CuPy's interface is highly compatible with NumPy; in most cases it can be

used as a drop-in replacement.

Blog Post: https://quansight.github.io/Episode-5-CuPy/

PyViz is a set of examples that help you see how various

Python packages can work together to solve a huge range

of visualization problems, from common situations to tricky

special cases. Although there is an installable package named pyviz,

it’s valuable mainly for its dependencies, which are a coherent set

of libraries designed to work well together. There is almost no code

actually in PyViz, apart from the examples in the tutorials.

SymPy is a Python library for symbolic mathematics. It

aims to become a full-featured computer algebra system

(CAS) while keeping the code as simple as possible in order to be

comprehensible and easily extensible. SymPy is written entirely in Python.

All purely symbolic mathematical computation is in scope for SymPy.

XND is an extensible framework of C libraries and Python modules for

handling typed memory blocks. XND recreates the foundations of NumPy as a

number of smaller libraries, combining the lessons learned in the past twenty

years of array computing in Python with the needs of newer applications.

XND's type system is more general than NumPy's and allows unpacking

arbitrarily nested Python type hierarchies directly to memory.

XND's computational kernels are multimethods and can be generated

and inserted into modular lookup tables at runtime. This opens the

possibility for a large variety of applications written in different

languages to use XND as a container backend.

Ongoing work connects

XND to Numba, Lapack/MKL and the CUDA kernel libraries. There is

interest in integrating XND with a variety of libraries like Dask,

xarray, Numba, Chainer, PyTorch, Tensorflow, PyMC4, TVM/NNVM,

Plasma Store, Apache Arrow, and Tensor Comprehensions.

NOTE: Please forgive the technical difficulties we had in recording this episode.

Carlos had some connectivity issues. We are releasing this episode because we

still want to bring you all of the news about Spyder!

Spyder is a powerful scientific environment written in Python, for Python, and designed by and for scientists, engineers and data analysts. It offers a unique combination of the advanced editing, analysis, debugging, and profiling functionality of a comprehensive development tool with the data exploration, interactive execution, deep inspection, and beautiful visualization capabilities of a scientific package.

Beyond its many built-in features, its abilities can be extended even further via its plugin system and API. Furthermore, Spyder can also be used as a PyQt5 extension library, allowing developers to build upon its functionality and embed its components, such as the interactive console, in their own PyQt software.

This episode features Bokeh, which is a web-based and interactive visualization

library for Python. Its goal is to provide elegant, concise construction of

versatile graphics, and to extend this capability with high-performance

interactivity over very large or streaming datasets. Bokeh can help anyone who

would like to quickly and easily create interactive plots, dashboards, and

data applications.