Python 3 and multi-environment support, new state of the art package dependency solver, and over 450 packages now available free for all users
Enthought is pleased to announce the release of Canopy 2.1, a significant feature release that includes Python 3 and multi-environment support, a new state of the art package dependency solver, and access to over 450 pre-built and tested scientific and analytic Python packages completely free for all users. We highly recommend that all current Canopy users upgrade to this new release.
Ready to dive in? Download Canopy 2.1 here.
For those currently familiar with Canopy, in this blog we’ll review the major new features in this exciting milestone release, and for those of you looking for a tool to improve your workflow with Python, or perhaps new to Python from a language like MATLAB or R, we’ll take you through the key reasons that scientists, engineers, data scientists, and analysts use Canopy to enable their work in Python.
First, let’s talk about the latest and greatest in Canopy 2.1!
- Support for Python 3 user environments: Canopy can now be installed with a Python 3.5 user environment. Users can benefit from all the Canopy features already available for Python 2.7 (syntax checking, debugging, etc.) in the new Python 3 environments. Python 3.6 is also available (and will be the standard Python 3 in Canopy 2.2).
- All 450+ Python 2 and Python 3 packages are now completely free for all users: Technical support, full installers with all packages for offline or shared installation, and the premium analysis environment features (graphical debugger and variable browser and Data Import Tool) remain subscriber-exclusive benefits. See subscription options here to take advantage of those benefits.
- Built in, state of the art dependency solver (EDM or Enthought Deployment Manager): the new EDM back end (which replaces the previous enpkg) provides additional features for robust package compatibility. EDM integrates a specialized dependency solver which automatically ensures you have a consistent package set after installation, removal, or upgrade of any packages.
- Environment bundles, which allow users to easily share environments directly with co-workers, or across various deployment solutions (such as the Enthought Deployment Server, continuous integration processes like Travis-CI and Appveyor, cloud solutions like AWS or Google Compute Engine, or deployment tools like Ansible or Docker). EDM environment bundles not only allow the user to replicate the set of installed dependencies but also support persistence for constraint modifiers, the list of manually installed packages, and the runtime version and implementation.
- Multi-environment support: with the addition of Python 3 environments and the new EDM back end, Canopy now also supports managing multiple Python environments from the user interface. You can easily switch between Python 2.7 and 3.5, or between multiple 2.7 or 3.5 environments. This is ideal especially for those migrating legacy code to Python 3, as it allows you to test as you transfer and also provides access to historical snapshots or libraries that aren’t yet available in Python 3.
Why Canopy is the Python platform of choice for scientists and engineers
Since 2001, Enthought has focused on making the scientific Python stack accessible and easy to use for both enterprises and individuals. For example, Enthought released the first scientific Python distribution in 2004, added robust and corporate support for NumPy on 64-bit Windows in 2011, and released Canopy 1.0 in 2013.
Since then, 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. Canopy’s all-in-one package distribution and analysis environment 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.