Open Source is at the Core of Modern Software
Innovation through open collaboration has changed the technology industry forever
At Continuum, we value open source software and believe it is a privilege to be able to share ideas-as-code with people around the world as we work together to build useful tools and products. We believe in building on the shoulders of giants and seek productive, sustainable ways to continue to strengthen the open source foundation and create the architecture of the future.
Travis Oliphant, our co-founder and CEO, is a key figure in the development of NumPy and SciPy. Most of our developers have a long history as open source contributors and have spent many, many volunteer hours writing open source software, giving talks at conferences, writing documentation, fixing bugs, answering questions, sharing thoughts on public mailing lists and generally working to ensure that ideas-as-code get shared as far and widely as possible. Our team of contributors incubate Continuum Analytics sponsored open source projects and contribute to many other OSS projects.
We take great satisfaction in empowering people solving some of the world’s greatest challenges to make the world a better place and improve the lives of others.
Incubating New Projects to Meet Enterprise Needs
While much of the software we write is open source from the beginning, some of our software is not freely available at first. Our sincere desire is for the features in our proprietary software to end up as open source software as soon as time and resources allow us to make it freely available. We believe that as a company we can contribute to open source software best by providing livelihoods for developers that allow them to focus on writing software that gets contributed to open source in time. Sometimes this means keeping software proprietary so that users of our software are also customers driving its development. This enables us to employ people full time on the creation and support of software that effectively becomes open source as more people buy it. This gives more and more people a chance to participate in the creation of open source software — not just developers with spare cycles.
If you find our software useful, we hope you'll download it and be satisfied not only with the software itself, but also in the knowledge that you are contributing to the present and future ecosystem of open source software.
A framework for automatic distribution and parallelization of Python
A framework for plots, interactive and real-time streaming visualizations
A framework for automatic distribution and parallelization of Python
A framework that enables parallelization of algorithms on modern architecture
A library for dynamic in-memory arrays that extends the NumPy data models
Dynamic, painless compilation of Python into machine code, via LLVM
A framework for building high performance, pluggable, desktop style web applications
Blaze scales NumPy to Big Data on multiple compute engines
Fast, scalable out-of-core computations on Big Data that extend NumPy and Pandas to distributed and streaming datasets.
Blaze extends NumPy's successful model of array-oriented programming to out-of-core and distributed data. Blaze allows analysts and scientists to productively write robust and efficient code, without getting bogged down in the details of how to distribute computation, or worse, how to transport and convert data between databases, formats, proprietary data warehouses, and other silos.
The core of Blaze is a generic N-dimensional array/table object with a very general “data type” and “data shape” descriptor for all kinds of data, but especially semi-structured, sparse, and columnar data. Blaze’s generalized calculation engine can iterate over the distributed array or table and dispatch to low-level kernels, selected via the dynamic data typing mechanism.
Blaze supports data stores and stream engines including:
- Bcolz - compressed columnar
- MongoDB – NoSQL store
- SQLAlchemy – SQL store
- Apache Spark – cluster computing framework
- PyTables - hierarchical data
- Streaming Python - streaming data
Bokeh scales visualization to Big Data
Interactive and real-time streaming visualization framework that scales to Big Data with data shading
Bokeh is a Python data visualization library combining the ideas of the Grammar of Graphics and Protovis, with enhancements to support interactive visualization. Its primary output backend is HTML5 Canvas.
There are many excellent plotting packages for Python, but they generally do not optimize for the particular needs of statistical plotting or multidimensional datasets. Additionally, advanced visual customization is typically difficult for non-programmers, and most libraries do not build a reified data processing pipeline that supports rich interactivity like linked brushing. Bokeh addresses these problems at their core by using a declarative data transformation scheme, and is engineered to operate in a client/server model for the modern web.
Conda packages Python, R, NumPy, SciPy easily
Eliminates package dependency and version control issues
Conda is an innovative package manager tool that allows users to mix-and-match different versions of Python, NumPy, SciPy, and other packages in isolated environments and easily switch between them.
The conda command is the primary interface for managing Anaconda installations. It is great for solving enterprise integration and application deployment challenges. It can query and search the Anaconda package index and current Anaconda installation, create new Anaconda environments, and install and update packages into existing Anaconda environments.
Dask parallelizes analytics on modern multi-core machines and distributed clusters
Makes it easy to write complex parallel algorithms for task execution
Dask is a framework used to easily parallelize algorithms that takes advantage of the available memory and computer power to maximize memory, execution time and performance of complex algorithms. Dask creates a task graph based on the data and then intelligently schedules the execution of the tasks to optimize throughput.
While developers can parallelize Python manually, Dask helps to automate the task with rich primitives that are aware of the execution environment and optimize the analytic execution. Dask collections build on Dask to provide dask.array and dask.dataframe, collections that mimic NumPy and pandas but operate in parallel and on larger-than-memory datasets.
Report bugs and make feature requests through the GitHub issue tracker. For community discussion, please use firstname.lastname@example.org
DyND is Dynamic ETL-on-read for unstructured data
Make unstructured and semi-structured just as easy to work with as structured data
DyND is a dynamic in-memory vector and array library for C++ and Python that makes it as efficient to process unstructured and semi-structured data as regularized structured data. This allows dynamic ETL-on-read to be performed where transformation operations are executed on CSV files, JSON and any other unstructured or semi-structured data.
Numba speeds up NumPy and SciPy
Compiles Python into machine code for lightening fast execution
Numba is a compiled version of NumPy and SciPy. It uses the LLVM compiler infrastructure to compile Python byte-code to machine code for use in the NumPy runtime and SciPy modules.
Contributing to OSS projects
Our developers enhance the Python ecosystem by actively contributing to a wide variety of open source software projects.
Create and share documents that contain live code, equations, visualizations and explanatory text
An easy-to-use interactive tool for publication-quality scientific plotting.
Fast vectors, matrices, and arrays in Python
Fast, flexible, and expressive data structures for working with relational or labeled data
A framework that enables parallelization of algorithms on modern architectures
Rich, powerful library for scientific computing
Powerful interactive development and numerical computing environment for Python
Symbolic mathematics for Python
Snapshot of our Contributors