PySpark

pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language. Tools for reading and writing data between in-memory data structures and different formats: CSV and text files, Microsoft Excel, SQL databases, and the fast HDF5 format. Intelligent data alignment and integrated handling of missing data: gain automatic label-based alignment in computations and easily manipulate messy data into an orderly form.Aggregating or transforming data with a powerful group by engine allowing split-apply-combine operations on data sets. Time series-functionality: date range generation and frequency conversion, moving window statistics, date shifting and lagging. Even create domain-specific time offsets and join time series without losing data.

Built and maintained by a team of differential privacy experts, and running in production at institutions like the U.S. Census Bureau. Runs on Spark and effortlessly supports input tables containing billions of rows. Supports a large and ever-growing list of aggregation functions, data transformation operators, and privacy definitions. Perform public and private joins, filters, or user-defined functions on your data. Compute counts, sums, quantiles, and more under multiple privacy models. Differential privacy is made easy, thanks to our simple tutorials and extensive documentation. Tumult Analytics is built on our sophisticated privacy foundation, Tumult Core, which mediates access to sensitive data and means that every program and application comes with an embedded proof of privacy. Built by composing small, easy-to-review components. Provably safe stability tracking and floating-point primitives. Uses a generic framework based on peer-reviewed research.