About this dataset

Context

The dataset tabulates the Brazil population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Brazil. The dataset can be utilized to understand the population distribution of Brazil by age. For example, using this dataset, we can identify the largest age group in Brazil.

Key observations

The largest age group in Brazil, IN was for the group of age 30 to 34 years years with a population of 683 (8.47%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Brazil, IN was the 80 to 84 years years with a population of 178 (2.21%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Brazil is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Brazil total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Brazil Population by Age. You can refer the same here

The total population in Brazil was estimated at 212.6 million people in 2024, according to the latest census figures and projections from Trading Economics. This dataset provides - Brazil Population - actual values, historical data, forecast, chart, statistics, economic calendar and news.

About this dataset

Context

The dataset tabulates the Brazil population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Brazil across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Brazil was 8,214, a 0.37% increase year-by-year from 2022. Previously, in 2022, Brazil population was 8,184, an increase of 0.21% compared to a population of 8,167 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Brazil increased by 328. In this period, the peak population was 8,214 in the year 2023. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Brazil is shown in this column.
• Year on Year Change: This column displays the change in Brazil population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Brazil Population by Year. You can refer the same here

The number of employed persons in Brazil increased to 103.30 Million in April of 2025 from 102.50 Million in March of 2025. This dataset provides the latest reported value for - Brazil Employed Persons - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

About this dataset

Context

The dataset tabulates the Brazil household income by age. The dataset can be utilized to understand the age-based income distribution of Brazil income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Brazil, IN annual median income by age groups dataset (in 2022 inflation-adjusted dollars)
• Age-wise distribution of Brazil, IN household incomes: Comparative analysis across 16 income brackets

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Brazil income distribution by age. You can refer the same here

Unemployment Rate in Brazil decreased to 6.20 percent in May from 6.60 percent in April of 2025. This dataset provides the latest reported value for - Brazil Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Dataset Description: Prices of Products in Brazil

This dataset spans a decade of detailed information on product prices across various states in Brazil. The data was collected by the National Supply Company (Conab) and provides a comprehensive view of price trends, enabling in-depth analyses of economic and trade evolution.

Key Attributes: 1. Product/Unit: Name of the product along with the corresponding unit of measurement. 2. U.F. (Federative Unit): Brazilian state where prices were recorded. 3. Commercialization Level: Indication of whether the product is intended for retail, wholesale, or other commercial channels. 4. Monthly Periods: Monthly records of product prices over the past decade, allowing for detailed temporal analysis.

Key Insights: - Regional Variations: The dataset reveals significant variations in product prices between different states, highlighting the influence of regional factors on the economy. - Impact of the Pandemic: A trend of price increases, especially during the pandemic, is observed, reflecting the economic impact of this period.

Responsible Use: This dataset is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. Users are encouraged to explore, analyze, and share the data while respecting the conditions of non-commercial use and proper attribution to the source (Conab).

Limitations and Considerations: - The quality of insights obtained will depend on the user's interpretation and in-depth analysis. - Some outliers may require specific treatment as needed.

This dataset provides a solid foundation for exploring the dynamics of product prices in Brazil, contributing to understanding economic trends and regional patterns over time.

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers. The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters. The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules. The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format.

Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datasets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of acquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc.

Depending on the intended use of a dataset, we recommend a few data processing steps before analysis: - Analyze missing data: Project Tycho datasets do not include time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported. - Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".

Brazilian E-Commerce Public Dataset by Olist Welcome! This is a Brazilian ecommerce public dataset of orders made at Olist Store. The dataset has information of 100k orders from 2016 to 2018 made at multiple marketplaces in Brazil. Its features allows viewing an order from multiple dimensions: from order status, price, payment and freight performance to customer location, product attributes and finally reviews written by customers. We also released a geolocation dataset that relates Brazilian zip codes to lat/lng coordinates.

This is real commercial data, it has been anonymised, and references to the companies and partners in the review text have been replaced with the names of Game of Thrones great houses.

Join it With the Marketing Funnel by Olist We have also released a Marketing Funnel Dataset. You may join both datasets and see an order from Marketing perspective now!

Instructions on joining are available on this Kernel.

Context This dataset was generously provided by Olist, the largest department store in Brazilian marketplaces. Olist connects small businesses from all over Brazil to channels without hassle and with a single contract. Those merchants are able to sell their products through the Olist Store and ship them directly to the customers using Olist logistics partners. See more on our website: www.olist.com

After a customer purchases the product from Olist Store a seller gets notified to fulfill that order. Once the customer receives the product, or the estimated delivery date is due, the customer gets a satisfaction survey by email where he can give a note for the purchase experience and write down some comments.

Attention An order might have multiple items. Each item might be fulfilled by a distinct seller. All text identifying stores and partners where replaced by the names of Game of Thrones great houses. Example of a product listing on a marketplace Example of a product listing on a marketplace

Data Schema The data is divided in multiple datasets for better understanding and organization. Please refer to the following data schema when working with it: Data Schema

Classified Dataset We had previously released a classified dataset, but we removed it at Version 6. We intend to release it again as a new dataset with a new data schema. While we don't finish it, you may use the classified dataset available at the Version 5 or previous.

Inspiration Here are some inspiration for possible outcomes from this dataset.

NLP:

This dataset offers a supreme environment to parse out the reviews text through its multiple dimensions.

Clustering:

Some customers didn't write a review. But why are they happy or mad?

Sales Prediction:

With purchase date information you'll be able to predict future sales.

Delivery Performance:

You will also be able to work through delivery performance and find ways to optimize delivery times.

Product Quality:

Enjoy yourself discovering the products categories that are more prone to customer insatisfaction.

Feature Engineering:

Create features from this rich dataset or attach some external public information to it.

Acknowledgements Thanks to Olist for releasing this dataset.

Original Data Source: Brazilian E-Commerce Public Dataset by Olist

Brazil recorded 702116 Coronavirus Deaths since the epidemic began, according to the World Health Organization (WHO). In addition, Brazil reported 37511921 Coronavirus Cases. This dataset includes a chart with historical data for Brazil Coronavirus Deaths.

Tourist Arrivals in Brazil increased to 6773.62 Thousand in 2025 from 5908 Thousand in 2023. This dataset provides - Brazil Tourist Arrivals- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This is the first product of the effort of more than 500 zoologists, experts in many different groups of animals that inhabit the Brazilian territory. Since the start of the Project, in 2013, the team of the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/fauna) has been working in an integrated manner to generate the first list of valid species found in the country. So far, we identified 124,438 taxonomically valid species.

Data on ecology, distribution are available in the system and taxonomic information will be soon be included in the catalogue (such as images and diagnoses). Visit the "tabs" in the catalogue site and learn a little more about the members of our team, coordinators, authors, and institutions involved.

The list is available for consultation by anyone. The system is dynamic and allows additions and corrections in real time, inclusion of newly discovered species, corrections associated with nomenclatural decisions, distribution expansion, among others.

Most species are arthropods (81.2% with more than 102,000 species) and chordates (8.84% with over 11,000 species). These taxa are followed by a cluster composed by Mollusca (3,570 species), Platyhelminthes (2,293 species), Annelida (1,833 species), and Nematoda (1,463 species). All remaining groups have less than 1,000 species reported in Brazil, with Cnidaria (823 species), Porifera (626 species), Bryozoa (520 species), and Rotifera (607), representing those with more than 500 species.

A quick analysis shows that we are far from knowing our fauna in a comprehensive manner. Much work is still necessary for our taxonomists, many inputs will still be entered into the system. We hope that the CTFB, therefore, represents a facilitator and induces further studies to describe more effectively the animal diversity in Brazil.

Brazilian? You can read a Portuguese version of this article here.

Context

Last year, while I was attending a data science course in Germany, my country was impeaching its president. My colleagues asked me to explain what was happening in Brazil and the possible political outcomes in South America. Although I was able to give a general context and tell multiple arguments in favor and against the impeachment, deep inside, my answer was "I really don't know".

Understanding what happens in Politics is something that takes a lot of effort and research. When I decided I had to use my tech skills to make myself a better citizen, I dived into government data and started Operation Serenata de Amor.

After reporting hundreds of politicians for small acts of corruption and learning how to encourage the population to engage in the democratic processes, my studies drove me to understand the legislative activity.

Brazilians elect 594 citizens to be their representatives in the National Congress. How can we be sure that they are not defending their own interests or those who paid for their campaigns? My way, as a data scientist, is to ask the data.

Content

The National Congress of Brazil is composed of a Lower (Chamber of Deputies) and an Upper House (Federal Senate). In the first version of this dataset, you are going to find data only from the Chamber of Deputies. With 513 representatives, 86% of the congresspeople, I hope you have enough data to explore for some time.

Would be impossible for me, a citizen without government ties, to collect this data without the help of public servants. I processed 9,717 fixed-width files and 73 XML's made officially available by the Chamber of Deputies and created 5 CSV's containing the same information. Multiple fields of the same file telling the same thing (e.g. body_id, body_name and body_abbreviation) were removed.

Data on session attendance, votes, and propositions since past century were collected and scripted in a reproducible manner. The data collection and pre-processing scripts are available in a GitHub repository, under an open source license.

Everything was collected from the Chamber of Deputies website at December 27, 2017, containing the whole legislative activity of the year. Presence and votes date from 1999, propositions go as far as 1946.

When in question about the legislative process and how the sessions work in real world, the Internal Regulation of the Chamber of Deputies is the best Portuguese documentation for research. It's free!

Acknowledgements

Since the data was collected from a government website and the Brazilian law states that access to this information is free to any citizen, I am placing my own work published here in Public Domain.

I'd like to thank the hundreds of people financially supporting the work of Operation Serenata de Amor and those responsible for passing the Information Access bill in 2011.

Inspiration

The legislative activity should tell the history while it's happening. How much has the Congress changed over the past decades? Do the congresspeople maintain the same political views or they vary on a weekly basis? Do people vote together with their state or party peers? How often? Can you model an algorithm to tell us the real parties inside Brazilian Congress?

The number of unemployed persons in Brazil decreased to 7.30 Million in April of 2025 from 7.70 Million in March of 2025. This dataset provides the latest reported value for - Brazil Unemployed Persons - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Context Forest fires are a serious problem for the preservation of the Tropical Forests. Understanding the frequency of forest fires in a time series can help to take action to prevent them. Brazil has the largest rainforest on the planet that is the Amazon rainforest.

Content This dataset report of the number of forest fires in Brazil divided by states. The series comprises the period of approximately 10 years (1998 to 2017). The data were obtained from the official website of the Brazilian government.

http://dados.gov.br/dataset/sistema-nacional-de-informacoes-florestais-snif

Acknowledgements We thank the brazilian system of forest information

Adjusted This dataset adjusted the first posted by Luiz Gustavo Modelli.

The Free Brazilian Repository for Open Soil Data – febr, www.ufsm.br/febr – is a centralized repository targeted at storing open soil data and serving it in a standardized and harmonized format. The repository infrastructure was built using open source and/or free (of cost) software, and was primarily designed for the individual management of datasets. A dataset-driven structure helps datasets authors to be properly acknowledged. Moreover, it gives the flexibility to accommodate many types of data of any soil variable. This is accomplished by storing each dataset using a collection of spreadsheets accessible through an online application. Spreadsheets are familiar to any soil scientist, the reason why it is easier to enter, manipulate and visualize soil data in febr. They also facilitate the participation of soil survey experts in the recovery and quality assessment of legacy data. Soil scientists can help in the definition of standards and data management choices through a public discussion forum, febr-forum@googlegroups.com. A comprehensive documentation is available to guide febr maintainers and data contributors. A detailed catalog gives access to the 14 477 soil observations – 42% of them from south and southeastern Brazil – from 232 datasets contained in febr. Global and dataset-specific visualization and search tools and multiple download facilities are available. The latter includes standard file formats and connections with R and QGIS through the febr package. Various products can be derived from data in febr: specialized databases, pedotransfer functions, fertilizer recommendation guides, classification systems, and detailed soil maps. By sharing data through a centralized soil data storing and sharing facility, soil scientists from different fields have the opportunity to increase collaboration and the much needed soil knowledge.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

National coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Brazil is 1002.

Mode of data collection

Landline and mobile telephone

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

The benchmark interest rate in Brazil was last recorded at 15 percent. This dataset provides - Brazil Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.