This lesson is being piloted (Beta version)

Python to package


Teaching: 20 min
Exercises: 5 min
  • How do we take code in a Jupyter Notebook or Python script and turn that into a package?

  • What are the minimum elements required for a Python package?

  • How do you set up tests?

  • Create and install a Python package

  • Create and run a test

Much research software is initially developed by hacking away in an interactive setting, such as in a Jupyter Notebook or a Python shell. However, at some point when you have a more-complicated workflow that you want to repeat, and/or make available to others, it makes sense to package your functions into modules and ultimately a software package that can be installed. This lesson will walk you through that process.

Consider the rescale() function written as an exercise in the Software Carpentry Programming with Python lesson.

First, as needed, create your virtual environment and install NumPy with

virtualenv .venv
source .venv/bin/activate
pip install numpy

Then, in a Python shell or Jupyter Notebook, declare the function:

import numpy as np

def rescale(input_array):
    """Rescales an array from 0 to 1.

    Takes an array as input, and returns a corresponding array scaled so that 0
    corresponds to the minimum and 1 to the maximum value of the input array.
    L = np.min(input_array)
    H = np.max(input_array)
    output_array = (input_array - L) / (H - L)
    return output_array

and call the function with:

rescale(np.linspace(0, 100, 5))

which provides the output:

array([ 0.  ,  0.25,  0.5 ,  0.75,  1.  ])

Creating our package in six lines

Let’s create a Python package that contains this function.

First, create a new directory for your software package, called package, and move into that:

$ mkdir package
$ cd package

You should immediately initialize an empty Git repository in this directory; if you need a refresher on using Git for version control, check out the Software Carpentry Version Control with Git lesson. (This lesson will not explicitly remind you to commit your work after this point.)

$ git init

Next, we want to create the necessary directory structure for your package. This includes:

$ mkdir -p src/rescale tests docs

(The -p flag tells mkdir to create the src parent directory for rescale.)

Putting the package directory and source code inside the src directory is not actually required; instead, if you put the <package_name> directory at the same level as tests and docs then you could actually import or call the package directory from that location. However, this can cause several issues, such as running tests with the local version instead of the installed version. In addition, this package structure matches that of compiled languages, and lets your package easily contain non-Python compiled code, if necessary.

Inside src/rescale, create the files and

$ touch src/rescale/ src/rescale/ is required to import this directory as a package, and should remain empty (for now). is the module inside this package that will contain the rescale() function; copy the contents of that function into this file. (Don’t forget the NumPy import!)

The last element your package needs is a pyproject.toml file. Create this with

$ touch pyproject.toml

and then provide the minimally required metadata, which include information about the build system (hatchling) and the package itself (name and version):

# contents of pyproject.toml
requires = ["hatchling"]
build-backend = ""

name = "package"
version = "0.1.0"

The package name given here, “package,” matches the directory package that contains our project’s code. We’ve chosen 0.1.0 as the starting version for this package; you’ll see more in a later episode about versioning, and how to specify this without manually writing it here.

The only elements of your package truly required to install and import it are the pyproject.toml,, and files. At this point, your package’s file structure should look like this:

├── docs/
├── pyproject.toml
├── src/
│   └── package/
│   │   ├──
│   │   └──
└── tests/

Installing and using your package

Now that your package has the necessary elements, you can install it into your virtual environment (which should already be active). From the top level of your project’s directory, enter

$ pip install -e .

The -e flag tells pip to install in editable mode, meaning that you can continue developing your package on your computer as you test it.

Then, in a Python shell or Jupyter Notebook, import your package and call the (single) function:

import numpy as np
from package.rescale import rescale

rescale(np.linspace(0, 100, 5))
array([0.  , 0.25, 0.5 , 0.75, 1.  ])

This matches the output we expected based on our interactive testing above! 😅

Your first test

Now that we have installed our package and we have manually tested that it works, let’s set up this situation as a test that can be automatically run using nox and pytest.

In the tests directory, create the file:

touch tests/

In this file, we need to import the package, and check that a call to the rescale function with our known input returns the expected output:

# contents of tests/
import numpy as np
from package.rescale import rescale

def test_rescale():
        rescale(np.linspace(0, 100, 5)),
        np.array([0.0, 0.25, 0.5, 0.75, 1.0]),

Next, take the you created in an earlier episode, and modify it to


# contents of
import nox

def tests(session):
    session.install("numpy", "pytest")

Now, with the added test file and, your package’s directory structure should look like:

├── docs/
├── pyproject.toml
├── src/
│   └── package/
│   │   ├──
│   │   └──
└── tests/

(You may also see some __pycache__ directories, which contain compiled Python bytecode that was generated when calling your package.)

Have nox run your tests with the command

$ nox

This should give you some information about what nox is doing, and show output along the lines of

nox > Running session tests
nox > Creating virtual environment (virtualenv) using python in .nox/tests
nox > python -m pip install numpy pytest
nox > python -m pip install .
nox > pytest
======================================================================= test session starts =================================================
platform darwin -- Python 3.9.13, pytest-7.1.2, pluggy-1.0.0
rootdir: /Users/niemeyek/Desktop/rescale
collected 1 item

tests/ .                                                                                                                [100%]

======================================================================== 1 passed in 0.07s ==================================================
nox > Session tests was successful.

This tells us that the output of the test function matches the expected result, and therefore the test passes! 🎉

We now have a package that is installed, can be interacted with properly, and has a passing test. Next, we’ll look at other files that should be included with your package.

Going further

We selected a specific backend - hatching. It had a good default configuration, is fast, and has some powerful features and supports a growing ecosystem of plugins. There are other backends too, including ones for compiled projects.

Key Points

  • Use a pyproject.toml file to describe a Python package