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Why migrate from MATLAB to Python?

Python has a lot of momentum. Many high profile projects use it and more are migrating to it all the time. Why? One reason is that Python is free, but more importantly, it is because Python has a thriving ecosystem of packages that allow developers to work faster and more efficiently. They can go from prototyping to production to scale on hardware ranging from a Raspberry Pi (or maybe micro controller) to a cluster, all using the same language. A large part of Python’s growth is driven by its excellent support for work in the fields of science, engineering, machine learning, and data science.

Understanding Python for Data Science

Enthought’s Python for Data Science training course is designed to accelerate the development of skill and confidence in using Python’s core data science tools — including the standard Python language, the fast array programming package NumPy, and the Pandas data analysis package, as well as tools for database access (DBAPI2, SQLAlchemy), machine learning (scikit-learn), and visual exploration (Matplotlib, Seaborn).

Published in the October 2016 edition of The Leading Edge magazine by the Society of Exploration Geophysicists. Read the full article here.

What is LabVIEW, and how does it integrate with Python?

LabVIEW is a software platform made by National Instruments, used widely in industries such as semiconductors, telecommunications, aerospace, manufacturing, electronics, and automotive for test and measurement applications. In August 2016, Enthought released the Python Integration Toolkit for LabVIEW, which is a “bridge” between the LabVIEW and Python environments.

On May 28, 2014 Phillip Cloud, core contributor for the Pandas data analytics Python library, spoke at a joint meetup of the New York Quantitative Python User’s Group (NY QPUG) and the NY Finance PUG. Enthought hosted and about 60 people joined us to listen to Phillip present some of the less-well-known, but really useful features that have come out since Pandas version 0.11 and some that are coming soon. We all learned more about how to take full advantage of the Pandas Python library, and got a better sense of how excited Phillip was to discover Pandas during his graduate work.

Earlier this month at the first New York Finance Python User’s Group (NY FPUG) meetup, Kelsey Jordahl talked about how PyQL streamlines the development of Python-based finance applications using QuantLib. There were about 30 people attending the talk at the Cornell Club in New York City. We have a recording of the presentation below.

We are excited to announce that Enthought is undertaking a multi-year project to bring the strengths of NumPy to high-performance distributed computing.  The goal is to provide a more intuitive and user-friendly interface to both distributed array computing and to high-performance parallel libraries.  We will release the project as open source, providing another tool in our toolbox to help with data processing, modeling, and simulation capabilities in the realm of big data.  The project is funded under a Phase II grant from the DOE SBIR program [0] [1], and is headed by Kurt Smith.