Enthought Canopy vs. DataGrip

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.

Incentivized

You will like DataGrip; it is the best software to channel and influence data systems and efficiently. It will help you be more efficient and productive in exporting computer data; it is very effective. There are no errors, and you will feel comfortable using them. It promotes data quality and has excellent graphics to represent your information through statistical processes.

Incentivized

• 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.

• I like and enjoy this tool because it is intuitive and straightforward; its interface and panels are for managing local and remote databases.
• It is beneficial and powerful for database administration and has extensive support for handling multiple database engines.
• DataGrip's features and functionalities are excellent; they make our work easier and faster.
• We can manage several databases at the same time.

Incentivized

• 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.

Incentivized

• An easier way to have a pool saved queries that can opened quickly

Incentivized

Usability has two aspects, being a complete tool and being easy... DataGrip has a lot of features but it is big, not clear how to use most of the things and most people won't need them. In my opinion, usability is good enough but not friendly.

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.

DataGrip provides a single UI for many DBMS platforms, instead of using one for each. Because of that, you can migrate things between platforms using the tool and "look across" all databases at once.

Incentivized

• 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.

Incentivized

• It has been perfect for detailed analysis of clients and companies; it gives us the competitiveness we were always looking for.

Incentivized