Anaconda vs. Microsoft Visual Studio Code

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

If your Source Control Software is Team Foundation Server then skip Visual Studio Code. If you're using GitHub and are creating small projects Visual Studio Code is the way to go. If you need to create a large, enterprise-level application, Visual Studio Code makes it easier to set up interactions between related projects (client & server). If you're interested in getting back to the old way of using the command line to create projects and you know what to enter in the console window then Visual Studio Code is great. Visual Studio Code is a better choice if you don't know the console commands and prefer to make selections from a menu.

Incentivized

• Installing packages is very easy with Anaconda. Anaconda comes with 'anaconda navigator', a terminal-like utility from which you can easily install R packages and python libraries.
• Launching R and python IDEs as well as Jupyter notebooks from anaconda navigator is simple, and Anaconda makes it very easy to keep these packages up-to-date.
• I really like the fact that if you don't want to install the full version of Anaconda, you can opt to install a lightweight version (called Miniconda) that includes less python libraries and only core conda. I've installed it when I didn't want to take up as much disk space as Anaconda requires, but it works just the same.

Incentivized

• Robust plugin architecture replete with fantastic add-ons that make developer life delightful.
• Integrated Terminal window allows you to stay in one application to perform most required tasks.
• Customization options are robust. It is easy to modify VS Code to your own specifications.
• It's free! Hard to believe such a well made, well maintained, robust software is a free download.
• Command+P/Command+Shift+P key commands will improve your workflow dramatically.

Incentivized

• More graphics need in Spyder book. If you work for couple of years then you will be bored with the graphics.
• Extra tools are required for making it secure. We uses extra tools for adding Username /Password to Jupyter.
• R Studio Hangs a lot when open from Anaconda Navigator.

Incentivized

• Unlike for most languages I have used, Ruby and Rails support available for Code users isn't great. The most popular Ruby extension is unofficial, and leaves much to desire. As an example, code navigation even with language server Solargraph installed isn't as good as IntelliJ's RubyMine.
• Even there is quite good support for a language or a framework, it is almost never as good as a dedicated IDE for it. In terms of the sheer number of features available, IntelliJ IDEs handily beat Code.
• Microsoft has close-sourced some of the extensions it develops for Code itself, e.g. Pylance for Python, and that has not been perceived as a good move for open-source.

Incentivized

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.

Incentivized

Solid tool that provides everything you need to develop most types of applications. The only reason not a 10 is that if you are doing large distributed teams on Enterprise level, Professional does provide more tools to support that and would be worth the cost.

Incentivized

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.

Incentivized

Looking at our current implementation, Microsoft Visual Studio Code is perfect for writing code and performing debug operations. Integration with SVN repository is easy and changes can be tracked effectively. Microsoft Visual Studio Code supports developers to write code productively using syntax check and easy customization. Microsoft Visual Studio Code also provides support for IntelliSense which prompts suggestions for code completion. It is easy to step through code using interactive debugger to inspect the root cause of error quickly.

Incentivized

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.

Incentivized

Active development means filing a bug on the GitHub repo typically gets you a response within 4 days. There are plugins for almost everything you need, whether it be linting, Vim emulation, even language servers (which I use to code in Scala). There is well-maintained official documentation. The only thing missing is forums. The closest thing is GitHub issues, which typically has the answers but is hard to sift through -- there are currently 78k issues.

Incentivized

One of the main competitors to Anaconda can be Google products such as Colab. Colab gives you the flexibility to handle large datasets gives it an edge over Anaconda. But again, the ease of access and usability of Anaconda stacks up against Colab. Besides, Anaconda relies more on your machine which makes it safe to use.

Incentivized

All the previously listed are incredible development environments that perfectly fulfill this function, but [Microsoft] Visual Studio Code goes one step ahead by providing flexibility, customization and adaptability to development environments with its own methodology, for all this productivity. of the work team is greatly increased helping to achieve the objectives set in the organization.

Incentivized

• Positive impact - Multiple options for data presenting , visualizing and sharing. (Eg: R-Markdown).
• Positive impact - Ease of access to build complex machine learning models. (I work in NLP, it has multiple built in models to analyze the various contexts).
• Positive impact - Conda package let's to deal with external packages which can be used in Jupyter.

Incentivized

• Saves money by replacing suites of tools such as Visual Studio, IntelliJ, etc.
• Speeds development time and developer environment setup time
• Strengthens code quality with integrated autoformatting and linting
• Strengths Git practices by keeping version control tightly connected with the code

Incentivized