Category Archives: Python

Webinar: Python for Scientists & Engineers: A Tour of Enthought’s Professional Training Course

What:  A guided walkthrough and Q&A about Enthought’s technical training course Python for Scientists & Engineers with Enthought’s VP of Training Solutions, Dr. Michael Connell

Who Should Watch: individuals, team leaders, and learning & development coordinators who are looking to better understand the options to increase professional capabilities in Python for scientific and engineering applications

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“Writing software is not my job…I just have to do it every day.”  
-21st Century Scientist or Engineer

Many scientists, engineers, and analysts today find themselves writing a lot of software in their day-to-day work even though that’s not their primary job and they were never formally trained for it. Of course, there is a lot more to writing software for scientific and analytic computing than just knowing which keyword to use and where to put the semicolon.

Software for science, engineering, and analysis has to solve the technical problem it was created to solve, of course, but it also has to be efficient, readable, maintainable, extensible, and usable by other people — including the original author six months later!

It has to be designed to prevent bugs and — because all reasonably complex software contains bugs — it should be designed so as to make the inevitable bugs quickly apparent, easy to diagnose, and easy to fix. In addition, such software often has to interface with legacy code libraries written in other languages like C or C++, and it may benefit from a graphical user interface to substantially streamline repeatable workflows and make the tools available to colleagues and other stakeholders who may not be comfortable working directly with the code for whatever reason.

Enthought’s Python for Scientists and Engineers is designed to accelerate the development of skill and confidence in addressing these kinds of technical challenges using some of Python’s core capabilities and tools, including:

  • The standard Python language
  • Core tools for science, engineering, and analysis, including NumPy (the fast array programming package), Matplotlib (for data visualization), and Pandas (for data analysis); and
  • Tools for crafting well-organized and robust code, debugging, profiling performance, interfacing with other languages like C and C++, and adding graphical user interfaces (GUIs) to your applications.

In this webinar, we give you the key information and insight you need to evaluate whether Enthought’s Python for Scientists and Engineers course is the right solution to take your technical skills to the next level, including:

  • Who will benefit most from the course
  • A guided tour through the course topics
  • What skills you’ll take away from the course, how the instructional design supports that
  • What the experience is like, and why it is different from other training alternatives (with a sneak peek at actual course materials)
  • What previous course attendees say about the course

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michael_connell-enthought-vp-trainingPresenter: Dr. Michael Connell, VP, Enthought Training Solutions

Ed.D, Education, Harvard University
M.S., Electrical Engineering and Computer Science, MIT


Python for Scientists & Engineers Training: The Quick Start Approach to Turbocharging Your Work

If you are tired of running repeatable processes manually and want to (semi-) automate them to increase your throughput and decrease pilot error, or you want to spend less time debugging code and more time writing clean code in the first place, or you are simply tired of using a multitude of tools and languages for different parts of a task and want to replace them with one comprehensive language, then Enthought’s Python for Scientists and Engineers is definitely for you!

This class has been particularly appealing to people who have been using other tools like MATLAB or even Excel for their computational work and want to start applying their skills using the Python toolset.  And it’s no wonder — Python has been identified as the most popular coding language for five years in a row for good reason.

One reason for its broad popularity is its efficiency and ease-of-use. Many people consider Python more fun to work in than other languages (and we agree!). Another reason for its popularity among scientists, engineers, and analysts in particular is Python’s support for rapid application development and extensive (and growing) open source library of powerful tools for preparing, visualizing, analyzing, and modeling data as well as simulation.

Python is also an extraordinarily comprehensive toolset – it supports everything from interactive analysis to automation to software engineering to web app development within a single language and plays very well with other languages like C/C++ or FORTRAN so you can continue leveraging your existing code libraries written in those other languages.

Many organizations are moving to Python so they can consolidate all of their technical work streams under a single comprehensive toolset. In the first part of this class we’ll give you the fundamentals you need to switch from another language to Python and then we cover the core tools that will enable you to do in Python what you were doing with other tools, only faster and better!

Additional Resources

Upcoming Open Python for Scientists & Engineers Sessions:

Albuquerque, NM, Sept 11-15, 2017
Washington, DC, Sept 25-29, 2017
Los Alamos, NM, Oct 2-6, 2017
Cambridge, UK, Oct 16-20, 2017
San Diego, CA, Oct 30-Nov 3, 2017
Albuquerque, NM, Nov 13-17, 2017
Los Alamos, NM, Dec 4-8, 2017
Austin, TX, Dec 11-15, 2017

Have a group interested in training? We specialize in group and corporate training. Contact us or call 512.536.1057.

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Download Enthought’s Machine Learning with Python’s Scikit-Learn Cheat Sheets
Enthought's Machine Learning with Python Cheat Sheets
Additional Webinars in the Training Series:

Python for Data Science: A Tour of Enthought’s Professional Technical Training Course

Python for Professionals: The Complete Guide to Enthought’s Technical Training Courses

An Exclusive Peek “Under the Hood” of Enthought Training and the Pandas Mastery Workshop

Download Enthought’s Pandas Cheat SheetsEnthought's Pandas Cheat Sheets

SciPy 2017 Conference to Showcase Leading Edge Developments in Scientific Computing with Python

Renowned scientists, engineers and researchers from around the world to gather July 10-16, 2017 in Austin, TX to share and collaborate to advance scientific computing tool


AUSTIN, TX – June 6, 2017 –
Enthought, as Institutional Sponsor, today announced the SciPy 2017 Conference will be held July 10-16, 2017 in Austin, Texas. At this 16th annual installment of the conference, scientists, engineers, data scientists and researchers will participate in tutorials, talks and developer sprints designed to foster the continued rapid growth of the scientific Python ecosystem. This year’s attendees hail from over 25 countries and represent academia, government, national research laboratories, and industries such as aerospace, biotechnology, finance, oil and gas and more.

“Since 2001, the SciPy Conference has been a highly anticipated annual event for the scientific and analytic computing community,” states Dr. Eric Jones, CEO at Enthought and SciPy Conference co-founder. “Over the last 16 years we’ve witnessed Python emerge as the de facto open source programming language for science, engineering and analytics with widespread adoption in research and industry. The powerful tools and libraries the SciPy community has developed are used by millions of people to advance scientific inquest and innovation every day.”

Special topical themes for this year’s conference are “Artificial Intelligence and Machine Learning Applications” and the “Scientific Python (SciPy) Tool Stack.” Keynote speakers include:

  • Kathryn Huff, Assistant Professor in the Department of Nuclear, Plasma, and Radiological Engineering at the University of Illinois at Urbana-Champaign  
  • Sean Gulick, Research Professor at the Institute for Geophysics at the University of Texas at Austin
  • Gaël Varoquaux, faculty researcher in the Neurospin brain research institute at INRIA (French Institute for Research in Computer Science and Automation)

In addition to the special conference themes, there will also be over 100 talk and poster paper speakers/presenters covering eight mini-symposia tracks including: Astronomy; Biology, Biophysics, and Biostatistics; Computational Science and Numerical Techniques; Data Science; Earth, Ocean, and Geo Sciences; Materials Science and Engineering; Neuroscience; and Open Data and Reproducibility.

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Webinar: Python for Data Science: A Tour of Enthought’s Professional Training Course

View Python for Data Science Webinar
What: A guided walkthrough and Q&A about Enthought’s technical training course “Python for Data Science and Machine Learning” with VP of Training Solutions, Dr. Michael Connell

Who Should Watch: individuals, team leaders, and learning & development coordinators who are looking to better understand the options to increase professional capabilities in Python for data science and machine learning applications

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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).

In this webinar, we give you the key information and insight you need to evaluate whether Enthought’s Python for Data Science course is the right solution to advance your professional data science skills in Python, including:

  • Who will benefit most from the course
  • A guided tour through the course topics
  • What skills you’ll take away from the course, how the instructional design supports that
  • What the experience is like, and why it is different from other training alternatives (with a sneak peek at actual course materials)
  • What previous course attendees say about the course

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michael_connell-enthought-vp-trainingPresenter: Dr. Michael Connell, VP, Enthought Training Solutions

Ed.D, Education, Harvard University
M.S., Electrical Engineering and Computer Science, MIT


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Handling Missing Values in Pandas DataFrames: the Hard Way, and the Easy Way

The Data Import Tool can highlight missing value cells, helping you easily identify columns or rows containing NaN valuesThis is the second blog in a series. See the first blog here: Loading Data Into a Pandas DataFrame: The Hard Way, and The Easy Way

No dataset is perfect and most datasets that we have to deal with on a day-to-day basis have values missing, often represented by “NA” or “NaN”. One of the reasons why the Pandas library is as popular as it is in the data science community is because of its capabilities in handling data that contains NaN values.

But spending time looking up the relevant Pandas commands might be cumbersome when you are exploring raw data or prototyping your data analysis pipeline. This is one of the places where the Canopy Data Import Tool helps make data munging faster and easier, by simplifying the task of identifying missing values in your raw data and removing/replacing them.

Why are missing values a problem you ask? We can answer that question in the context of machine learning. scikit-learn and TensorFlow are popular and widely used libraries for machine learning in Python. Both of them caution the user about missing values in their datasets. Various machine learning algorithms expect all the input values to be numerical and to hold meaning. Both of the libraries suggest removing rows and/or columns that contain missing values.

If removing the missing values is not an option, given the size of your dataset, then they suggest replacing the missing values. The scikit-learn library provides an Imputer class, which can be used to replace missing values. See the sci-kit learn documentation for an example of how the Imputer class is used. Similarly, the decode_csv function in the TensorFlow library can be passed a record_defaults argument, which will replace missing values in the dataset. See the TensorFlow documentation for specifics.

The Data Import Tool provides capabilities to handle missing values in your dataset because we strongly believe that discovering and handling missing values in your dataset is a part of the data import and cleaning phase and not the analysis phase of the data science process.

Digging into the specifics, here we’ll compare how you can go about handling missing values with three typical scenarios, first using the Pandas library, then contrasting with the Data Import Tool:

  1. Identifying missing values in data
  2. Replacing missing values in data, and
  3. Removing missing values from data.

Note : Pandas’ internal representation of your data is called a DataFrame. A DataFrame is simply a tabular data structure, similar to a spreadsheet or a SQL table.

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Webinar: Using Python and LabVIEW Together to Rapidly Solve Engineering Problems

What: Presentation, demo, and Q&A with Collin Draughon, Software Product Manager, National Instruments, and Andrew Collette, Scientific Software Developer, Enthought

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Engineers and scientists all over the world are using Python and LabVIEW to solve hard problems in manufacturing and test automation, by taking advantage of the vast ecosystem of Python software.  But going from an engineer’s proof-of-concept to a stable, production-ready version of Python, smoothly integrated with LabVIEW, has long been elusive.

In this on-demand webinar and demo, we take a LabVIEW data acquisition app and extend it with Python’s machine learning capabilities, to automatically detect and classify equipment vibration.  Using a modern Python platform and the Python Integration Toolkit for LabVIEW, we show how easy and fast it is to install heavy-hitting Python analysis libraries, take advantage of them from live LabVIEW code, and finally deploy the entire solution, Python included, using LabVIEW Application Builder.


Python_LabVIEW_VI_Diagram

In this webinar, you’ll see how easy it is to solve an engineering problem by using LabVIEW and Python together.

What You’ll Learn:

  • How Python’s machine learning libraries can simplify a hard engineering problem
  • How to extend an existing LabVIEW VI using Python analysis libraries
  • How to quickly bundle Python and LabVIEW code into an installable app

Who Should Watch:

  • Engineers and managers interested in extending LabVIEW with Python’s ecosystem
  • People who need to easily share and deploy software within their organization
  • Current LabVIEW users who are curious what Python brings to the table
  • Current Python users in organizations where LabVIEW is used

How LabVIEW users can benefit from Python:

  • High-level, general purpose programming language ideally suited to the needs of engineers, scientists, and analysts
  • Huge, international user base representing industries such as aerospace, automotive, manufacturing, military and defense, research and development, biotechnology, geoscience, electronics, and many more
  • Tens of thousands of available packages, ranging from advanced 3D visualization frameworks to nonlinear equation solvers
  • Simple, beginner-friendly syntax and fast learning curve

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Presenters:

Collin Draughon, National Instruments, Software Product Manager Collin Draughon, National Instruments
Software Product Manager
Andrew Collette, Enthought, Scientific Software Developer Andrew Collette, Enthought
Scientific Software Developer
Python Integration Toolkit for LabVIEW core developer

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Webinar – Python for Professionals: The Complete Guide to Enthought’s Technical Training Courses

View the Python for Professionals Webinar

What: Presentation and Q&A with Dr. Michael Connell, VP, Enthought Training Solutions
Who Should Watch: Anyone who wants to develop proficiency in Python for scientific, engineering, analytic, quantitative, or data science applications, including team leaders considering Python training for a group, learning and development coordinators supporting technical teams, or individuals who want to develop their Python skills for professional applications

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Python is an uniquely flexible language – it can be used for everything from software engineering (writing applications) to web app development, system administration to “scientific computing” — which includes scientific analysis, engineering, modeling, data analysis, data science, and the like.

Unlike some “generalist” providers who teach generic Python to the lowest common denominator across all these roles, Enthought specializes in Python training for professionals in scientific and analytic fields. In fact, that’s our DNA, as we are first and foremost scientists, engineers, and data scientists ourselves, who just happen to use Python to drive our daily data wrangling, modeling, machine learning, numerical analysis, simulation, and more.

If you’re a professional using Python, you’ve probably had the thought, “how can I be better, smarter, and faster in using Python to get my work done?” That’s where Enthought comes in – we know that you don’t just want to learn generic Python syntax, but instead you want to learn the key tools that fit the work you do, you want hard-won expert insights and tips without having to discover them yourself through trial and error, and you want to be able to immediately apply what you learn to your work.

Bottom line: you want results and you want the best value for your invested time and money. These are some of the guiding principles in our approach to training.

In this webinar, we’ll give you the information you need to decide whether Enthought’s Python training is the right solution for your or your team’s unique situation, helping answer questions such as:

  • What kinds of Python training does Enthought offer? Who is it designed for? 
  • Who will benefit most from Enthought’s training (current skill levels, roles, job functions)?
  • What are the key things that make Enthought’s training different from other providers and resources?
  • What are the differences between Enthought’s training courses and who is each one best for?
  • What specific skills will I have after taking an Enthought training course?
  • Will I enjoy the curriculum, the way the information is presented, and the instructor?
  • Why do people choose to train with Enthought? Who has Enthought worked with and what is their feedback?

We’ll also provide a guided tour and insights about our our five primary course offerings to help you understand the fit for you or your team:

View Recording  


michael_connell-enthought-vp-training

Presenter: Dr. Michael Connell, VP, Enthought Training Solutions

Ed.D, Education, Harvard University
M.S., Electrical Engineering and Computer Science, MIT


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Traits and TraitsUI: Reactive User Interfaces for Rapid Application Development in Python

 The open-source pi3Diamond application built with Traits, TraitsUI and Chaco by Swabian Instruments.The Enthought Tool Suite team is pleased to announce the release of Traits 4.6. Together with the release of TraitsUI 5.1 last year, these core packages of Enthought’s open-source rapid application development tools are now compatible with Python 3 as well as Python 2.7.  Long-time fans of Enthought’s open-source offerings will be happy to hear about the recent updates and modernization we’ve been working on, including the recent release of Mayavi 4.5 with Python 3 support, while newcomers to Python will be pleased that there is an easy way to get started with GUI programming which grows to allow you to build applications with sophisticated, interactive 2D and 3D visualizations.

A Brief Introduction to Traits and TraitsUI

Traits is a mature reactive programming library for Python that allows application code to respond to changes on Python objects, greatly simplifying the logic of an application.  TraitsUI is a tool for building desktop applications on top of the Qt or WxWidgets cross-platform GUI toolkits. Traits, together with TraitsUI, provides a programming model for Python that is similar in concept to modern and popular Javascript frameworks like React, Vue and Angular but targeting desktop applications rather than the browser.

Traits is also the core of Enthought’s open source 2D and 3D visualization libraries Chaco and Mayavi, drives the internal application logic of Enthought products like Canopy, Canopy Geoscience and Virtual Core, and Enthought’s consultants appreciate its the way it facilitates the rapid development of desktop applications for our consulting clients. It is also used by several open-source scientific software projects such as the HyperSpy multidimensional data analysis library and the pi3Diamond application for controlling diamond nitrogen-vacancy quantum physics experiments, and in commercial projects such as the PyRX Virtual Screening software for computational drug discovery.

 The open-source pi3Diamond application built with Traits, TraitsUI and Chaco by Swabian Instruments.

The open-source pi3Diamond application built with Traits, TraitsUI and Chaco by Swabian Instruments.

Traits is part of the Enthought Tool Suite of open source application development packages and is available to install through Enthought Canopy’s Package Manager (you can download Canopy here) or via Enthought’s new edm command line package and environment management tool. Running

edm install traits

at the command line will install Traits into your current environment.

Traits

The Traits library provides a new type of Python object which has an event stream associated with each attribute (or “trait”) of the object that tracks changes to the attribute.  This means that you can decouple your application model much more cleanly: rather than an object having to know all the work which might need to be done when it changes its state, instead other parts of the application register the pieces of work that each of them need when the state changes and Traits automatically takes care running that code.  This results in simpler, more modular and loosely-coupled code that is easier to develop and maintain.

Traits also provides optional data validation and initialization that dramatically reduces the amount of boilerplate code that you need to write to set up objects into a working state and ensure that the state remains valid.  This makes it more likely that your code is correct and does what you expect, resulting in fewer subtle bugs and more immediate and useful errors when things do go wrong.

When you consider all the things that Traits does, it would be reasonable to expect that it may have some impact on performance, but the heart of Traits is written in C and knows more about the structure of the data it is working with than general Python code. This means that it can make some optimizations that the Python interpreter can’t, the net result of which is that code written with Traits is often faster than equivalent pure Python code.

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New Year, New Enthought Products!

We’ve had a number of major product development efforts underway over the last year, and we’re pleased to share a lot of new announcements for 2017:

A New Chapter for the Enthought Python Distribution (EPD):
Python 3 and Intel MKL 2017

In 2004, Enthought released the first “Python: Enthought Edition,” a Python package distribution tailored for a scientific and analytic audience. In 2008 this became the Enthought Python Distribution (EPD), a self-contained installer with the "enpkg" command-line tool to update and manage packages. Since then, over a million users have benefited from Enthought’s tested, pre-compiled set of Python packages, allowing them to focus on their science by eliminating the hassle of setting up tools.

Enthought Python Distribution logo

Fast forward to 2017, and we now offer over 450 Python packages and a new era for the Enthought Python Distributionaccess to all of the packages in the new EPD is completely free to all users and includes packages and runtimes for both Python 2 and Python 3 with some exciting new additions. Our ever-growing list of packages includes, for example, the 2017 release of the MKL (Math Kernel Library), the fruit of an ongoing collaboration with Intel.

The New Enthought Deployment Server:
Secure, Onsite Access to EPD and Private Packages

enthought-deployment-server-centralized-management-illustration-v2

For those who are interested in having a private copy of the Enthought Python Distribution behind their firewall, as well as the ability to upload and manage internal private packages alongside it, we now offer the Enthought Deployment Server, an onsite version of the server we have been using for years to serve millions of Python packages to our users.

enthought-deployment-server-logoWith a local Enthought Deployment Server, your private copy will periodically synchronize with our master repository, on a schedule of your choosing, to keep you up to date with the latest releases. You can also set up private package repositories and control access to them using your existing LDAP or Active Directory service in a way that suits your organization.  We can even give you access to the packages (and their historical versions) inside of air-gapped networks! See our webinar introducing the Enthought Deployment Server.

Command Line Access to the New EPD and Flat Environments
via the Enthought Deployment Manager (EDM)

In 2013, we expanded the original EPD to introduce Enthought Canopy, coupling an integrated analysis environment with additional features such as a graphical package manager, documentation browser, and other user-friendly tools together with the Enthought Python Distribution to provide even more features to help “make science and analysis easy.”

With its MATLAB-like experience, Canopy has enabled countless engineers, scientists and analysts to perform sophisticated analysis, build models, and create cutting-edge data science algorithms. The all-in-one analysis platform for Python has also been widely adopted in organizations who want to provide a single, unified platform that can be used by everyone from data analysts to software engineers.

But we heard from a number of you that you also still wanted the capability to have flat, standalone environments not coupled to any editor or graphical tool. And we listened!  

enthought-deployment-manager-cli-screenshot2So last year, we finished building out our next-generation command-line tool that makes producing flat, standalone Python environments super easy.  We call it the Enthought Deployment Manager (or EDM for short), because it’s a tool to quickly deploy one or multiple Python environments with the full control over package versions and runtime environments.

EDM is also a valuable tool for use cases such as command line deployment on local machines or servers, web application deployment on AWS using Ansible and Amazon CloudFormation, rapid environment setup on continuous integration systems such as Travis-CI, Appveyor, or Jenkins/TeamCity, and more.

Finally, a new state-of-the-art package dependency solver included in the tool guarantees the consistency of your environment, and if your workflow requires switching between different environments, its sandboxed architecture makes it a snap to switch contexts.  All of this has also been designed with a focus on providing robust backward compatibility to our customers over time.  Find out more about EDM here.

Enthought Canopy 2.0:
Python 3 packages and New EDM Back End Infrastructure

Enthought Canopy LogoThe new Enthought Python Distribution (EPD) and Enthought Deployment Manager (EDM) will also provide additional benefits for Canopy.  Canopy 2.0 is just around the corner, which will be the first version to include Python 3 packages from EPD.

In addition, we have re-worked Canopy’s graphical package manager to use EDM as its back end, to take advantage of both the consistency and stability of the environments EDM provides, as well as its new package dependency solver.  By itself, this will provide a big boost in stability for users (ever found yourself wrapped up in a tangle of inconsistent package versions?).  Alongside the conversion of Canopy’s back end infrastructure to EDM, we have also included a substantial number of stability improvements and bug fixes.

Canopy’s Graphical Debugger adds external IPython kernel debugging support

On the integrated analysis environment side of Canopy, the graphical debugger and variable browser, first introduced in 2015, has gotten some nifty new features, including the ability to connect to and debug an external IPython kernel, in addition to a number of stability improvements.  (Weren’t aware you could connect to an external process?  Look for the context menu in the IPython console, use it to connect to the IPython kernel running, say, a Jupyter notebook, and debug away!)

Canopy Data Import Tool adds CSV exports and input file templates

Enthought Canopy Data Import ToolAlso, we’ve continued to add new features to the Canopy Data Import Tool since its initial release in May of 2016. The Data Import Tool allows users to quickly and easily import CSVs and other structured text files into Pandas DataFrames through a graphical interface, manipulate the data, and create reusable Python scripts to speed future data wrangling.

The latest version of the tool (v. 1.0.9, shipping with Canopy 2.0) has some nice new features like CSV exporting, input file templates, and more. See Enthought’s blog for some great examples of how the Data Import Tool speeds data loading, wrangling and analysis.

What to Look Forward to in 2017

So where are we headed in 2017?  We have put a lot of effort into building a strong foundation with our core suite of products, and now we’re focused on continuing to deliver new value (our enterprise users in particular have a number of new features to look forward to).  First up, for example, you can look for expanded capabilities around Python environments, making it easy to manage multiple environments, or even standardize and distribute them in your organization.  With the tremendous advancements in our core products that took place in 2016, there are a lot of follow-on features we can deliver. Stay tuned for updates!

Have a specific feature you’d like to see in one of Enthought’s products? E-mail our product team at canopy.support@enthought.com and tell us about it!