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

Time serie (cumulative) Time series (G20)
MAP (OECD) Histogram

It is designed to be accessible to non-specialists: teenagers learning Python™, students, science journalists, even scientists who are not familiar in data access methods. A simple analysis can be performed out of the box, as well as a more complex analysis for people familiar with Python™ programming. As an example, after installing pycoa to your framework, the following few lines of code produce the four figures introducing this short documentation.

import coa.front as cf
# default database is JHU
cf.plot(option='sumall') # default is 'deaths', for all countries
cf.plot(where='g20') # managing region'oecd',what='daily',when='01/02/2021',which='confirmed')

cf.setwhom('owid') # changing database
cf.hist(which='total_vaccinations') # default is for all countries

Since the v2.0 version, PyCoA manages also local data like JHU-USA for the United-States, SPF or OpenCovid19 for France. Then we get plots like the ones just below.

SPF data JHU-USA data
cf.setwhom('spf') # Santé Publique France database'tot_vacc',tile='esri') # Vaccinations, map view optional tile 

cf.setwhom('jhu-usa',visu='folium') # JHU USA database # deaths, map view with folium visualization output

PyCoA works currently inside Jupyter notebook, over a local install or on online platforms such as Google Colab.

A basic demo code is available as a notebook on GitHub logo GitHub, on Google colab logo Google Colab, or on NbViewer logo Jupyter NbViewer. Other notebooks are provided in our coabook page.

Full documentation is on the Wiki.