91 datasets found
  1. Consumer Price Index (CPI) Trends in India Feb'24

    • kaggle.com
    Updated Aug 24, 2024
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    Prathamjyot Singh (2024). Consumer Price Index (CPI) Trends in India Feb'24 [Dataset]. https://www.kaggle.com/datasets/prathamjyotsingh/state-level-consumer-price-index
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 24, 2024
    Dataset provided by
    Kaggle
    Authors
    Prathamjyot Singh
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Area covered
    India
    Description

    Explanation of CPI and the Dataset:

    What is CPI?

    CPI (Consumer Price Index) measures the average change in prices over time that consumers pay for a basket of goods and services. It is a key indicator of inflation and is used by governments and central banks to monitor price stability and for inflation targeting. Components: The construction of CPI involves two main components: Weighting Diagrams: These represent the consumption patterns of households. Price Data: This is collected at regular intervals to track changes in prices.

    Role of the Central Statistics Office (CSO):

    The CSO, under the Ministry of Statistics and Programme Implementation, is responsible for releasing CPI data. The indices are released for Rural, Urban, and Combined sectors for all-India and individual States/UTs.

    Dataset Alignment:

    Sectors: The dataset includes a "Sector" column that categorizes data into "Rural," "Urban," and "Rural+Urban," aligning with the CPI data released by the CSO. Time Period: The "Year" and "Name" (which appears to represent months) columns in the dataset track the data over time, consistent with the monthly release schedule by the CSO starting from January 2011. State/UT Data: Each column corresponding to a state or union territory likely represents the CPI values for that region. The numeric values under each state/UT column represent the CPI index values, with a base of 2010=100. Purpose: This data can be used to analyze inflation trends, price stability, and the impact on economic policies, such as adjustments to dearness allowance for employees. Practical Use of This Data: Inflation Analysis: By examining the changes in CPI values across different states, analysts can study regional inflation trends and compare them to the national average. Policy Making: Governments and central banks can use this data to design and adjust policies aimed at controlling inflation, targeting specific regions or sectors that are experiencing higher inflation. Wage Indexation: Companies and governments can use CPI data to adjust wages and allowances in line with inflation, ensuring that purchasing power is maintained.

  2. T

    United States Consumer Price Index (CPI)

    • tradingeconomics.com
    • fa.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jun 11, 2025
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    TRADING ECONOMICS (2025). United States Consumer Price Index (CPI) [Dataset]. https://tradingeconomics.com/united-states/consumer-price-index-cpi
    Explore at:
    xml, csv, excel, jsonAvailable download formats
    Dataset updated
    Jun 11, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 31, 1950 - May 31, 2025
    Area covered
    United States
    Description

    Consumer Price Index CPI in the United States increased to 321.47 points in May from 320.80 points in April of 2025. This dataset provides the latest reported value for - United States Consumer Price Index (CPI) - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

  3. d

    Consumer price index (CPI) for main groups, Peninsular Malaysia,Sabah and...

    • archive.data.gov.my
    Updated Mar 10, 2021
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    (2021). Consumer price index (CPI) for main groups, Peninsular Malaysia,Sabah and Sarawak (Monthly) - Dataset - MAMPU [Dataset]. https://archive.data.gov.my/data/dataset/consumer-price-index-cpi-for-main-groups-peninsular-malaysiasabah-and-sarawak-monthly
    Explore at:
    Dataset updated
    Mar 10, 2021
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Peninsular Malaysia, Sarawak, Sabah, Malaysia
    Description

    This dataset shows the Consumer Price Index (2010 =100), index number for main groups, 2017 - 2023 (Jan-Jul) Malaysia, Peninsular Malaysia, Sabah and Sarawak (Monthly) Footnote Main Group classification is based on the Classification of Individual Consumption According by Purpose (COICOP) January 2017 - December 2020 : Data includes W.P. Labuan. Weight: Main Groups Weight Total 100 Food and Non- Alcoholic Beverages 29.5 Alcoholic Beverages and Tobacco 2.4 Clothing and Footware 3.2 Housing, Water, Electricity, Gas and Other Fuels 23.8 Furnishings, Households Equipment and Routine Household Maintenance 4.1 Health 1.9 Transport 14.6 Communication 4.8 Recreation Services and Culture 4.8 Education 1.3 Restaurants and Hotels 2.9 Miscellaneous Goods and Services 6.7 Source : Department of Statistics, Malaysia No. of Views : 277

  4. A

    ‘🚊 Consumer Price Index’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Aug 28, 2013
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2013). ‘🚊 Consumer Price Index’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-consumer-price-index-ba9d/latest
    Explore at:
    Dataset updated
    Aug 28, 2013
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘🚊 Consumer Price Index’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/consumer-price-indexe on 13 February 2022.

    --- Dataset description provided by original source is as follows ---

    About this dataset

    9The Consumer Price Index for All Urban Consumers: All Items (CPIAUCSL) is a measure of the average monthly change in the price for goods and services paid by urban consumers between any two time periods.(1) It can also represent the buying habits of urban consumers. This particular index includes roughly 88 percent of the total population, accounting for wage earners, clerical workers, technical workers, self-employed, short-term workers, unemployed, retirees, and those not in the labor force.(1)

    The CPIs are based on prices for food, clothing, shelter, and fuels; transportation fares; service fees (e.g., water and sewer service); and sales taxes. Prices are collected monthly from about 4,000 housing units and approximately 26,000 retail establishments across 87 urban areas.(1) To calculate the index, price changes are averaged with weights representing their importance in the spending of the particular group. The index measures price changes (as a percent change) from a predetermined reference date.(1) In addition to the original unadjusted index distributed, the Bureau of Labor Statistics also releases a seasonally adjusted index. The unadjusted series reflects all factors that may influence a change in prices. However, it can be very useful to look at the seasonally adjusted CPI, which removes the effects of seasonal changes, such as weather, school year, production cycles, and holidays.(1)

    The CPI can be used to recognize periods of inflation and deflation. Significant increases in the CPI within a short time frame might indicate a period of inflation, and significant decreases in CPI within a short time frame might indicate a period of deflation. However, because the CPI includes volatile food and oil prices, it might not be a reliable measure of inflationary and deflationary periods. For a more accurate detection, the core CPI (Consumer Price Index for All Urban Consumers: All Items Less Food & Energy [CPILFESL]) is often used. When using the CPI, please note that it is not applicable to all consumers and should not be used to determine relative living costs.(1) Additionally, the CPI is a statistical measure vulnerable to sampling error since it is based on a sample of prices and not the complete average.(1)

    Attribution: US. Bureau of Labor Statistics from The Federal Reserve Bank of St. Louis

    For more information on the consumer price indexes, see:

    This dataset was created by Finance and contains around 900 samples along with Consumer Price Index For All Urban Consumers: All Items, Title:, technical information and other features such as: - Consumer Price Index For All Urban Consumers: All Items - Title: - and more.

    How to use this dataset

    • Analyze Consumer Price Index For All Urban Consumers: All Items in relation to Title:
    • Study the influence of Consumer Price Index For All Urban Consumers: All Items on Title:
    • More datasets

    Acknowledgements

    If you use this dataset in your research, please credit Finance

    Start A New Notebook!

    --- Original source retains full ownership of the source dataset ---

  5. d

    Consumer Price Index (CPI), index numbers for main groups, urban and rural,...

    • archive.data.gov.my
    Updated Mar 14, 2021
    + more versions
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    (2021). Consumer Price Index (CPI), index numbers for main groups, urban and rural, Malaysia (Monthly) - Dataset - MAMPU [Dataset]. https://archive.data.gov.my/data/dataset/consumer-price-index-cpi-index-numbers-for-main-groups-urban-and-rural-malaysia-monthly
    Explore at:
    Dataset updated
    Mar 14, 2021
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Malaysia
    Description

    This dataset shows the Consumer Price Index (2010=100), Index Numbers For Main Groups, Urban and Rural, 2017- 2023 (Jan-Jul) Malaysia (Monthly) Footnote Weight: Main Groups Weight Urban Total 100.0 Food and Non- Alcoholic Beverages 28.4 Alcoholic Beverages and Tobacco 2.3 Clothing and Footware 3.2 Housing, Water, Electricity, Gas and Other Fuels 24.5 Furnishings, Households Equipment and Routine Household Maintenance 4.2 Health 1.8 Transport 14.6 Communication 4.9 Recreation Services and Culture 5.0 Education 1.4 Restaurants and Hotels 3.0 Miscellaneous Goods and Services 6.7 Weight Rural Total 100.0 Food and Non- Alcoholic Beverages 35.6 Alcoholic Beverages and Tobacco 3.0 Clothing and Footware 3.6 Housing, Water, Electricity, Gas and Other Fuels 19.9 Furnishings, Households Equipment and Routine Household Maintenance 3.7 Health 2.0 Transport 14.6 Communication 4.4 Recreation Services and Culture 3.6 Education 0.9 Restaurants and Hotels 2.4 Miscellaneous Goods and Services 6.3 Source : Department of Statistics, Malaysia No. of Views : 455

  6. T

    United States Inflation Rate

    • tradingeconomics.com
    • fa.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jun 11, 2025
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    TRADING ECONOMICS (2025). United States Inflation Rate [Dataset]. https://tradingeconomics.com/united-states/inflation-cpi
    Explore at:
    json, excel, xml, csvAvailable download formats
    Dataset updated
    Jun 11, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 31, 1914 - May 31, 2025
    Area covered
    United States
    Description

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

  7. Consumer Price Index 2020 - West Bank and Gaza

    • pcbs.gov.ps
    Updated Jan 2, 2022
    + more versions
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    Palestinian Central Bureau of Statistics (2022). Consumer Price Index 2020 - West Bank and Gaza [Dataset]. https://www.pcbs.gov.ps/PCBS-Metadata-en-v5.2/index.php/catalog/706
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    Dataset updated
    Jan 2, 2022
    Dataset authored and provided by
    Palestinian Central Bureau of Statisticshttp://pcbs.gov.ps/
    Time period covered
    2020
    Area covered
    Palestine, West Bank
    Description

    Abstract

    The Consumer price surveys primarily provide the following: Data on CPI in Palestine covering the West Bank, Gaza Strip and Jerusalem J1 for major and sub groups of expenditure. Statistics needed for decision-makers, planners and those who are interested in the national economy. Contribution to the preparation of quarterly and annual national accounts data.

    Consumer Prices and indices are used for a wide range of purposes, the most important of which are as follows: Adjustment of wages, government subsidies and social security benefits to compensate in part or in full for the changes in living costs. To provide an index to measure the price inflation of the entire household sector, which is used to eliminate the inflation impact of the components of the final consumption expenditure of households in national accounts and to dispose of the impact of price changes from income and national groups. Price index numbers are widely used to measure inflation rates and economic recession. Price indices are used by the public as a guide for the family with regard to its budget and its constituent items. Price indices are used to monitor changes in the prices of the goods traded in the market and the consequent position of price trends, market conditions and living costs. However, the price index does not reflect other factors affecting the cost of living, e.g. the quality and quantity of purchased goods. Therefore, it is only one of many indicators used to assess living costs. It is used as a direct method to identify the purchasing power of money, where the purchasing power of money is inversely proportional to the price index.

    Geographic coverage

    Palestine West Bank Gaza Strip Jerusalem

    Analysis unit

    The target population for the CPI survey is the shops and retail markets such as grocery stores, supermarkets, clothing shops, restaurants, public service institutions, private schools and doctors.

    Universe

    The target population for the CPI survey is the shops and retail markets such as grocery stores, supermarkets, clothing shops, restaurants, public service institutions, private schools and doctors.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    A non-probability purposive sample of sources from which the prices of different goods and services are collected was updated based on the establishment census 2017, in a manner that achieves full coverage of all goods and services that fall within the Palestinian consumer system. These sources were selected based on the availability of the goods within them. It is worth mentioning that the sample of sources was selected from the main cities inside Palestine: Jenin, Tulkarm, Nablus, Qalqiliya, Ramallah, Al-Bireh, Jericho, Jerusalem, Bethlehem, Hebron, Gaza, Jabalia, Dier Al-Balah, Nusseirat, Khan Yunis and Rafah. The selection of these sources was considered to be representative of the variation that can occur in the prices collected from the various sources. The number of goods and services included in the CPI is approximately 730 commodities, whose prices were collected from 3,200 sources. (COICOP) classification is used for consumer data as recommended by the United Nations System of National Accounts (SNA-2008).

    Sampling deviation

    Not apply

    Mode of data collection

    Computer Assisted Personal Interview [capi]

    Research instrument

    A tablet-supported electronic form was designed for price surveys to be used by the field teams in collecting data from different governorates, with the exception of Jerusalem J1. The electronic form is supported with GIS, and GPS mapping technique that allow the field workers to locate the outlets exactly on the map and the administrative staff to manage the field remotely. The electronic questionnaire is divided into a number of screens, namely: First screen: shows the metadata for the data source, governorate name, governorate code, source code, source name, full source address, and phone number. Second screen: shows the source interview result, which is either completed, temporarily paused or permanently closed. It also shows the change activity as incomplete or rejected with the explanation for the reason of rejection. Third screen: shows the item code, item name, item unit, item price, product availability, and reason for unavailability. Fourth screen: checks the price data of the related source and verifies their validity through the auditing rules, which was designed specifically for the price programs. Fifth screen: saves and sends data through (VPN-Connection) and (WI-FI technology).

    In case of the Jerusalem J1 Governorate, a paper form has been designed to collect the price data so that the form in the top part contains the metadata of the data source and in the lower section contains the price data for the source collected. After that, the data are entered into the price program database.

    Cleaning operations

    The price survey forms were already encoded by the project management depending on the specific international statistical classification of each survey. After the researcher collected the price data and sent them electronically, the data was reviewed and audited by the project management. Achievement reports were reviewed on a daily and weekly basis. Also, the detailed price reports at data source levels were checked and reviewed on a daily basis by the project management. If there were any notes, the researcher was consulted in order to verify the data and call the owner in order to correct or confirm the information.

    At the end of the data collection process in all governorates, the data will be edited using the following process: Logical revision of prices by comparing the prices of goods and services with others from different sources and other governorates. Whenever a mistake is detected, it should be returned to the field for correction. Mathematical revision of the average prices for items in governorates and the general average in all governorates. Field revision of prices through selecting a sample of the prices collected from the items.

    Response rate

    Not apply

    Sampling error estimates

    The findings of the survey may be affected by sampling errors due to the use of samples in conducting the survey rather than total enumeration of the units of the target population, which increases the chances of variances between the actual values we expect to obtain from the data if we had conducted the survey using total enumeration. The computation of differences between the most important key goods showed that the variation of these goods differs due to the specialty of each survey. For example, for the CPI, the variation between its goods was very low, except in some cases such as banana, tomato, and cucumber goods that had a high coefficient of variation during 2019 due to the high oscillation in their prices. The variance of the key goods in the computed and disseminated CPI survey that was carried out on the Palestine level was for reasons related to sample design and variance calculation of different indicators since there was a difficulty in the dissemination of results by governorates due to lack of weights. Non-sampling errors are probable at all stages of data collection or data entry. Non-sampling errors include: Non-response errors: the selected sources demonstrated a significant cooperation with interviewers; so, there wasn't any case of non-response reported during 2019. Response errors (respondent), interviewing errors (interviewer), and data entry errors: to avoid these types of errors and reduce their effect to a minimum, project managers adopted a number of procedures, including the following: More than one visit was made to every source to explain the objectives of the survey and emphasize the confidentiality of the data. The visits to data sources contributed to empowering relations, cooperation, and the verification of data accuracy. Interviewer errors: a number of procedures were taken to ensure data accuracy throughout the process of field data compilation: Interviewers were selected based on educational qualification, competence, and assessment. Interviewers were trained theoretically and practically on the questionnaire. Meetings were held to remind interviewers of instructions. In addition, explanatory notes were supplied with the surveys. A number of procedures were taken to verify data quality and consistency and ensure data accuracy for the data collected by a questioner throughout processing and data entry (knowing that data collected through paper questionnaires did not exceed 5%): Data entry staff was selected from among specialists in computer programming and were fully trained on the entry programs. Data verification was carried out for 10% of the entered questionnaires to ensure that data entry staff had entered data correctly and in accordance with the provisions of the questionnaire. The result of the verification was consistent with the original data to a degree of 100%. The files of the entered data were received, examined, and reviewed by project managers before findings were extracted. Project managers carried out many checks on data logic and coherence, such as comparing the data of the current month with that of the previous month, and comparing the data of sources and between governorates. Data collected by tablet devices were checked for consistency and accuracy by applying rules at item level to be checked.

    Data appraisal

    Other technical procedures to improve data quality: Seasonal adjustment processes

  8. T

    United States Core Inflation Rate

    • tradingeconomics.com
    • id.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Apr 10, 2025
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    TRADING ECONOMICS (2025). United States Core Inflation Rate [Dataset]. https://tradingeconomics.com/united-states/core-inflation-rate
    Explore at:
    excel, csv, json, xmlAvailable download formats
    Dataset updated
    Apr 10, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Feb 28, 1957 - May 31, 2025
    Area covered
    United States
    Description

    Core consumer prices in the United States increased 2.80 percent in May 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.

  9. Consumer Price Index, annual average, not seasonally adjusted

    • www150.statcan.gc.ca
    • datasets.ai
    • +3more
    Updated Jan 21, 2025
    + more versions
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    Government of Canada, Statistics Canada (2025). Consumer Price Index, annual average, not seasonally adjusted [Dataset]. http://doi.org/10.25318/1810000501-eng
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    Dataset updated
    Jan 21, 2025
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    Area covered
    Canada
    Description

    Annual indexes for major components and special aggregates of the Consumer Price Index (CPI), for Canada, provinces, Whitehorse, Yellowknife and Iqaluit. Data are presented for the last five years. The base year for the index is 2002=100.

  10. d

    Consumer price index (CPI) for main groups (CHGS) Malaysia, Peninsular...

    • archive.data.gov.my
    Updated Jul 6, 2023
    + more versions
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    (2023). Consumer price index (CPI) for main groups (CHGS) Malaysia, Peninsular Malaysia, Sabah and Sarawak - Dataset - MAMPU [Dataset]. https://archive.data.gov.my/data/dataset/consumer-price-index-cpi-for-main-groups-chgs-malaysia-peninsular-malaysia-sabah-and-sarawak
    Explore at:
    Dataset updated
    Jul 6, 2023
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Peninsular Malaysia, Sarawak, Malaysia, Sabah
    Description

    This dataset shows the Consumer price index (CPI) for main groups (CHGS), 1980 - 2005, Malaysia, Peninsular Malaysia, Sabah and Sarawak

  11. T

    China Inflation Rate

    • tradingeconomics.com
    • de.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jul 2, 2025
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    TRADING ECONOMICS (2025). China Inflation Rate [Dataset]. https://tradingeconomics.com/china/inflation-cpi
    Explore at:
    json, excel, csv, xmlAvailable download formats
    Dataset updated
    Jul 2, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 31, 1986 - Jun 30, 2025
    Area covered
    China
    Description

    Inflation Rate in China increased to 0.10 percent in June from -0.10 percent in May of 2025. This dataset provides - China Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

  12. o

    Quarterly Consumer Price Index (CPI), 2023 (Q4) (2018=100)

    • qatar.opendatasoft.com
    • data.gov.qa
    csv, excel, json
    Updated May 12, 2025
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    (2025). Quarterly Consumer Price Index (CPI), 2023 (Q4) (2018=100) [Dataset]. https://qatar.opendatasoft.com/explore/dataset/quarterly-consumer-price-index-cpi-2023-q4-2018-100/table/
    Explore at:
    json, excel, csvAvailable download formats
    Dataset updated
    May 12, 2025
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Quarterly Consumer Price Index (CPI) by major group for Q4 2023 (2018=100)

  13. k

    Consumer Price Indices For Main Divisions (2018 = 100)

    • data.kapsarc.org
    • datasource.kapsarc.org
    Updated Dec 18, 2024
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    (2024). Consumer Price Indices For Main Divisions (2018 = 100) [Dataset]. https://data.kapsarc.org/explore/dataset/consumer-price-indices-for-main-divisions/?flg=ar-001
    Explore at:
    Dataset updated
    Dec 18, 2024
    Description

    Explore the Consumer Price Indices for main divisions dataset, including information on Clothing and Footwear, Quarterly, Miscellaneous Goods and Services, and more. View data for Saudi Arabia.

    Clothing and Footwear, Quarterly, Miscellaneous Goods and Services, Tobacco, Transport, Food and beverages, Monthly, Housing, Water, Electricity, Gas, and other fuels, Annually, Restaurants and Hotels, Furnishings, Household equipment, General Index, Education, Communication, Recreation and Culture, Health, CPI, Main Division, SAMA Monthly

    Saudi ArabiaFollow data.kapsarc.org for timely data to advance energy economics research..

  14. A

    ‘Extra Datasets for TPS Jan 2022| GDP-CPI-Holidays’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Jan 28, 2022
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2022). ‘Extra Datasets for TPS Jan 2022| GDP-CPI-Holidays’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-extra-datasets-for-tps-jan-2022-gdp-cpi-holidays-798b/f16a111a/?iid=003-130&v=presentation
    Explore at:
    Dataset updated
    Jan 28, 2022
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘Extra Datasets for TPS Jan 2022| GDP-CPI-Holidays’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sardorabdirayimov/extra-datasets-for-tps-jan-2022-gdpcpiholidays on 28 January 2022.

    --- Dataset description provided by original source is as follows ---

    I edited several datasets in Kaggle and make them more user-friendly. Current Datasets are very convenient to add them to main dataset by single pandas method | merge(on=['date'|'year', 'country']) Example notebook: https://www.kaggle.com/sardorabdirayimov/adding-extra-datasets-to-main Good luck on competition!

    --- Original source retains full ownership of the source dataset ---

  15. Consumer Price Index, monthly, not seasonally adjusted

    • www150.statcan.gc.ca
    • open.canada.ca
    • +2more
    Updated Jun 24, 2025
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    Government of Canada, Statistics Canada (2025). Consumer Price Index, monthly, not seasonally adjusted [Dataset]. http://doi.org/10.25318/1810000401-eng
    Explore at:
    Dataset updated
    Jun 24, 2025
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    Area covered
    Canada
    Description

    Monthly indexes for major components and special aggregates of the Consumer Price Index (CPI), not seasonally adjusted, for Canada, provinces, Whitehorse, Yellowknife and Iqaluit. Data are presented for the current month and previous four months. The base year for the index is 2002=100.

  16. Consumer price inflation tables

    • ons.gov.uk
    • cy.ons.gov.uk
    xlsx
    Updated Jun 18, 2025
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    Office for National Statistics (2025). Consumer price inflation tables [Dataset]. https://www.ons.gov.uk/economy/inflationandpriceindices/datasets/consumerpriceinflation
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Jun 18, 2025
    Dataset provided by
    Office for National Statisticshttp://www.ons.gov.uk/
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Description

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

  17. T

    Thailand Consumer Price Index (CPI)

    • tradingeconomics.com
    • ko.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jun 15, 2025
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    TRADING ECONOMICS (2025). Thailand Consumer Price Index (CPI) [Dataset]. https://tradingeconomics.com/thailand/consumer-price-index-cpi
    Explore at:
    json, csv, excel, xmlAvailable download formats
    Dataset updated
    Jun 15, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 31, 2025 - Jun 30, 2025
    Area covered
    Thailand
    Description

    Consumer Price Index CPI in Thailand increased to 100.42 points in June from 100.40 points in May of 2025. This dataset provides the latest reported value for - Thailand Consumer Price Index (CPI) - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

  18. N

    Fort Myers, FL Median Household Income Trends (2010-2021, in 2022...

    • neilsberg.com
    csv, json
    Updated Jan 11, 2024
    + more versions
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    Neilsberg Research (2024). Fort Myers, FL Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars) [Dataset]. https://www.neilsberg.com/research/datasets/90cfd6d4-73f0-11ee-949f-3860777c1fe6/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Jan 11, 2024
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Fort Myers, Florida
    Variables measured
    Median Household Income, Median Household Income Year on Year Change, Median Household Income Year on Year Percent Change
    Measurement technique
    The data presented in this dataset is derived from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. It presents the median household income from the years 2010 to 2021 following an initial analysis and categorization of the census data. Subsequently, we adjusted these figures for inflation using the Consumer Price Index retroactive series via current methods (R-CPI-U-RS). For additional information about these estimations, please contact us via email at research@neilsberg.com
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset illustrates the median household income in Fort Myers, spanning the years from 2010 to 2021, with all figures adjusted to 2022 inflation-adjusted dollars. Based on the latest 2017-2021 5-Year Estimates from the American Community Survey, it displays how income varied over the last decade. The dataset can be utilized to gain insights into median household income trends and explore income variations.

    Key observations:

    From 2010 to 2021, the median household income for Fort Myers increased by $4,871 (9.55%), as per the American Community Survey estimates. In comparison, median household income for the United States increased by $4,559 (6.51%) between 2010 and 2021.

    Analyzing the trend in median household income between the years 2010 and 2021, spanning 11 annual cycles, we observed that median household income, when adjusted for 2022 inflation using the Consumer Price Index retroactive series (R-CPI-U-RS), experienced growth year by year for 7 years and declined for 4 years.

    https://i.neilsberg.com/ch/fort-myers-fl-median-household-income-trend.jpeg" alt="Fort Myers, FL median household income trend (2010-2021, in 2022 inflation-adjusted dollars)">

    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

    Years for which data is available:

    • 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021

    Variables / Data Columns

    • Year: This column presents the data year from 2010 to 2021
    • Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific year
    • YOY Change($): Change in median household income between the current and the previous year, in 2022 inflation-adjusted dollars
    • YOY Change(%): Percent change in median household income between current and the previous year

    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 Fort Myers median household income. You can refer the same here

  19. T

    Japan Inflation Rate

    • tradingeconomics.com
    • zh.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jul 9, 2025
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    TRADING ECONOMICS (2025). Japan Inflation Rate [Dataset]. https://tradingeconomics.com/japan/inflation-cpi
    Explore at:
    csv, json, excel, xmlAvailable download formats
    Dataset updated
    Jul 9, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 31, 1958 - May 31, 2025
    Area covered
    Japan
    Description

    Inflation Rate in Japan decreased to 3.50 percent in May from 3.60 percent in April of 2025. This dataset provides the latest reported value for - Japan Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

  20. k

    Saudi Arabia Consumer Price Indices For Main Divisions (2018 = 100)

    • datasource.kapsarc.org
    csv, excel, json
    Updated Jan 5, 2023
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    (2023). Saudi Arabia Consumer Price Indices For Main Divisions (2018 = 100) [Dataset]. https://datasource.kapsarc.org/explore/dataset/consumer-price-indices-for-main-divisions/
    Explore at:
    json, excel, csvAvailable download formats
    Dataset updated
    Jan 5, 2023
    Area covered
    Saudi Arabia
    Description

    This dataset contains Saudi Arabia Consumer Price Index by Main Division (2018=100). Data from from the Saudi Central Bank (SAMA) Follow datasource.kapsarc.org for timely data to advance energy economics research.

Share
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Prathamjyot Singh (2024). Consumer Price Index (CPI) Trends in India Feb'24 [Dataset]. https://www.kaggle.com/datasets/prathamjyotsingh/state-level-consumer-price-index
Organization logo

Consumer Price Index (CPI) Trends in India Feb'24

Tracking Inflation Trends and Price Stability Across Indian States and UTs

Explore at:
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Aug 24, 2024
Dataset provided by
Kaggle
Authors
Prathamjyot Singh
License

Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically

Area covered
India
Description

Explanation of CPI and the Dataset:

What is CPI?

CPI (Consumer Price Index) measures the average change in prices over time that consumers pay for a basket of goods and services. It is a key indicator of inflation and is used by governments and central banks to monitor price stability and for inflation targeting. Components: The construction of CPI involves two main components: Weighting Diagrams: These represent the consumption patterns of households. Price Data: This is collected at regular intervals to track changes in prices.

Role of the Central Statistics Office (CSO):

The CSO, under the Ministry of Statistics and Programme Implementation, is responsible for releasing CPI data. The indices are released for Rural, Urban, and Combined sectors for all-India and individual States/UTs.

Dataset Alignment:

Sectors: The dataset includes a "Sector" column that categorizes data into "Rural," "Urban," and "Rural+Urban," aligning with the CPI data released by the CSO. Time Period: The "Year" and "Name" (which appears to represent months) columns in the dataset track the data over time, consistent with the monthly release schedule by the CSO starting from January 2011. State/UT Data: Each column corresponding to a state or union territory likely represents the CPI values for that region. The numeric values under each state/UT column represent the CPI index values, with a base of 2010=100. Purpose: This data can be used to analyze inflation trends, price stability, and the impact on economic policies, such as adjustments to dearness allowance for employees. Practical Use of This Data: Inflation Analysis: By examining the changes in CPI values across different states, analysts can study regional inflation trends and compare them to the national average. Policy Making: Governments and central banks can use this data to design and adjust policies aimed at controlling inflation, targeting specific regions or sectors that are experiencing higher inflation. Wage Indexation: Companies and governments can use CPI data to adjust wages and allowances in line with inflation, ensuring that purchasing power is maintained.

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