Canopy - Python Scientific and Analytic Environment

Enthought Canopy provides an open, intuitive Python environment for scientific and analytic computing.

Enthought CanopyEnthought Canopy is a comprehensive Python-based analysis environment for scientists, engineers and analysts. It provides easy installation of the core analytic and scientific Python packages for rapid data collection, manipulation, analysis and visualization, algorithm design, and application development.

Canopy is the follow on to the Enthought Python Distribution (EPD) that has been widely used for scientific and analytic computing with Python. EPD is popular within energy and finance fields, industrial automation, aerospace and government organizations. Canopy took EPD's Python computing stack and supplemented it with valuable tools creating a robust platform you can explore, develop, and visualize on. Main Enthought Canopy features include:

  • An advanced text editor with syntax highlighting, Python code auto-completion, and error checking.
  • IPython Notebook Support and integrated IPython shell that facilitates interactive execution and exploration.
  • Interactive graphical Python code debugger with variable browser that enables users to understand and investigate code and data.
  • One-click Python package deployment with a graphical package manager, which also notifies of updates, helps to rollback package versions, and report bugs.
  • Convenient Documentation Browser with user's guide and code examples.

Tools like advanced editor, graphical debugger with variable browser, and integrated IPython create a powerful integrated analysis environment. Canopy streamlines data analysis, visualization, algorithm algorithm prototyping and testing, application development. Scripting and plotting become more straightforward.

Produced by Enthought, Canopy is available for free and under a commercial license. A free Canopy variant includes integrated IPython, advanced Code Editor and application development platform. Scientists, engineers, quantitative and data analysts can choose the most appropriate option. For more information see

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