Pipenio is simpler than raw pipe but still less polished than poetry.
Poetry vs. Hatch
Hatch Python is another modern tool for managing projects. It also uses pyproject.tomlso it follows the criteria according to poetry.
Hatch is known to be flexible and fast. It is popular among users who handle packaging, testing, and versioning.
Hatch can create an environment using:
hatch env create
Dependencies are managed using sections in the configuration file. Hatch can feel overdeveloped, and it focuses more on packaging than dependency management.
The main difference is that Poetry tries to be an all-in-one tool for dependency management, environment, building and publishing. The hatch gives more control but less of a guided experience.
For beginners and teams, poetry feels seamless. Hatch is powerful for advanced users who want more customization.
Why is poetry fun to use?
One of the reasons developers enjoy poetry is the sense of clarity. Everything is clean, predictable and organized. When you open a poetry project, you always know where to look.
You know that dependencies are managed properly. You know your build will work. It reduces stress and makes you more confident.
Poetry takes care of things you might forget about. It creates environments, controls versions, and keeps your workspace clean. It also has a friendly command line interface that guides you through helpful messages.
Another benefit is how easy it is to share your project. Anyone who wants to run your project just needs to run:
poetry install
This brings stability to teams and avoids many common problems.
When poetry may not be the best choice
Rhyme is great for most projects, but there are cases where it may not be the best fit.
If your project is extremely small, you may not need additional structures. If you work in an environment that already uses PIP, Konda, or another rigid workflow, introducing rhyme can cause friction.
Poetry itself also tries to manage the environment. Some users prefer manual control. In these cases, tools like Hatch or Simple PIP may be a better fit.
But for most Python developers, Poetry brings great value with very little setup.
The result
Poetry is one of the most obvious and useful tools in the Python ecosystem. It helps you manage dependencies, create environments, build packages and publish with ease. It brings structure and reliability to your projects, making your code more stable and easier to share.
If you’re looking for a better workflow for your Python projects, Poetry is a great tool to use. It keeps your setup clean, prevents versioning problems, and gives you a smooth path from development to publication. With a few commands, you can create robust and repeatable Python projects without the usual headaches.
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