(df_concepts)=
# RDataFrame concepts
**(5 minutes)**

Let's start with some definitions. For purposes of this tutorial, an
{dfn}`n-tuple`, a {dfn}`spreadsheet`, and a {dfn}`dataframe` are all
the same thing.[^switch]$^,$[^f103] It's something that looks like this:

[^switch]: I frequently switch from one term to the other, sometimes
    within the middle of the same sentence. 

[^f103]: The term "dataframe" is also an important component of the
    Python data analysis package
    [pandas](https://pandas.pydata.org/docs/getting_started/index.html),
    the [R programming
    language](https://www.tutorialspoint.com/r/r_data_frames.htm),
    and the [HDF5](https://www.neonscience.org/resources/learning-hub/tutorials/about-hdf5) file format.
    It pretty much means the same thing in all these environments.

    If you're curious why high-energy physics prefers to use the ROOT
    file format compared to HDF5, here's a [2018
    paper](https://iopscience.iop.org/article/10.1088/1742-6596/1085/3/032020/pdf)
    comparing the use of different file formats and databases in a
    typical analysis. The TL;DR version: HDF5 is better at storing
    large multi-dimensional arrays, often found in {abbr}`HPC (High
    Performance Computing)` applications associated with Deep
    Learning. ROOT is a better choice for storing complex data
    structures.

:::{figure-md} spreadsheet-fig
:align: center

<img src="Spreadsheet.png" alt="A spreadsheet" width="75%">

You saw this in the class introduction. These are the first few 
rows and columns in the n-tuple `tree1` in file {file}`experiment.root`.
:::

Some more equivalences: a {dfn}`row` in the spreadsheet can also be
called an {dfn}`entry` in the n-tuple; a {dfn}`column` in the spreadsheet
is a {dfn}`branch` in the n-tuple. 

:::{note}
In ROOT, the individual cells can have full-fledged C++ structures
in them. To keep things simple I'm sticking with numeric values
({dfn}`leaves` in ROOT's terminology) for this tutorial. 
:::

Since we can think of {numref}`Figure %s <spreadsheet-fig>` as a spreadsheet,
let's think of the kinds of physics-analysis tasks we might do with the
columns and rows in a program like [Microsoft Excel](https://blog.hubspot.com/marketing/microsoft-excel),
[Google Sheets](https://www.google.com/sheets/about/), or [Apple Numbers](https://support.apple.com/guide/numbers/intro-to-numbers-tan0eca1a9ab/mac):

   - Sum the values in a column. While this comes up a lot in the
     business world, it's not common in a physics analysis.

   - Statistics: Take the mean or standard deviation of a column, or find its minimum or maximum value.

   - Make a histogram of the values in a column. You've already done this if you
     went through the {ref}`TreeViewer <treeviewer>` section. 

   - Add new columns to the spreadsheet, with the new columns derived from 
     formulas based on existing columns. 

The idea behind [RDataFrame](https://root.cern/doc/master/classROOT_1_1RDataFrame.html) is to
provide a simple way to perform tasks like these.