There are some advantages to pyproject.toml

1. You can differenciate between different dependency groups like build dependencies, dev dependencies, test dependencies and regular dependencies. So if someone uses some dependency only in dev previously you either had to install that manually or your requirements.txt installed it for you without you needing it.

2. It adds a common description for project metadata that can be used

3. Adds a place where tool settings like those of a linter or a formatter can be stored (e.g. ruff and black)

4. Its format is standardized and allows it to be integrated with multiple build tools, toml is a bit more standardized than whatever custom file syntax python used before

virtualenvs are great, but they're not great on their own. requirements.txt work sorta, but then any package with more than 50 requirements requires a non-trivial amount of manual labor to maintain. (Hell even 20 deps are a pain)

Astral uv and poetry both maintain the pyproject.toml for you -- and as a bonus, they maintain the virtualenv underneath.

Then for the complete python newbs, they can run 'uv sync' or 'poetry install' and they don't have to understand what a virtualenv is -- and they don't need root, and they don't have to worry about conflicts, or which virtualenv is which, etc.

So the simple case:

    mkdir test
    cd test

    # init a new project with python 3.13
    uv init -p 3.13

    # Add project deps
    uv add numpy
    uv add ...

    # Delete the venv
    rm -rf .venv

    # reinstall everything (with the exact versions)
    uv sync

    # Install a test package in your venv
    uv pip install poetry

    # force the virtualenv back into a sane state (removing poetry and all it's deps)
    uv sync

    # update all deps
    rm uv.lock
    uv lock

Now cat your pyproject.toml, and you'll see something like this:

    [project]
    name = "test"
    version = "0.1.0"
    description = "Add your description here"
    readme = "README.md"
    requires-python = ">=3.13"
    dependencies = [
        "numpy>=2.2.5",
        "pillow>=11.2.1",
        "scipy>=1.15.2",
    ]