5 datasets found
  1. kimonaim

    • kaggle.com
    Updated Aug 10, 2022
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    Ariel Paz-Sawicki (2022). kimonaim [Dataset]. https://www.kaggle.com/datasets/arielpazsawicki/kimonaim/discussion
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 10, 2022
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Ariel Paz-Sawicki
    Description

    August 2022 update

    1. The updated database has monthly prices from over 400 supermarkets across six chains (all Shufersal stores, plus ~20 per chain for the other chains), from March 2022-August 2022. Before march there is a steady increase in the number of stores.
    2. The current database has combined all the supermarket chains, which required adding two columns - 'ChainName' is the supermarket chain, and 'store_code' is a concatenation of the store_id and the beginning of the chain name, to solve the duplicate store_ids.
    3. A new table was constructed to try and identify the manufacturers of each product, given the significant discrepancies in the retailers' reporting. This table ("manufacturer_finder") has identified the likely supplier of around half of the products in the database, and can be used to track specific suppliers (producers/importers).

    This dataset was created to analyze changes in prices in the Israeli grocery retail market. It was created based on the files retailers are legally required to upload, available here: https://www.gov.il/he/departments/legalInfo/cpfta_prices_regulations

    The data is not complete and downloads increased gradually. Beginning in May 2020 there are sporadic files for three specific Shufersal stores. Starting in November 2021 Downloads increased, ~20-50 stores downloaded at various times from Shufersal, and ~5-10 stores downloaded from a few other retailers.

    Different table for each retailer. The table "snifim" specifies the names for stores for Shufersal (in the main table you can find store_id which can be joined to the names).

    Description of columns in the Prices tables: Filename - original file name (without the xml extension) store_id - ID of the store upload_date - date of file download. Upload dates before 2020 - unclear what they are, probably of stores which shut down.
    PriceUpdateDate - Last date of price change of the item. ItemCode - a unique ID of the item. ItemName - name. ManufacturerName - manufacturer. These data are messy. ManufactureCountry - country of production. ManufacturerItemDescription - similar to ItemName UnitQty - unit of measure Quantity - quantity. UnitOfMeasure - also unit of measure ItemPrice - price (NIS) UnitOfMeasurePrice - price divided by quantity AllowDiscount - boolean/dummy variable.

    Supplementary data can be found here: https://docs.google.com/spreadsheets/d/1LYyCt3BTJ-QInja-4iN1vqZ91xV6TAwhywgJxecSOkM/edit?usp=sharing Including: - Analysis of suppliers - different labels associated with each supplier - A table linking Shufersal stores with their store_id - A table with details on how many price files (stores) were downloaded each date.

    What are we looking for? - Price collusion - producers raising prices at the same time. - Which producers saw the greatest price increase? - Which is the most expensive store? - Which products are most promoted? You can go to the source and find "promo" tables. - Can you create a user-friendly tool to analyze these data for non-data scientists?

  2. Monthly average retail prices for selected products

    • www150.statcan.gc.ca
    • datasets.ai
    • +2more
    Updated Jul 2, 2025
    + more versions
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    Government of Canada, Statistics Canada (2025). Monthly average retail prices for selected products [Dataset]. http://doi.org/10.25318/1810024501-eng
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    Dataset updated
    Jul 2, 2025
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    Area covered
    Canada
    Description

    Monthly average retail prices for selected products, for Canada and provinces. Prices are presented for the current month and the previous four months. Prices are based on transaction data from Canadian retailers, and are presented in Canadian current dollars.

  3. F

    Average Price: Bread, White, Pan (Cost per Pound/453.6 Grams) in U.S. City...

    • fred.stlouisfed.org
    json
    Updated Jul 15, 2025
    + more versions
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    (2025). Average Price: Bread, White, Pan (Cost per Pound/453.6 Grams) in U.S. City Average [Dataset]. https://fred.stlouisfed.org/series/APU0000702111
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jul 15, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Area covered
    United States
    Description

    All white pre-packaged regular bread regardless of size. Includes frozen and fresh bread.

  4. F

    Average Price: Milk, Fresh, Whole, Fortified (Cost per Gallon/3.8 Liters) in...

    • fred.stlouisfed.org
    json
    Updated Jul 15, 2025
    + more versions
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    (2025). Average Price: Milk, Fresh, Whole, Fortified (Cost per Gallon/3.8 Liters) in U.S. City Average [Dataset]. https://fred.stlouisfed.org/series/APU0000709112
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jul 15, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Area covered
    United States
    Description

    Fresh whole milk, fortified, sold per gallon regardless of packaging type. Includes organic and non-organic milk."

  5. Average retail price for ground beef in Canada 2017-2024

    • statista.com
    Updated May 29, 2024
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    Statista (2024). Average retail price for ground beef in Canada 2017-2024 [Dataset]. https://www.statista.com/statistics/443386/average-retail-price-for-ground-beef-in-canada/
    Explore at:
    Dataset updated
    May 29, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 2017 - Mar 2024
    Area covered
    Canada
    Description

    In Canada, one kilogram of ground beef cost an average 11.72 Canadian dollars in March 2024. Ground, otherwise known as minced, beef is often considered a cheaper and more versatile alternative to other cuts of beef. For example, prime rib roast cost an average 43.16 Canadian dollars per kilogram in February 2022, over three times as much as ground beef. Trends in Canadian meat consumption As of 2022, beef was the second most consumed meat in Canada, after chicken. Beef consumption has been decreasing though, at just about 25.7 kilograms per capita in 2022. This is compared to almost 40 kilograms per capita in1980. Just under half of Canadians stated that they consume meat daily when surveyed. With up and coming trends in eating behavior in Canada , such as vegetarianism and veganism, it is possible that consumption will decrease in the coming years and decades.

  6. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Click to copy link
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Ariel Paz-Sawicki (2022). kimonaim [Dataset]. https://www.kaggle.com/datasets/arielpazsawicki/kimonaim/discussion
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kimonaim

price data from Israeli grocery retailers, November 2021-February 2022

Explore at:
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Aug 10, 2022
Dataset provided by
Kagglehttp://kaggle.com/
Authors
Ariel Paz-Sawicki
Description

August 2022 update

  1. The updated database has monthly prices from over 400 supermarkets across six chains (all Shufersal stores, plus ~20 per chain for the other chains), from March 2022-August 2022. Before march there is a steady increase in the number of stores.
  2. The current database has combined all the supermarket chains, which required adding two columns - 'ChainName' is the supermarket chain, and 'store_code' is a concatenation of the store_id and the beginning of the chain name, to solve the duplicate store_ids.
  3. A new table was constructed to try and identify the manufacturers of each product, given the significant discrepancies in the retailers' reporting. This table ("manufacturer_finder") has identified the likely supplier of around half of the products in the database, and can be used to track specific suppliers (producers/importers).

This dataset was created to analyze changes in prices in the Israeli grocery retail market. It was created based on the files retailers are legally required to upload, available here: https://www.gov.il/he/departments/legalInfo/cpfta_prices_regulations

The data is not complete and downloads increased gradually. Beginning in May 2020 there are sporadic files for three specific Shufersal stores. Starting in November 2021 Downloads increased, ~20-50 stores downloaded at various times from Shufersal, and ~5-10 stores downloaded from a few other retailers.

Different table for each retailer. The table "snifim" specifies the names for stores for Shufersal (in the main table you can find store_id which can be joined to the names).

Description of columns in the Prices tables: Filename - original file name (without the xml extension) store_id - ID of the store upload_date - date of file download. Upload dates before 2020 - unclear what they are, probably of stores which shut down.
PriceUpdateDate - Last date of price change of the item. ItemCode - a unique ID of the item. ItemName - name. ManufacturerName - manufacturer. These data are messy. ManufactureCountry - country of production. ManufacturerItemDescription - similar to ItemName UnitQty - unit of measure Quantity - quantity. UnitOfMeasure - also unit of measure ItemPrice - price (NIS) UnitOfMeasurePrice - price divided by quantity AllowDiscount - boolean/dummy variable.

Supplementary data can be found here: https://docs.google.com/spreadsheets/d/1LYyCt3BTJ-QInja-4iN1vqZ91xV6TAwhywgJxecSOkM/edit?usp=sharing Including: - Analysis of suppliers - different labels associated with each supplier - A table linking Shufersal stores with their store_id - A table with details on how many price files (stores) were downloaded each date.

What are we looking for? - Price collusion - producers raising prices at the same time. - Which producers saw the greatest price increase? - Which is the most expensive store? - Which products are most promoted? You can go to the source and find "promo" tables. - Can you create a user-friendly tool to analyze these data for non-data scientists?

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