Python Modules

Gregor von Laszewski (

Often you may need functionality that is not present in Python’s standard library. In this case, you have two option:

  • implement the features yourself
  • use a third-party library that has the desired features.

Often you can find a previous implementation of what you need. Since this is a common situation, there is a service supporting it: the Python Package Index (or PyPi for short).

Our task here is to install the autopep8 tool from PyPi. This will allow us to illustrate the use of virtual environments using the venv and installing and uninstalling PyPi packages using pip.

Updating Pip

You must have the newest version of pip installed for your version of python. Let us assume your python is registered with python and you use venv, than you can update pip with

pip install -U pip

without interfering with a potential system-wide installed version of pip that may be needed by the system default version of python. See the section about venv for more details

Using pip to Install Packages

Let us now look at another important tool for Python development: the Python Package Index, or PyPI for short. PyPI provides a large set of third-party Python packages.

To install a package from PyPI, use the pip command. We can search for PyPI for packages:

$ pip search --trusted-host autopep8 pylint

It appears that the top two results are what we want, thus install them:

$ pip install --trusted-host autopep8 pylint

This will cause pip to download the packages from PyPI, extract them, check their dependencies and install those as needed, then install the requested packages.



Install guizero with the following command:

sudo pip install guizero

For a comprehensive tutorial on guizero, click here.


You can install Kivy on macOS as follows:

brew install pkg-config sdl2 sdl2_image sdl2_ttf sdl2_mixer gstreamer
pip install -U Cython
pip install kivy
pip install pygame

A hello world program for kivy is included in the cloudmesh.robot repository. Which you can find here

To run the program, please download it or execute it in cloudmesh.robot as follows:

cd cloudmesh.robot/projects/kivy

To create stand-alone packages with kivy, please see:


Formatting and Checking Python Code

First, get the bad code:

$ wget --no-check-certificate -O

Examine the code:

$ emacs

As you can see, this is very dense and hard to read. Cleaning it up by hand would be a time-consuming and error-prone process. Luckily, this is a common problem so there exist a couple of packages to help in this situation.

Using autopep8

We can now run the bad code through autopep8 to fix formatting problems:

$ autopep8 >

Let us look at the result. This is considerably better than before. It is easy to tell what the example1 and example2 functions are doing.

It is a good idea to develop a habit of using autopep8 in your python-development workflow. For instance: use autopep8 to check a file, and if it passes, make any changes in place using the -i flag:

$ autopep8    # check output to see of passes
$ autopep8 -i # update in place

If you use pyCharm you can use a similar function while pressing on Inspect Code.

Writing Python 3 Compatible Code

To write python 2 and 3 compatible code we recommend that you take a look at:

Using Python on FutureSystems

This is only important if you use Futuresystems resources.

To use Python you must log in to your FutureSystems account. Then at the shell prompt execute the following command:

$ module load python

This will make the python and virtualenv commands available to you.

The details of what the module load command does are described in the future lesson modules.



The Python Package Index is a large repository of software for the Python programming language containing a large number of packages, many of which can be found on pypi. The nice thing about pypi is that many packages can be installed with the program ‘pip.’

To do so you have to locate the <package_name> for example with the search function in pypi and say on the command line:

$ pip install <package_name>

where package_name is the string name of the package. an example would be the package called cloudmesh_client which you can install with:

$ pip install cloudmesh_client

If all goes well the package will be installed.

Alternative Installations

The basic installation of python is provided by However, others claim to have alternative environments that allow you to install python. This includes

Typically they include not only the python compiler but also several useful packages. It is fine to use such environments for the class, but it should be noted that in both cases not every python library may be available for install in the given environment. For example, if you need to use cloudmesh client, it may not be available as conda or Canopy package. This is also the case for many other cloud-related and useful python libraries. Hence, we do recommend that if you are new to python to use the distribution from, and use pip and virtualenv.

Additionally, some python versions have platform-specific libraries or dependencies. For example, coca libraries, .NET, or other frameworks are examples. For the assignments and the projects, such platform-dependent libraries are not to be used.

If however, you can write a platform-independent code that works on Linux, macOS, and Windows while using the version but develop it with any of the other tools that are just fine. However, it is up to you to guarantee that this independence is maintained and implemented. You do have to write requirements.txt files that will install the necessary python libraries in a platform-independent fashion. The homework assignment PRG1 has even a requirement to do so.

In order to provide platform independence we have given in the class a minimal python version that we have tested with hundreds of students: If you use any other version, that is your decision. Additionally, some students not only use but have used iPython which is fine too. However, this class is not only about python, but also about how to have your code run on any platform. The homework is designed so that you can identify a setup that works for you.

However, we have concerns if you for example wanted to use chameleon cloud which we require you to access with cloudmesh. cloudmesh is not available as conda, canopy, or other framework packages. Cloudmesh client is available form pypi which is standard and should be supported by the frameworks. We have not tested cloudmesh on any other python version than which is the open-source community standard. None of the other versions are standard.

In fact, we had students over the summer using canopy on their machines and they got confused as they now had multiple python versions and did not know how to switch between them and activate the correct version. Certainly, if you know how to do that, then feel free to use canopy, and if you want to use canopy all this is up to you. However, the homework and project require you to make your program portable to If you know how to do that even if you use canopy, anaconda, or any other python version that is fine. Graders will test your programs on a installation and not canopy, anaconda, ironpython while using virtualenv. It is obvious why. If you do not know that answer you may want to think about that every time they test a program they need to do a new virtualenv and run vanilla python in it. If we were to run two installs in the same system, this will not work as we do not know if one student will cause a side effect for another. Thus we as instructors do not just have to look at your code but code of hundreds of students with different setups. This is a non-scalable solution as every time we test out code from a student we would have to wipe out the OS, install it new, install a new version of whatever python you have elected, become familiar with that version, and so on and on. This is the reason why the open-source community is using We follow best practices. Using other versions is not a community best practice, but may work for an individual.

We have however in regards to using other python versions additional bonus projects such as

  • deploy run and document cloudmesh on ironpython
  • deploy run and document cloudmesh on anaconda, develop script to generate a conda package form github
  • deploy run and document cloudmesh on canopy, develop script to generate a conda package form github
  • deploy run and document cloudmesh on ironpython
  • other documentation that would be useful


If you are unfamiliar with programming in Python, we also refer you to some of the numerous online resources. You may wish to start with Learn Python or the book Learn Python the Hard Way. Other options include Tutorials Point or Code Academy, and the Python wiki page contains a long list of references for learning as well. Additional resources include:

A very long list of useful information is also available from

This list may be useful as it also contains links to data visualization and manipulation libraries, and AI tools and libraries. Please note that for this class you can reuse such libraries if not otherwise stated.

Jupyter Notebook Tutorials

A Short Introduction to Jupyter Notebooks and NumPy To view the notebook, open this link in a background tab and copy and paste the following link in the URL input area Then hit Go.



Write a python program called that accepts an integer n from the command line. Pass this integer to a function called iterate.

The iterate function should then iterate from 1 to n. If the i-th number is a multiple of three, print multiple of 3, if a multiple of 5 print multiple of 5, if a multiple of both print multiple of 3 and 5, else print the value.


  1. Create a pyenv or virtualenv ~/ENV
  1. Modify your ~/.bashrc shell file to activate your environment upon login.
  1. Install the docopt python package using pip
  1. Write a program that uses docopt to define a command line program. Hint: modify the iterate program.
  1. Demonstrate the program works.
Last modified June 20, 2021 : spelling (d59b3b2d)