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Enthought Canopy

Enthought Canopy

Overview

What is Enthought Canopy?

Austin based Enthought offers their flagship scientific Python distribution, Canopy. The Canopy Geoscience (or Canopy Geo) variant of the product is a data analysis, exploration and visualization package optimized for geologists & geophysicists, and researchers in petroleum science.

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Pricing

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Enthought Canopy

Enthought Canopy

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What is Enthought Canopy?

Austin based Enthought offers their flagship scientific Python distribution, Canopy. The Canopy Geoscience (or Canopy Geo) variant of the product is a data analysis, exploration and visualization package optimized for geologists & geophysicists, and researchers in petroleum science.

Entry-level set up fee?

  • No setup fee

Offerings

  • Free Trial
  • Free/Freemium Version
  • Premium Consulting / Integration Services

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Product Details

What is Enthought Canopy?

Enthought Canopy Technical Details

Operating SystemsUnspecified
Mobile ApplicationNo
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Comparisons

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Reviews and Ratings

(3)

Reviews

(1-2 of 2)
Companies can't remove reviews or game the system. Here's why
March 28, 2018

Canopy is highly recommended for data analysts

Verified User
Engineer in Engineering
Information Technology and Services Company, 201-500 employees
Score 6 out of 10
Vetted Review
Verified User
Incentivized
Use Cases and Deployment Scope
I currently use Enthought Canopy as my IDE for writing, editing and running Python scripts. It helps me keep track of all my Python programs and projects in one space. Canopy is good for data analysis and playing around with data to achieve a specific purpose. Currently I am the only one using Enthought Canopy at my team.
Pros and Cons
  • Canopy's editor has an integrated environment of having an editor and a Python shell attached to it
  • The Documentation Browser is useful
  • The analytic Python package distribution is definitely a plus
  • Canopy does not support Python 3
  • There were times the Python shell crashed, and I would have to restart it
  • Some Python libraries are slow.
Likelihood to Recommend
Enthought Canopy is best suites for scripting data analytical concepts. It has a wide range of data analytical libraries and also is good for data visualization. I would not recommend using Enthought Canopy only as an IDE, there may be better options available. If you're looking for a good data simulation & visualization package, Canopy it is.
Return on Investment
  • Its easier to define KPI's with Enthought
  • It is good for reiteration and building on top of existing scripts
  • Its dedicated Python console makes it easier to execute projects.
Alternatives Considered
  • Sublime
I was using Sublime Text Editor initially to edit and write most of my Python scripts, but a colleague recommended Enthought Canopy and I have been using it ever since then, because of its integrated packages for data analysis, its easier to execute and write relevant scripts in one space. It definitely makes work life easier and effective.
Other Software Used
February 11, 2016

Data analysis and scientific applications with python integrated in a beautifully designed environment.

JA
Jesús Aponte
Ayudante Académico Nivel II
Universidad Simón Bolívar (Higher Education, 1001-5000 employees)
Score 9 out of 10
Vetted Review
Verified User
Use Cases and Deployment Scope
Enthought Canopy is the programming platform of choice by myself for python 2.7, and is the tool I'm currently using for my project. We are keeping track of a bunch of data and making a lot of analysis on them. I chose Canopy for its ease of use, and because they provide several libraries which are perfect for our purpose, specially for data analysis. Besides that Canopy offers several training courses which are very helpful. Canopy facilitates the incorporation of scientific libraries it works locally and provides its own compiler, that's why configuring a common environment for all the developers stop being a problem.
Pros and Cons
  • Providing scientific libraries, both open source and Enthought's own libraries which are excellent.
  • Training. They provide several courses in python for general use and for data analysis.
  • Debugging tools. Several IDEs provides tools for debugging, but I think they are insufficient or too general. Canopy has a special debugging tool, specially design for python.
  • Lack of python 3 support. I hope this will chance in the near future.
  • Canopy is perfect for scientific application, but it would be even more perfect if they include more general purpose libraries.
Likelihood to Recommend
Canopy is perfect for data analysis in general. If you are a programmer for general purpose commercial application perhaps Canopy is not the best choice. But if you are just beginning programming or making simulation with python I recommend using Canopy and its training platform, they have excellent teachers with impressive curriculum.
Return on Investment
  • Canopy increased our efficiency in processing , analyzing and plotting our data.
  • Canopy reduced the cost of developing several libraries.
Alternatives Considered
Before Canopy with its python we were working with Matlab. We decided for Canopy against Matlab for two reasons: First, we believe that python together with NumPy or SciPy can achieve the objectives with less code and therefore less training, and second the prizes are much lower than matlab which is most robust, expensive and less intuitive. It's clear we are making the comparison with python and it has nothing to with canopy. But with Canopy you feel you have all those tools close together without the problem of configuration, besides a lot of personalized libraries that complements a typical python environment.
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