Overview

Blockchain Statistics - Blockchain is a decentralized and distributed digital ledger that records transactions across numerous computers or nodes.

It is the technology that underpins cryptocurrencies such as Bitcoin and Ethereum, but its potential applications go beyond digital money.

A blockchain, at its heart, is a chain of blocks, each of which contains a list of transactions. These blocks are connected via cryptographic hashes, resulting in an immutable and transparent record of all transactions.

Blockchain technology is forecast to increase to nearly 1,000 trillion U.S. dollars by 2032, but this was lower than in a previous forecast. This is according to a market research forecast, focusing on blockchain with cloud applications for specific business segments. The numbers do not include decentralized applications such as blockchain gaming. Originally, a forecast from June 2022 predicted "blockchain technology" would reach 1,235 billion U.S. dollars by 2030, at a CAGR of 82.8 percent. A newer forecast from December 2023 predicts a value of 943 billion U.S. dollars in 2032 with a CAGR of 56.1 percent. The source does not explain this difference.

A blockchain is a distributed ledger with growing lists of records (blocks) that are securely linked together via cryptographic hashes. Each block contains a cryptographic hash of the previous block, a timestamp, and transaction data (generally represented as a Merkle tree, where data nodes are represented by leaves). Since each block contains information about the previous block, they effectively form a chain (compare linked list data structure), with each additional block linking to the ones before it. Consequently, blockchain transactions are irreversible in that, once they are recorded, the data in any given block cannot be altered retroactively without altering all subsequent blocks.

Inspiration

Bitcoin's Blockchain is public. Anybody can run a node locally and get access to all the blocks, since genesis to the current height. However, this process can take some time as the current size exceeds 400 gigabytes and managing the data can be complex as most implementations require the data to be decoded.

The purpose of this dataset is to aid individual researches and developers to be able to analyze the blockchain deeply or even train machine learning models in just a few minutes, instead of several hours/days.

Content

dataset.csv

In this dataset, you will get access to the most relevant information about the Bitcoin Blockchain to kick off your data science project or machine learning model. It is also very easy to subset the data if you're looking to focus on a specific date range.

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Discover key blockchain statistics, including adoption rates, transaction volumes, industry use cases, market growth, and technology trends!

Cryptocurrency web scraping involves extracting data related to digital currencies from various online sources such as cryptocurrency exchanges, news websites, forums, and social media platforms. This data can encompass a wide range of information, including real-time price data, trading volumes, market sentiment, blockchain statistics, ICO details, and more.

Cryptocurrency web scraping is utilized by traders, analysts, researchers, and developers to gather insights, conduct market research, develop trading strategies, build financial models, and create data-driven applications. By collecting and analyzing large volumes of cryptocurrency data, stakeholders can make informed decisions and stay up-to-date with the rapidly evolving crypto market landscape.

As of 2021, ** percent of respondents stated that their companies were working on secure information exchange as a use case based on blockchain technology, making it the most popular use case of the technology. Digital currency such as Bitcoin and Ethereum, asset tracking and management etc. are also common blockchain use cases.

Blockchain technology has evolved far beyond cryptocurrency, shaping the backbone of digital trust and decentralized systems across industries. From supply chain traceability in logistics to secure identity verification in finance and healthcare, blockchain's utility is reshaping how businesses handle data, automation, and compliance. In this article, we’ll explore the most...

The 6 datasets (average gas limit, average gas price, network transaction fee, total gas used, market capitalization, and price) were obtained from etherscan.io’s API in JSON format. The dataset includes the last 7 years (from Friday, August 7, 2015 12:00:00 AM (GMT) to Sunday, November 13, 2022 12:00:00 AM (GMT)) of the following: the Ethereum blockchain’s - Average gas limit - Average gas price - Network transaction fee - Total gas used - ETH’s market capitalization - Price

Context

A cryptocurrency, crypto-currency, or crypto is a collection of binary data which is designed to work as a medium of exchange. Individual coin ownership records are stored in a ledger, which is a computerized database using strong cryptography to secure transaction records, to control the creation of additional coins, and to verify the transfer of coin ownership. Cryptocurrencies are generally fiat currencies, as they are not backed by or convertible into a commodity. Some crypto schemes use validators to maintain the cryptocurrency. In a proof-of-stake model, owners put up their tokens as collateral. In return, they get authority over the token in proportion to the amount they stake. Generally, these token stakes get additional ownership in the token overtime via network fees, newly minted tokens, or other such reward mechanisms.

Cryptocurrency does not exist in physical form (like paper money) and is typically not issued by a central authority. Cryptocurrencies typically use decentralized control as opposed to a central bank digital currency (CBDC). When a cryptocurrency is minted or created prior to issuance or issued by a single issuer, it is generally considered centralized. When implemented with decentralized control, each cryptocurrency works through distributed ledger technology, typically a blockchain, that serves as a public financial transaction database

A cryptocurrency is a tradable digital asset or digital form of money, built on blockchain technology that only exists online. Cryptocurrencies use encryption to authenticate and protect transactions, hence their name. There are currently over a thousand different cryptocurrencies in the world, and many see them as the key to a fairer future economy.

Bitcoin, first released as open-source software in 2009, is the first decentralized cryptocurrency. Since the release of bitcoin, many other cryptocurrencies have been created.

Content

This Dataset is a collection of records of 3000+ Different Cryptocurrencies. * Top 395+ from 2021 * Top 3000+ from 2023

Structure of the Dataset

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Acknowledgements

This Data is collected from: https://finance.yahoo.com/. If you want to learn more, you can visit the Website.

Cover Photo by Worldspectrum: https://www.pexels.com/photo/ripple-etehereum-and-bitcoin-and-micro-sdhc-card-844124/

Solana Blockchain Dataset

Insights into Blockchain Usage, Adoption, and Growth

About this dataset

This dataset provides valuable insights into the usage of the Solana blockchain. The data includes information on blockchain activity, new users, and programs. This data can be used to create dashboards and visualizations to better understand the Solana ecosystem.

The data shows that the Solana ecosystem is growing steadily, with new users and programs being created every week. The data also shows that the majority of users are active on the blockchain, with only a small number of users who have not used the blockchain in recent days.

This dataset provides valuable insights into the usage of the Solana blockchain and can be used to create dashboards and visualizations to better understand the Solana ecosystem

How to use the dataset

This data provides valuable insights into the usage of the Solana blockchain. The data includes information on blockchain activity, new users, and programs. This data can be used to create dashboards and visualizations to better understand the Solana ecosystem.

When exploring the data, you may want to focus on specific columns such as 'Blockchain', 'Creator', 'Address', or 'Label Type'. You can use filters to look at only certain rows of data that are relevant to your research question.

Once you have filtered the data down to a manageable set, you can begin creating visualizations. Visualizations can help you spot trends and patterns that might not be immediately apparent from looking at raw data. For example, if you create a line graph of weekly program creation over time, you might notice an overall upward trend indicating increasing adoption of Solana by developers.

Research Ideas

• This data can be used to create a dashboard that tracks the number of new users of the Solana blockchain.
• This data can be used to create a dashboard that tracks the number of new programs created on the Solana blockchain.
• This data can be used to create a dashboard that tracks the number of days since the last use of the Solana blockchain

Columns

File: program_flipside_labels.csv | Column name | Description | |:------------------|:-----------------------------------------------| | BLOCKCHAIN | The blockchain the transaction is on. (String) | | CREATOR | The creator of the transaction. (String) | | ADDRESS | The address the transaction is from. (String) | | LABEL_TYPE | The type of label. (String) | | LABEL_SUBTYPE | The subtype of label. (String) | | ADDRESS_NAME | The name of the address. (String) |

File: program_solana_fm_labels.csv | Column name | Description | |:-----------------|:-------------------------------------------------| | ADDRESS | The address the transaction is from. (String) | | FriendlyName | The name of the blockchain. (String) | | Abbreviation | The abbreviation for the blockchain. (String) | | Category | The category the blockchain falls into. (String) | | Flag | The flag for the blockchain. (String) | | LogoURI | The logo for the blockchain. (String) |

File: weekly_days_active.csv | Column name | Description | |:------------------|:----------------------------------------------------------| | CREATION_DATE | The date the account was created. (Date) | | Days Active | The number of days the account has been active. (Numeric) |

File: weekly_days_since_last_use.csv | Column name | Description | |:------------------------|:--------------------------------------------------------------| | CREATION_DATE | The date the account was created. (Date) | | Days since last use | The number of days since the account was last used. (Integer) | | Days since creation | The number of days since the account was created. (Integer) |

File: weekly_new_program.csv | Column name | Description | |:-----------------|:--------------------------------------------------------| | WEEK | The week the data is from. (String) | | New Programs | The number of new programs on the blockchain. (Integer) |

File: weekly_new_users.csv | Column name | Description | |:--------------|:------------------------------------------------| | WEEK | The week the data is from. (String) | | NEW_USERS | The number of new users for the week. (Intege...

Introduction: The "Cryptocurrency Price Analysis Dataset: BTC, ETH, XRP, LTC (2018-2023)" is a comprehensive dataset that captures the daily price movements of six popular cryptocurrencies. It covers a period from January 1, 2018, to May 31, 2023, providing a valuable resource for researchers, analysts, and enthusiasts interested in studying the historical price behavior of these digital assets.

Description: This dataset contains a wealth of information for six major cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Litecoin (LTC). The data spans a time frame of over five years, enabling users to explore long-term trends, analyze volatility patterns, and gain insights into market dynamics.

Columns:

1. Crypto: This column specifies the name of the cryptocurrency (e.g., BTC, ETH, XRP, LTC).
2. Date: The date on which the price data was recorded.
3. Open: The opening price of the cryptocurrency at the beginning of the day.
4. High: The highest price reached by the cryptocurrency during the day.
5. Low: The lowest price reached by the cryptocurrency during the day.
6. Close: The closing price of the cryptocurrency at the end of the day.

Use Cases: The dataset offers numerous possibilities for analysis and research within the field of cryptocurrencies. Here are a few potential use cases:

1. Price Analysis: Researchers can investigate the historical price movements of each cryptocurrency to identify trends, patterns, and potential correlations between different assets.
2. Volatility Study: The dataset enables the study of volatility in cryptocurrency markets, helping users understand the frequency and magnitude of price fluctuations.
3. Market Performance: Analysts can analyze the performance of individual cryptocurrencies over time, comparing returns and risk measures to assess their investment potential.
4. Trading Strategies: Traders can utilize the dataset to develop and backtest trading strategies based on technical indicators, price patterns, or machine learning algorithms.
5. Sentiment Analysis: Combine this dataset with external sentiment data to explore the relationship between market sentiment and cryptocurrency price movements. By sharing this dataset on Kaggle, you are providing a valuable resource to the data science community, encouraging collaborative research, and enabling the development of innovative models and solutions within the cryptocurrency domain.

Please note that this dataset is for educational and research purposes only and should not be used for making financial decisions without thorough analysis and consultation with financial professionals.

In 2021, global spending on blockchain solutions is projected to reach *** billion dollars. Forecasts suggest that spending on blockchain solutions will continue to grow in the coming years, reaching almost ** billion U.S. dollars by 2024. What is blockchain? Widely known for its association with cryptocurrencies such as Bitcoin, blockchain technology is simply an electronic list of connected records and verified records. Some of the benefits of this electronic “ledger” are that it is tamper-evident and can be efficiently updated online due to its nature as a decentralized network across many devices. These features make the technology perfect for data validation, data access, and identity protection, which serve as blockchain’s most common use cases. Enterprises around the world have begun to adopt private blockchain for internal purposes such as record keeping and intra-company transactions, as well as public blockchain like Bitcoins in their payment processes. The business of blockchain Given the potential of the technology and the widespread business interest in the capabilities it can provide, blockchain has become a considerable market in its own right, even at this relatively early stage of the technology’s development. Promising blockchain startup companies regularly accumulate hundreds of millions of dollars of investment in their initial offerings, with particularly successful ones such as EOS raking in multiple billions.

Context:

I gathered around 4k data by extracting information from Coingecko and created a CSV file. If anyone is interested, they can review the file on this GitHub link.

Objective & Inspiration:

Analyzing cryptocurrencies is essential for making informed decisions in a rapidly evolving and complex ecosystem. It allows stakeholders to navigate risks, seize opportunities, and contribute to the responsible development of this innovative space. Later i utilized the scraped data to understand the following Trends and relations using Tableau Dashboard:

Performance Trends: Visualize and compare the performance trends (1h, 24h, 7d, 30d) of different cryptocurrencies based on their respective Coin Names.

Market Metrics Overview: Create comprehensive visualizations comparing Top Coin Names against essential metrics, including Price, 24-hour Volume, Circulating Supply, and Market Cap.

Aggregate Metrics: Calculate and visualize aggregate metrics such as Total Market Cap, Total 24-hour Volume, and Total Circulating Supply across all cryptocurrencies.

Columns Name of the data :

Rank: The "Rank" column indicates the ranking of each cryptocurrency based on certain criteria. It helps users understand the relative standing of each coin in comparison to others.

Coin Name: The "Coin Name" column contains the names of various cryptocurrencies. Each row represents a different digital asset, such as Bitcoin, Ethereum, or other altcoins.

Symbol: The "Symbol" column typically represents the shorthand symbol or abbreviation associated with each cryptocurrency. For example, the symbol for Bitcoin is "BTC," and for Ethereum, it's "ETH."

Price: The "Price" column shows the current or latest market price of each cryptocurrency. It is the value at which the coin is traded on the market.

1h, 24h, 7d, 30d: These columns ("1h," "24h," "7d," "30d") represent the percentage change in the price of each cryptocurrency over different time intervals. They provide insights into short-term and long-term price fluctuations.

24h Volume: The "24h Volume" column indicates the total trading volume (in terms of the cryptocurrency) over the last 24 hours. It reflects the total value of all transactions within that time frame.

Circulating Supply: The "Circulating Supply" column specifies the number of units of a cryptocurrency that are currently available and in circulation. It helps assess the liquidity and availability of the cryptocurrency.

Total Supply: The "Total Supply" column represents the total number of units of a cryptocurrency that will ever exist. It provides information about the maximum supply limit of the cryptocurrency.

Market Cap: The "Market Cap" column represents the total market capitalization of each cryptocurrency. It is calculated by multiplying the current price by the circulating supply and provides an overall valuation of the cryptocurrency in the market.

Visualization

Tableau visualization of this dataset can also be found in this link

Blockchain data query: Statistics by BlockChain

Introduction

Bitcoin Statistics: Bitcoin, often called "digital gold" or a "cryptocurrency," emerged in 2009, created by an enigmatic individual known as Satoshi Nakamoto.

It functions on a decentralized blockchain network, notable for its capped supply of 21 million coins, establishing it as a deflationary asset.

Bitcoin enables direct peer-to-peer transactions without intermediaries, employing robust cryptographic security measures.

It has garnered recognition as a digital store of value and a medium of exchange, attracting investments and offering potential solutions for remittances and financial inclusivity.

Despite encountering obstacles like regulatory scrutiny and scalability concerns, Bitcoin's continual development is disrupting conventional financial systems, and its enduring influence on the worldwide economy remains a topic of profound interest and pioneering advancements.

Bitcoin's blockchain size was close to reaching 673.58 gigabytes in September 2025, as the database saw exponential growth by nearly one gigabyte every few days. The Bitcoin blockchain contains a continuously growing and tamper-evident list of all Bitcoin transactions and records since its initial release in January 2009. Bitcoin has a set limit of 21 million coins, the last of which will be mined around 2140, according to a forecast made in 2017. Bitcoin mining: A somewhat uncharted world Despite interest in the topic, there are few accurate figures on how big Bitcoin mining is on a country-by-country basis. Bitcoin's design philosophy is at the heart of this. Created out of protest against governments and central banks, Bitcoin's blockchain effectively hides both the country of origin and the destination country within a (mining) transaction. Research involving IP addresses placed the United States as the world's most Bitcoin mining country in 2022, but the source admits IP addresses can easily be manipulated using VPN. Note that mining figures are different from figures on Bitcoin trading: Africa and Latin America were more interested in buying and selling BTC than some of the world's developed economies. Bitcoin developments Bitcoin's trade volume slowed in the second quarter of 2023 after hitting a noticeable growth at the beginning of the year. The coin outperformed most of the market. Some attribute this to the announcement in June 2023 that BlackRock filed for a Bitcoin ETF. This iShares Bitcoin Trust was to use Coinbase Custody as its custodian. Regulators in the United States had not yet approved any applications for spot ETFs on Bitcoin.

This statistic displays the value of blockchain in the agriculture and food market worldwide in 2020, with forecasted figures for 2026. The global market value of blockchain in the food and agriculture market amounted to about 140 million U.S. dollars in 2020, and is projected to grow to about 1.5 billion U.S. dollars by 2026.

🚀 Top 50 Cryptocurrencies — Historical Market Data

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Your support motivates more high-quality open datasets for the community.

📊 Subtitle

Historical OHLCV Prices, Returns & Market Metrics for Leading Digital Assets

🧾 Dataset Description

This dataset provides clean, structured, and analysis-ready historical market data for the Top 50 cryptocurrencies, collected using Yahoo Finance (via yfinance).

It is designed for: - 📈 Exploratory Data Analysis (EDA) - ⏳ Time-Series Analysis - 🤖 Machine Learning & Forecasting - 📚 Academic & Educational Use - 💼 Financial & Crypto Market Research

The dataset includes daily OHLCV (Open, High, Low, Close, Volume) price data over the maximum available historical period for each cryptocurrency, enabling analysis of long-term trends, volatility, correlations, and market cycles.

All files are provided in CSV format, ensuring compatibility with Python, R, Excel, Power BI, Tableau, and other analytics tools.

🪙 Cryptocurrencies Included (Top 50)

The dataset covers major cryptocurrencies across multiple categories such as store-of-value assets, smart-contract platforms, DeFi tokens, Layer-2 solutions, and meme coins.

Examples include Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Solana (SOL), Cardano (ADA), XRP (XRP), Dogecoin (DOGE), Polygon (MATIC), Avalanche (AVAX), Polkadot (DOT), Chainlink (LINK), Uniswap (UNI), Shiba Inu (SHIB), Pepe (PEPE), and many more — 50 assets in total.

Each cryptocurrency is stored as an individual CSV file, allowing independent analysis or easy merging into a master dataset.

🧱 Dataset Structure

crypto_data/
├── bitcoin.csv
├── ethereum.csv
├── solana.csv
├── dogecoin.csv
├── ...
└── (50 CSV files in total)

📑 Column Descriptions

Each CSV file contains the following columns:

All prices are denominated in US Dollars (USD).

✨ Key Features

• Daily historical OHLCV data
• Covers top 50 cryptocurrencies by market relevance
• Clean, readable, and well-structured CSV files
• Ready for EDA, ML models, and forecasting
• Ideal for Kaggle notebooks and datasets

🎯 Use Cases

This dataset can be used for: - Cryptocurrency price trend analysis
- Volatility and risk modeling
- Correlation and portfolio analysis
- Time-series forecasting (ARIMA, Prophet, LSTM)
- Machine learning and deep learning experiments
- Teaching and academic research

📜 Data Source & License

• Source: Yahoo Finance (via yfinance)
• License: Provided for educational and research purposes
• Update Frequency: Static snapshot

⚠️ Notes

• Some cryptocurrencies may have shorter historical coverage depending on launch date
• Missing or zero values may appear in early trading periods
• Trading volume availability varies across assets and time

⭐ Why This Dataset Is Valuable

This dataset offers a standardized, long-term view of the cryptocurrency market, making it a strong resource for financial analytics, crypto economics, and data-driven market insights.

🙏 If this dataset helps your work, please consider upvoting 👍
It helps the Kaggle community and motivates future datasets.

Cryptocurrency historical datasets from January 2012 (if available) to October 2021 were obtained and integrated from various sources and Application Programming Interfaces (APIs) including Yahoo Finance, Cryptodownload, CoinMarketCap, various Kaggle datasets, and multiple APIs. While these datasets used various formats of time (e.g., minutes, hours, days), in order to integrate the datasets days format was used for in this research study. The integrated cryptocurrency historical datasets for 80 cryptocurrencies including but not limited to Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Cardano (ADA), Tether (USDT), Ripple (XRP), Solana (SOL), Polkadot (DOT), USD Coin (USDC), Dogecoin (DOGE), Tron (TRX), Bitcoin Cash (BCH), Litecoin (LTC), EOS (EOS), Cosmos (ATOM), Stellar (XLM), Wrapped Bitcoin (WBTC), Uniswap (UNI), Terra (LUNA), SHIBA INU (SHIB), and 60 more cryptocurrencies were uploaded in this online Mendeley data repository. Although the primary attribute of including the mentioned cryptocurrencies was the Market Capitalization, a subject matter expert i.e., a professional trader has also guided the initial selection of the cryptocurrencies by analyzing various indicators such as Relative Strength Index (RSI), Moving Average Convergence/Divergence (MACD), MYC Signals, Bollinger Bands, Fibonacci Retracement, Stochastic Oscillator and Ichimoku Cloud. The primary features of this dataset that were used as the decision-making criteria of the CLUS-MCDA II approach are Timestamps, Open, High, Low, Closed, Volume (Currency), % Change (7 days and 24 hours), Market Cap and Weighted Price values. The available excel and CSV files in this data set are just part of the integrated data and other databases, datasets and API References that was used in this study are as follows: [1] https://finance.yahoo.com/ [2] https://coinmarketcap.com/historical/ [3] https://cryptodatadownload.com/ [4] https://kaggle.com/philmohun/cryptocurrency-financial-data [5] https://kaggle.com/deepshah16/meme-cryptocurrency-historical-data [6] https://kaggle.com/sudalairajkumar/cryptocurrencypricehistory [7] https://min-api.cryptocompare.com/data/price?fsym=BTC&tsyms=USD [8] https://min-api.cryptocompare.com/ [9] https://p.nomics.com/cryptocurrency-bitcoin-api [10] https://www.coinapi.io/ [11] https://www.coingecko.com/en/api [12] https://cryptowat.ch/ [13] https://www.alphavantage.co/ This dataset is part of the CLUS-MCDA (Cluster analysis for improving Multiple Criteria Decision Analysis) and CLUS-MCDAII Project: https://aimaghsoodi.github.io/CLUSMCDA-R-Package/ https://github.com/Aimaghsoodi/CLUS-MCDA-II https://github.com/azadkavian/CLUS-MCDA

This comprehensive dataset comprises daily historical price data for over 50 major cryptocurrencies, providing a robust foundation for market analysis, machine learning models, and the development of trading strategies. Whether you're building price prediction algorithms, conducting market research, or exploring crypto trends. The dataset offers clean, structured data ready for immediate use.

🎯 What's Inside

• 50+ cryptocurrencies, including Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and other top market cap coins.
• Daily OHLCV data: Open, High, Low, Close prices, trading Volume, and Counts.
• Multi-year coverage: Historical data spanning multiple years for comprehensive time series analysis.

💡 Use Cases

✅ Machine Learning & AI: Train predictive models for price forecasting and trend analysis.

✅ Trading Strategies: Backtest algorithmic trading strategies and portfolio optimization.

✅ Market Analysis: Explore correlations, volatility patterns, and market dynamics.

✅ Data Visualization: Create compelling charts and dashboards.

✅ Research & Education: Academic studies on cryptocurrency markets and blockchain economics.

✅ Risk Management: Analyze historical volatility and develop hedging strategies.

📈 Key Features

• High-quality data: Sourced from reliable cryptocurrency exchanges
• Consistent format: Uniform structure across all cryptocurrencies for easy comparison
• Time series ready: Indexed by date for seamless integration with pandas, NumPy, and ML libraries
• Wide coverage: Diverse selection of coins across different market caps and categories (DeFi, Layer 1, Meme coins, etc.)

🧾 Data Features (Column Descriptions)

Each CSV file contains the following columns:

These features are essential for: - Candlestick and OHLC analysis
- Volatility and momentum indicators
- Time-series forecasting models
- Market activity and liquidity analysis

🔧 Technical Specifications

• File Format: CSV
• Frequency: Daily
• **Columns: Time, Date, Open, High, Low, Close, Volume, Trading Count.

⚠️ Disclaimer

This dataset is provided for educational and research purposes only. Past performance does not guarantee future results. Always conduct your own research before making investment decisions.