Countries

A simple, clean, and machine-readable reference dataset containing 195 countries with essential geographic, administrative, currency, and language information.

This dataset is designed to be lightweight and easy to use in Python, data analysis, AI applications, machine learning projects, search systems, RAG systems, knowledge bases, educational applications, and software development.

Dataset Overview

Property	Value
Dataset name	Countries
Repository	krishanthb/countries
Number of countries	195
File format	CSV
Main file	countries.csv
License	Apache License 2.0
Primary language	English
Dataset type	Structured reference data
Size category	n<1K
Primary domain	Geography
Secondary domains	Currencies, Capitals, Languages, Countries

Purpose

The purpose of this dataset is to provide a small, clean, and reusable country reference table.

It can serve as a basic geographic reference layer for larger datasets and applications.

For example, this dataset can be used as a foundation for:

• Country selection menus
• Geographic search
• Country lookup systems
• AI knowledge bases
• RAG systems
• Entity matching
• Data normalization
• Country code lookup
• Currency lookup
• Capital city lookup
• Language lookup
• Geographic analytics
• Educational applications
• Travel applications
• International business applications
• Database prototypes
• Software development
• Dataset experimentation

Data Structure

The dataset contains the following columns.

Column	Type	Description
country	string	Common country name
country_code	string	ISO 3166-1 alpha-2 country code
iso3	string	ISO 3166-1 alpha-3 country code
capital	string	Capital city
continent	string	Continent associated with the country
currency	string	Primary currency
currency_code	string	ISO 4217 currency code
official_language	string	Official or principal language information

Example Records

country,country_code,iso3,capital,continent,currency,currency_code,official_language
India,IN,IND,New Delhi,Asia,Indian Rupee,INR,Hindi; English
Japan,JP,JPN,Tokyo,Asia,Japanese Yen,JPY,Japanese
Germany,DE,DEU,Berlin,Europe,Euro,EUR,German
Brazil,BR,BRA,Brasília,South America,Brazilian Real,BRL,Portuguese
Australia,AU,AUS,Canberra,Oceania,Australian Dollar,AUD,English

Country Coverage

The dataset contains 195 country records covering:

• Africa
• Asia
• Europe
• North America
• South America
• Oceania

The dataset is intended to provide a practical global country reference list.

Geographic Coverage

Countries are grouped into the following continental categories:

Africa

Countries located primarily on the African continent.

Asia

Countries located primarily in Asia, including countries in Western Asia, Central Asia, South Asia, East Asia, and Southeast Asia.

Europe

Countries located primarily in Europe.

North America

Countries located in North America, Central America, and the Caribbean.

South America

Countries located in South America.

Oceania

Countries and island nations located in the Pacific/Oceania region.

Country Codes

The dataset provides two commonly used country identifiers:

ISO Alpha-2

The country_code column contains two-letter country codes.

Examples:

India → IN
United States → US
Japan → JP
Germany → DE
Australia → AU

ISO Alpha-3

The iso3 column contains three-letter country codes.

Examples:

India → IND
United States → USA
Japan → JPN
Germany → DEU
Australia → AUS

These identifiers are useful for:

• APIs
• Databases
• Web applications
• Geographic systems
• Data integration
• Entity matching
• International datasets

Capital Cities

The capital column provides the capital city associated with each country.

Examples:

India → New Delhi
France → Paris
Japan → Tokyo
Brazil → Brasília
Australia → Canberra

Currency Information

The dataset provides basic currency information through:

• currency
• currency_code

Example:

India
Currency: Indian Rupee
Code: INR

Japan
Currency: Japanese Yen
Code: JPY

United States
Currency: US Dollar
Code: USD

Currency information can be useful for:

• Financial applications
• E-commerce
• International business
• Currency conversion systems
• Travel applications
• Geographic databases

Language Information

The official_language field provides the official or principal language information used for the country.

Some countries have multiple official or principal languages. Multiple languages are represented as a semicolon-separated value.

Example:

India → Hindi; English
Belgium → Dutch; French; German
Switzerland → German; French; Italian; Romansh
Singapore → English; Malay; Mandarin; Tamil

Applications should treat this field as a simple reference field rather than a complete linguistic database.

Recommended Use

This dataset works particularly well as a base reference dataset.

For example, a larger geographic data system could connect:

Country
   ↓
State / Province
   ↓
District / County
   ↓
City
   ↓
Postal Code

The country_code and iso3 fields can be used as identifiers when connecting this dataset with other datasets.

Example Data Architecture

A larger geographic knowledge system could eventually use:

countries
    │
    ├── states
    │      │
    │      ├── districts
    │      │       │
    │      │       └── cities
    │      │
    │      └── regions
    │
    ├── languages
    │
    ├── currencies
    │
    └── timezones

This dataset can therefore act as the country-level foundation of a larger geographic database.

Loading the Dataset

Python

Install the Hugging Face datasets library:

pip install datasets

Then load the dataset:

from datasets import load_dataset

dataset = load_dataset("krishanthb/countries")

print(dataset)

Access the training split:

countries = dataset["train"]

print(countries)

Display the first record:

print(countries[0])

Pandas

The CSV file can also be loaded directly using pandas:

import pandas as pd

df = pd.read_csv("countries.csv")

print(df.head())

Display the number of records:

print(len(df))

Expected result:

195

Finding a Country

Example using pandas:

india = df[df["country"] == "India"]

print(india)

Using the country code:

india = df[df["country_code"] == "IN"]

print(india)

Using the ISO-3 code:

india = df[df["iso3"] == "IND"]

print(india)

Filtering by Continent

Example:

asia = df[df["continent"] == "Asia"]

print(asia)

Europe:

europe = df[df["continent"] == "Europe"]

print(europe)

Africa:

africa = df[df["continent"] == "Africa"]

print(africa)

Finding Countries by Currency

Example:

usd = df[df["currency_code"] == "USD"]

print(usd)

Finding Countries by Language

Example:

english = df[
    df["official_language"].str.contains(
        "English",
        case=False,
        na=False
    )
]

print(english)

Data Quality

The dataset is intentionally designed to be:

• Simple
• Lightweight
• Structured
• Machine-readable
• Easy to integrate
• Easy to query
• Easy to understand

Each row represents one country-level entity.

Country codes follow commonly used ISO country-code conventions.

Currency codes follow commonly used ISO 4217 conventions.

Important Data Notes

Country names, capital cities, currencies, and language classifications can change over time.

Some geopolitical entities have complex or disputed international status.

Therefore, this dataset should be treated as a general-purpose reference dataset, not as a legal or diplomatic authority.

For applications requiring authoritative geopolitical information, users should verify the relevant information against appropriate official sources.

Limitations

This dataset intentionally does not attempt to provide:

• Population data
• GDP data
• Area measurements
• Geographic coordinates
• Timezones
• Postal codes
• Administrative divisions
• ISO numeric codes
• Internet country domains
• Telephone calling codes
• Detailed language statistics
• Historical country information
• Political information
• Military information
• Economic indicators

These can be provided in separate specialized datasets.

Future Dataset Expansion

Future versions may introduce additional fields such as:

iso_numeric
internet_tld
calling_code
latitude
longitude
timezone
region
subregion
population
area

However, these fields are intentionally not included in the current version to keep the dataset simple.

Suggested Related Datasets

This dataset can later be combined with additional geographic datasets such as:

indian-states
indian-districts
indian-cities
world-cities
languages
currencies
timezones
airports
postal-codes

A larger knowledge system could connect these datasets using standardized identifiers.

Intended Applications

Possible applications include:

Artificial Intelligence

• AI knowledge bases
• RAG systems
• Entity resolution
• Question answering
• Geographic assistants
• Training-data preparation

Software

• Country dropdowns
• Country selectors
• APIs
• Backend databases
• Search systems
• Data validation

Business

• International market databases
• Country segmentation
• Global customer databases
• International commerce

Education

• Geography applications
• Country quizzes
• Learning platforms
• Geographic reference tools

Travel

• Country lookup
• Destination databases
• Travel applications
• International location search

Dataset Philosophy

The goal of this dataset is simplicity first.

Rather than creating one extremely large dataset containing hundreds of attributes, this repository focuses on a small number of fundamental country-level attributes.

This makes the dataset:

• Easy to download
• Easy to inspect
• Easy to understand
• Easy to integrate
• Easy to maintain
• Easy to use with AI systems

Versioning

Dataset changes should be tracked through the Hugging Face repository revision history.

Recommended future versioning:

v1.0.0
v1.1.0
v1.2.0
v2.0.0

Major changes to the schema should result in a major version increment.

Minor additions or corrections can use minor or patch versions.

License

This dataset is released under the Apache License 2.0.

The Apache License 2.0 permits use, modification, distribution, and reuse subject to the terms and conditions of the license.

See the repository license for the complete legal terms.

Citation

If you use this dataset in a project, you can reference the Hugging Face repository:

Krishanth B. Countries Dataset.
Hugging Face: krishanthb/countries

Repository:

https://huggingface.co/datasets/krishanthb/countries

Repository Structure

The repository currently contains:

countries/
│
├── README.md
└── countries.csv

The primary data file is:

countries.csv

Maintainer

Krishanth B.

Hugging Face:

krishanthb

Dataset repository:

krishanthb/countries

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Summary

The Countries dataset provides a lightweight global country reference table containing 195 records and eight fundamental attributes:

country
country_code
iso3
capital
continent
currency
currency_code
official_language

It is designed to serve as a simple and reusable foundation for geographic databases, AI systems, applications, research, education, and future structured datasets.