EPython: Extending Python to the Future
We will create an embedded domain specific language (DSL) using the Python language itself along with the typing module and specialized objects as necessary to allow nearly all the existing extensions to be written using this DSL. We will then port several key libraries such as NumPy, SciPy, and Pandas to use this DSL. We will also provide a runtime so these extensions can work on PyPy, C-Python, Jython, and RustPython.
The initial MVP will be completed in 6 months and then a version of NumPy will be ported in the next 12 months. An initial run-time for PyPy and Jython will be completed in the next 6 months. The remaining 3.5 years of the funded project will be improving the language by porting existing modules to use the new.
Numba gives you the power to speed up your applications with high performance functions written directly in Python.
Cython is an optimising static compiler for both the Python programming language and the extended Cython programming language (based on Pyrex). It makes writing C extensions for Python as easy as Python itself.
pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. pandas’ data analysis and modeling features enable users to carry out their entire data analysis workflow in Python without having to switch to a more domain-specific language like R.
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.
SciPy provides many user-friendly and efficient numerical routines for Python such as routines for numerical integration, interpolation, optimization, linear algebra and statistics.
N-dimensional array and computational libraries for Python.