subgrounds-gnosis 1.9.2
A Pythonic data access layer for applications querying data from The Graph Network.
Requires Python: <4.0,>=3.10
Keywords: graph, subgrounds, graphql, subgraph

# Subgrounds

Fork of https://github.com/0xPlaygrounds/subgrounds with small changes due to pending PRs in the original repository.

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<!-- start elevator-pitch -->
An intuitive Python library for interfacing with subgraphs and GraphQL.

## Features
- **Simple**: Leverage a Pythonic API to easily build queries and transformations without the need for raw GraphQL manipulation.
- **Automated**: Automatically handle pagination and schema introspection for effortless data retrieval.
- **Powerful**: Create sophisticated queries using the `SyntheticFields` transformation system.
<!-- end elevator-pitch -->

## Resources
- [**Docs**](http://docs.playgrounds.network/): User guide and API documentation
- [**Snippets**](https://github.com/gnosis/subgrounds/tree/main/examples): A list of examples showcasing Subgrounds integration with Dash and Plotly
- [**Examples**](http://docs.playgrounds.network/subgrounds/examples/): An ever growing list of projects created by our community members and team
- [**Videos**](https://docs.playgrounds.network/subgrounds/videos/): Video workshops on Subgrounds

## Installation
> Subgrounds **requires** atleast Python 3.10+

Subgrounds is available on PyPi. To install it, run the following:<br>
`pip install subgrounds-gnosis`.

Subgrounds also comes bundled with extra modules that may require extra libraries. You can get all functionality of `subgrounds` via the following:<br>
`pip install subgrounds-gnosis[all]`.

## Simple example
<!-- start simple-example -->
```python
>>> from subgrounds import Subgrounds

>>> sg = Subgrounds()

>>> # Load
>>> aave_v3 = sg.load_subgraph("https://api.thegraph.com/subgraphs/name/messari/aave-v3-ethereum")

>>> # Construct the query
>>> largest_markets = aave_v3.Query.markets(
...     orderBy=aave_v3.Market.totalValueLockedUSD,
...     orderDirection='desc',
...     first=5,
... )

>>> # Return query to a dataframe
>>> sg.query_df([
...     largest_markets.name,
...     largest_markets.totalValueLockedUSD,
... ])
          markets_name  markets_totalValueLockedUSD
0 Aave Ethereum wstETH                 1.915090e+09
1   Aave Ethereum WETH                 1.047417e+09
2   Aave Ethereum WBTC                 6.049655e+08
3   Aave Ethereum USDC                 4.482461e+08
4   Aave Ethereum USDT                 4.280103e+08
```
<!-- end simple-example -->


## About Us
Playgrounds Analytics is a data solutions company providing data infrastructures and services for data teams, analysts, and engineers. Check us out [here](https://playgrounds.network/) to learn more!


## Acknowledgments
This software project would not be possible without the support of The Graph Foundation. You can learn more about The Graph and its mission [here](https://thegraph.com/).