Anaconda 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

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.

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

• 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.

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

• PyCharm text editor automatically inserts whitespace at the end of each line which can cause issues when doing text comparisons.
• The package requirement checker and installer does not work well all the time and can be improved
• Integration with GitLab pipelines can be made better.

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

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

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

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.

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

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

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

It is more complete and can handle more projects at the same time. On the other hand, Visual Studio Code has better integration with LMS to help you code. PyCharm allows you to integrate with many external tools and external servers that Visual Studio Code has difficulties with.

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

• Improved efficiency with coding assistance (templates, code completion, documentation), which helps us avoid 'reinventing the wheel' with new projects.
• Extensive support for other packages/integrations: Docker support to test code, Git repo creation (for version control), and integration with different database systems (Postgres, MySQL).

Incentivized