Welcome to the crandas documentation!

The Virtual Data Lake (VDL) is software that allows for secure data collaboration. To interact with the VDL, we have developed a Python package called crandas. This package allows users to encrypt and upload data to the VDL, 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 (MPC) 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.

Getting started

• Getting Started
  • crandas and pandas
  • Installation (local python)
  • First steps
  • Combining data
  • Approvals
  • Deployments

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 missing data
  • Categorical data
  • Merge, join, and concatenate
  • Group by: split-apply-combine
  • Binomial Logistic Regression
  • Linear regression
  • Authorization
  • Tips and tricks

Reference

• API documentation

Tutorials

• Tutorials
  • Joining data
  • Regression
  • Authorization
  • Data exploration

Download the zip file for tutorial data and notebooks (Jupyter).

Index

• Index

• Index of functions per column type