Overview

The data refers to Daily prices of various commodities in India like Tomato, Potato, Brinjal, Wheat etc. It has the wholesale maximum price, minimum price and modal price on daily basis. the prices in the dataset refer to the wholesale prices of various commodities per quintal (100 kg) in Indian rupees. The wholesale price is the price at which goods are sold in large quantities to retailers or distributors.

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Features of the dataset include:

• State: The state in India where the market is located.
• District: The district in India where the market is located.
• Market: The name of the market.
• Commodity: The name of the commodity.
• Variety: The variety of the commodity.
• Grade: The grade or quality of the commodity.
• Min Price: (INR) The minimum wholesale price of the commodity on a given day, per quintal (100 kg).
• Max Price: (INR) The maximum wholesale price of the commodity on a given day, per quintal (100 kg).
• Modal Price: (INR) The most common or representative wholesale price of the commodity on a given day, per quintal (100 kg).

1 INR = 0.012 USD (as on 17 August, 2023)

Use Cases

Market analysis: You can use this dataset to analyze trends and patterns in the wholesale prices of various commodities across different markets in India. This can help you understand factors that affect prices, such as supply and demand, seasonality, and market conditions. Commodity recommendation: Develop recommender systems that suggest the best markets or commodities for farmers or traders to sell or buy based on their location, preferences, and market conditions.

Licensed under the Government Open Data License - India (GODL) https://data.gov.in/government-open-data-license-india

Feel free to download the data and use it in your work. I will wait for interesting notebooks from your side. Thank you

Graph and download economic data for Global Price Index of All Commodities (PALLFNFINDEXQ) from Q1 1992 to Q4 2025 about World, commodities, price index, indexes, and price.

GSCI fell to 719.33 Index Points on March 27, 2026, down 0.11% from the previous day. Over the past month, GSCI's price has risen 13.77%, and is up 28.34% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. GSCI Commodity Index - values, historical data, forecasts and news - updated on March of 2026.

Context

Time series of major commodity prices and indices including iron, cooper, wheat, gold, oil

Content

Dataset contains Monthly prices for 53 commodities and 10 indexes, starting from 1980 to 2016, Last updated on march 17, 2016. The reference year for indexes are 2005 (meaning the value of indexes are 100 and all other values are relative to that year).

Inspiration

This is a challenging dataset with a fair share of NaN values. Some really good potential for EDA and also Time Series Analysis!

CRB Index rose to 457.52 Index Points on March 26, 2026, up 1.55% from the previous day. Over the past month, CRB Index's price has risen 16.06%, and is up 22.67% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. CRB Commodity Index - values, historical data, forecasts and news - updated on March of 2026.

Graph and download economic data for Global Price Index of All Commodities (PALLFNFINDEXM) from Jan 1998 to Jan 2026 about World, commodities, price index, indexes, and price.

The price index of natural gas dropped sharply in October 2022 after having reached around 893 points in August 2022 relative to the base year of 2016. By February 2026, precious metals had the highest consumer price index of the selected commodities at 396.32. In other words, precious metals prices worldwide were nearly four times higher in that month than in 2016. The cost of several commodities, especially energy resources, rose at the end of February 2022 after the Russian invasion of Ukraine.

View monthly updates and historical trends for All Commodities Price Index. Source: International Monetary Fund. Track economic data with YCharts analytic…

Nominal prices in USD for selected key international commodity prices relevant to Pacific Island Countries and Territories, extracted from World bank Commodity Prices (« pink sheets ») and from FAO GLOBEFISH European Fish Price Report.

Find more Pacific data on PDH.stat.

This dataset aggregates daily wholesale price data for a wide spectrum of agricultural commodities traded across India’s regulated markets (mandis). It captures minimum, maximum, and modal prices, enabling detailed analysis of price dispersion and volatility over time. Data is sourced directly from the AGMARKNET portal and made available under the National Data Sharing and Accessibility Policy (NDSAP). With over 165,000 views and nearly 400,000 downloads, it’s a cornerstone resource for economists, agronomists, and data scientists studying India’s commodity markets.

This dataset provides daily wholesale minimum, maximum, and modal prices for a wide variety of agricultural commodities across India’s mandis, sourced from the AGMARKNET portal and published on Data.gov.in under NDSAP, with records dating back to 2013 and updated as of 19 May 2025 via a REST API; it includes key fields like Arrival_Date, State, District, Market, Commodity, Variety, Min_Price, Max_Price, and Modal_Price, making it ideal for time-series analysis, price-trend visualizations, and commodity forecasting.

This statistic depicts global commodity price indexes for energy, metal, and agriculture from January 2018 to November 2019. In November 2019, the commodity index for energy stood at 87.7, compared to 86.1 for metals, and 98.4 for agriculture.

This chartbook is an update of Jacks, D.S. (2019), “From Boom to Bust: A Typology of Real Commodity Prices in the Long Run.” Cliometrica 13(2), 201-220. It analyses an accompanying dataset on 42 commodities, comprising 7.43 trillion USD worth of production in 2019 and spanning the years from 1850 to 2024. It also presents evidence on three commodity price indices using various weighting schemes for the period from 1900 to 2024. Applying weights drawn from the value of production in 1975, real commodity prices are estimated to have increased by 37.22% (or 0.26% per annum) from 1900, 43.73% (or 0.50% per annum) from 1950, and 12.54% (or 0.25% per annum) from 1975. The data also indicates the presence of three complete commodity price cycles, entailing multi-year positive deviations from the long-run trend. The most recently completed cycle began in 1996, reached its peak in 2010, and is now likely near its trough.

Commodity prices are updated in the second business day of the month. Commodity price forecasts are updated twice a year (April and October). The Manufacture Unit Value Index (MUV), also updated twice a year, can be found in the in the worksheet “Annual Price” excel file, “Annual Indices (Real)” worksheet. This dataset includes data previously published as the "Global Economic Monitor (GEM) Commodities" and "Manufactures Unit Value Index (MUV Index)". | This dataset contains important information and resources. For comprehensive details, documentation, and inquiries, please contact data@worldbank.org. Additional metadata and related resources are available on this page.

The commodity prices displayed in Trading Economics are based on over-the-counter (OTC) and contract for difference (CFD) financial instruments. Our market prices are intended to provide you with a reference only, rather than as a basis for making trading decisions. Trading Economics does not verify any data and disclaims any obligation to do so. This dataset provides a table with prices for several commodities including the latest price for the nearby futures contract, yesterday close, plus weekly, monthly and yearly percentage changes. This dataset provides a table with prices for several commodities including the latest price for the nearby futures contract, yesterday close, plus weekly, monthly and yearly percentage changes.

Weekly Commodity Prices are made up of four excel spreadsheets and graphs split into commodity groups. Source agency: Environment, Food and Rural Affairs Designation: National Statistics Language: English Alternative title: Commodity Price Movements If you require datasets in a more accessible format, please contact prices@defra.gsi.gov.uk.

📈 Global Commodities Market Dataset (1997–Present)

Overview

This dataset provides a comprehensive historical record of global commodity prices across energy, metals, agriculture, and construction-related commodities. It is designed for time-series analysis, forecasting, econometrics, quantitative finance, and machine learning research.

The dataset aggregates daily market data spanning more than two decades, enriched with engineered temporal features to support predictive modeling and pattern discovery.

📊 What’s Inside

• Time Span: October 1997 → Recent years
• Frequency: Daily
• Total Rows: 7,103
• Total Features: 131 columns

Each commodity includes standard OHLCV-style market data where available.

🛢️ Covered Commodity Categories

Energy

• Crude Oil (WTI)
• Brent Oil
• Natural Gas

Precious & Industrial Metals

• Gold
• Silver
• Copper
• Aluminum
• Zinc
• Nickel
• Lead

Agricultural Commodities

• Corn
• Wheat
• Soybeans
• Rice
• Cotton
• Sugar
• Coffee
• Cocoa

Construction & Industrial Inputs

• Lumber

📌 Feature Breakdown

1. Market Price Data

For each commodity, the dataset may include: - Open - High - Low - Close - Volume

Note: Some commodities have missing values during early years due to data availability.

2. Temporal & Calendar Features

To enable advanced time-series modeling and seasonality analysis: - Year - Month - Quarter - Day_of_Week - Week_of_Year

These features are pre-engineered for immediate use in ML pipelines.

🧠 Ideal Use Cases

• Commodity price forecasting
• Inflation and macroeconomic analysis
• Volatility modeling
• Correlation and co-movement studies
• Feature engineering practice for time-series ML
• LSTM / Transformer-based forecasting models
• Algorithmic trading research (educational)

⚠️ Notes on Data Quality

• Missing values exist, especially in earlier years for some commodities.
• Users are encouraged to apply:
  • Forward-filling
  • Interpolation
  • Asset-specific filtering
• Prices are nominal and not inflation-adjusted.

📂 File Format

• CSV
• UTF-8 encoded
• Ready for use with:
  • Pandas
  • NumPy
  • Scikit-learn
  • PyTorch / TensorFlow
  • R

Commodity Prices YoY in Australia increased to 3.40 percent in February from 2.70 percent in January of 2026. This dataset includes a chart with historical data for Australia Commodity Prices YoY.

Daily market prices of agricultural commodities across India from 2001-2025. Contains 75+ million records covering 374 unique commodities and 1,504 varieties from various mandis (wholesale markets). Commodity Like: Vegetables, Fruits, Grains, Spices, etc.

Cleaned, deduplicated, and sorted by date and commodity for analysis.

Column Schema

Data sourced from the Government of India's Open Data Platform.

License: Government Open Data License - India (GODL-India) https://www.data.gov.in/Godl

The 'Commodity Prices YoY' in Australia measures the year-over-year change in the prices of key commodities exported by the country, such as iron ore, coal, and natural gas.

A dataset of various commodity price Licensed Data provided by EZB / CC-BY-4.0 by Worldbank