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https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F128750%2F66baee67b3e35bf9656ff816e692527e%2Fsnapshot_worldometer_july4.png?generation=1593988535797227&alt=media" alt="">
The dataset contains data about the numbers of tests, cases, deaths, serious/critical cases, active cases and recovered cases in each country for every day since April 18, and also contains the population of each country to calculate per-capita penetration of the virus
I've removed data from the "Diamond Princess" and "MS Zaandam" since they are not countries
Additionally, an auxiliray table with information about the fraction of the general population at different age groups for every country is added (taken from Wikipedia). This is specifically relevant since COVID-19 death rate is very much age dependent.
The people at "www.worldometers.info" collecting and maintaining this site really are doing very important work "https://www.worldometers.info/coronavirus/#countries">https://www.worldometers.info/coronavirus/#countries
Data about age structure for every country comes from wikipedia
It's possible to use this dataset for various purposes and analyses My goal will be to use the additional data about the number of tests performed in each country to estimate the true death and infection rates of COVID-19
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License information was derived automatically
Analysis of ‘World Population by Year’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sansuthi/world-population-by-year on 28 January 2022.
--- Dataset description provided by original source is as follows ---
Source of content: www.worldometers.info
--- Original source retains full ownership of the source dataset ---
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Analysis of ‘Population by Country - 2020’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/tanuprabhu/population-by-country-2020 on 28 January 2022.
--- Dataset description provided by original source is as follows ---
I always wanted to access a data set that was related to the world’s population (Country wise). But I could not find a properly documented data set. Rather, I just created one manually.
Now I knew I wanted to create a dataset but I did not know how to do so. So, I started to search for the content (Population of countries) on the internet. Obviously, Wikipedia was my first search. But I don't know why the results were not acceptable. And also there were only I think 190 or more countries. So then I surfed the internet for quite some time until then I stumbled upon a great website. I think you probably have heard about this. The name of the website is Worldometer. This is exactly the website I was looking for. This website had more details than Wikipedia. Also, this website had more rows I mean more countries with their population.
Once I got the data, now my next hard task was to download it. Of course, I could not get the raw form of data. I did not mail them regarding the data. Now I learned a new skill which is very important for a data scientist. I read somewhere that to obtain the data from websites you need to use this technique. Any guesses, keep reading you will come to know in the next paragraph.
https://fiverr-res.cloudinary.com/images/t_main1,q_auto,f_auto/gigs/119580480/original/68088c5f588ec32a6b3a3a67ec0d1b5a8a70648d/do-web-scraping-and-data-mining-with-python.png" alt="alt text">
You are right its, Web Scraping. Now I learned this so that I could convert the data into a CSV format. Now I will give you the scraper code that I wrote and also I somehow found a way to directly convert the pandas data frame to a CSV(Comma-separated fo format) and store it on my computer. Now just go through my code and you will know what I'm talking about.
Below is the code that I used to scrape the code from the website
https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3200273%2Fe814c2739b99d221de328c72a0b2571e%2FCapture.PNG?generation=1581314967227445&alt=media" alt="">
Now I couldn't have got the data without Worldometer. So special thanks to the website. It is because of them I was able to get the data.
As far as I know, I don't have any questions to ask. You guys can let me know by finding your ways to use the data and let me know via kernel if you find something interesting
--- Original source retains full ownership of the source dataset ---
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Demographic data set of countries of the world (1955-2020). This dataset is created using Web Scraping technics on webpage: https://www.worldometers.info/population/.
UPDATED till 10/04/2020 23:59:59
Worldometer Covid-19 Data is available as csv file. Uploading it here for using it in Kaggle kernels and getting insights from the broader DS community.
(2019-nCoV) is a virus (more specifically, a coronavirus) identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China. Early on, many of the patients in the outbreak in Wuhan, China reportedly had some link to a large seafood and animal market, suggesting animal-to-person spread. However, a growing number of patients reportedly have not had exposure to animal markets, indicating person-to-person spread is occurring. At this time, it’s unclear how easily or sustainably this virus is spreading between people - CDC
Country - List of countries affected by covid-19 Total Cases - Cumulative number of confirmed cases till date New Cases - New confirmed cases each day Total Deaths - Cumulative number of deaths till date New Deaths - New death cases each day Total Recovered - Cumulative number of recovered cases till date Active Cases - Cumulative number of recovered cases till date Serious, Critical - Cumulative number of Serious/Critical cases till date Tot Cases/1M pop - Cumulative number of confirmed cases till date per million population Deaths/1M pop - Cumulative number of deaths till date per million population Total Tests - Cumulative number of test till date Tests/1M pop - Cumulative number of test till date per million population
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This dataset, titled "Global COVID-19 Statistics - Jan 2025," contains the latest COVID-19 statistics collected from the Worldometer website on Jan 09, 2025. The data includes crucial metrics such as the total number of cases, deaths, recoveries, and active cases for countries around the world. The information is extracted from the comprehensive table provided by Worldometer, which is widely regarded as a reliable source for real-time coronavirus statistics. Source and Collection Date Source: Worldometer Coronavirus Page Date of Collection: Jan 09, 2025
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In this dataset we can find information related to the population of all the countries listed in the website Worldometers. The dataset is composed, among others, with information like Country, Total Cases, New Cases or TotalDeaths. The dataset was created with the idea to implement it in any project where this information could help to fight against Covid-19.
The 2019–20 coronavirus pandemic is an ongoing global pandemic of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus first emerged in Wuhan, Hubei, China, in December 2019. On 11 March 2020, the World Health Organization declared the outbreak a pandemic. As of 11 March 2020, over 126,000 cases have been confirmed in more than 110 countries and territories, with major outbreaks in mainland China, Italy, South Korea, and Iran. More than 4,600 have died from the disease and 67,000 have recovered.
2019 Novel Coronavirus (2019-nCoV) is a virus (more specifically, a coronavirus) identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China. Early on, many of the patients in the outbreak in Wuhan, China reportedly had some link to a large seafood and animal market, suggesting animal-to-person spread. However, a growing number of patients reportedly have not had exposure to animal markets, indicating person-to-person spread is occurring. At this time, it’s unclear how easily or sustainably this virus is spreading between people - CDC
This dataset has information on the number of affected cases, deaths and recovery from 2019 novel coronavirus. Please note that this data was scrapped from https://www.worldometers.info/coronavirus/.This data is solely for education purposes only.
This data is solely belongs to https://www.worldometers.info/coronavirus/. for licensing visit https://www.worldometers.info/licensing/
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Analysis of ‘Covid19 in World Countries-Latest Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/anandhuh/covid19-in-world-countrieslatest-data on 12 November 2021.
--- Dataset description provided by original source is as follows ---
This dataset contains Covid-19 data of world countries as on November 10, 2021
Link : https://www.worldometers.info/coronavirus/#countries
Link : https://www.kaggle.com/anandhuh/datasets
Upvote if you find it useful 🙏
--- Original source retains full ownership of the source dataset ---
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The Africa Population (Live) counter shows a continuously updated estimate of the current population of Africa delivered by Worldometer's RTS algorithm, which processes data collected from the United Nations Population Division. From https://www.worldometers.info/world-population/africa-population/
https://github.com/disease-sh/API/blob/master/LICENSEhttps://github.com/disease-sh/API/blob/master/LICENSE
In past 24 hours, Sweden, Europe had N/A new cases, N/A deaths and 18 recoveries.
As the world is fighting against this invisible enemy a lot of data-driven students like me want to study it as well as we can. There is an enormous number of data set available on covid19 today but as a beginner, in this field, I wanted to find some more simple data. So here I come up with this covid19 data set which I scrapped from "https://www.worldometers.info/coronavirus". It is my way of learning by doing. This data is till 5/17/2020. I will keep on updating it.
The dataset contains 194 rows and 12 columns which are described below:-
Country: Contains the name of all Countries. Total_Cases: It contains the total number of cases the country has till 5/17/2020. Total_Deaths: Total number of deaths in that country till 5/17/2020. Total_Recovered: Total number of individuals recovered from covid19. Active_Cases: Total active cases in the country on 5/17/2020. Critical_Cases: Number of patients in critical condition. Cases/Million_Population: Number of cases per million population of that country. Deaths/Million_Population: Number of deaths per million population of that country. Total_Tests: Total number of tests performed 5/17/2020 Tests/Million_Population: Number of tests performed per million population. Population: Population of the country Continent: Continent in which the country lies.
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Analysis of ‘Covid-19 Weekly Trends In Europe - Latest Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/anandhuh/covid19-weekly-trends-in-europe-latest-data on 28 January 2022.
--- Dataset description provided by original source is as follows ---
This dataset contains data of weekly trend of Covid-19 in Europe (January 01 - January 07, 2022)
Link : https://www.worldometers.info/coronavirus/weekly-trends/#weekly_table
Link : https://www.kaggle.com/anandhuh/datasets
Please appreciate the effort with an upvote 👍
--- Original source retains full ownership of the source dataset ---
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Estimated population data based on the latest United Nations Population Division estimates and http://www.worldometers.info/world-population/population-by-country/
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Analysis of ‘Covid in African Countries - Latest Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/anandhuh/covid-in-african-countries-latest-data on 28 January 2022.
--- Dataset description provided by original source is as follows ---
This dataset contains Covid-19 data of African countries as on January 26, 2022
Link : https://www.worldometers.info/coronavirus/#countries
Link : https://www.kaggle.com/anandhuh/datasets
If you find it useful, please support by upvoting 👍
--- Original source retains full ownership of the source dataset ---
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This dataset is a three dimensional dataset in wich we analyze the evolution of some data related with COVID-19 along the time.
We analyse how a type of data behave along the time in the different countries.
In each csv, we have kind of varibale (Cases, recovered, deaths) by country and date (from 03/30 to 05/04). Howevwe, the data its up to date in https://github.com/AdrianArnaiz/scrap_uoc (updated automatically every day).
So we have 5 time series by country: one for each kind of data.
The csv contais the information related with a kind of data, and are described by the other two dimensions: country and date.
We obtained this dataset scrapping Worldometers.
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Associated with manuscript titled: Fifty Muslim-majority countries have fewer COVID-19 cases and deaths than the 50 richest non-Muslim countriesThe objective of this research was to determine the difference in the total number of COVID-19 cases and deaths between Muslim-majority and non-Muslim countries, and investigate reasons for the disparities. Methods: The 50 Muslim-majority countries had more than 50.0% Muslims with an average of 87.5%. The non-Muslim country sample consisted of 50 countries with the highest GDP while omitting any Muslim-majority countries listed. The non-Muslim countries’ average percentage of Muslims was 4.7%. Data pulled on September 18, 2020 included the percentage of Muslim population per country by World Population Review15 and GDP per country, population count, and total number of COVID-19 cases and deaths by Worldometers.16 The data set was transferred via an Excel spreadsheet on September 23, 2020 and analyzed. To measure COVID-19’s incidence in the countries, three different Average Treatment Methods (ATE) were used to validate the results. Results published as a preprint at https://doi.org/10.31235/osf.io/84zq5(15) Muslim Majority Countries 2020 [Internet]. Walnut (CA): World Population Review. 2020- [Cited 2020 Sept 28]. Available from: http://worldpopulationreview.com/country-rankings/muslim-majority-countries (16) Worldometers.info. Worldometer. Dover (DE): Worldometer; 2020 [cited 2020 Sept 28]. Available from: http://worldometers.info
https://github.com/disease-sh/API/blob/master/LICENSEhttps://github.com/disease-sh/API/blob/master/LICENSE
In past 24 hours, N. Korea, Asia had N/A new cases, N/A deaths and N/A recoveries.
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Covid-19 cases per country snapshot
13-Apr-2020 at 14:19 CET
Data source: https://www.worldometers.info/coronavirus/
Obtained by web-scraping
Contains header on 1st row.
Columns:
https://github.com/disease-sh/API/blob/master/LICENSEhttps://github.com/disease-sh/API/blob/master/LICENSE
In past 24 hours, Réunion, Africa had N/A new cases, N/A deaths and N/A recoveries.
Attribution-NonCommercial-NoDerivs 4.0 (CC BY-NC-ND 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/
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https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F128750%2F66baee67b3e35bf9656ff816e692527e%2Fsnapshot_worldometer_july4.png?generation=1593988535797227&alt=media" alt="">
The dataset contains data about the numbers of tests, cases, deaths, serious/critical cases, active cases and recovered cases in each country for every day since April 18, and also contains the population of each country to calculate per-capita penetration of the virus
I've removed data from the "Diamond Princess" and "MS Zaandam" since they are not countries
Additionally, an auxiliray table with information about the fraction of the general population at different age groups for every country is added (taken from Wikipedia). This is specifically relevant since COVID-19 death rate is very much age dependent.
The people at "www.worldometers.info" collecting and maintaining this site really are doing very important work "https://www.worldometers.info/coronavirus/#countries">https://www.worldometers.info/coronavirus/#countries
Data about age structure for every country comes from wikipedia
It's possible to use this dataset for various purposes and analyses My goal will be to use the additional data about the number of tests performed in each country to estimate the true death and infection rates of COVID-19