Worldometer¶

Get live, population, geography, projected, and historical data from around the world.

About¶

The worldometer package accesses various counters and live data available throughout the worldometers.info website and provides them through simple and self-describing classes, methods and attributes.

Access data on:

The world 🌍
Population 👥
Geography 🗺️
Projections 🔮
Historical 📜

Install¶

Use pip to install the worldometer package:

$ pip install worldometer

API Reference¶

The worldometer package is divided into several specific sub-packages for each dataset. So if you are looking for information on a specific package, class, or method, this part of the documentation is for you.

worldometer package¶

The worldometer package accesses various counters and live data available throughout the https://www.worldometers.info website and provides them through self-describing classes, methods and attributes.

Examples

Get the data from the live counters available on the homepage:

>>> from worldometer.world import WorldCounters

>>> wc = WorldCounters()

>>> wc.world_population.current_population
8065299074

>>> wc.government_and_economics.computers_produced_this_year
180248430

>>> wc.society_and_media.internet_users_in_the_world_today
5895566559

Reload data to get the latest:

>>> wc.reload_data()
>>> wc.world_population.current_population
8065300592

Get help and view information about mapped sections:

>>> help(wc)

Notes

Check https://www.worldometers.info/about for more information about the data source, how live counters work, and more related information.

world package ¶

world is the main API of the worldometer package, where you can access various live counters and data available on the https://www.worldometers.info website through self-descriptive classes, methods, and attributes.

Live Counters ¶

Get the data provided by the live counters.

Each section is represented by an attribute in the WorldCounters class, and these attributes are instances of data classes that store the counter values.

Each of these data classes has attributes that describe the data stored in them.

>>> from worldometer.world import WorldCounters

>>> wc = WorldCounters()

>>> wc.world_population.current_population
8065299074

>>> wc.government_and_economics.computers_produced_this_year
180248430

>>> wc.society_and_media.internet_users_in_the_world_today
5895566559

class worldometer.world.counters.WorldCounters¶

Contains a reference to each section of the home page counters.

source_path¶

The source path for the counters.

Type:: str

world_population¶

An instance of the WorldPopulation class that stores all counters related to population data.

Type:: WorldPopulation

government_and_economics¶

An instance of the GovernmentAndEconomics class that stores all counters related to government and economic data.

Type:: GovernmentAndEconomics

society_and_media¶

An instance of the SocietyAndMedia class that stores all counters related to society and media data.

Type:: SocietyAndMedia

environment¶

An instance of the Environment class that stores all counters related to environmental data.

Type:: Environment

food¶

An instance of the Food class that stores all counters related to food data.

Type:: Food

water¶

An instance of the Water class that stores all counters related to water data.

Type:: Water

energy¶

An instance of the Energy class that stores all counters related to energy data.

Type:: Energy

health¶

An instance of the Health class that stores all counters related to health data.

Type:: Health

Notes

For precise and up-to-date information on each section and its counters, please check the worldometers homepage.

class worldometer.world.counters.WorldPopulation(_data: dataclasses.InitVar[Dict[str, Union[int, float, NoneType]]])¶

Counters related to world population data.

current_population¶

Type:: Union[int, float, None]

births_today¶

Type:: Union[int, float, None]

births_this_year¶

Type:: Union[int, float, None]

deaths_today¶

Type:: Union[int, float, None]

deaths_this_year¶

Type:: Union[int, float, None]

net_population_growth_today¶

Type:: Union[int, float, None]

net_population_growth_this_year¶

Type:: Union[int, float, None]

class worldometer.world.counters.GovernmentAndEconomics(_data: dataclasses.InitVar[Dict[str, Union[int, float, NoneType]]])¶

Counters related to government and economic data.

public_healthcare_expenditure_today¶

Type:: Union[int, float, None]

public_education_expenditure_today¶

Type:: Union[int, float, None]

public_military_expenditure_today¶

Type:: Union[int, float, None]

cars_produced_this_year¶

Type:: Union[int, float, None]

bicycles_produced_this_year¶

Type:: Union[int, float, None]

computers_produced_this_year¶

Type:: Union[int, float, None]

class worldometer.world.counters.SocietyAndMedia(_data: dataclasses.InitVar[Dict[str, Union[int, float, NoneType]]])¶

Counters related to society and media data.

new_book_titles_published_this_year¶

Type:: Union[int, float, None]

newspapers_circulated_today¶

Type:: Union[int, float, None]

tv_sets_sold_worldwide_today¶

Type:: Union[int, float, None]

cellular_phones_sold_today¶

Type:: Union[int, float, None]

money_spent_on_videogames_today¶

Type:: Union[int, float, None]

internet_users_in_the_world_today¶

Type:: Union[int, float, None]

emails_sent_today¶

Type:: Union[int, float, None]

blog_posts_written_today¶

Type:: Union[int, float, None]

tweets_sent_today¶

Type:: Union[int, float, None]

google_searches_today¶

Type:: Union[int, float, None]

class worldometer.world.counters.Environment(_data: dataclasses.InitVar[Dict[str, Union[int, float, NoneType]]])¶

Counters related to environmental data.

forest_loss_this_year¶

Type:: Union[int, float, None]

land_lost_to_soil_erosion_this_year¶

Type:: Union[int, float, None]

co2_emissions_this_year¶

Type:: Union[int, float, None]

desertification_this_year¶

Type:: Union[int, float, None]

toxic_chemicals_released_in_the_environment_this_year¶

Type:: Union[int, float, None]

class worldometer.world.counters.Food(_data: dataclasses.InitVar[Dict[str, Union[int, float, NoneType]]])¶

Counters related to food data.

undernourished_people_in_the_world¶

Type:: Union[int, float, None]

overweight_people_in_the_world¶

Type:: Union[int, float, None]

obese_people_in_the_world¶

Type:: Union[int, float, None]

people_who_died_of_hunger_today¶

Type:: Union[int, float, None]

money_spent_for_obesity_related_diseases_in_the_usa_today¶

Type:: Union[int, float, None]

money_spent_on_weight_loss_programs_in_the_usa_today¶

Type:: Union[int, float, None]

class worldometer.world.counters.Water(_data: dataclasses.InitVar[Dict[str, Union[int, float, NoneType]]])¶

Counters related to water data.

water_used_this_year¶

Type:: Union[int, float, None]

deaths_caused_by_water_related_diseases_this_year¶

Type:: Union[int, float, None]

people_with_no_access_to_a_safe_drinking_water_source¶

Type:: Union[int, float, None]

class worldometer.world.counters.Energy(_data: dataclasses.InitVar[Dict[str, Union[int, float, NoneType]]])¶

Counters related to energy data.

energy_used_today¶

Type:: Union[int, float, None]

non_renewable_sources¶

Type:: Union[int, float, None]

renewable_sources¶

Type:: Union[int, float, None]

solar_energy_striking_earth_today¶

Type:: Union[int, float, None]

oil_pumped_today¶

Type:: Union[int, float, None]

oil_left¶

Type:: Union[int, float, None]

days_to_the_end_of_oil¶

Type:: Union[int, float, None]

natural_gas_left¶

Type:: Union[int, float, None]

days_to_the_end_of_natural_gas¶

Type:: Union[int, float, None]

coal_left¶

Type:: Union[int, float, None]

days_to_the_end_of_coal¶

Type:: Union[int, float, None]

class worldometer.world.counters.Health(_data: dataclasses.InitVar[Dict[str, Union[int, float, NoneType]]])¶

Counters related to health data.

communicable_disease_deaths_this_year¶

Type:: Union[int, float, None]

seasonal_flu_deaths_this_year¶

Type:: Union[int, float, None]

deaths_of_children_under_5_this_year¶

Type:: Union[int, float, None]

abortions_this_year¶

Type:: Union[int, float, None]

deaths_of_mothers_during_birth_this_year¶

Type:: Union[int, float, None]

hiv_aids_infected_people¶

Type:: Union[int, float, None]

deaths_caused_by_hiv_aids_this_year¶

Type:: Union[int, float, None]

deaths_caused_by_cancer_this_year¶

Type:: Union[int, float, None]

deaths_caused_by_malaria_this_year¶

Type:: Union[int, float, None]

cigarettes_smoked_today¶

Type:: Union[int, float, None]

deaths_caused_by_smoking_this_year¶

Type:: Union[int, float, None]

deaths_caused_by_alcohol_this_year¶

Type:: Union[int, float, None]

suicides_this_year¶

Type:: Union[int, float, None]

money_spent_on_illegal_drugs_this_year¶

Type:: Union[int, float, None]

road_traffic_accident_fatalities_this_year¶

Type:: Union[int, float, None]

Country Codes ¶

All countries have specific codes that represent them in some way. Get all of these codes with the CountryCodes class:

>>> from worldometer.world import CountryCodes

>>> cc = CountryCodes()

>>> cc.data[0]
CountryCodesData(
    country='Afghanistan',
    calling_code='93',
    three_letter_iso='AF',
    two_letter_iso='AFG',
    three_digit_iso_numeric=4
)

class worldometer.world.country_codes.CountryCodes¶

Represents the data table of some codes used by each country.

source_path¶

The data source path.

Type:: str

new_column_names¶

The new column names that will be used to replace those of the original table.

Type:: tuple

Notes

Check the source table in Worldometers Country Codes.

property data: List[CountryCodesData]¶

Get a list of all the data from the table.

Each index in the list contains an object representing a data row of the table.

class worldometer.world.country_codes.CountryCodesData(country: str, calling_code: str, three_letter_iso: str, two_letter_iso: str, three_digit_iso_numeric: str)¶

Represents a data row from the respective table.

country¶

Type:: str

calling_code¶

Type:: str

three_letter_iso¶

Type:: str

two_letter_iso¶

Type:: str

three_digit_iso_numeric¶

Type:: str

population package ¶

The population package provides access to various data related to world populations, regions, and their countries. Additionally, it includes historical data ranging from 1950 to the present day and future projections extending to 2050.

To understand the significance of each of these data sets, their sources, and any other related information, please visit the official page at https://www.worldometers.info/population

Population by country ¶

>>> from worldometer.world.population import CountriesByPopulation

>>> cp = CountriesByPopulation()

>>> cp.data[0]
CountriesByPopulationData(
    idx=1,
    country='India',
    population=1428627663,
    yearly_change='0.81 %',
    net_change=11454490,
    density=481,
    land_area=2973190,
    migrants=-486136,
    fertility_rate=2.0,
    median_age=28.0,
    urban_population='36 %',
    world_share='17.76 %'
)

class worldometer.world.population.countries_by_population.CountriesByPopulation¶

Represents the data table of countries in the world by population.

source_path¶

The data source path.

Type:: str

new_column_names¶

The new column names that will be used to replace those of the original table.

Type:: tuple

Notes

Check the source table in Countries in the world by population.

property data: List[CountriesByPopulationData]¶

Get a list of all the data from the table.

Each index in the list contains an object representing a data row of the table.

class worldometer.world.population.countries_by_population.CountriesByPopulationData(idx: int, country: str, population: int, yearly_change: str, net_change: int, density: int, land_area: int, migrants: int, fertility_rate: float, median_age: float, urban_population: str, world_share: str)¶

Represents a data row from the respective table.

idx¶

Type:: int

country¶

Type:: str

population¶

Type:: int

yearly_change¶

Type:: str

net_change¶

Type:: int

density¶

Type:: int

land_area¶

Type:: int

migrants¶

Type:: int

fertility_rate¶

Type:: float

median_age¶

Type:: float

urban_population¶

Type:: str

world_share¶

Type:: str

Population by region ¶

>>> from worldometer.world.population import WorldPopulationByRegion

>>> pr = WorldPopulationByRegion()

>>> pr.current()[0]
CurrentWorldPopulationByRegionData(
    idx=1,
    region='Asia',
    population=4753079727,
    yearly_change='0.64 %',
    net_change=30444963,
    density=153,
    area=31033131,
    migrants=-1487191,
    fertility_rate=1.934,
    median_age=32,
    urban_population='52.6 %',
    world_share='59.1 %'
)

>>> pr.past()[0]
PastWorldPopulationByRegionData(
    idx=1,
    region='Asia',
    population=1379048370,
    world_share='55.2 %'
)

>>> pr.future()[0]
FutureWorldPopulationByRegionData(
    idx=1,
    region='Asia',
    population=5292947571,
    world_share='54.5 %'
)

class worldometer.world.population.by_region.WorldPopulationByRegion¶

Represents the data table of regions in the world by population.

source_path¶

The data source path.

Type:: str

new_column_names¶

The new column names that will be used to replace those of the original table.

Type:: tuple

Notes

Check the source table in Regions in the world by population.

current() → List[CurrentWorldPopulationByRegionData]¶

Get a list of all the current data from the table.

These data are related to the current year.

Each index in the list contains an object representing a data row of the table.

future() → List[FutureWorldPopulationByRegionData]¶

Get a list of all future data from the table.

These data are an estimate for the year 2050.

Each index in the list contains an object representing a data row of the table.

past() → List[PastWorldPopulationByRegionData]¶

Get a list of all historical data from the table.

These data pertain to the year 1950.

Each index in the list contains an object representing a data row of the table.

class worldometer.world.population.by_region.CurrentWorldPopulationByRegionData(idx: int, region: str, population: int, yearly_change: str, net_change: int, density: int, area: int, migrants: int, fertility_rate: float, median_age: int, urban_population: str, world_share: str)¶

Represents a data row from the respective table.

idx¶

Type:: int

region¶

Type:: str

population¶

Type:: int

yearly_change¶

Type:: str

net_change¶

Type:: int

density¶

Type:: int

area¶

Type:: int

migrants¶

Type:: int

fertility_rate¶

Type:: float

median_age¶

Type:: int

urban_population¶

Type:: str

world_share¶

Type:: str

class worldometer.world.population.by_region.PastWorldPopulationByRegionData(idx: int, region: str, population: int, world_share: str)¶

Represents a data row from the respective table.

idx¶

Type:: int

region¶

Type:: str

population¶

Type:: int

world_share¶

Type:: str

class worldometer.world.population.by_region.FutureWorldPopulationByRegionData(idx: int, region: str, population: int, world_share: str)¶

Represents a data row from the respective table.

idx¶

Type:: int

region¶

Type:: str

population¶

Type:: int

world_share¶

Type:: str

Population by year ¶

>>> from worldometer.world.population import WorldPopulationByYear

>>> py = WorldPopulationByYear()

>>> py.data[0]
WorldPopulationByYearData(
    year=2023,
    world_population=8045311447,
    yearly_change='0.88 %',
    net_change=70206291.0,
    density=54.0
)

class worldometer.world.population.by_year.WorldPopulationByYear¶

Represents the data table of the world population by year.

source_path¶

The data source path.

Type:: str

new_column_names¶

The new column names that will be used to replace those of the original table.

Type:: tuple

Notes

Check the source table in the world population by year.

property data: List[WorldPopulationByYearData]¶

Get a list of all the data from the table.

Each index in the list contains an object representing a data row of the table.

class worldometer.world.population.by_year.WorldPopulationByYearData(year: int, world_population: int, yearly_change: str, net_change: float, density: float)¶

Represents a data row from the respective table.

year¶

Type:: int

world_population¶

Type:: int

yearly_change¶

Type:: str

net_change¶

Type:: float

density¶

Type:: float

Largest cities in the world ¶

>>> from worldometer.world.population import LargestCities

>>> lc = LargestCities()

>>> lc.data[0]
LargestCitiesData(
    rank=1,
    urban_area='Tokyo-Yokohama',
    population_estimate=37843000,
    country='Japan',
    land_area=8547,
    density=4400
)

class worldometer.world.population.largest_cities.LargestCities¶

Represents the data table of the largest cities in the world.

source_path¶

The data source path.

Type:: str

new_column_names¶

The new column names that will be used to replace those of the original table.

Type:: tuple

Notes

Check the source table in the largest cities in the world.

property data: List[LargestCitiesData]¶

Get a list of all the data from the table.

Each index in the list contains an object representing a data row of the table.

class worldometer.world.population.largest_cities.LargestCitiesData(rank: int, urban_area: str, population_estimate: str, country: str, land_area: int, density: int)¶

Represents a data row from the respective table.

rank¶

Type:: int

urban_area¶

Type:: str

population_estimate¶

Type:: str

country¶

Type:: str

land_area¶

Type:: int

density¶

Type:: int

Most populous countries ¶

>>> from worldometer.world.population import MostPopulousCountries

>>> pc = MostPopulousCountries()

>>> pc.current()[0]
CurrentMostPopulousCountriesData(
    idx=1,
    country='India',
    population=1428627663,
    yearly_change='0.81 %',
    world_share='17.8 %'
)

>>> pc.past()[0]
PastMostPopulousCountriesData(
    idx=1,
    country='China',
    population=543979233,
    world_share='21.8 %',
    rank='(2)'
)

>>> pc.future()[0]
FutureMostPopulousCountriesData(
    idx=1,
    country='India',
    population=1670490596,
    world_share='17.2 %',
    rank='(1)'
)

class worldometer.world.population.most_populous_countries.MostPopulousCountries¶

Represents the data table of most populous countries in the world.

source_path¶

The data source path.

Type:: str

new_column_names¶

The new column names that will be used to replace those of the original table.

Type:: tuple

Notes

Check the source table in Most populous countries in the world.

current() → List[CurrentMostPopulousCountriesData]¶

Get a list of all the current data from the table.

These data are related to the current year.

Each index in the list contains an object representing a data row of the table.

future() → List[FutureMostPopulousCountriesData]¶

Get a list of all future data from the table.

These data are an estimate for the year 2050.

Each index in the list contains an object representing a data row of the table.

past() → List[PastMostPopulousCountriesData]¶

Get a list of all historical data from the table.

These data pertain to the year 1950.

Each index in the list contains an object representing a data row of the table.

class worldometer.world.population.most_populous_countries.CurrentMostPopulousCountriesData(idx: int, country: str, population: int, yearly_change: str, world_share: str)¶

Represents a data row from the respective table.

idx¶

Type:: int

country¶

Type:: str

population¶

Type:: int

yearly_change¶

Type:: str

world_share¶

Type:: str

class worldometer.world.population.most_populous_countries.PastMostPopulousCountriesData(idx: int, country: str, population: int, world_share: str, rank: str)¶

Represents a data row from the respective table.

idx¶

Type:: int

country¶

Type:: str

population¶

Type:: int

world_share¶

Type:: str

rank¶

Type:: str

class worldometer.world.population.most_populous_countries.FutureMostPopulousCountriesData(idx: int, country: str, population: int, world_share: str, rank: str)¶

Represents a data row from the respective table.

idx¶

Type:: int

country¶

Type:: str

population¶

Type:: int

world_share¶

Type:: str

rank¶

Type:: str

World population projections ¶

>>> from worldometer.world.population import WorldPopulationProjections

>>> pp = WorldPopulationProjections()

>>> pp.data[0]
WorldPopulationProjectionsData(
    year=2023,
    world_population=8045311447,
    yearly_change='0.88 %',
    net_change=70206291,
    density=54
)

class worldometer.world.population.projections.WorldPopulationProjections¶

Represents the data table of the world population projections.

source_path¶

The data source path.

Type:: str

new_column_names¶

The new column names that will be used to replace those of the original table.

Type:: tuple

Notes

Check the source table in World Population Projections.

property data: List[WorldPopulationProjectionsData]¶

Get a list of all the data from the table.

Each index in the list contains an object representing a data row of the table.

class worldometer.world.population.projections.WorldPopulationProjectionsData(year: int, world_population: int, yearly_change: str, net_change: int, density: int)¶

Represents a data row from the respective table.

year¶

Type:: int

world_population¶

Type:: int

yearly_change¶

Type:: str

net_change¶

Type:: int

density¶

Type:: int

Regions population ¶

>>> from worldometer.world.population import (
        AsiaPopulation,
        AfricaPopulation,
        EuropePopulation,
        LatinAmericanAndTheCaribbeanPopulation,
        NorthernAmericanPopulation,
        OceaniaPopulation
    )

>>> ap = AsiaPopulation()

>>> ap.live()
4762699828

>>> ap.subregions()[0]
SubregionData(
    area='Southern Asia',
    population='(2,027,578,876)'
)

>>> ap.historical()[0]
HistoricalData(
    year=2023,
    population=4753079727,
    yearly_change_percent='0.64 %',
    yearly_change=30444963,
    migrants=-1487191,
    median_age=31.9,
    fertility_rate=1.93,
    density=153,
    urban_population_percent='52.6 %',
    urban_population=2500201501,
    world_share='59.1 %',
    world_population=8045311447,
    rank=nan
)

>>> ap.forecast()[0]
ForecastData(
    year=2025,
    population=4816249054,
    yearly_change_percent='0.64 %',
    yearly_change=30384996,
    migrants=-1555419,
    median_age=32.7,
    fertility_rate=1.93,
    density=155,
    urban_population_percent='53.8 %',
    urban_population=2589655469,
    world_share='61.4 %',
    world_population=8191988453,
    rank=nan
)

class worldometer.world.population.regions.AsiaPopulation¶

Represents the data tables of Asia’s populations.

source_path¶

The data source path.

Type:: str

new_column_names¶

The new column names that will be used to replace those of the original tables.

Type:: tuple

Notes

Check the source tables in Asia Population.

forecast() → List[ForecastData]¶

Get a list of all forecast data from the table.

Each index in the list contains an object representing a data row of the table.

historical() → List[HistoricalData]¶

Get a list of all historical data from the table.

Each index in the list contains an object representing a data row of the table.

live() → int | float | None¶: Get a live population counter for the respective region.

subregions() → List[SubregionData]¶

Get a list of all the subregions’ data from the table.

Each index in the list contains an object representing a data row of the table.

class worldometer.world.population.regions.AfricaPopulation¶

Represents the data tables of Africa’s populations.

source_path¶

The data source path.

Type:: str

new_column_names¶

The new column names that will be used to replace those of the original tables.

Type:: tuple

Notes

Check the source tables in Africa Population.

forecast() → List[ForecastData]¶

Get a list of all forecast data from the table.

Each index in the list contains an object representing a data row of the table.

historical() → List[HistoricalData]¶

Get a list of all historical data from the table.

Each index in the list contains an object representing a data row of the table.

live() → int | float | None¶: Get a live population counter for the respective region.

subregions() → List[SubregionData]¶

Get a list of all the subregions’ data from the table.

Each index in the list contains an object representing a data row of the table.

class worldometer.world.population.regions.EuropePopulation¶

Represents the data tables of Europe’s populations.

source_path¶

The data source path.

Type:: str

new_column_names¶

The new column names that will be used to replace those of the original tables.

Type:: tuple

Notes

Check the source tables in Europe Population.

forecast() → List[ForecastData]¶

Get a list of all forecast data from the table.

Each index in the list contains an object representing a data row of the table.

historical() → List[HistoricalData]¶

Get a list of all historical data from the table.

Each index in the list contains an object representing a data row of the table.

live() → int | float | None¶: Get a live population counter for the respective region.

subregions() → List[SubregionData]¶

Get a list of all the subregions’ data from the table.

Each index in the list contains an object representing a data row of the table.

class worldometer.world.population.regions.LatinAmericanAndTheCaribbeanPopulation¶

Represents the data tables of Latin American and Caribbean populations.

source_path¶

The data source path.

Type:: str

new_column_names¶

The new column names that will be used to replace those of the original tables.

Type:: tuple

Notes

Check the source tables in Latin America and the Caribbean Population.

forecast() → List[ForecastData]¶

Get a list of all forecast data from the table.

Each index in the list contains an object representing a data row of the table.

historical() → List[HistoricalData]¶

Get a list of all historical data from the table.

Each index in the list contains an object representing a data row of the table.

live() → int | float | None¶: Get a live population counter for the respective region.

subregions() → List[SubregionData]¶

Get a list of all the subregions’ data from the table.

Each index in the list contains an object representing a data row of the table.

class worldometer.world.population.regions.NorthernAmericanPopulation¶

Represents the data tables of Northern American populations.

source_path¶

The data source path.

Type:: str

new_column_names¶

The new column names that will be used to replace those of the original tables.

Type:: tuple

Notes

Check the source tables in Northern American Population.

forecast() → List[ForecastData]¶

Get a list of all forecast data from the table.

Each index in the list contains an object representing a data row of the table.

historical() → List[HistoricalData]¶

Get a list of all historical data from the table.

Each index in the list contains an object representing a data row of the table.

live() → int | float | None¶: Get a live population counter for the respective region.

subregions() → List[SubregionData]¶

Get a list of all the subregions’ data from the table.

Each index in the list contains an object representing a data row of the table.

class worldometer.world.population.regions.OceaniaPopulation¶

Represents the data tables of the Oceania populations.

source_path¶

The data source path.

Type:: str

new_column_names¶

The new column names that will be used to replace those of the original tables.

Type:: tuple

Notes

Check the source tables in Oceania Population.

forecast() → List[ForecastData]¶

Get a list of all forecast data from the table.

Each index in the list contains an object representing a data row of the table.

historical() → List[HistoricalData]¶

Get a list of all historical data from the table.

Each index in the list contains an object representing a data row of the table.

live() → int | float | None¶: Get a live population counter for the respective region.

subregions() → List[SubregionData]¶

Get a list of all the subregions’ data from the table.

Each index in the list contains an object representing a data row of the table.

class worldometer.world.population.regions.SubregionData(area: int, population: str)¶

Represents a data row from the respective table.

area¶

Type:: int

population¶

Type:: str

class worldometer.world.population.regions.HistoricalData(year: int, population: int, yearly_change_percent: str, yearly_change: int, migrants: int, median_age: float, fertility_rate: float, density: int, urban_population_percent: str, urban_population: int, world_share: str, world_population: int, rank: int)¶

Represents a data row from the respective table.

year¶

Type:: int

population¶

Type:: int

yearly_change_percent¶

Type:: str

yearly_change¶

Type:: int

migrants¶

Type:: int

median_age¶

Type:: float

fertility_rate¶

Type:: float

density¶

Type:: int

urban_population_percent¶

Type:: str

urban_population¶

Type:: int

world_share¶

Type:: str

world_population¶

Type:: int

rank¶

Type:: int

class worldometer.world.population.regions.ForecastData(year: int, population: int, yearly_change_percent: str, yearly_change: int, migrants: int, median_age: float, fertility_rate: float, density: int, urban_population_percent: str, urban_population: int, world_share: str, world_population: int, rank: int)¶

Represents a data row from the respective table.

year¶

Type:: int

population¶

Type:: int

yearly_change_percent¶

Type:: str

yearly_change¶

Type:: int

migrants¶

Type:: int

median_age¶

Type:: float

fertility_rate¶

Type:: float

density¶

Type:: int

urban_population_percent¶

Type:: str

urban_population¶

Type:: int

world_share¶

Type:: str

world_population¶

Type:: int

rank¶

Type:: int

geography package ¶

The geography package provides access to various data related to geographic information about the world, regions, and their countries.

To understand the significance of each of these data sets, their sources, and any other related information, please visit the official page at https://www.worldometers.info/geography/how-many-countries-are-there-in-the-world

Countries in the world ¶

>>> from worldometer.world.geography import (
        WorldCountries,
        AsiaCountries,
        AfricaCountries,
        EuropeCountries,
        LatinAmericanAndTheCaribbeanCountries,
        NorthernAmericanCountries,
        OceaniaCountries
    )

>>> wc = WorldCountries()

>>> wc.total
195

>>> wc.countries()[0]
WorldCountriesData(
    idx=1,
    country='India',
    population=1428627663,
    world_share='17.76 %',
    land_area=2973190
)

>>> ac = AfricaCountries()

>>> ac.total
54

>>> ac.countries()[0]
CountryData(
    idx=1,
    country='Nigeria',
    population=223804632,
    subregion='Western Africa'
)

>>> ac.dependencies()[0]
DependencyData(
    idx=1,
    territory='Réunion',
    population=981796,
    dependency_of='France'
)

class worldometer.world.geography.countries.WorldCountries¶

Represents the data table of a list of countries in the world.

source_path¶

The data source path.

Type:: str

new_column_names¶

The new column names that will be used to replace those of the original table.

Type:: tuple

total¶

The total number of countries in the world.

Type:: int

Notes

Check the source table in List of countries.

countries() → List[WorldCountriesData]¶

Get a list of all the countries’ data from the table.

Each index in the list contains an object representing a data row of the table.

class worldometer.world.geography.countries.AsiaCountries¶

Represents the data tables of Asia’s countries.

source_path¶

The data source path.

Type:: str

new_column_names¶

The new column names that will be used to replace those of the original tables.

Type:: tuple

total¶

The total number of countries in the region.

Type:: int

Notes

Check the source tables in Countries in Asia.

countries() → List[CountryData]¶

Get a list of all the data for countries in the region from the table.

Each index in the list contains an object representing a data row of the table.

dependencies() → List[DependencyData]¶

Get a list of all the data for dependencies in the region from the table.

Each index in the list contains an object representing a data row of the table.

class worldometer.world.geography.countries.AfricaCountries¶

Represents the data tables of Africa’s countries.

source_path¶

The data source path.

Type:: str

new_column_names¶

The new column names that will be used to replace those of the original tables.

Type:: tuple

total¶

The total number of countries in the region.

Type:: int

Notes

Check the source tables in Countries in Africa.

countries() → List[CountryData]¶

Get a list of all the data for countries in the region from the table.

Each index in the list contains an object representing a data row of the table.

dependencies() → List[DependencyData]¶

Get a list of all the data for dependencies in the region from the table.

Each index in the list contains an object representing a data row of the table.

class worldometer.world.geography.countries.EuropeCountries¶

Represents the data tables of Europe’s countries.

source_path¶

The data source path.

Type:: str

new_column_names¶

The new column names that will be used to replace those of the original tables.

Type:: tuple

total¶

The total number of countries in the region.

Type:: int

Notes

Check the source tables in Countries in Europe.

countries() → List[CountryData]¶

Get a list of all the data for countries in the region from the table.

Each index in the list contains an object representing a data row of the table.

dependencies() → List[DependencyData]¶

Get a list of all the data for dependencies in the region from the table.

Each index in the list contains an object representing a data row of the table.

class worldometer.world.geography.countries.LatinAmericanAndTheCaribbeanCountries¶

Represents the data tables of Latin American And The Caribbean countries.

source_path¶

The data source path.

Type:: str

new_column_names¶

The new column names that will be used to replace those of the original tables.

Type:: tuple

total¶

The total number of countries in the region.

Type:: int

Notes

Check the source tables in Countries in Latin American And The Caribbean.

countries() → List[CountryData]¶

Get a list of all the data for countries in the region from the table.

Each index in the list contains an object representing a data row of the table.

dependencies() → List[DependencyData]¶

Get a list of all the data for dependencies in the region from the table.

Each index in the list contains an object representing a data row of the table.

class worldometer.world.geography.countries.NorthernAmericanCountries¶

Represents the data tables of Northern American countries.

source_path¶

The data source path.

Type:: str

new_column_names¶

The new column names that will be used to replace those of the original tables.

Type:: tuple

total¶

The total number of countries in the region.

Type:: int

Notes

Check the source tables in Countries in Northern American.

countries() → List[CountryData]¶

Get a list of all the data for countries in the region from the table.

Each index in the list contains an object representing a data row of the table.

dependencies() → List[DependencyData]¶

Get a list of all the data for dependencies in the region from the table.

Each index in the list contains an object representing a data row of the table.

class worldometer.world.geography.countries.OceaniaCountries¶

Represents the data tables of the Oceania countries.

source_path¶

The data source path.

Type:: str

new_column_names¶

The new column names that will be used to replace those of the original tables.

Type:: tuple

total¶

The total number of countries in the region.

Type:: int

Notes

Check the source tables in Countries in Oceania.

countries() → List[CountryData]¶

Get a list of all the data for countries in the region from the table.

Each index in the list contains an object representing a data row of the table.

dependencies() → List[DependencyData]¶

Get a list of all the data for dependencies in the region from the table.

Each index in the list contains an object representing a data row of the table.

class worldometer.world.geography.countries.WorldCountriesData(idx: int, country: str, population: int, world_share: str, land_area: int)¶

Represents a data row from the respective table.

idx¶

Type:: int

country¶

Type:: str

population¶

Type:: int

world_share¶

Type:: str

land_area¶

Type:: int

class worldometer.world.geography.countries.CountryData(idx: int, country: str, population: int, subregion: str)¶

Represents a data row from the respective table.

idx¶

Type:: int

country¶

Type:: str

population¶

Type:: int

subregion¶

Type:: str

class worldometer.world.geography.countries.DependencyData(idx: int, territory: str, population: int, dependency_of: str)¶

Represents a data row from the respective table.

idx¶

Type:: int

territory¶

Type:: str

population¶

Type:: int

dependency_of¶

Type:: str

Largest countries in the world ¶

>>> from worldometer.world.geography import LargestCountries

>>> lc = LargestCountries()

>>> lc.data[0]
LargestCountriesData(
    idx=1,
    country='Russia',
    total_area_km2=17098242,
    total_area_mi2=6601665,
    land_area_km2=16376870,
    land_area_mi2=6323142,
    percentage_of_world_landmass='11.0 %'
)

class worldometer.world.geography.largest_countries.LargestCountries¶

Represents the data table of the largest countries in the world (by area).

source_path¶

The data source path.

Type:: str

new_column_names¶

The new column names that will be used to replace those of the original table.

Type:: tuple

Notes

Check the source table in the Largest Countries in the World.

property data: List[LargestCountriesData]¶

Get a list of all the data from the table.

Each index in the list contains an object representing a data row of the table.

class worldometer.world.geography.largest_countries.LargestCountriesData(idx: int, country: str, total_area_km2: int, total_area_mi2: int, land_area_km2: int, land_area_mi2: int, percentage_of_world_landmass: str)¶

Represents a data row from the respective table.

idx¶

Type:: int

country¶

Type:: str

total_area_km2¶

Type:: int

total_area_mi2¶

Type:: int

land_area_km2¶

Type:: int

land_area_mi2¶

Type:: int

percentage_of_world_landmass¶

Type:: str

Demo¶

Note

The first time you run any function/method or class, it will download Chromium to ~/.local/share/pyppeteer directory. It only happens once. After, it will only open the chromium to render the contents of worldometers.info.

Get the data from the live counters available on the homepage:

>>> from worldometer.world import WorldCounters

>>> wc = WorldCounters()

>>> wc.world_population.current_population
8065299074

>>> wc.government_and_economics.computers_produced_this_year
180248430

>>> wc.society_and_media.internet_users_in_the_world_today
5895566559

Reload data to get the latest:

>>> wc.reload_data()
>>> wc.world_population.current_population
8065300592

Get help and view information about mapped sections:

>>> help(wc)

worldometers.info¶

“Worldometer is run by an international team of developers, researchers, and volunteers with the goal of making world statistics available in a thought-provoking and time relevant format to a wide audience around the world. It is published by a small and independent digital media company based in the United States. We have no political, governmental, or corporate affiliation. Furthermore, we have no investors, donors, grants, or backers of any type. We are completely independent and self-financed through automated programmatic advertising sold in real time on multiple ad exchanges.”

More info: worldometers.info/about

Data Sources¶

[adapted]: “worldometers.info collects its statistics and data from the most reputable national and international organizations, including the United Nations, the World Health Organization, the Food and Agriculture Organization, OECD and others.

Each Worldometer counter has its specific set of sources, which are listed on its dedicated page (accessible by clicking on the counter text link, when available).

Data, estimates, and projections displayed on worldometers.info counters are for the most part provided by organizations included in the following list of United Nations Statistics Division’s partners.”

More info about data source: worldometers.info/sources

Contributions¶

All contributions are welcome!

Found a problem, want to give a tip? open an issue

Do you have a solution to the problem? Send me a PR

Did you like this project? Click on the star ⭐

License¶

This project is using the MIT license, see in MIT LICENSE

worldometer

Table of Contents

Related Topics

Worldometer¶

About¶

Install¶

API Reference¶

worldometer package¶

world package ¶

Live Counters ¶

Country Codes ¶

population package ¶

Population by country ¶

Population by region ¶

Population by year ¶

Largest cities in the world ¶

Most populous countries ¶

World population projections ¶

Regions population ¶

geography package ¶

Countries in the world ¶

Largest countries in the world ¶

Demo¶

worldometers.info¶

Data Sources¶

Contributions¶

License¶

Indices and tables¶