For more than a decade Enthought has helped solve technical computing challenges in industries such as energy, manufacturing, biotechnology, aerospace, consumer products, technology, finance, and more. See how we can help you too.
Have a team transitioning to Python from another language such as R or MATLAB? Need to level the Python skill set across a group for better productivity?
Register for one of our 2017 open training classes. Choose from topics including:
The Enthought Deployment Server provides enterprise-grade tools groups and organizations using Python need, including: secure, onsite access to a private copy of the proven 450+ package Enthought Python Distribution, centralized management and control of packages and Python installations, private repositories for sharing and deployment of proprietary Python packages, and support for the software development workflow with Continuous Integration and development, testing, and production repositories.
The Canopy Data Import Tool allows you to easily import .csv / structured text files and perform common (and time-consuming!) data manipulation and cleaning tasks through a graphical interface, while capturing the interaction as Python (Pandas) code for re-use. See the new features and download a free 7 day trial.
PyXLL is an Excel add-in that enables you to extend Excel using nothing but Python code. The new PyXLL 3.0 release adds even more features, including the ability to turn an Excel spreadsheet into a live data stream dashboard and Excel ribbon integration for an intuitive user experience. See the new features in PyXLL 3.0 and download a free 30 day trial.
LabVIEW is a software platform made by National Instruments, used widely in industry for test and measurement applications. Now, Enthought's new Python Integration Toolkit for LabVIEW seamlessly brings the power of the Python ecosystem of scientific and engineering tools to LabVIEW. See how it works and download a free 30 day trial.
Canopy Geoscience integrates data I/O, visualization, and programming in an easy-to-use environment. Geoscientific data is accessible from an embedded Python environment, and can be analyzed, modified, and immediately visualized with simple Python commands. See the latest release details.
Virtual Core automates aspects of core description for geologists and petrophysicists, drastically reducing the time and effort required for core description. Its unified visualization interface displays cleansed whole-core CT data alongside core photographs and well logs. See the latest release details.
Enthought’s Pandas Mastery Workshop is designed to accelerate the development of skill and confidence with Python’s Pandas data analysis package — in just three days, you’ll look like an old pro! This course was created ground up by our training experts based on insights from the science of human learning, as well as what we’ve learned from over a decade of extensive practical experience of teaching thousands of scientists, engineers, and analysts to use Python effectively in their everyday work.
In this webinar, we give you the key information and insight you need to evaluate whether the Pandas Mastery Workshop is the right solution to advance your data analysis skills in Python, including:
Built on 15 years of experience of Python packaging and deployment for Fortune 500 companies, the NEW Enthought Deployment Server provides enterprise-grade tools that take the pain out of Python deployment.
In this webinar, you'll see an overview of Enthought Deployment Server features, including:
LabVIEW users can now have seamless, bi-directional connectivity to the Python ecosystem of scientific and engineering tools
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. Earlier this month, Enthought released the Python Integration Toolkit for LabVIEW, which is a “bridge” between the LabVIEW and Python environments.
SciPy 2017, the 16th annual Scientific Computing with Python conference, was held July 10-16, 2017 in Austin, Texas, and had over 700 participants from industry, academia, and government who showcased their latest projects, learned from skilled users and developers, and collaborated on code development.
Major topic tracks included: Machine Learning & AI and SciPy Tools. Mini-Symposia tracks included: Astronomy; Biology; Biophysics and Biostatistics; Computational Science and Numerical Techniques; Data Science; Earth, Ocean and Geoscience; Materials Science and Engineering; Neuroscience; and Open Data and Reproducibility.