The data is from:

https://simplemaps.com/data/world-cities

We're proud to offer a simple, accurate and up-to-date database of the world's cities and towns. We've built it from the ground up using authoritative sources such as the NGIA, US Geological Survey, US Census Bureau, and NASA.

Our database is:

• Up-to-date: It was last refreshed on May 11, 2025.
• Comprehensive: Over 4 million unique cities and towns from every country in the world (about 48 thousand in basic database).
• Accurate: Cleaned and aggregated from official sources. Includes latitude and longitude coordinates.
• Simple: A single CSV file, concise field names, only one entry per city.

All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

The dataset provided is a comprehensive list of 200 capital cities from around the world, detailing their respective countries, continents, and precise geographical coordinates in terms of latitude and longitude. This information is organized in a tabular format with the following columns:

Sno: This column represents the serial number, providing a unique identifier for each row in the dataset. Capital City: This column lists the name of the capital city for each entry. Country: This column specifies the country in which each capital city is located. Continent: This column indicates the continent on which each capital city is situated. Latitude: This column provides the latitude coordinates of each capital city. Longitude: This column provides the longitude coordinates of each capital city

"This dataset presents a comprehensive list of cities and locations from various countries around the world. Each entry includes the city's name, the corresponding country, the specific region or locality, and a unique numerical code assigned to each location. The data provides essential information for various applications, such as geographical analysis, research, or database management."

About this Dataset

This is the most accurate and complete database of major places on earth

defined as

('continent','country','state', 'city','town','region','county', 'village')

Data fields

country_code, latitude,longitude, name, (multi-lingual) place_id, parent_place_id, linked_place_id, importance, geometry_sector, rank_address, rank_search, osm_id, osm_type, class, type, admin_level, address, extratags, area, wikipedia, token_info, housenumber, postcode

sample data available at https://4.ipv6.systems/data/basic_places.csv.zip

Category

Location Lists

Keywords

cities of world,world locations,world places,places,coordinates

Row Count

1614395

Price

$50.00

Jeu de données payant contenant les noms de villes, coordonnées, régions et divisions administratives des villes du monde. Disponible aux formats Excel (.xlsx), CSV, JSON, XML et SQL après achat.

DescriptionOpenFlights Airports Database contains over 70,000 airports. The data as generated by from DAFIF (October 2006 cycle) and OurAirports, plus timezone information from EarthTools. All DST information added manually. Significant revisions and additions made by the users of OpenFlights. LimitationsBlank.AttributesOBJECTID: Assigned by WWF. Unique identifier id: Unique OpenFlights identifier for this airport ident: ICAO airport code or location indicator type: Type of the airport. Value "airport" for air terminals, "station" for train stations, "port" for ferry terminals and "unknown" if not known. In airports.csv, only type=airport is included name: Name of airport. May or may not contain the city name latitude_d: Latitude in decimal degrees longitude_: Longitude in decimal degrees elevation_: Altitude in feet continent: Continent code where the airport is located. Cross-reference to ISO 3166-1 codes here iso_countr: Country code where airport is located. Cross-reference to ISO 3166-1 codes here iso_region: Region code where airport is located. Cross-reference to ISO 3166-1 municipali: Name of the municipality where the airport is located scheduled_: Availability of airline schedule data service gps_code: GPS airport code iata_code: IATA airport code, also know as an IATA location identifier, IATA station code or simply a location identifier local_code: Local airport code. Can be the same IATA code. home_link: Website of the airport in question wikipedia_: Source of Airport Data keywords x: X location in the original coordinate system of the database (WGS 1984) y: Y location in the original coordinate system of the database (WGS 1984)

The table Global Temperatures by City is part of the dataset Climate Change: Earth Surface Temperature Data, available at https://columbia.redivis.com/datasets/1e0a-f4931vvyg. It contains 8599212 rows across 7 variables.

The world’s largest noise complaint dataset including labeled noise sources. Ideal for AI training in acoustic event detection and urban noise analysis. Available via CSV, S3, and API (coming soon). GDPR-compliant.

Abstract

Compilation of Earth Surface temperatures historical. Source: https://www.kaggle.com/berkeleyearth/climate-change-earth-surface-temperature-data

Documentation

Data compiled by the Berkeley Earth project, which is affiliated with Lawrence Berkeley National Laboratory. The Berkeley Earth Surface Temperature Study combines 1.6 billion temperature reports from 16 pre-existing archives. It is nicely packaged and allows for slicing into interesting subsets (for example by country). They publish the source data and the code for the transformations they applied. They also use methods that allow weather observations from shorter time series to be included, meaning fewer observations need to be thrown away.

In this dataset, we have include several files:

Global Land and Ocean-and-Land Temperatures (GlobalTemperatures.csv):

• Date: starts in 1750 for average land temperature and 1850 for max and min land temperatures and global ocean and land temperatures

%3C!-- --%3E

• LandAverageTemperature: global average land temperature in celsius

%3C!-- --%3E

• LandAverageTemperatureUncertainty: the 95% confidence interval around the average

%3C!-- --%3E

• LandMaxTemperature: global average maximum land temperature in celsius

%3C!-- --%3E

• LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature

%3C!-- --%3E

• LandMinTemperature: global average minimum land temperature in celsius

%3C!-- --%3E

• LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature

%3C!-- --%3E

• LandAndOceanAverageTemperature: global average land and ocean temperature in celsius

%3C!-- --%3E

• LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

%3C!-- --%3E

**Other files include: **

• Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)

%3C!-- --%3E

• Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)

%3C!-- --%3E

• Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)

%3C!-- --%3E

• Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

%3C!-- --%3E

The raw data comes from the Berkeley Earth data page.

Hyper-local street noise-level data for urban planning, covering 200+ countries. Built from 35B real-world datapoints and AI interpolation. Available as CSV, high-resolution maps, or S3 delivery. Ideal for sustainable, people-centered city design. The sample above is raw data and not processed.

The world’s largest noise complaint dataset with over 160K reports including labeled noise sources. Ideal for AI training in acoustic event detection and urban noise analysis. Available via CSV, S3, and API (coming soon). GDPR-compliant.

Welcome to New York City, one of the most-visited cities in the world. There are many Airbnb listings in New York City to meet the high demand for temporary lodging for travelers, which can be anywhere between a few nights to many months. In this project, we will take a closer look at the New York Airbnb market by combining data from multiple file types like .csv, .tsv, and .xlsx.

Recall that CSV, TSV, and Excel files are three common formats for storing data. Three files containing data on 2019 Airbnb listings are available to you:

data/airbnb_price.csv This is a CSV file containing data on Airbnb listing prices and locations.

listing_id: unique identifier of listing price: nightly listing price in USD nbhood_full: name of borough and neighborhood where listing is located data/airbnb_room_type.xlsx This is an Excel file containing data on Airbnb listing descriptions and room types.

listing_id: unique identifier of listing description: listing description room_type: Airbnb has three types of rooms: shared rooms, private rooms, and entire homes/apartments data/airbnb_last_review.tsv This is a TSV file containing data on Airbnb host names and review dates.

listing_id: unique identifier of listing host_name: name of listing host last_review: date when the listing was last reviewed

This dataset was created to simulate a market basket dataset, providing insights into customer purchasing behavior and store operations. The dataset facilitates market basket analysis, customer segmentation, and other retail analytics tasks. Here's more information about the context and inspiration behind this dataset:

Context:

Retail businesses, from supermarkets to convenience stores, are constantly seeking ways to better understand their customers and improve their operations. Market basket analysis, a technique used in retail analytics, explores customer purchase patterns to uncover associations between products, identify trends, and optimize pricing and promotions. Customer segmentation allows businesses to tailor their offerings to specific groups, enhancing the customer experience.

Inspiration:

The inspiration for this dataset comes from the need for accessible and customizable market basket datasets. While real-world retail data is sensitive and often restricted, synthetic datasets offer a safe and versatile alternative. Researchers, data scientists, and analysts can use this dataset to develop and test algorithms, models, and analytical tools.

Dataset Information:

The columns provide information about the transactions, customers, products, and purchasing behavior, making the dataset suitable for various analyses, including market basket analysis and customer segmentation. Here's a brief explanation of each column in the Dataset:

• Transaction_ID: A unique identifier for each transaction, represented as a 10-digit number. This column is used to uniquely identify each purchase.
• Date: The date and time when the transaction occurred. It records the timestamp of each purchase.
• Customer_Name: The name of the customer who made the purchase. It provides information about the customer's identity.
• Product: A list of products purchased in the transaction. It includes the names of the products bought.
• Total_Items: The total number of items purchased in the transaction. It represents the quantity of products bought.
• Total_Cost: The total cost of the purchase, in currency. It represents the financial value of the transaction.
• Payment_Method: The method used for payment in the transaction, such as credit card, debit card, cash, or mobile payment.
• City: The city where the purchase took place. It indicates the location of the transaction.
• Store_Type: The type of store where the purchase was made, such as a supermarket, convenience store, department store, etc.
• Discount_Applied: A binary indicator (True/False) representing whether a discount was applied to the transaction.
• Customer_Category: A category representing the customer's background or age group.
• Season: The season in which the purchase occurred, such as spring, summer, fall, or winter.
• Promotion: The type of promotion applied to the transaction, such as "None," "BOGO (Buy One Get One)," or "Discount on Selected Items."

Use Cases:

• Market Basket Analysis: Discover associations between products and uncover buying patterns.
• Customer Segmentation: Group customers based on purchasing behavior.
• Pricing Optimization: Optimize pricing strategies and identify opportunities for discounts and promotions.
• Retail Analytics: Analyze store performance and customer trends.

Note: This dataset is entirely synthetic and was generated using the Python Faker library, which means it doesn't contain real customer data. It's designed for educational and research purposes.

Eurovision Contest Winners: A Look at the Data

World's Longest Running Tv Programme

About this dataset

The Eurovision Song Contest is an annual music competition that began in 1956. It is one of the longest-running television programmes in the world and is watched by millions of people every year. The contest's winner is determined using numerous voting techniques, including points awarded by juries or televoters.

Since 2004, the contest has included a televised semi-final::— In 2004 held on the Wednesday before the final:— Between 2005 and 2007 held on the Thursday of Eurovision Week n2 - Since 2008 the contest has included two semi-finals, held on the Tuesday and Thursday before the final.

The Eurovision Song Contest is a truly global event, with countries from all over Europe (and beyond) competing for the coveted prize. Over the years, some truly amazing performers have taken to the stage, entertaining audiences with their catchy songs and stunning stage performances.

So who will be crowned this year's winner? Tune in to find out!

How to use the dataset

This dataset contains information on all of the winners of the Eurovision Song Contest from 1956 to the present day. The data includes the year that the contest was held, the city that hosted it, the winning song and performer, the margin of points between the winning song and runner-up, and the runner-up country.

This dataset can be used to study patterns in Eurovision voting over time, or to compare different winning songs and performers. It could also be used to study how hosting the contest affects a country's chances of winning

Research Ideas

• In order to studyEurovision Song Contest winners, one could use this dataset to train a machine learning model to predict the winner of the contest given a set of features about the song and the performers.
• This dataset could be used to study how different voting methods (e.g. jury vs televoters) impact the outcome of the Eurovision Song Contest.
• This dataset could be used to study trends in music over time by looking at how the style ofwinner songs has changed since the contest began in 1956

Acknowledgements

Data from eurovision_winners.csv was scraped from Wikipedia on April 4, 2020.

The dataset eurovision_winners.csv contains a list of all the winners of the Eurovision Song Contest from 1956 to the present day

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: eurovision_winners.csv | Column name | Description | |:--------------|:---------------------------------------------------------------------------------------------| | Year | The year in which the contest was held. (Integer) | | Date | The date on which the contest was held. (String) | | Host City | The city in which the contest was held. (String) | | Winner | The country that won the contest. (String) | | Song | The song that won the contest. (String) | | Performer | The performer of the winning song. (String) | | Points | The number of points that the winning song received. (Integer) | | Margin | The margin of victory (in points) between the winning song and the runner-up song. (Integer) | | Runner-up | The country that placed second in the contest. (String) |