web3-data-center 0.7.3

Web3 data center integrating multiple APIs for blockchain data analysis

Web3 Data Center

Web3 Data Center is a Python package that integrates multiple APIs to provide comprehensive blockchain data analysis capabilities. It offers a unified interface to access data from various sources, making it easier for developers to gather and analyze blockchain-related information.

Features

• Integration with multiple blockchain data providers (GeckoTerminal, GMGN, Birdeye, Solscan, GoPlus, DexScreener)
• Asynchronous API calls for improved performance
• Persistent file-based caching system for optimized data retrieval
• Caching mechanism to reduce API calls and improve response times
• Support for multiple blockchains (Ethereum, Solana, and more)
• Token information retrieval (price, market cap, holders, etc.)
• Transaction analysis
• Token security checks

Installation

You can install Web3 Data Center using pip:

pip install data_center

Quick Start

Here's a simple example of how to use Web3 Data Center:

import asyncio
from web3_data_center import DataCenter
async def main():
data_center = DataCenter()
# Get token info
token_address = "CzLSujWBLFsSjncfkh59rUFqvafWcY5tzedWJSuypump" # Wrapped SOL
token_info = await data_center.get_token_info(token_address)
print(f"Token Info: {token_info}")
# Get top holders
top_holders = await data_center.get_top_holders(token_address, limit=10)
print(f"Top 10 Holders: {top_holders}")
asyncio.run(main())

Caching System

Web3 Data Center includes a robust file-based caching system to improve performance and reduce API calls. The cache is stored in ~/.web3_data_center/cache/ and is automatically managed.

Cached Operations

The following operations are cached by default:

• Root funder lookups (24-hour cache)
• Funding path queries (24-hour cache)
• Funding relationship checks (24-hour cache)

Using the Cache

The cache is automatically used when calling the relevant methods. You can also use the caching decorator for your own functions:

from web3_data_center import file_cache

@file_cache(namespace="my_cache", ttl=3600)  # 1-hour cache
async def my_function():
    # Your code here
    pass

Cache Management

To clear the cache for a specific function:

data_center.get_root_funder.cache_clear()

To get the cache directory:

from web3_data_center import get_cache_dir
cache_dir = get_cache_dir()

The cache automatically manages:

• Entry expiration (TTL-based)
• Size limits
• Cleanup of old entries

Documentation

For detailed documentation, please refer to the docs directory.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Thanks to all the API providers that make this project possible.
• Special thanks to the open-source community for their invaluable tools and libraries.