Welcome to the crandas documentation!¶
Roseman Labs is software for secure data collaboration, based on multi-party computation (MPC). To interact with the MPC engine, we have developed a Python package called crandas. This package allows users to encrypt and upload data to the engine, perform privacy-preserving data analyses over the encrypted data, and obtain the computational results. It offers a seamless way to perform secure computations on private data while maintaining a familiar pandas-like syntax. Users can leverage the power of multi-party computation tools for data analysis without having to worry about the cryptography.
If you are new to crandas, the Getting Started guide will walk you through the process of installing the crandas library, creating and manipulating tables. This guide also provides information on MPC and some design choices in crandas. For more in-depth information about functionalities, consult the User Guide. Additionally, you can go through some Tutorials to familiarize yourself with the different features present in crandas. Finally, for more generic information about Roseman Labs, the components it consists of and the role that crandas has as a part of this, the user is referred to the product page of the Roseman Labs knowledge base.
Getting started
User Guide
- User Guide
- Importing/exporting data
- Data structures in crandas
- Basic table operations
- Selecting data
- Data types
- Working with numeric data
- Working with text data
- Working with dates
- Working with missing data
- Categorical data
- Merge, join, and concatenate
- Group by: split-apply-combine
- Binomial logistic regression
- Multinomial logistic regression
- Ordinal logistic regression
- Linear regression
- K-nearest neighbors
- Authorization
- Tips and tricks
Reference
Download the zip file for tutorial data and notebooks (Jupyter).