semvec/pypi-packages huggingface dataset:
bq-tabulate 0.0.2
A tool to format BQ results as tables
Requires Python: >=3.8
Keywords: BigQuery,tabulate

# BQ tabulate

[![PyPI - Version](https://img.shields.io/pypi/v/bq-tabulate.svg)](https://pypi.org/project/bq-tabulate)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/bq-tabulate.svg)](https://pypi.org/project/bq-tabulate)

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**Table of Contents**

- [Installation](#installation)
- [License](#license)

## Installation

```console
pip install bq-tabulate
```

## License

`bq-tabulate` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
dbt-snapshot-analysis 0.2.13
A package for analyzing snapshots

</br>

<p align="center">
  <a href="https://www.data-drift.io">
    <img src="https://github.com/data-drift/data-drift/blob/13a11b48ed7191b63f3c688eef67fbf843103323/datadrift-logo-dark.png?raw=true" width="200px" alt="Datadrift logo" />
  </a>
</p>

<h2 align="center" >dbt Snapshot Analysis</h3>

<p align="center"><a href="https://data-drift.io">Website</a> · <a href="https://www.data-drift.io/blog">Blog</a></p>
</br>

# dbt Snapshot Analysis

[![PyPI version](https://badge.fury.io/py/dbt-snapshot-analysis.svg)](https://badge.fury.io/py/dbt-snapshot-analysis)

**Analyse the stability of your key metrics**
</br>
Changes of a metric’s historical value harms data consumers’ trust.

dbt Snapshot Analysis is Python package to get insight on the quality and stability of the metrics your report.

</br>
<p align="center">
  <a href="https://pypi.org/project/dbt-snapshot-analysis">
    <img src="https://github.com/data-drift/data-drift/blob/main/tools/dbt-snapshot-analysis/dbt-snapshot-analysis.gif?raw=true" alt="dbt snapshot analysis gif" />
  </a>
</p>

# 🚀 Quickstart

## Installation

You can install DBT Snapshot Analysis using `pip`:

```sh
pip install dbt-snapshot-analysis
```

## Usage

```sh
dbt_snapshot_analysis
```

</br>

This project is developped by the DataDrift team with 💚. Check out our [main repository](https://github.com/data-drift/data-drift).