# More to explore

## uproot

As opposed to {ref}`rdataframe`, let's go to the other end of the
spectrum: ROOT I/O without using ROOT. The Python
[uproot](https://pypi.org/project/uproot/) package reads ROOT
files using Python and some commonly-used packages:
[numpy](https://numpy.org/doc/stable/user/index.html#user),
[awkward](https://awkward-array.org/doc/main/getting-started/index.html),
and
[pandas](https://pandas.pydata.org/docs/user_guide/index.html#user-guide). Uproot
is particularly handy if you were already a Python expert before
taking this ROOT tutorial, and would rather not have to touch ROOT
again if you can help it.

I've installed
[uproot](https://uproot.readthedocs.io/en/latest/basic.html) in the
Python installations available at Nevis.[^review]

[^review]: I've played around with uproot. My initial (and
    highly-biased) opinion is that you have to be a full dues-paying
    member of the "ROOT-haters club" to do a serious ROOT-based
    analysis using uproot. For the kinds of simplistic analyses that I
    present in this tutorial, uproot is fine. But if you want to do
    work like that described in {ref}`Exercise 15 <data-reduction>`, the uproot
    code may be even more cryptic than the equivalent
    [pyroot](https://root.cern/manual/python/)-based code.

    Another possible pitfall is that one has to think carefully about
    when uproot performs the event loop. As I described in
    {ref}`lazy-evaluation` with `RDataFrame`, it's possible to invoke
    uproot operations in the wrong order and get very inefficient
    code.

    According to the [uproot development
    page](https://github.com/scikit-hep/uproot5), uproot is primarily
    intended as a way to reorganize data in ROOT files into a form suitable to
    pass on to machine-learning libraries. From what I see of the package,
    its benefits (and limitations) are well-suited for that purpose.

## coffea

The [coffea](https://coffeateam.github.io/coffea/) package is
column-based analysis system specifically designed for high-energy
physics. I think of it as a Python-only equivalent to
{ref}`RDataFrames <rdataframe>`.

:::{figure-md} data_pipeline-fig
:class: align-center

<img src="https://imgs.xkcd.com/comics/data_pipeline.png" alt="xkcd data_pipeline" width="80%">

<https://xkcd.com/2054/> by Randall Munroe. 
Hopefully your use of ROOT data will be more rational. 
:::