English version / Version française
PyCoA (Python Covid Analysis) is a Python™ framework which provides:
- a simple access to common Covid-19 databases;
- tools to represent and analyse Covid-19 data such as time series plots, histograms and maps.
|Time serie (cumulative)||Time series (G20)|
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 cf.map(where='oecd',what='daily',when='01/02/2021',which='confirmed') cf.setwhom('owid') # changing database cf.hist(which='total_vaccinations') # default is for all countries
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 cf.map(which='tot_vacc',tile='esri') # Vaccinations, map view optional tile cf.setwhom('jhu-usa',visu='folium') # JHU USA database cf.map() # 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, on Google Colab, or on Jupyter NbViewer. Other notebooks are provided in our coabook page.
Full documentation is on the Wiki.
- Tristan Beau - Université de Paris - LPNHE laboratory
- Julien Browaeys - Université de Paris - MSC laboratory
- Olivier Dadoun - CNRS - Sorbonne Université - LPNHE laboratory