Introducing Enthought Canopy

-Eric Jones, Enthought CEO

Yesterday we launched Enthought Canopy, our next-generation, Python-based analysis environment and our follow-on to EPD.

Since 2003, the Enthought Python Distribution (EPD) has helped hundreds of thousands of scientists, engineers and analysts develop and deploy with Python. 2013 is its 10th anniversary! It’s hard to believe it’s been that long. Time flies when you are having fun.

Over the years, we’ve watched customers use Python for a variety of scientific and analytic computing applications. Their tasks fell into three areas: exploration, development, and visualization. We developed Canopy to specifically help with these 3 key analysis tasks.


Python makes it straightforward to acquire and manage data from a variety of sources like the web, databases and files on the computer. Using Canopy’s IPython interactive prompt, you can interactively access, combine and explore data from all these sources. With the IPython Notebook, you can capture your sessions, re-use them later or share them with others.


Analysts develop algorithms and scripts to work with their data and to deliver results. Canopy’s editor includes syntax highlighting to help identify elements in the Python code, auto completion to speed programming, and error checking to reduce time spent hunting bugs. You can execute entire scripts or selected lines from the editor at the IPython prompt to test and debug your code.

Python’s scientific and analytic computing ecosystem is quite large, with hundreds, if not thousands, of packages. Canopy itself ships with over 100 math, science, and analytic packages. And the ecosystem continues to grow and evolve. The Canopy Package Manager makes it simple to find, install and update these Python packages.


Beyond the numbers and the scripts, one of the best ways to understand and explain data is with visualization. Python provides many tools and packages to help visualize data and results. Within Canopy, Matplotlib works seamlessly with IPython and provides an extensive toolset for data plotting and presentation.

For more extensive graphical user interfaces, Canopy includes 2-D and 3-D visualization packages like Chaco and Mayavi.

We’re really excited about Canopy’s design. It brings together the full extent and power of Python in an easy-to-use analysis environment today and provides an analysis platform on which to build for the next 10 years. For more details, check the Canopy web-page.


19 thoughts on “Introducing Enthought Canopy

    1. avatarbmurphy

      Completely agree with the “document” and “share/deploy”. And I can see your point about optimizing before deploying. Actually our vision for Canopy includes addressing these steps. Thank you for the comment!

  1. avatarMohammad Ishfaque Jahan Rafee

    I tried to switch to canopy on its launch. Currently I use PyScripter. But lack of auto completion and being unable to install OpenCV forced me back to PyScripter. Ialso want to know whether there is a way to change IPython interpreter options, e.g. I want to change pylab to inline mode, i.e. –pylab=inline. Unless these features are implemented or known, I am sticking with PyScripter for now.

    1. avatarJason McCampbell

      Hi Mohammad, you can turn on –pylab=inline via the preferences in the editor. On Windows and Linux go to Edit -> Preferences and on Mac OS see Canopy -> Preferences. In both cases the ‘Python’ tab has an option labeled ‘Pylab Backend’. You can select between Interactive / Qt (the default), Interactive / wx, or inline.

      The code editor does support auto-completion of variable names, locally defined functions, and module names. Are you not seeing this? Pressing ‘tab’ in the editor should complete the names where possible.

      Unfortunately we don’t have OpenCV as a part of the Canopy packages yet, but stay tuned.

  2. avatarChris Nelson

    What’s the difference between Enthought’s Canopy and Continuum’s Anaconda? It seems that there’s a lot of overlap and duplication between the two Python Science “distros”.


    1. avatarbmurphy

      Yes, both Canopy and Anaconda include Python distributions targeted at scientific and analytic computing. However, I would state it as “Canopy includes a Python distribution” and “Anaconda is a Python distribution”.

      Canopy is an analysis environment (analysis desktop, package manager, documention browser as well as Python distro). It is aimed at scientists, engineers and analysts, and its purpose is to make their scientific and analytic computing tasks simpler. With this as the vision, we will continue to flesh out the capabilities of the environment.

  3. avatarGeorge Allen

    I just finished attempting to install Canopy and my anti-viral software (Avast) flagged flapack.pyd as malware and terminated the installation. Please look into this and if it is definitely clean let me know and I will notify Avast and attempt to reinstall. I currently use the old version and really like it so am looking forward to this upgrade.



  4. avatarTroels

    A tip for installing Canopy with admin privileges on win7.
    Write ‘cmd’ at start button. Right click, “Run as administrator”.
    > cd %HOMEPATH%\Downloads
    > msiexec /i canopy-1.0.0-win-64.msi

  5. avatarEthan Fosse

    As a quantitative sociologist, I highly recommend Enthought’s products. It’s a great way to learn programming languages while progressing on substantively-meaningful analyses.

  6. avatarS Durve

    It was nice to see all the necessary packages together – ready to use. A serious engineering user can easily benefit from this and even develop more versatile software instead of wasting time in writing spreadsheets.

    Visualisation of complex data is the real power of Canopy and it will be nice to see more development on graphics in future.

    1. avatarbmurphy

      Yes, we plan to flesh out the analysis capabilities of the environment as we go forward. If you have specific ideas or challenges, let us know. Thanks!

  7. avatarChris


    Canopy is great, but I have a few feature requests:

    (1) Better control over appearance of editing window from the preferences pane (e.g. syntax coloring options, custom themes, repositioning of panes to take advantage of wide screen monitors).

    (2) Vim bindings

    (3) Ability to create snippets for tag completion

    Canopy will have to add some minimal IDE-like features for me to use it as a replacement for Vim/Terminal. Right now the biggest value-add of Enthought products is avoiding the need to build and install all those pesky packages myself.

    You might want to check out RStudio for inspiration as you further develop Canopy. That software really shines because it offers power users the editing features they expect, but also reduces the learning curve for newbies.

    Best wishes,

  8. avatarbmurphy

    Thanks for the feedback Chris!
    We are working on 1) and looking at 2). 3) we’ll add to the list. Right now we’re focused on building out features for scientists, engineers and analysts so the more advanced editor features will come along later most likely.

      1. avatarbmurphy

        You can use pdb from the command line if you want to grab it yourself and install. There are some Knowledge Base articles on our support pages that discuss how to install external packages from the command line.

        We are working on a graphical debugger that will come out later this year. Sorry for the wait!

  9. avatarChinmay

    Few things I need in a good IDE.

    Integrated pdb
    documentation by selecting the keyword and F1
    auto completion
    unit testing framework
    should be better than VIM. 🙂

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  11. avatarRowland

    How will I load a file in this new Enthought Canopy.. I am quite new to using Enthought Canopy and I just learned Python for a research project I am working on.

    1. avatarbmurphy

      You can open file from the File menu or through the File Manager on the Editor page. Hope this helps!


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