semvec/pypi-packages huggingface dataset:
dagster-iceberg 0.3.11
Package for PyIceberg-specific Dagster framework op and resource components.
Requires Python: >=3.10

![dagster-iceberg](docs/assets/dagster-iceberg-main.png)

> ⚠️ This project has [preview status](https://docs.dagster.io/api/api-lifecycle)

[Dagster](https://dagster.io/) IO manager for managing [Iceberg](https://iceberg.apache.org/) tables with [PyIceberg](https://github.com/apache/iceberg-python).

## Examples

See:

- [example with postgresql catalog](https://github.com/JasperHG90/dagster-pyiceberg-example-postgres)
- [example with Polaris catalog](https://github.com/JasperHG90/dagster-pyiceberg-example-polaris)

## Installation

See [Installing dagster-iceberg](https://jasperhg90.github.io/community-integrations/installation/).

## Documentation

Documentation can be built locally by cloning this repository, navigating to the 'dagster-iceberg' folder, and running `make docs`. It is also available on [here](https://jasperhg90.github.io/community-integrations/).

## Features

Available features can be found [here](https://jasperhg90.github.io/community-integrations/features/).

## Status

This library is currently in development and has [preview status](https://docs.dagster.io/api/api-lifecycle).
fair-mango 0.2.2
Explore your AI model's fairness
Requires Python: >=3.10
Keywords: fairness, AI, ML, machine learning, XAI, explanaible AI

# Fair Mango

Fair Mango is a Python package that helps developers evaluate their model's performance and fairness across different groups.

## Supported Fairness Metrics

- Demographic Parity / Statistical Parity
- Disparate Impact
- Equalised Odds
- Equality of Opportunity
- Predictive Rate Parity
- Group Benefit Disparity
- False Positive Rate
- False Negative Rate
- True Positive Rate / Sensitivity
- True Negative Rate / specificity