API Reference¶
Welcome to the VAST Orbit API Reference. This comprehensive guide covers all public objects, functions, and methods available in VAST Orbit for data science at scale on the VAST AI OS.
Tip
New to VAST Orbit? Start with Getting Started for installation and quick examples, then explore the User Guide for in-depth tutorials.
Core Components¶
Essential modules for connecting to VAST and working with data.
Connect to VAST DataBase. Manage connections, catalogs, and authentication.
The main data structure - pandas-like API for VAST DataBase. 400+ functions for data manipulation.
Load data from files (Parquet, CSV, JSON), databases, and streaming sources into VAST.
Machine Learning & Analytics¶
Tools for building, training, and deploying ML models at scale.
Train models with sklearn/Spark. Deploy for in-database inference. 10 supported algorithms.
Statistical functions for hypothesis testing, distributions, and advanced analytics.
Visualization & Exploration¶
Create interactive charts and explore data visually.
Comprehensive charting library supporting Plotly and Matplotlib backends.
Interactive widgets, magic commands, and enhanced notebook functionality for VAST Orbit.
Built-in sample datasets (Titanic, Iris, Amazon) for learning and testing.
Data Management¶
Utilities for managing data in VAST DataBase.
Helper functions for data ingestion, schema management, and table operations.
Execute raw SQL queries against VAST DataBase with federated query support.
Work with data samples for exploratory analysis and rapid prototyping.
Advanced Topics¶
Exception types and error handling patterns in VAST Orbit.
Quick Reference¶
Most Used Functions:
import vastorbit as vo
# Connection
vo.new_connection({...})
vo.current_connection()
# Data Loading
vdf = vo.VastFrame('table_name')
vdf = vo.VastFrame('SELECT * FROM ...')
# Analysis
vdf.describe()
vdf.agg(func="max", columns)
vdf.scatter(['x', 'y'])
# Machine Learning
from vastorbit.machine_learning import RandomForest
model = RandomForest()
model.fit(vdf, 'target', ['feature1', 'feature2'])
predictions = model.predict(vdf)
Key Modules:
vastorbit.VastFrame- Main data structurevastorbit.machine_learning- ML algorithmsvastorbit.plot- Visualization functionsvastorbit.sql- SQL execution utilitiesvastorbit.stats- Statistical functions
Note
API Stability: VAST Orbit is currently in pre-release (0.1.x). APIs may change before the 1.0.0 release. After 1.0.0, VAST Orbit will follow semantic versioning with stable public APIs. Breaking changes will be announced in advance and deprecated gradually.
See also
Getting Started - Installation and setup guide
User Guide - Comprehensive tutorials
Examples - Hands-on code examples
Chart Gallery - Visualization gallery
Full API Documentation¶
Detailed documentation for every module, class, and function.