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.
Read full review It's easy to create virtual environments and install packages for different projects as we may need project-specific packages for doing our experiments, also it's easy to see what changes we have made and create pull requests faster. But sometimes we want some light python editor like Jupiter notebook as PyCharm is relatively heavier, also Jupiter notebooks are a good option when we need to run remote code on local machines.
Read full review Pros 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. Read full review Git integration is really essential as it allows anyone to visually see the local and remote changes, compare revisions without the need for complex commands. Complex debugging tools are basked into the IDE. Controls like break on exception are sometimes very helpful to identify errors quickly. Multiple runtimes - Python, Flask, Django, Docker are native the to IDE. This makes development and debugging and even more seamless. Integrates with Jupyter and Markdown files as well. Side by side rendering and editing makes it simple to develop such files. Read full review Cons 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. Read full review The biggest complaint I have about PyCharm is that it can use a lot of RAM which slows down the computer / IDE. I use the paid version, and have otherwise found nothing to complain about the interface, utility, and capabilities. Read full review Likelihood to Renew It's perfect for our needs, cuts development time, is really helpful for newbies to understand projects structure
Read full review Support Rating I rate 10/10 because I have never needed a direct customer support from the JetBrains so far. Whenever and for whatever kind of problems I came across, I have been able to resolve it within the internet community, simply by Googling because turns out most of the time, it was me who lacked the proper information to use the IDE or simply make the proper configuration. I have never came across a bug in PyCharm either so it deserves 10/10 for overall support
Read full review 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.
Read full review PyCharm is the best IDE for python development. PyCharm offers various features: source code completion, support for unit testing, integration with Docker/GitLab/Git, ability to manage and configure virtual environments, auto-indentation, and re-factoring code with ease. Support for JSON/Shell scripts and support for Flask/Django Other tools are effective for creating isolated scripts but not for handling projects with more than two scripts.
Read full review 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. Read full review Buying the licensed pro version is a bit costly, but overall because of its features and its speed, the time taken by a developer to develop something can be improved. Indirectly getting a good return of Investment. Considering the team size and its features, one can go for the licensed version as the ROI is high. Customer support is also good for a licensed version, thereby saving the time, which in turn shows ROI as high. Read full review ScreenShots