Dataset Description: Worldometer Data Introduction This dataset contains detailed information on the population statistics of various countries, compiled from Worldometer. It includes demographic data such as yearly population changes, migration numbers, fertility rates, and urbanization metrics over multiple years.

Dataset Overview Total Entries: 4,104 Total Columns: 14 Columns Description country (object):

The name of the country. Example: 'India', 'China'. year (float64):

The year for which the data is recorded. Example: 2024, 2023. population (object):

The total population for the given year. Example: '1,441,719,852', '1,428,627,663'. yearly_change_pct (object):

The percentage change in population from the previous year. Example: '0.92%', '0.81%'. yearly_change (object):

The absolute change in population from the previous year. Example: '13,092,189', '11,454,490'. migrants (object):

The net number of migrants for the given year. Example: '-486,784', '-486,136'. median_age (object):

The median age of the population. Example: '28.6', '28.2'. fertility_rate (object):

The fertility rate for the given year. Example: '1.98', '2.00'. density_p_km2 (object):

The population density per square kilometer. Example: '485', '481'. urban_pop_pct (object):

The percentage of the population living in urban areas. Example: '36.8%', '36.3%'. urban_pop (object):

The total urban population for the given year. Example: '530,387,142', '518,239,122'. share_of_world_pop_pct (object):

The country's share of the world's population as a percentage. Example: '17.76%', '17.77%'. world_pop (object):

The total world population for the given year. Example: '8,118,835,999', '8,045,311,447'. global_rank (float64):

The global population rank of the country for the given year. Example: '1.0', '2.0'. Data Quality Missing Values:

Some columns have missing values which need to be handled before analysis. Columns with significant missing data: year, population, yearly_change_pct, yearly_change, migrants, median_age, fertility_rate, density_p_km2, urban_pop_pct, urban_pop, share_of_world_pop_pct, world_pop, global_rank. Data Types:

Most columns are of type object due to the presence of commas and percentage signs. Conversion to appropriate numeric types (e.g., integers, floats) is required for analysis. Potential Uses Demographic Analysis: Study population growth trends, migration patterns, and changes in fertility rates. Urbanization Studies: Analyze urban population growth and density changes over time. Global Ranking: Evaluate and compare the population statistics of different countries. Conclusion This dataset provides a comprehensive view of the world population trends over the years. Cleaning and preprocessing steps, including handling missing values and converting data types, will be necessary to prepare the data for analysis. This dataset can be valuable for researchers, demographers, and data scientists interested in population studies and demographic trends.

File Details Filename: worldometer_data.csv Size: 4104 rows x 14 columns Format: CSV Source Website: Worldometer Scraped Using: Scrapy

Dataset

This dataset was created by Sainath Kamble

Contents

This dataset was obtained through web scraping from Worldometer, a website that provides real-time global statistics. The data was collected in September 2025.

Column Description

• Population: The total number of inhabitants of a country in a given year.
• Yearly Change (%): The percentage growth in population per year compared to the previous year.
• Net Change: The difference in the number of inhabitants added each year (in numbers, not percentages).
• Density (P/Km²): Population density, calculated as the number of people per square kilometer (people per km²).
• Land Area (Km²): The land area of a country in square kilometers (excluding water areas).
• Migrants (net): Net migration figures (immigrants minus emigrants). Positive → more people entering, Negative → more people leaving.
• Fertility Rate: The average number of children born to a woman throughout her lifetime.
• Median Age: The middle age of the population (half are younger than this number, half are older).
• Urban Population (%): The percentage of the population living in urban areas.
• World Share (%): The percentage of a country's population compared to the total world population.

Context

This dataset is a clean CSV file with the most recent estimates of the population of the countries according to Wolrdometer. The data is taken from the following link: https://www.worldometers.info/world-population/population-by-country/

Content

The data has been generated by websraping the aforementioned link on the 16th August 2021. Below is the code used to make CSV data in Python 3.8: import requests from bs4 import BeautifulSoup import pandas as pd url = "https://www.worldometers.info/world-population/population-by-country/" r = requests.get(url) soup = BeautifulSoup(r.content) countries = soup.find_all("table")[0] dataframe = pd.read_html(str(countries))[0] dataframe.to_csv("countries_by_population_2021.csv", index=False)

Acknowledgements

The creation of this dataset would not be possible without a team of Worldometers, a data aggregation website.

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

Manually collected daily snapshots of worldometer COVID-19 data (since April 18)

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F128750%2F66baee67b3e35bf9656ff816e692527e%2Fsnapshot_worldometer_july4.png?generation=1593988535797227&alt=media" alt="">

Content

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.

Acknowledgements

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

Inspiration

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

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/.

The high sensitivity of COVID-19 and the need for high accuracy calculations necessitate collecting the required data sets from reliable sources. Thus, all information was collected and categorized from reputable sources such as WHO (World Health Organization) and worldometers site (www.worldometers.info). The worldometers site contains information such as daily mortality statistics, recovery, and newly confirmed cases. Research data including observation data is obtained from a collection of Iranian samples’ reports in three parts (i.e. death, confirmed and recovered). This countrywide daily information is confirmed by the WHO. It should be noted that the relevant data was collected between February 19 and May 16, 2020.

worldometers.info is ranked #7626 in US with 5.14M Traffic. Categories: . Learn more about website traffic, market share, and more!

Context

The files provided are daily datasets that I scraped from the COVID-19 tracking website Worldometer over the course of 3 days—08/04/21–08/06/21. The dates don't necessarily have to contain the most recent data because that is not the intent of this dataset.

Inspiration

For me, I find making data visualizations very satisfying. Seeing a neat and tidy graph come out of an enormous CSV file is very inspirational to me. The goal is simply to use this data to make visualizations of how COVID-19 is continuing to affect each country throughout the world.

The Data

I made a pandas DataFrame out of the table on the website, and I included all 21 of their columns. Descriptions for each column are provided below.

• Country: String. Name of each country.
• TotalCases: Integer. Total number of cases
• NewCases: Integer. Number of new additional cases
• TotalDeaths: Integer. Total number of deaths due to COVID-19
• NewDeaths: Integer. Number of new additional deaths
• TotalRecovered: Integer. Total number of patients recovered from COVID-19
• NewRecovered: Integer. Number of new additional recovered patients
• ActiveCases: Integer. Number of current active cases
• Critical: Integer. Number of critically ill patients
• Tot Cases/1M pop: Integer. Total cases per 1M (one million) population
• Deaths/1M pop: Float. Deaths per 1M population
• TotalTests: Integer Total number of COVID-19 tests administered
• Tests/1M pop: String. Tests per 1M population
• Population: Integer. Population of country
• Continent: String. Continent on which the country is located
• 1 Case Every X ppl: Integer. Gives us an idea of the rate of cases per country
• 1 Death Every X ppl: Integer. Gives us an idea of the rate of death due to COVID-19
• 1 Test Every X ppl: Integer. Gives us an idea of the rate of testing per country
• New Cases/1M pop: Float. New cases per 1M population
• New Deaths/1M pop: Integer. New deaths per 1M population
• Active Cases/1M pop: Integer. Active cases per 1M population

Sources

This data was collected from https://www.worldometers.info/coronavirus/

In past 24 hours, Sweden, Europe had N/A new cases, N/A deaths and 18 recoveries.

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.

These data files have been web scraped from https://www.worldometers.info/coronavirus using R. All information about column and datasets can be found on the worldometer website. Links to specific data files can be found in the "covid-19.R" file.

In past 24 hours, Finland, Europe had N/A new cases, N/A deaths and 17 recoveries.

Estimated population data based on the latest United Nations Population Division estimates and http://www.worldometers.info/world-population/population-by-country/

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit the following sources:Global: World Health Organization (WHO)U.S.: U.S. Centers for Disease Control and Prevention (CDC)For more information, visit the Johns Hopkins Coronavirus Resource Center.This feature layer contains the most up-to-date COVID-19 cases for the US and Canada. Data sources: WHO, CDC, ECDC, NHC, DXY, 1point3acres, Worldometers.info, BNO, state and national government health departments, and local media reports. This layer is created and maintained by the Center for Systems Science and Engineering (CSSE) at the Johns Hopkins University. This feature layer is supported by the Esri Living Atlas team and JHU Data Services. This layer is opened to the public and free to share. Contact Johns Hopkins.IMPORTANT NOTICE: 1. Fields for Active Cases and Recovered Cases are set to 0 in all locations. John Hopkins has not found a reliable source for this information at the county level but will continue to look and carry the fields.2. Fields for Incident Rate and People Tested are placeholders for when this becomes available at the county level.3. In some instances, cases have not been assigned a location at the county scale. those are still assigned a state but are listed as unassigned and given a Lat Long of 0,0.Data Field Descriptions by Alias Name:Province/State: (Text) Country Province or State Name (Level 2 Key)Country/Region: (Text) Country or Region Name (Level 1 Key)Last Update: (Datetime) Last data update Date/Time in UTCLatitude: (Float) Geographic Latitude in Decimal Degrees (WGS1984)Longitude: (Float) Geographic Longitude in Decimal Degrees (WGS1984)Confirmed: (Long) Best collected count of Confirmed Cases reported by geographyRecovered: (Long) Not Currently in Use, JHU is looking for a sourceDeaths: (Long) Best collected count for Case Deaths reported by geographyActive: (Long) Confirmed - Recovered - Deaths (computed) Not Currently in Use due to lack of Recovered dataCounty: (Text) US County Name (Level 3 Key)FIPS: (Text) US State/County CodesCombined Key: (Text) Comma separated concatenation of Key Field values (L3, L2, L1)Incident Rate: (Long) People Tested: (Long) Not Currently in Use Placeholder for additional dataPeople Hospitalized: (Long) Not Currently in Use Placeholder for additional data

In past 24 hours, Réunion, Africa had N/A new cases, N/A deaths and N/A recoveries.

This feature layer contains the most up-to-date COVID-19 cases and the latest trend plot. It covers the US (county or state level), China, Canada, Australia (province/state level), and the rest of the world (country/region level, represented by either the country centroids or their capitals). Data sources are WHO, CDC, ECDC, NHC, DXY, 1point3acres, Worldometers.info, BNO, the COVID Tracking Project (testing and hospitalizations), state and national government health departments, and local media reports. This layer is created and maintained by the Center for Systems Science and Engineering (CSSE) at the Johns Hopkins University. This feature layer is supported by Esri Living Atlas team, JHU APL and JHU Data Services. This layer is opened to the public and free to share. Contact us.

Dataset

This dataset was created by RVK

Contents

In past 24 hours, Montenegro, Europe had N/A new cases, N/A deaths and N/A recoveries.