Inflation Rate in the United States decreased to 2.30 percent in April from 2.40 percent in March of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Context

Understanding global economic dynamics, specifically the trends in inflation rates, is paramount for policymakers, economists, and researchers. This dataset, covering the years 1980 to 2024, offers a comprehensive perspective on inflation across various countries. The primary focus is on dissecting the data based on country-specific indicators, providing valuable insights into the multifaceted factors influencing economic environments on a global scale.

Content

The dataset comprises crucial columns including country name, indicator type, and annual average inflation rates from 1980 to 2024. This extensive collection of information facilitates detailed analysis and correlation studies, enabling researchers to uncover patterns and trends. By examining the nuanced relationships between country-specific indicators and inflation rates, valuable conclusions can be drawn about the complexities of global economic dynamics over the years. This dataset serves as a valuable resource for anyone seeking to delve into the intricacies of inflation trends and their implications across diverse nations.

Dataset Structure:

This dataset (global_inflation_data.csv) covering from 1980 to 2024 consists of the following columns:

Acknowledgment

The primary dataset was retrieved from the World Bank. I sincerely thank the team for providing the core data used in this dataset.

© Image credit: Freepik

Core consumer prices in the United States increased 2.80 percent in April of 2025 over the same month in the previous year. This dataset provides - United States Core Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Inflation Rate in Norway decreased to 2.50 percent in April from 2.60 percent in March of 2025. This dataset provides - Norway Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Description: This dataset provides a comprehensive overview of monthly inflation rates in Nigeria from March 2003 to June 2024, alongside key economic indicators such as crude oil prices, production levels, and various Consumer Price Index (CPI) components. The data captures important economic trends and is suitable for time series analysis, forecasting, and economic modeling.

The dataset includes the following features:

Inflation Rate: The monthly inflation rate in Nigeria, reflecting the change in consumer prices.

Crude Oil Price: The monthly average price of crude oil, which plays a significant role in Nigeria's economy.

Production and Export: Monthly crude oil production and export figures, representing key components of Nigeria's GDP.

CPI Components: Detailed breakdown of the Consumer Price Index, including food, energy, health, transport, communication, and education.

This dataset is ideal for economists, data scientists, and analysts interested in exploring the dynamics of inflation in a developing economy heavily influenced by oil prices and production. Potential applications include inflation forecasting, economic policy analysis, and studying the impact of global oil prices on domestic inflation.

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Inflation is generally defined as the continued increase in the average prices of goods and services in a given region. Following the extremely high global inflation experienced in the 1980s and 1990s, global inflation has been relatively stable since the turn of the millennium, usually hovering between three and five percent per year. There was a sharp increase in 2008 due to the global financial crisis now known as the Great Recession, but inflation was fairly stable throughout the 2010s, before the current inflation crisis began in 2021. Recent years Despite the economic impact of the coronavirus pandemic, the global inflation rate fell to 3.26 percent in the pandemic's first year, before rising to 4.66 percent in 2021. This increase came as the impact of supply chain delays began to take more of an effect on consumer prices, before the Russia-Ukraine war exacerbated this further. A series of compounding issues such as rising energy and food prices, fiscal instability in the wake of the pandemic, and consumer insecurity have created a new global recession, and global inflation in 2024 is estimated to have reached 5.76 percent. This is the highest annual increase in inflation since 1996. Venezuela Venezuela is the country with the highest individual inflation rate in the world, forecast at around 200 percent in 2022. While this is figure is over 100 times larger than the global average in most years, it actually marks a decrease in Venezuela's inflation rate, which had peaked at over 65,000 percent in 2018. Between 2016 and 2021, Venezuela experienced hyperinflation due to the government's excessive spending and printing of money in an attempt to curve its already-high inflation rate, and the wave of migrants that left the country resulted in one of the largest refugee crises in recent years. In addition to its economic problems, political instability and foreign sanctions pose further long-term problems for Venezuela. While hyperinflation may be coming to an end, it remains to be seen how much of an impact this will have on the economy, how living standards will change, and how many refugees may return in the coming years.

Measures of monthly UK inflation data including CPIH, CPI and RPI. These tables complement the consumer price inflation time series dataset.

Inflation Rate in Indonesia decreased to 1.60 percent in May from 1.95 percent in April of 2025. This dataset provides - Indonesia Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Danish residential house prices (1992-2024)

About the dataset (cleaned data)

The dataset (parquet file) contains approximately 1,5 million residential household sales from Denmark during the periode from 1992 to 2024. All cleaned data is merged into one parquet file here on Kaggle. Note some cleaning might still be nessesary, see notebook under code.

Also, added a random sample (100k) of the dataset as a csv file.

Done in Python version: 2.6.3.

Raw data

Raw data and more info is avaible on Github repositary: https://github.com/MartinSamFred/Danish-residential-housingPrices-1992-2024.git

The dataset has been scraped and cleaned (to some extent). Cleaned files are located in: \Housing_data_cleaned \ named DKHousingprices_1 and 2. Saved in parquet format (and saved as two files due to size).

Cleaning from raw files to above cleaned files is outlined in BoligsalgConcatCleanigGit.ipynb. (done in Python version: 2.6.3)

Webscraping script: Webscrape_script.ipynb (done in Python version: 2.6.3)

Provided you want to clean raw files from scratch yourself:

Uncleaned scraped files (81 in total) are located in \Housing_data_raw \ Housing_data_batch1 and 2. Saved in .csv format and compressed as 7-zip files.

Additional files added/appended to the Cleaned files are located in \Addtional_data and named DK_inflation_rates, DK_interest_rates, DK_morgage_rates and DK_regions_zip_codes. Saved in .xlsx format.

Content

Each row in the dataset contains a residential household sale during the period 1992 - 2024.

“Cleaned files” columns:

0 'date': is the transaction date

1 'quarter': is the quarter based on a standard calendar year

2 'house_id': unique house id (could be dropped)

3 'house_type': can be 'Villa', 'Farm', 'Summerhouse', 'Apartment', 'Townhouse'

4 'sales_type': can be 'regular_sale', 'family_sale', 'other_sale', 'auction', '-' (“-“ could be dropped)

5 'year_build': range 1000 to 2024 (could be narrowed more)

6 'purchase_price': is purchase price in DKK

7 '%_change_between_offer_and_purchase': could differ negatively, be zero or positive

8 'no_rooms': number of rooms

9 'sqm': number of square meters

10 'sqm_price': 'purchase_price' divided by 'sqm_price'

11 'address': is the address

12 'zip_code': is the zip code

13 'city': is the city

14 'area': 'East & mid jutland', 'North jutland', 'Other islands', 'Capital, Copenhagen', 'South jutland', 'North Zealand', 'Fyn & islands', 'Bornholm'

15 'region': 'Jutland', 'Zealand', 'Fyn & islands', 'Bornholm'

16 'nom_interest_rate%': Danish nominal interest rate show pr. quarter however actual rate is not converted from annualized to quarterly

17 'dk_ann_infl_rate%': Danish annual inflation rate show pr. quarter however actual rate is not converted from annualized to quarterly

18 'yield_on_mortgage_credit_bonds%': 30 year mortgage bond rate (without spread)

Uses

Various (statistical) analysis, visualisation and I assume machine learning as well.

Practice exercises etc.

Uncleaned scraped files are great to practice cleaning, especially string cleaning. I’m not an expect as seen in the coding ;-).

Disclaimer

The data and information in the data set provided here are intended to be used primarily for educational purposes only. I do not own any data, and all rights are reserved to the respective owners as outlined in “Acknowledgements/sources”. The accuracy of the dataset is not guaranteed accordingly any analysis and/or conclusions is solely at the user's own responsibly and accountability.

Acknowledgements/sources

All data is publicly available on:

Boliga: https://www.boliga.dk/

Finans Danmark: https://finansdanmark.dk/

Danmarks Statistik: https://www.dst.dk/da

Statistikbanken: https://statistikbanken.dk/statbank5a/default.asp?w=2560

Macrotrends: https://www.macrotrends.net/

PostNord: https://www.postnord.dk/

World Data: https://www.worlddata.info/

Dataset picture / cover photo: Nick Karvounis (https://unsplash.com/)

Have fun… :-)

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