In 2024, approximately 11.9 million people lived in São Paulo, making it the largest municipality in Brazil and one of the most populous cities in the world. The homonymous state of São Paulo was also the most populous federal entity in the country. Brazil's cities Brazil is home to two large metropolises: São Paulo with close to 11.9 million inhabitants, and Rio de Janeiro with around 6.7 million inhabitants. It also contains a number of smaller but well-known cities, such as Brasília, Salvador, Belo Horizonte, and many others, which report between 2 and 3 million inhabitants each. As a result, the country's population is primarily urban, with nearly 88 percent of inhabitants living in cities. While smaller than some of the other cities, Brasília was chosen to be the capital because of its relatively central location. The city is also well-known for its modernist architecture and utopian city plan, which is quite controversial - criticized by many and praised by others. Sports venues capitals A number of Brazil’s medium-sized and large cities were chosen as venues for the 2014 World Cup, and the 2015 Summer Olympics also took place in Rio de Janeiro. Both of these events required large sums of money to support infrastructure and enhance mobility within a number of different cities across the country. Billions of dollars were spent on the 2014 World Cup, which went primarily to stadium construction and renovation but also to a number of different mobility projects. Other short-term spending on infrastructure for the World Cup and the Rio Olympic Games was estimated at 50 billion U.S. dollars. While these events have poured a lot of money into urban infrastructure, a number of social and economic problems within the country remain unsolved.

Context

This dataset has information about brazilian cities.

Content

• city: the name of the city
• lat: the latitude of each city
• lng: the longitude of each city
• country: the country of each city (all Brazil in this case)
• iso2: country code (BR)
• admin: state of each city
• capital: wheter the city is a capital or not
• population: amount of inhabitants of each city
• population_proper: area contained within city limits

Context

While working with geographical data in my internship, I had to analyze information regarding the cities of Brazil, using their respective latitudes and longitudes. The data was obtained from varied sources.

Content

Schema: 1. cd_ibge ("IBGE" code of the City) 2. nm_municipio (name of each City) 3. nm_uf (name of each State) 4. cd_uf (State name abbreviation) 5. bl_capital (boolean indicating if the city is the State capital or not) 6. regiao_uf (region of the State) 7. lat_municipio (latitude of the City) 8. long_municipio (longitude of the City) 9. lat_long_municipio (lat/long of the City) 10. lat_central_uf (latitude of the State - centralized) 11. long_central_uf (longitude of the State - centralized) 12. lat_long_central_uf (lat/long of the State - centralized)

Postal Codes Dataset for Brazil, BR including name of the city, town, or place, various administrative divisions and alternative city names.

Brazilian cities (pt: municípios) and their corresponding states according IBGE as of Feb/2023, along with their respective geolocation (latitude, longitude) coordinates and populations. Also contains IBGE metadata that can be used to query specific information about cities on IBGE website.

Context

The dataset extracted from the website of the Brazilian Institute of Geography and Statistics (IBGE) contains all demographic, economic, geographic and human development information on Brazilian cities.

Content

There was no complete dataset to download all this information. So, I did a webscrapping that entered all the pages of each Brazilian cities and got all the information available. After that, I consolidated everything into a single file and now share with you to serve as research and studies of Brazil's performance on development, economics, and other topics.

This file contains 14 columns and 5571 rows (with headers):

• IBGECode - ID of the cities
• LocalCidade - Name of the cities
• LocalUF - State initials of the cities
• LocalEstado - Full name of city states
• RegiaoBrasil - IBGE's Region for each city
• Latitude - city ​​latitude
• Longitude - city ​​longitude
• Gentilico - name given to people born in the city
• PopEstimada_2018 - Population estimated in 2018
• PopCenso 2010 - Population in 2010
• IDHM - HDI of each city
• ReceitasRealizadas_2014 - Revenues realized by cities
• DespesasEmpenhadas_2014 - Expenditure incurred by cities
• Pib_2014 - GDP by cities

Acknowledgements

I thank my co-workers who helped me develop web scrapping and distribute the consolidated information to all of you.

Inspiration

Questions to be answered about this dataset:

1. What is the forecast of the GDP of each city in Brazil?
2. Can we group the cities by some specific division related to HDI or GDP?
3. What will be the total population of the Brazilian in the South of the Country in 2020?

And so on.

In 2025, approximately 23 million people lived in the São Paulo metropolitan area, making it the biggest in Latin America and the Caribbean and the sixth most populated in the world. The homonymous state of São Paulo was also the most populous federal entity in the country. The second place for the region was Mexico City with 22.75 million inhabitants. Brazil's cities Brazil is home to two large metropolises, only counting the population within the city limits, São Paulo had approximately 11.45 million inhabitants, and Rio de Janeiro around 6.21 million inhabitants. It also contains a number of smaller, but well known cities such as Brasília, Salvador, Belo Horizonte and many others, which report between 2 and 3 million inhabitants each. As a result, the country's population is primarily urban, with nearly 88 percent of inhabitants living in cities. Mexico City Mexico City's metropolitan area ranks sevenths in the ranking of most populated cities in the world. Founded over the Aztec city of Tenochtitlan in 1521 after the Spanish conquest as the capital of the Viceroyalty of New Spain, the city still stands as one of the most important in Latin America. Nevertheless, the preeminent economic, political, and cultural position of Mexico City has not prevented the metropolis from suffering the problems affecting the rest of the country, namely, inequality and violence. Only in 2023, the city registered a crime incidence of 52,723 reported cases for every 100,000 inhabitants and around 24 percent of the population lived under the poverty line.

List of telephone area codes by city and state for pre-processing data and filling in missing information.

This data set contains actual sales data for a chain of Brazilian stores. I modified the names of products, customers, and employees to preserve their identity. I am making this data available so that they can help me get the most out of it, analysis such as:

• Sales forecast

• Customer segmentation

• Employee productivity

• Profitable products

• And everything else that can be extracted from it.

Columns description

Company Code - Affiliate code that sold Order Number - Unique code to identify the sale Employee - Employee who made the sale Product - Name of product sold Product Category - category the product belongs to Client - Name of the customer who made the purchase Client City - City name of the customer who made the purchase Sale Date Time - Date and time the sale was made Product Cost - Cost per unit sold Discount Amount - Total sale discount Amount - Item Quantity Total - Total item value Form of payment - Form of payment

The column values: - Client - Client City - Employee They were exchanged for fictitious names.

The category of the products was maintained, but translated into English, the name of the product consists of the name of the category to which it belongs concatenated with a random number. The rule does not apply to products in the Fuel category, for these, fictitious names were invented.

📄 Dataset Description

This dataset contains climate and air quality data collected from 10 Brazilian cities, covering a wide range of environmental variables. It aims to support data science projects related to weather analysis, air pollution monitoring, public health research, and environmental modeling.

Each row represents a single timestamped observation for a specific city.

The data was collected through web scraping from a public website using an Apache Airflow pipeline, which fetched the data hourly between May 25, 2025 and June 1, 2025.

🌍 Cities Included

• Fernandópolis
• Ribeirão Preto
• São Paulo
• Porto Alegre
• Rio de Janeiro
• Manaus
• Belo Horizonte
• Fortaleza
• Campo Grande
• Curitiba

🌡️ Columns

• city: Name of the Brazilian city where the data was collected.
• timestamp: Date and time (ISO format) of the observation.
• temperature: Ambient temperature in degrees Celsius (°C).
• wind: Wind speed in meters per second (m/s).
• humidity: Relative humidity as a percentage (%).
• dew_point: Dew point temperature in degrees Celsius (°C).
• pressure: Atmospheric pressure in hectopascals (hPa).
• uv_index: Ultraviolet radiation index (scale from 0 to 11+).

🧪 Air Quality Indicators

Each pollutant has two associated fields:

• [pollutant]_aqi: Air Quality Index value for the pollutant, following local environmental standards.
• [pollutant]_medida: Measured concentration of the pollutant (in µg/m³ or ppm, depending on the pollutant).

Pollutants included:

• O3_aqi, O3_medida: Ozone (O₃)
• CO_aqi, CO_medida: Carbon Monoxide (CO)
• NO2_aqi, NO2_medida: Nitrogen Dioxide (NO₂)
• PM10_aqi, PM10_medida: Particulate Matter ≤10 µm
• PM2_5_aqi, PM2_5_medida: Particulate Matter ≤2.5 µm
• SO2_aqi, SO2_medida: Sulfur Dioxide (SO₂)

Feel free to use this dataset for exploratory analysis, modeling, forecasting, or visualization projects related to air and climate conditions in urban Brazil.