Anaconda Review: "A simple and powerful open-source Python distribution"https://www.trustradius.com/data-scienceAnacondaUnspecified8.7251012018-07-12T15:27:14.157Z
Overall Satisfaction with Anaconda
Anaconda is used by most members of my department who use Python. Since Anaconda is a cross-platform program, it makes collaboration among Mac/PC/Linux users relatively painless. Anaconda's package management system helps us maintain the most up-to-date Python libraries, which is important for working on code development within our department. Anaconda helps us avoid problems with installing python libraries that sometimes arise when installing libraries using pip. This, in turn, allows us to spend more of our time developing code and building software rather than troubleshoot issues with installing libraries. Anaconda offers several IDEs for python (and R), which makes writing code and debugging easier.
- 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.
- Although I have generally had positive experiences with Anaconda, I have had trouble installing specific python libraries. I tried to remedy the solution by updating other packages, but in the end, things got really messed up, and I ended up having to uninstall and reinstall a total of about 4 times over the past 2 years.
- If you have the free version of Anaconda, there is not much support. Googling questions and error messages are helpful, but there were times when I wished I would have been able to ask technical support to help me troubleshoot issues.
- There were a few times when I tried to install tensorflow and tensorboard via Anaconda on a PC, but I could not get them to install properly. Anaconda allows you to create 'environments' , which allow you to install specific versions of python and associated libraries. You can keep your environments separate so they do not conflict with one another. Anyway, I ended up having to create several 'conda envrionments' just so I could use tensforflow/tensorboard and a few other utilities to avoid errors. This was somewhat annoying, because every time I wanted to run a specific model, I'd have to open up the specific conda environment with the appropriate python libraries.
- It has increased our productivity by allowing us to spend more time on code development and less time on troubleshooting library installation issues.
- Because Anaconda helped us increase our efficiency while developing code / statistical models, we were able to complete our research objectives quickly. This allowed us to write manuscripts and publish our results quickly..
- Anaconda makes it very easy to share code through Jupyter notebooks. This has been particularly valuable for helping other members of our department (not directly involved with software development) understand what the software developers are doing. This step also doubles as quality control, as a new set of eyes can spot small software bugs. Getting rid of software bugs early is extremely important and helps us save time and money.
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!
Anaconda is great for academic and private organizations that cannot afford more expensive Python/R package managers. Also, it is more appropriate for intermediate to advanced Python users--Anaconda can be somewhat frustrating for beginners, as it takes some practice to get comfortable with the workflow. I find it particularly useful for working in teams, because if everyone uses the same package manager, it is easier to troubleshoot issues and makes for reproducible research. For wealthier organizations, a premium package management system (with tech support) would be ideal. Anaconda is also great for people working independently on code development.