Anaconda vs. Enthought Canopy vs. PyCharm

I have asked all my juniors to work with Anaconda and Pycharm only, as this is the best combination for now. Coming to use cases: 1. When you have multiple applications using multiple Python variants, it is a really good tool instead of Venv (I never like it). 2. If you have to work on multiple tools and you are someone who needs to work on data analytics, development, and machine learning, this is good. 3. If you have to work with both R and Python, then also this is a good tool, and it provides support for both.

Incentivized

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.

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PyCharm is well suited to developing and deploying Python applications in the cloud using Kubernetes or serverless pipelines. The integration with GitLab is great; merges and rebates are easily done and help the developer move quickly. The search engine that allows you to search inside your code is also great. It is less appropriate for other languages.

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• Anaconda is a one-stop destination for important data science and programming tools such as Jupyter, Spider, R etc.
• Anaconda command prompt gave flexibility to use and install multiple libraries in Python easily.
• Jupyter Notebook, a famous Anaconda product is still one of the best and easy to use product for students like me out there who want to practice coding without spending too much money.

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• 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.

• 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.

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• It can have a cloud interface to store the work.
• Compatible for large size files.
• I used R Studio for building Machine Learning models, Many times when I tried to run the entire code together the software would crash. It would lead to loss of data and changes I made.

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• 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.

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• 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.

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It's really good at data processing, but needs to grow more in publishing in a way that a non-programmer can interact with. It also introduces confusion for programmers that are familiar with normal Python processes which are slightly different in Anaconda such as virtualenvs.

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It's perfect for our needs, cuts development time, is really helpful for newbies to understand projects structure

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I am giving this rating because I have been using this tool since 2017, and I was in college at that time. Initially, I hesitated to use it as I was not very aware of the workings of Python and how difficult it is to manage its dependency from project to project. Anaconda really helped me with that. The first machine-learning model that I deployed on the Live server was with Anaconda only. It was so managed that I only installed libraries from the requirement.txt file, and it started working. There was no need to manually install cuda or tensor flow as it was a very difficult job at that time. Graphical data modeling also provides tools for it, and they can be easily saved to the system and used anywhere.

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It's pretty easy to use, but if it's your first time using it, you need time to adapt. Nevertheless, it has a lot of options, and everything is pretty easy to find. The console has a lot of advantages and lets you accelerate your development from the first day.

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Anaconda provides fast support, and a large number of users moderate its online community. This enables any questions you may have to be answered in a timely fashion, regardless of the topic. The fact that it is based in a Python environment only adds to the size of the online community.

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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

Incentivized

I have experience using RStudio oustide of Anaconda. RStudio can be installed via anaconda, but I like to use RStudio separate from Anaconda when I am worin in R. I tend to use Anaconda for python and RStudio for working in R. Although installing libraries and packages can sometimes be tricky with both RStudio and Anaconda, I like installing R packages via RStudio. However, for anything python-related, Anaconda is my go to!

Incentivized

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.

When it comes to development and debugging PyCharm is better than Spyder as it provides good debugging support and top-quality code completion suggestions. Compared to Jupiter notebook it's easy to install required packages in PyCharm, also PyChram is a good option when we want to write production-grade code because it provides required suggestions.

Incentivized

• It has helped our organization to work collectively faster by using Anaconda's collaborative capabilities and adding other collaboration tools over.
• By having an easy access and immediate use of libraries, developing times has decreased more than 20 %
• There's an enormous data scientist shortage. Since Anaconda is very easy to use, we have to be able to convert several professionals into the data scientist. This is especially true for an economist, and this my case. I convert myself to Data Scientist thanks to my econometrics knowledge applied with Anaconda.

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• 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.

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• PyCharm has a very positive ROI for our BU. It has increased developer productivity exponentially.
• Software quality has significantly improved. We are able to refactor/test/debug the code quicker/faster/better.
• Our business unit is able to deliver faster. Customers are happier than ever.

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