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PyCoA (Python Covid Analysis) is a Python™ code framework which provides:

This environment is designed to be accessible to non-specialists: high-school students who are learning Python™, university students, data journalists, even scientists who are not particularly familiar with extracting data from online databases. Simple analyses can be readily performed, and deeper ones may be programmed by people with a reasonable Python™ proficiency.

The PyCoA tool provides access to several databases and reformats its data. PyCoA transparently joins the data with geo-localization databases (management of country or region names, creation of maps). This geolocation information can also be used for other applications outside the Covid19 problematics.

The PyCoA tool is designed to be used in a jupyter environment, installed locally or on a remote server (like Google colaboratory or Binder). This simplifies installation and ensures, thanks to the Bokeh library, powerful graphical outputs with very few lines of code for the user, as the following examples attest.

import coa.front as cf
cf.plot(where=['France', 'Italy', 'United kingdom'])'world',what='daily',when='01/04/2020')
cf.hist(where='middle africa', which='tot_confirmed',what='cumul')
cf.get(where='usa', what='daily', which='tot_recovered',output='pandas')

About us

Physicists on particle physics experiments at CERN or studying complex matter, used to managing big data, we wished to share our skills in statistics and data management to the largest number of people interested in the analysis of data related to the Covid19 pandemic. We believe that data mining and statistics should help everyone to better understand one of the most important phenomena in recent history.

This is the reason why we have initiated the PyCoA project, which stands for Python™ Covid19 Analysis. This tool that can be used online, with a simple interface and clear modelling schemes. It is available online through Google Colaboratory or Binder, a Python™ notebook infrastructure, with no installation effort. Collaborative work can be therefore performed, with access to a huge computational infrastructure (like GPUs for deep learning analyses of Covid19 data).


The PyCoA project, initially named CoCoA, was born from the participation in April 2020 to the hackathon organized by Selected in final phase, the project is free and open source. It has continued to evolve since then and underwent a major update during the Hackathon Covid19 organized by the Direction interministérielle de la transformation publique in April 2021.

Its code is public as well as the notebooks and are used as references or examples of use during animations or workshops (during the Salon Culture et Jeux Mathématiques 2021 or during the Fête de la Science 2021).

PyCoA: generic software for numerical analysis in Python.

The project was originally developed for epidemiological studies related to Covid19. However, it can be used for other types of studies.
Any analyses involving time series data associated with numerical and geographical variables can use PyCoA. Studies will be greatly simplified with clear and precise graphical representations.


The PyCoA project is under MIT license.




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