# flotpython exos: data-science 

this repo contains exercises and TPs that [complement the Python MOOC](https://www.fun-mooc.fr/en/cours/python-3-des-fondamentaux-aux-concepts-avances-du-langage/)

excluded are the auto-corrected exercises mentioned in the MOOC
[these are bundled in the main course repo](https://github.com/flotpython/course)

in any case, in the present repo we try to gather all the other, generally more
informal, material for **practising the Data-Science ecosystem** (for stuff about pure Python, see the other repo <https://github.com/flotpython/exos-python>)

## there is no runtime tool here !

also note that, as opposed to the auto-corrected exercises mentioned above,
there is a **deliberate choice to not provide a notebooks infrastructure**  
this is because we want our students to become autonomous, so it means they are
supposed to solve all these problems **on their own laptop**, where they are
expected to have acquired the skills for installing and managing a decent
software stack (typically bash + vscode + python + ipython + jupyter)  
the fact that most of the material is written as a notebook is mostly a
convenience, both for authoring (outputs are up-to-date), and of course in cases
where the starting material is a notebook itself

## contents

the material is organized along these rather vague categories:

* `exos`: short, simple one-shot assignments
* `tps`: more elaborate assignments, with several steps, that let students
  achieve something
* `howtos`: more for reading than for practising, that can be recipes to achieve
  some common tasks

as well as, less interesting probably, some low-order categories like `samples`, `reading`, `quizzes`, etc.

**historical note**: in an older version, this repo contained material about both pure Python and the Data Science tools; it has been split in two to ease its maintenance - github slugs are now `flotpython/exos-python` and `flotpython/exos-ds` respectively

## formats & jupytext

as noted above, most of the contents is written as notebooks; all notebooks are
jupytext-encoded using either `py:percent` or `md:myst` formats  
you will **need to `pip install jupytext`** to be able to read those as notebooks  
also all notebooks have their filename prefix ending in `-nb` to help the
distinction between notebooks and pure Python or pure markdown

(label-autoreload)=
## note on autoreload in ipython or notebooks

if you use IPython or Jupyter on your laptop, make sure to read  
<https://intro.info-mines.paris/1-1-installations/#-configuration-de-lautoreload>

it's in French, but gives you the recipe to get IPython and the notebooks to play along if you're doing module development (otherwise you'll be bitten by the caching of modules)