EPJ Web Conf.
Volume 214, 201923rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018)
|Number of page(s)||8|
|Section||T6 - Machine learning & analysis|
|Published online||17 September 2019|
Pandas DataFrames for a FAST binned analysis at CMS
University of Bristol
2 Rutherford Appleton Laboratory
* e-mail: email@example.com
Published online: 17 September 2019
Binned data frames are a generalisation of multi-dimensional histograms, represented in a tabular format with one category per row containing the labels, bin contents, uncertainties and so on. Pandas is an industry-standard tool, which provides a data frame implementation complete with routines for data frame manipultion, persistency, visualisation, and easy access to “big data” scientific libraries and machine learning tools. FAST (the Faster Analysis Software Taskforce) has developed a generic approach for typical binned HEP analyses, driving the summary of ROOT Trees to multiple binned DataFrames with a yaml-based analysis description. Using Continuous Integration to run subsets of the analysis, we can monitor and test changes to the analysis itself, and deploy documentation automatically. This report describes this approach using examples from a public CMS tutorial and details the benefit over traditional methods.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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