# covid_worldwide
import pandas as pd
df = pd.read_csv('covid_worldwide.csv')
df.head()
| Serial Number | Country | Total Cases | Total Deaths | Total Recovered | Active Cases | Total Test | Population | |
|---|---|---|---|---|---|---|---|---|
| 0 | 1 | USA | 104,196,861 | 1,132,935 | 101,322,779 | 1,741,147 | 1,159,832,679 | 334,805,269 |
| 1 | 2 | India | 44,682,784 | 530,740 | 44,150,289 | 1,755 | 915,265,788 | 1,406,631,776 |
| 2 | 3 | France | 39,524,311 | 164,233 | 39,264,546 | 95,532 | 271,490,188 | 65,584,518 |
| 3 | 4 | Germany | 37,779,833 | 165,711 | 37,398,100 | 216,022 | 122,332,384 | 83,883,596 |
| 4 | 5 | Brazil | 36,824,580 | 697,074 | 35,919,372 | 208,134 | 63,776,166 | 215,353,593 |
This proposal is based on the COVID-19 pandemic and seeks to minimize deaths or cases. We approach this by looking at the countries of the world as well as their situation in regards to COVID-19.
Serial Number: index
Country: the name of the country
Total Cases: the total number of COVID cases
Total Deaths: the total number of COVID deaths
Total Recovered: the total number of people who've caught and recovered from COVID
Active Cases: the number of people who are currently infected
Total Test: the total nubmer of tests the country has performed
Population: the population of the country
Hopefully, by analyizing this data, we are able to conclude which countries handle the pandemic the best. We can do this by examining the active cases, total cases, and total deaths in relation to the total testing population and the general population. From this, we can perhaps go a step further and, for example, see what the best countries are doing and to implement their policies to the rest of the world.