In the COVID-19 repository I share a set of little software tools written in Python3 to visualize the evolution of the COVID-19 outbreak around the world. They are designed to show data from the next databases:
I loaded the data (until October 1st 2020) from the National reports by Ministerio de
Salud de la Nación Argentina manually in a spreadsheet exported to a *csv* file afterwards. Note that this
file has a lot of holes because the reports are not complete (some data is not separated by province).
Confirmed cases and deaths series are complete, but active cases, laboratory tests and
dropped cases are not. If there is a difference between the total and the sum of all districts I add it
in UNKNOWN field.
To visualize data from 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE or from the Datasets in datos.gob.ar you need to download that files first. Data on Argentina.csv is processed and save to a set of csv files with disctint information. For example: daily confirmed cases, daily deaths or dialy confirmed cases trend (taking averages for 3 or 5 days).
You can obtain different charts to visualize the data. You can plot the data by date or aligning it when a certain condition is fulfilled (selected number of confirmed cases or deahts). You can control which charts to show/save with a set of booblean varialbles you will find in the code. I save some of them in Argentina_Data/actual_charts.
The csv files and the charts made to show the data for the outbreak evolution in Argentina contains certain categories. Understand each one clearly is important to get the facts right. Take into account these definitions: