Union Pandera Integrations

FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints. Fast: Very high performance, on par with NodeJS and Go (thanks to Starlette and Pydantic). One of the fastest Python frameworks available. Minimize code duplication, multiple features from each parameter declaration.

GeoPandas is an open-source project to make working with geospatial data in python easier. GeoPandas extends the datatypes used by pandas to allow spatial operations on geometric types. Geometric operations are performed by shapely. Geopandas further depends on fiona for file access and matplotlib for plotting. The goal of GeoPandas is to make working with geospatial data in python easier. It combines the capabilities of pandas and shapely, providing geospatial operations in pandas and a high-level interface to multiple geometries to shapely. GeoPandas enables you to easily do operations in python that would otherwise require a spatial database such as PostGIS. GeoPandas is a community-led project written, used and supported by a wide range of people from all around of world of a large variety of backgrounds. GeoPandas will always be 100% open source software, free for all to use and released under the liberal terms of the BSD-3-Clause license.

PySpark is an interface for Apache Spark in Python. It not only allows you to write Spark applications using Python APIs, but also provides the PySpark shell for interactively analyzing your data in a distributed environment. PySpark supports most of Spark’s features such as Spark SQL, DataFrame, Streaming, MLlib (Machine Learning) and Spark Core. Spark SQL is a Spark module for structured data processing. It provides a programming abstraction called DataFrame and can also act as distributed SQL query engine. Running on top of Spark, the streaming feature in Apache Spark enables powerful interactive and analytical applications across both streaming and historical data, while inheriting Spark’s ease of use and fault tolerance characteristics.

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.

The Fugue Platform empowers teams with the tools to build, deploy and maintain cloud security at every stage of the development lifecycle. We're so confident that you'll get immediate value with Fugue that we guarantee it. Fugue leverages the open source Open Policy Agent (OPA) standard for IaC and cloud infrastructure policy as code. Build IaC checks into git workflows and CI/CD pipelines with Regula—an open-source tool powered by OPA. Develop custom rules—including multi-resource checks—using Rego, the simple and powerful open source language of OPA. Govern your IaC security for cloud resources, Kubernetes, and containers in one place and ensure consistent policy enforcement across the development lifecycle. View the results of security and compliance checks on IaC across your organization. Access and export tenant-wide, IaC-specific security and compliance reports.

Dask is open source and freely available. It is developed in coordination with other community projects like NumPy, pandas, and scikit-learn. Dask uses existing Python APIs and data structures to make it easy to switch between NumPy, pandas, scikit-learn to their Dask-powered equivalents. Dask's schedulers scale to thousand-node clusters and its algorithms have been tested on some of the largest supercomputers in the world. But you don't need a massive cluster to get started. Dask ships with schedulers designed for use on personal machines. Many people use Dask today to scale computations on their laptop, using multiple cores for computation and their disk for excess storage. Dask exposes lower-level APIs letting you build custom systems for in-house applications. This helps open source leaders parallelize their own packages and helps business leaders scale custom business logic.