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Welcome to **Introduction to Python**! Python is one of the most popular and powerful programming languages. It is used extensively in computational and data science, and you will use it in almost all modules during your MSc studies. This self-guided course will help you to learn the basics of Python.

This course consists of four lectures (1, 2, 3, and 4), and we strongly recommend completing them in that order.

There are three possibilities for how you can learn Python with our self-guided course. **JupyterHub** and **Binder** options allow you to run tutorials in the cloud - you do not need to install anything, and no files will be created on your machine. All you need is a web browser and internet connection. However, you need to download Jupyter notebooks you worked in to ensure your work is not lost at the end of each session. The third option is to create a Python environment and run all tutorials on your machine. There is no difference in what you will learn, and we give you complete freedom to choose how you want to run tutorials.

1. **Create a GitHub account**. In the email you received, we sent you your unique username. Create a GitHub account and make sure your GitHub account uses that username.

3. **Install Anaconda**. We recommend installation using conda package manager. If you do not already have it installed, download [Anaconda](https://www.anaconda.com/products/individual) Python 3 for your operating system and follow the instructions to install it. After the installation is complete, in Terminal (Linux and MacOS) or Anaconda Prompt (Windows), navigate to the course directory, create a new conda environment, and activate it:

After (almost) each exercise, there are two cells containing some code. In those cells, we test your solution using two different testing packages:

Although **PyBryt** provides much more detailed feedback, it is possible it complains about your code even though **okpy** confirms your code is correct. This is because PyBryt testing is based on comparing your solution to reference solutions. Since there is virtually an infinite number of alternative solutions, it is possible that your solution is not in our references. On the other hand, **okpy** looks at the final result of your code and if **okpy** is happy with your result, your code is correct and you do not have to address **PyBryt**'s error messages (if any).

- python=3.9 # Python 3.10 expected to work as well, but we are being conservative here.

- jupyterlab

- pybryt>=0.7.0

"metadata": {},

"Welcome to **Introduction to Python**! Python is one of the most popular and powerful programming languages. It is used extensively in computational and data science, and you will use it in almost all modules during your MSc studies. This self-guided course will help you to learn the basics of Python.\n",

"This course consists of four lectures:\n",

"After (almost) each exercise, there are two cells containing some code. In those cells, we test your solution using two different testing packages:\n",

"Although **PyBryt** provides much more detailed feedback, it is possible it complains about your code even though **okpy** confirms your code is correct. This is because PyBryt testing is based on comparing your solution to reference solutions. Since there is virtually an infinite number of alternative solutions, it is possible that your solution is not in our references. On the other hand, **okpy** looks at the final result of your code and if **okpy** is happy with your result, your code is correct and you do not have to address **PyBryt**'s error messages (if any).\n",

"\n",

"> **A:** Click the `Issues` tab next to top of the page, then click the green `New Issue` button. Ask your question in the title and comment fields, then click `Submit new issue`. Congratulations, you submitted your question! We will try to get back to you shortly.\n",

]

}

import pathlib

args = assignment.Settings(server="clewolffautook21.eastus.cloudapp.azure.com/okpy")

ok = Notebook("./lecture.ok", args)

"""Function for obtaining the ``.pkl`` PyBryt reference.

dirname = pathlib.Path("pybryt-references")

basename = f"exercise-{lecture}_{exercise}"

pyfilename = dirname / f"{basename}.py"

pklfilename = dirname / f"{basename}.pkl"

if pyfilename.is_file():

pybryt.ReferenceImplementation.compile(str(pyfilename)).dump(str(pklfilename))

elif not pklfilename.is_file():

msg = f"PyBryt reference {pklfilename!r} not found."

raise FileNotFoundError(msg)

return str(pklfilename)