Graph and download economic data for Inflation, consumer prices for the World (FPCPITOTLZGWLD) from 1981 to 2024 about World, consumer, CPI, inflation, price index, indexes, and price.

Graph and download economic data for Inflation, consumer prices for the United States from 1960 to 2024 about consumer, CPI, inflation, price index, indexes, price, and USA.

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

Geographic coverage notes

The data cover the following areas: Afghanistan, Armenia, Bangladesh, Burkina Faso, Burundi, Cameroon, Central African Republic, Chad, Congo, Dem. Rep., Congo, Rep., Gambia, The, Guinea, Guinea-Bissau, Haiti, Indonesia, Iraq, Kenya, Lao PDR, Lebanon, Liberia, Libya, Malawi, Mali, Mauritania, Mozambique, Myanmar, Niger, Nigeria, Philippines, Senegal, Somalia, South Sudan, Sri Lanka, Sudan, Syrian Arab Republic, Yemen, Rep.

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations.
This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

Graph and download economic data for Inflation, consumer prices for the United Kingdom (FPCPITOTLZGGBR) from 1960 to 2024 about United Kingdom, consumer, CPI, inflation, price index, indexes, and price.

Inflation, consumer prices (annual %) in Brazil was reported at 4.3675 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Brazil - Inflation, consumer prices (annual %) - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

This dataset enlists the details about annual inflation percentage of most countries in the world by consumer prices from 1960 through 2018. Indicator Name as per the World Bank for this dataset is "Inflation, consumer prices (annual %)" and its code is "FP.CPI.TOTL.ZG".

Graph and download economic data for Inflation, consumer prices for Lebanon (FPCPITOTLZGLBN) from 2009 to 2024 about Lebanon, consumer, CPI, inflation, price index, indexes, and price.

Inflation, consumer prices (annual %) in Egypt was reported at 28.27 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Egypt - Inflation, consumer prices (annual %) - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

Actual value and historical data chart for World Inflation Consumer Prices Annual Percent

In September 2025, global inflation rates and central bank interest rates showed significant variation across major economies. Most economies initiated interest rate cuts from mid-2024 due to declining inflationary pressures. The U.S., UK, and EU central banks followed a consistent pattern of regular rate reductions throughout late 2024. In September 2025, Russia maintained the highest interest rate at 17 percent, while Japan retained the lowest at 0.5 percent. Varied inflation rates across major economies The inflation landscape varies considerably among major economies. China had the lowest inflation rate at -0.3 percent in September 2025. In contrast, Russia maintained a high inflation rate of 8 percent. These figures align with broader trends observed in early 2025, where China had the lowest inflation rate among major developed and emerging economies, while Russia's rate remained the highest. Central bank responses and economic indicators Central banks globally implemented aggressive rate hikes throughout 2022-23 to combat inflation. The European Central Bank exemplified this trend, raising rates from 0 percent in January 2022 to 4.5 percent by September 2023. A coordinated shift among major central banks began in mid-2024, with the ECB, Bank of England, and Federal Reserve initiating rate cuts, with forecasts suggesting further cuts through 2025 and 2026.

This is the Dataset for the world inflation from 1960 to 2021, obtained from world bank website for analyzing inflation rate around the world and in major economics. This data can be used for analytical purpose or project building for selves.

Graph and download economic data for Inflation, consumer prices for Switzerland (FPCPITOTLZGCHE) from 1960 to 2024 about Switzerland, consumer, CPI, inflation, price index, indexes, and price.

Inflation, consumer prices (annual %) in Palestine was reported at 53.67 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Palestine - Inflation, consumer prices (annual %) - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

Pakistan PK: Inflation:(GDP) Gross Domestic ProductDeflator data was reported at 4.001 % in 2017. This records an increase from the previous number of 0.400 % for 2016. Pakistan PK: Inflation:(GDP) Gross Domestic ProductDeflator data is updated yearly, averaging 7.526 % from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 25.437 % in 1974 and a record low of -1.312 % in 1963. Pakistan PK: Inflation:(GDP) Gross Domestic ProductDeflator data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Pakistan – Table PK.World Bank.WDI: Inflation. Inflation as measured by the annual growth rate of the GDP implicit deflator shows the rate of price change in the economy as a whole. The GDP implicit deflator is the ratio of GDP in current local currency to GDP in constant local currency.; ; World Bank national accounts data, and OECD National Accounts data files.; Median;

Overview

This dataset provides a comprehensive collection of time series data sourced from the World Bank Open Data Platform, covering a wide range of global indicators from 1960 to the most recently published year. It includes economic, social, environmental, and demographic metrics, making it an ideal resource for researchers, data scientists, and policymakers interested in global development trends, economic forecasting, or socio-economic analysis.

A tutorial on how to combined the dataset topics together into one large dataset can be found here

Why this Dataset?

My motivation for this project was to curate a high-quality collection of datasets for World Bank indicators organized by topics and structured in time-series, making them more accessible for data science projects. Since the World Bank’s Kaggle datasets have not been updated since 2019 https://www.kaggle.com/organizations/theworldbank, I saw an opportunity to provide more current data for the data analysis community.

Dataset Collection Contents

This collection brings together more than 800 World Bank indicators organized into 18 topic‑specific CSV files. Each file is structured as a country‑year panel: every row represents a unique combination of year (1960‑present) and ISO‑3 country code, while the columns hold the topic’s indicators.

The collection includes datasets with a variety of indicators, such as: - Economic Metrics: GDP growth (%), GDP per capita, consumer price inflation, merchandise trade, gross capital formation, and more.
- Social Metrics: School enrollment (primary, secondary, tertiary), infant mortality rate, maternal mortality rate, poverty headcount, and more.
- Environmental Metrics: Forest area, renewable energy consumption, food production indices, and more.
- Demographic Metrics: Urban population, life expectancy, net migration, and more.

Usage

This dataset is ideal for a variety of applications, including: - Economic forecasting and trend analysis (e.g., GDP growth, inflation).
- Socio-economic studies (e.g., education, health, poverty).
- Environmental impact analysis (e.g., renewable energy adoption).
- Demographic research (e.g., population trends, migration).

Topic datasets can be merged with each other using year and country code. This tutorial with notebook code can help you get started quickly.

Collection Methodology

The data is collected via a custom software application that discovers and groups high-quality indicators with rules-based logic & artificial intelligence, generates metadata, and performs ETL for the data from the World Bank API. The result is a clean, up‑to‑date collection of World Bank indicators in time-series format that is ready for analysis—no manual downloads or data wrangling required.

Modifications

The original World Bank data has been aggregated and transformed for ease of use. Missing values have been preserved as provided by the World Bank, and no significant transformations have been applied beyond formatting and aggregation into a single file.

Source & Attribution

The World Bank: World Development Indicators

This dataset is publicly available and sourced from the World Bank Open Data Platform and is made available under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. When using this data, please attribute the World Bank as follows: "Data sourced from the World Bank, licensed under CC BY 4.0." For more details on the World Bank’s terms of use, visit: https://www.worldbank.org/en/about/legal/terms-of-use-for-datasets.

License

This dataset is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Feel free to use this data in Kaggle notebooks, academic research, or policy analysis. If you create a derived dataset or analysis, I encourage you to share it with the Kaggle community.

Inflation, consumer prices (annual %) in Tanzania was reported at 3.057 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Tanzania - Inflation, consumer prices (annual %) - actual values, historical data, forecasts and projections were sourced from the World Bank on October of 2025.

Turkey TR: Inflation:(GDP) Gross Domestic ProductDeflator data was reported at 10.809 % in 2017. This records an increase from the previous number of 8.098 % for 2016. Turkey TR: Inflation:(GDP) Gross Domestic ProductDeflator data is updated yearly, averaging 21.930 % from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 143.693 % in 1998 and a record low of 2.352 % in 1964. Turkey TR: Inflation:(GDP) Gross Domestic ProductDeflator data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Turkey – Table TR.World Bank.WDI: Inflation. Inflation as measured by the annual growth rate of the GDP implicit deflator shows the rate of price change in the economy as a whole. The GDP implicit deflator is the ratio of GDP in current local currency to GDP in constant local currency.; ; World Bank national accounts data, and OECD National Accounts data files.; Median;

Zimbabwe ZW: Inflation:(GDP) Gross Domestic ProductDeflator: Linked Series data was reported at 3.798 % in 2017. This records an increase from the previous number of 1.310 % for 2016. Zimbabwe ZW: Inflation:(GDP) Gross Domestic ProductDeflator: Linked Series data is updated yearly, averaging 1.330 % from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 24.675 % in 2009 and a record low of -27.049 % in 1998. Zimbabwe ZW: Inflation:(GDP) Gross Domestic ProductDeflator: Linked Series data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Zimbabwe – Table ZW.World Bank.WDI: Inflation. Inflation as measured by the annual growth rate of the GDP implicit deflator shows the rate of price change in the economy as a whole. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years.; ; World Bank staff estimates based on World Bank national accounts data archives, OECD National Accounts, and the IMF WEO database.; ;

Abstract

Energy price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes energy price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

Geographic coverage notes

The data cover the following sub-national areas: Badakhshan, Badghis, Baghlan, Balkh, Bamyan, Daykundi, Farah, Faryab, Paktya, Ghazni, Ghor, Hilmand, Hirat, Nangarhar, Jawzjan, Kabul, Kandahar, Kapisa, Khost, Kunar, Kunduz, Laghman, Logar, Wardak, Nimroz, Nuristan, Paktika, Panjsher, Parwan, Samangan, Sar-e-pul, Takhar, Uruzgan, Zabul, Market Average, Armavir, Ararat, Aragatsotn, Tavush, Gegharkunik, Shirak, Kotayk, Syunik, Lori, Vayotz Dzor, Yerevan, Kanifing Municipal Council, Central River, Upper River, West Coast, North Bank, Lower River, Bafata, Tombali, Cacheu, Sector Autonomo De Bissau, Biombo, Oio, Gabu, Bolama, Quinara, Anbar, Babil, Baghdad, Basrah, Diyala, Dahuk, Erbil, Ninewa, Kerbala, Kirkuk, Missan, Muthanna, Najaf, Qadissiya, Salah al-Din, Sulaymaniyah, Thi-Qar, Wassit, Attapeu, Louangnamtha, Champasack, Bokeo, Bolikhamxai, Khammouan, Oudomxai, Phongsaly, Vientiane, Xiengkhouang, Louangphabang, Salavan, Savannakhet, Sekong, Vientiane Capital, Houaphan, Xaignabouly, Akkar, Mount Lebanon, Baalbek-El Hermel, North, Beirut, Bekaa, El Nabatieh, South, Nimba, Grand Kru, Grand Cape Mount, Gbarpolu, Grand Bassa, Rivercess, Montserrado, River Gee, Lofa, Bomi, Bong, Sinoe, Maryland, Margibi, Grand Gedeh, Abia, Borno, Yobe, Katsina, Kano, Kaduna, Gombe, Adamawa, Jigawa, Kebbi, Oyo, Sokoto, Zamfara, Lagos, Shabelle Hoose, Juba Hoose, Bay, Banadir, Shabelle Dhexe, Gedo, Hiraan, Woqooyi Galbeed, Awdal, Bari, Juba Dhexe, Togdheer, Nugaal, Galgaduud, Bakool, Sanaag, Mudug, Sool, , Warrap, Unity, Jonglei, Northern Bahr el Ghazal, Upper Nile, Eastern Equatoria, Central Equatoria, Western Bahr el Ghazal, Western Equatoria, Lakes, Aleppo, Dar'a, Quneitra, Homs, Deir-ez-Zor, Damascus, Ar-Raqqa, Al-Hasakeh, Hama, As-Sweida, Rural Damascus, Tartous, Idleb, Lattakia, Al Dhale'e, Aden, Al Bayda, Al Maharah, Lahj, Al Jawf, Raymah, Al Hudaydah, Hajjah, Amran, Shabwah, Dhamar, Ibb, Sana'a, Al Mahwit, Marib, Hadramaut, Sa'ada, Amanat Al Asimah, Socotra, Taizz, Abyan