Enthought Canopy
Scientific and Analytic Python Deployment with Integrated Analysis Environment
Enthought Canopy is a comprehensive Python
analysis environment that
provides easy
installation of the core
scientific analytic and scientific Python packages, creating a robust platform
you can explore, develop, and visualize on. In addition to its pre-built,
tested Python distribution, Enthought Canopy has valuable tools for
iterative data analysis, visualization and application development including:
Plus, Canopy works seamlessly with the NEW Enthought
Python Training on Demand for a
hands-on, interactive learning experience.
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For Scientists & Engineers
A comprehensive, Python-based analysis desktop and Python distribution,
Canopy provides an open, intuitive environment for scientific and
analytic computing. Since it's Python, your algorithms, scripts and
programs will never be locked into a proprietary language. And with the
analysis desktop, data analysis, scripting and plotting are more
straightforward. |
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For Quantitative & Data Analysts
Python functionality spans from desktop algorithm development and
testing to web server application development.
With the Canopy desktop and Python distribution, data ingestion,
manipulation and analysis are simplified, and algorithm prototyping and
testing are streamlined. |
For Enterprises
As a comprehensive Python-based analysis environment, Canopy puts
powerful, yet cost-effective tools in the hands of analysts, scientists
and engineers. As a robust application platform, it streamlines
technical computing application development and deployment for your
organization and for your customers.
With the popular, intuitive Python language and the comprehensive Canopy
application platform, your organization can deploy new applications,
algorithms and analysis tools much faster than with standard software
languages and platforms. Users, especially power users, can extend and
innovate with scripting and open platform APIs, driving the creation and
sharing of innovative techniques and tools. |
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