When developing code to accomplish some goal, researchers will typically work within a single script or even something like a Jupyter notebook. Often, the code written here will be useful in another situation, by the same person, a collaborator, or a student. Frequently, that script or notebook gets copied from place to place and edited as a matter of course. This can be problematic for a number of reasons. And it can be a challenge to keep track of which version of the script was used for what task.

Learning how to properly package your code in a way that others (or even yourself) can install into one or more environments and used everywhere is powerful. Not only will it be easier to track which version is needed for which project, but having a centralized location to collaborate on that code makes it easier to manage and deploy those changes.

This section introduces a simple code example. We will discuss the anatomy of a Python package and how Python looks for and deals with installed assets. Version control (i.e., Git and GitHub) and managing deployed assets (e.g., deploying to PyPI with Twine) are not covered.

Starting Point

The following code snippet will be the starting point of this tutorial and developed on throughout. It is entirely contrived and purposefully made to be both simple and initially “bad” as a teaching aid.

Here we have an example bit of code written to compute the cumulative product of an array of numeric values.
# original data
array = [3, 4, 6, 9]

# compute cumulative product
result = array[:1]
last_value = array[-1]
for value in array[1:]:
    result.append(result[-1] * value)
    if value == last_value:


There are of course many things that could be improved about this bit of code. We will get to those soon enough! Let’s just say for the sake of argument that it meets the needs of our use-case initially. We’ve written this bit of code in a file somewhere and can execute it to display our result.

$ python
[3, 12, 72, 648]


Different kinds of code snippets and examples will be shown throughout the tutorial. Anything that looks like Python code should be interpreted as residing in a file. If it includes In[1] style markings that is to be executed at the IPython console. If it includes a $ prompt it represents an unspecified, generic shell prompt; these lines should be executed at the command-line.


In [1]: print('Hello, world!')
Hello, world!
$ echo 'Hello, world!'
Hello, world!


The first thing to tackle here is how to make this code re-usable. We’ve hard-coded the input data, and if we wanted to compute the cumulative product on more than one set of data we would need to duplicate those lines of code each time.

Functions facilitate code reuse. Whenever you see yourself typing the same code twice in the same program or project, it is a clear indication that the code belongs in a function.

A good function:

  • has a descriptive name. cumulative_product is a better name than alg32.

  • is small – no more than a couple of dozen lines – and does one thing. If a function is doing too much, then it should probably be broken into smaller functions.

  • can be easily tested – more on this soon.

  • is well documented – more on this later.
def cumulative_product(array):
    result = array[:1]
    last_value = array[-1]
    for value in array[1:]:
        result.append(result[-1] * value)
        if value == last_value:
    return result

print(cumulative_product([3, 4, 6, 9]))
print(cumulative_product([1, 8, 2, 7]))

Now we can do more work without duplicating those lines of code. But we’re still hard-coding the input data. We’ll explore making this function better in the next section when we discuss testing. For now, let’s focus on making this code available as part of an installable package.

Creating a Python Package

In order to make this code broadly available to Python, it needs to be “packaged” in a particular way and “installed” into a particular location that Python knows where to find it. There are many different types of files Python can import from and there is more than one location Python can look for packages.


Before diving into the mechanics of Python packaging it is helpful to talk about terminology. When not accepting input directly via an interactive console, Python needs to find code in a file somewhere. In the simplest case we are speaking of a .py file. If we execute such a file directly (as we did just now) it is referred to as a script. If instead we want to import some code (either in an interactive session or within another file) it is typically referred to as a module. Python files can in fact be both a script and a module simultaneously, depending on context; more on this soon! A folder containing a collection of Python modules is a package. In order for that folder to be understood by Python as a package there are a few criteria, more on that in a minute. A package can in fact be a nesting of such folders (or sub-packages).

How does Python Find Packages

What we want to be able to do is import our function as library code to use somewhere else.

In [4]: from cumulative_product import cumulative_product

In [5]: cumulative_product([1, 2, 3])
Out[5]: [1, 2, 6]

In this case, the file is acting as a module and we’ve imported a function from it. Python will complain that there is no module named “cumulative_product” if it’s not found on one of the designated paths it knows about.

In [6]: import cumulative_product
ModuleNotFoundError                       Traceback (most recent call last)
<ipython-input-6-7f58dd7fb72e> in <module>
----> 1 import cumulative_product

ModuleNotFoundError: No module named 'cumulative_product'

The notion of a “path” (and environment variables that can supplement them) is ubiquitous on most platforms (including Windows) and used by many systems and tools. In Python, there are a few pre-defined values that will show up on your Python path. You can view and manipulate these paths by accessing the sys.path list from within a Python session or module.

In [2]: import sys

In [3]: sys.path

The exact values depend on both how Python was installed and what platform you are using (i.e., Windows, MacOS, Linux, etc.). But there are some common patterns to observe. Your system libraries will occur first on your path; that’s the .zip, top-level library, and lib-dynload you see (this will be slightly different on Windows). These represent Python itself and the built-ins. Discussion of these is beyond the scope of this tutorial.

The final three paths are what’s important here. The empty string represents your current working directory. Python can always import from a module or package that exists within your current working directory. Next are two site-packages directories which are essentially fixed and represent the target for installed packages. By default, your user site packages folder is first which allows you to install extra packages without needing write permissions to the system path.


When using the word system we don’t necessarily mean the Python installation used by the operating system. We mean the location where the Python you are invoking resides. If your Python installation is in your home directory, that is the system location.

Technically, we could create a special folder somewhere on our system and populate it with .py files and be able to import code from them by adding it to our Python path.

In [7]: sys.path.append('/opt/lab/python/modules')

In [8]: import cumulative_product
Out[8]: <module 'cumulative_product' from '/opt/lab/python/modules/'>

Alternatively, we could automate this by defining (or extending) the PYTHONPATH environment variable before launching a Python session or script.

$ export PYTHONPATH=/opt/lab/python/modules:$PYTHONPATH

This isn’t the best solution however because it requires access to a special location (/opt/lab/python) to make use of the library. If we wanted to be able to use our algorithm in another project without having to hard-code the location of the installed dependency in the project, we should instead make it so our code is installable and automatically placed in the appropriate location (wherever that happens to be).

Organizing a Python Project

To make your module or package installable we will use setuptools. There is a long and storied history regarding the development tools used to package Python libraries. We’ll neglect that here and instead simply recommend the current “best practice”.

The picture looks a little different if you are trying to make a single .py file installable as a module. In either case we need a present at the top-level of our project. The tooling and systems we will use expect a file with this exact name to exist. Think of it as a configuration file.

$ tree .

In this case, a minimum viable setup file would look something like the following.
from setuptools import setup


For a small number of functions this approach is fine, and even recommended, but as the size and scope of the project grows, it will become necessary to organize the code across multiple files. Let’s assume this to be the case from the outset and restructure our project. We’ll use the name python201 for the purposes of this tutorial, but your package of course should take on a name relevant to the project.

$ tree .
├── python201/
│   ├──
│   └──

Notice the presence of the file. A detail neglected until this point, the existence of a file with this special name is what defines a folder as a package. This file must be present throughout the package/sub-package hierarchy at every level. An entire tutorial could be devoted to the purpose and use of these modules. Suffice it to say these files can literally be empty.

Now our import would look like the following.

In [1]: from python201.algorithms import cumulative_product

In [2]: cumulative_product([1, 2, 3])
Out[2]: [1, 2, 6]

Our setup file would then instead need to be the following.
from setuptools import setup


The packages keyword argument is similarly a list of strings, but now representing packages (and any sub-packages therein). The modules below a package will automatically be included, but sub-packages will not. To automatically enumerate our package structure should we choose to expand our project, we can use a handy tool included in setuptools that does exactly what it sounds like.
from setuptools import setup, find_packages



Ultimately, it is highly recommended to nest your source package root with a src directory. Doing so prevents a lot of common pitfalls you are likely to run into, as discussed on good integration practices at, which links to an excellent blog post by Ionel Cristian Mărieș.


To install a Python package we will use the command-line tool, pip, which is typically included out-of-the-box with any Python installation. You are likely familiar with the use of pip to install packages from Python’s online package index, PyPI. There are in fact numerous types of targets that can be specified to pip, including local source code repositories you have on your system, such as ours.

$ pip install .

Here the dot represents the current working directory. In practice, you can point to any folder path that contains a file. This will install our package to the system site-packages path. To install to our user site-packages, we can include the --user flag.

$ pip install . --user

Finally, an often used feature of developers is to install your package in editable mode using the -e flag. This allows you to work on the code and see the changes without needing to re-install it every time.

$ pip install -e . --user


As a side note, using pip’s --user functionality might have some unexpected consequences and could cause package conflicts at some point later in the future. As discussed previously, your user site-packages always come first on your sys.path. So long as you are using the same Python version (e.g., 3.6), those packages will be preferred.

  • First, if you’re using some kind of virtual environment, it may pick up packages that you didn’t intend that conflict with other libraries you’re using.

  • Second, other applications that happen to be running against that particular version of Python may pick up those packages and there could be a conflict. A common example from a shared HPC system: the centrally-maintained JupyterHub instance could be running the same version of Python as your environment. If you install a web-based plotting library that pulls down tornado as a dependency, now all of your kernels are “broken” in JupyterHub.

For this tutorial, if you’re using Anaconda or a vanilla virtualenv, we recommend you use

$ pip install -e .

while you have the environment activated. If you’re using Pipenv, you can add your package to your managed virtualenv with

$ pipenv install -e .

If you’re using Poetry, your current project is automatically included in developer mode when installing from your lock file.

$ poetry install


We’ve arrived now at the conclusion of the essentials for our package. Here are some extra things that you might want to consider for your project, particularly if you plan on sharing it with others.


A staple of open-source projects, a README file is a plain text file included in your project that explains what is included in the project and typically some guidance on how to use it. Many online hosting services including PyPI itself understand what these files are and will render them in different formats.


A Python package for numerical algorithms.

Version Control

Even if you plan to work on the project alone, you definitely should consider developing your code using some kind of version control system. These days, git is ubiquitous. If you’ll be sharing the code, online hosting services such as and offer sophisticated features.


You should pick a software license that best suites your project. A license specifies the terms of use of the code. Choosing a license is beyond the scope of this tutorial. Once you’ve decided on an appropriate license, most commonly used licenses are recognized by hosting services like PyPI and

You should include the license in full in a LICENSE file at the top-level of your project. It may be appropriate to include a brief snippet pertaining to the license at the top of some or all of your code files.

$ tree .
├── python201/
│   ├──
│   └──
├── README.rst

It doesn’t affect the installed package, but if you plan to upload your package to PyPI you can define the license within the setup function using the appropriate name and classifier.
from setuptools import setup, find_packages

    license='Apache Software License',
        'License :: OSI Approved :: Apache Software License'

More Details

There are many options one can and may need to use within the setup function to make a package function as desired, such as dependencies, non-python installed assets (such as data files or man pages), etc. You also should consider including additional information if you plan to upload your package to the package index.
from setuptools import setup, find_packages

with open('README.rst', mode='r') as readme:
    long_description =

    name             = 'python201',
    version          = '0.0.1',
    author           = 'Geoffrey Lentner',
    author_email     = '[email protected]',
    description      = 'A Python package for numerical algorithms.',
    license          = 'Apache Software License',
    keywords         = 'tutorial packaging example',
    url              = '',
    packages         = find_packages(),
    include_package_data = True,
    long_description = long_description,
    long_description_content_type = 'text/x-rst',
    classifiers      = ['Development Status :: 4 - Beta',
                        'Programming Language :: Python :: 3.8',
                        'Programming Language :: Python :: 3.9',
                        'Programming Language :: Python :: 3.10',
                        'Operating System :: POSIX :: Linux',
                        'Operating System :: MacOS',
                        'Operating System :: Microsoft :: Windows',
                        'License :: OSI Approved :: Apache Software License', ],
    install_requires = ['numpy', 'numba', ],
    extras_require   = {
        'dev': ['ipython', 'pytest', 'hypothesis', 'pylint', 'sphinx',

Upload to PyPI

You can validate, register, and upload your package to the Python package index using the Twine command-line tool. This is the officially supported way of doing so. It’s perfectly acceptable to not host your package via PyPI, and instead merely instruct users how to install directly via GitHub, for example.

In the most basic scenario, assuming your project is pure-python, you would want to build both the a “binary distribution wheel” and a “source distribution” using your script.

$ python bdist_wheel sdist

This creates distribution files that you can upload to PyPI. All necessary information should be contained within the file you used to “build” the distribution assets. Upload them with twine.

$ twine upload dist/*


Be sure to check out the documentation for doing this before attempting. You’ll want to validate your assets before you push them to PyPI. There is a “test” server you can push to that is more-or-less a black hole for testing purposes. It will let you check that everything is setup properly before actually pushing to the world.