Numba is an open source JIT compiler that translates a subset of

Python and NumPy code into fast machine code. Numba translates

Python functions to optimized machine code at runtime using the

Industry-standard LLVM compiler library. Numba-compiled numerical

algorithms in Python can approach the speeds of C or FORTRAN.

Users do not need to replacethe Python interpreter, run a separate

compilation step, or even have a C/C++ compiler installed. Applying

one of the Numba decorators to a Python function is all that is needed.

ITK is an open-source, cross-platform system that provides developers with

an extensive suite of software tools for image analysis. Developed through

extreme programming methodologies, ITK employs leading-edge algorithms for

registering and segmenting multidimensional data.

Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more.

The Spark Python API (PySpark) exposes the Spark programming model to Pytho

Dask provides advanced parallelism for analytics, enabling performance at scale for the tools you love. Dask is open source and freely available. It is developed in coordination with other community projects like Numpy, Pandas, and SciKit-Learn.

The aim of PyData/Sparse is to create sparse containers that implement the ndarray interface. Traditionally in the PyData ecosystem, sparse arrays have been provided by the scipy.sparse submodule. All containers there depend on and emulate the numpy.matrix interface. This means that they are limited to two dimensions and also do not work well in places where numpy.ndarray would work.

PyData/Sparse is well on its way to replacing scipy.sparse as the de-facto sparse array implementation in the PyData ecosystem.

Datashader is a graphics pipeline system for creating meaningful

representations of large datasets quickly and flexibly. Datashader

breaks the creation of images into a series of explicit steps that

allow computations to be done on intermediate representations.

This approach allows accurate and effective visualizations to be

produced automatically without trial-and-error parameter tuning,

and also makes it simple for data scientists to focus on particular

data and relationships of interest in a principled way.

SciPy is open-source software for mathematics, science, and engineering.

It includes modules for statistics, optimization, integration, linear algebra,

Fourier transforms, signal and image processing, ODE solvers, and more.

SciPy provides many user-friendly and efficient numerical routines such

as for numerical integration and optimization. SciPy runs on all popular

operating systems, is easy to use, and powerful enough to be depended

upon by the world's leading scientists & engineers.

GeoViews is a Python library that makes it easy to explore and visualize

geographical, meteorological, and oceanographic datasets, such as those

used in weather, climate, and remote sensing research. GeoViews is built

on the HoloViews library for building flexible visualizations of

multidimensional data. GeoViews adds a family of geographic plot

types based on the Cartopy library, plotted using either the

Matplotlib or Bokeh packages. With GeoViews, you can now work

easily and naturally with large, multidimensional geographic

datasets, instantly visualizing any subset or combination of them,

while always being able to access the raw data underlying any plot.

Intake will appeal to different groups

but is useful for all and acts as a common platform that everyone can

use to smooth the progression of data from developers and providers to users.