mage-ai 0.0.6

Mage - An open-source data management platform that helps you clean data and prepare it for training AI/ML models

Intro

Mage is an open-source data management platform that helps you clean data and prepare it for training AI/ML models.

Join us on Slack

Table of contents

1. Quick start
2. Features
3. Roadmap
4. Contributing
5. Community

🏃‍♀️ Quick start

• Try a demo of Mage in Google Colab.
• Try a hosted version of Mage

1. Install Mage

$ pip install mage-ai

2. Load and connect data

import mage_ai
from mage_ai.sample_datasets import load_dataset


df = load_dataset('titanic_survival.csv')
mage_ai.connect_data(df, name='titanic dataset')

3. Launch tool

mage_ai.launch()

Open http://localhost:5789 in your browser to access the tool locally.

If you’re launching Mage in a notebook, the tool will render in an iFrame.

4. Clean data

After building a data cleaning pipeline from the UI, you can clean your data anywhere you can execute Python code:

mage_ai.clean(df, pipeline_uuid='pipeline name')

Demo video (2 min)

More resources

Here is a 🗺️ step-by-step guide on how to use the tool.

1. Jupyter notebook example
2. Google Colaboratory (Colab) example

Check out the 📚 tutorials to quickly become a master of magic.

🔮 Features

1. Data visualizations
2. Reports
3. Cleaning actions
4. Data cleaning suggestions

1. Data visualizations

Inspect your data using different charts (e.g. time series, bar chart, box plot, etc.).

Here’s a list of available charts.

2. Reports

Quickly diagnose data quality issues with summary reports.

Here’s a list of available reports.

3. Cleaning actions

Easily add common cleaning functions to your pipeline with a few clicks. Cleaning actions include imputing missing values, reformatting strings, removing duplicates, and many more.

If a cleaning action you need doesn’t exist in the library, you can write and save custom cleaning functions in the UI.

Here’s a list of available cleaning actions.

4. Data cleaning suggestions

The tool will automatically suggest different ways to clean your data and improve quality metrics.

Here’s a list of available suggestions.

🗺️ Roadmap

Big features being worked on or in the design phase.

1. Encoding actions (e.g. one-hot encoding, label hasher, ordinal encoding, embeddings, etc.)
2. Data quality monitoring and alerting
3. Apply cleaning actions to columns and values that match a condition

Here’s a detailed list of 🪲 features and bugs that are in progress or upcoming.

🙋‍♀️ Contributing

We welcome all contributions to Mage; from small UI enhancements to brand new cleaning actions. We love seeing community members level up and give people power-ups!

Check out the 🎁 contributing guide to get started by setting up your development environment and exploring the code base.

Got questions? Live chat with us in Slack

Anything you contribute, the Mage team and community will maintain. We’re in it together!

🧙 Community

We love the community of Magers (/ˈmājər/); a group of mages who help each other realize their full potential!

To live chat with the Mage team and community, please join the free Mage Slack channel.

For real-time news and fun memes, check out the Mage Twitter.

To report bugs or add your awesome code for others to enjoy, visit GitHub.

🪪 License

See the LICENSE file for licensing information.