.. include:: links.rst

Getting Started
===============

What
----

Taste is a |pure_python|_ library with a hard dependency on Matplotlib_ that is
most commonly present in every Jupyter_ installation that uses plots. It does
not use any |c_cpp_ext|_ directly but as Matplotlib_ is a |c_cpp_ext|_ library,
on some platforms (or in Docker_ container) pip_ might not correctly detect
what Wheel_ to choose and you might need to install a compiler (such as GCC_)
from your OS' package manager or compiler's official website.

It attempts to provide a simple interface and out-of-box available set of plots
even for beginners or people who come from environments where plots are created
via a drag&drop approach and the learning curve of the underlying Matplotlib_
engine isn't feasible for them at the moment.

Where
-----

Feel free to come and say *Hi!* or contribute:

- on PyPI_ as |taste_pypi|_
- on GitLab_ as |taste_repo|_
- on Matrix_ as |taste_matrix|_

Why
---

Using (Jupyter_) notebooks is easy to learn and allows people to keep the code
bundled with results in a single place which allows reproducibility in a way or
at least documenting *how* the results were obtained if the code becomes
outdated. In combination with Matplotlib_ the notebook is a powerful tool to
quickly assemble results starting from simple reports pulled from CSV or DB up
to some hard-core scientific experiments. It has some noticable quirks though.

When displaying the data there are two most common situations:

- Python_ object representation (|repr|_) is leaking to the notebook
- the plot isn't ready to be displayed *yet*, but the notebook is already
  rendering it

Getting rid of the object representations is quite easy, just assign it to some
variable. The second situation is a little bit harder to resolve and each
approach known to me is rather ugly because it either requires keeping a util
library within any of my projects or the notebook itself is polluted with
helper functions.

How
---

Taste addresses these issues by exposing only high-level classes (such as
:class:`taste.BarPlot`) which expect the plot attributes set in an intuitive
way as well as provide helper methods to adjust the underlying Matplotlib_ plot
stored there.

It uses |agg|_ backend to draw the images in the background, therefore it does
not polute your output with unnecessary stringified Python object
representations nor renders prematurely incomplete plots to the notebook. *You*
get to choose when to render in the notebook, not the other way around.