Data analysis and scientific applications with python integrated in a beautifully designed environment.
February 11, 2016
Data analysis and scientific applications with python integrated in a beautifully designed environment.
Score 9 out of 10
Vetted Review
Verified User
Overall Satisfaction with Enthought Canopy
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.
- 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.
- Canopy increased our efficiency in processing , analyzing and plotting our data.
- Canopy reduced the cost of developing several libraries.
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.