Anaconda vs. Enthought Canopy vs. Spyder

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.

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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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Spyder is an open-source Python IDE designed for the movement of data science work. Spyder comes with an Anaconda package manager distribution, so depending on your setup you may have installed it on your machine.

Spyder includes most of the "standard IDE" features you can expect, such as a strong syntax code editor, Python code rendering, and an integrated text browser.

Spyder is used when we want to develop a code that is useful and able to explore proper documentation of the code that has been written. We use Spyder to perform data-related operations like filtration, cleaning, and enhancing the data qualities. There some cases where it is less appropriate like working in an environment, creating dashboards of data visualizations and plots.

• 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.

• Provides wider screen to read and write code and flexibility to adjust size as per requirement.
• While running the code it provide the variable overview and memory overview
• Lightweight and easily available in Anaconda Navigator
• Multiple compilation options are available
• Works well for data analytics, Django, Flask and Fast API frameworks

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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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• Colors in code format
• Add a broadcast to share the project with friends
• Contains more than one important language such as Python

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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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 is fairly straightforward to use. Pretty much good to go as soon as you install it. The IDE itself is very user friendly, and it is only limited by whatever limitations Python has as a language. Great for those who want to run their scripts quickly or do some Python programming without fussing.

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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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Most of data scientists or data engineers are either using ec2 on the cloud or Atom or PyCharm locally. It is a bit hard to find people who are still using Spyder and have the sight of the IDE and can help you to answer your question.

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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!

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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.

I think Spyder doesn't stack up as well as other IDEs due to its many limitations. But it is available for free and that is one advantage it has over its competitors

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• 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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• It has helped me learn python quickly
• The ability to generate figures quickly and interact with them is helpful

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