The following table compares Python vs ActiveState Python vs Anaconda:
Python vs ActiveState Python vs Anaconda – Which Python to download?
| Python | ActiveState | Anaconda | |
| Pre-built Distributions | Multiple Python cores | Multiple ActivePython distributions | Anaconda/ MiniConda |
| Usage | General purpose | General purpose | Data science focused |
| Package Manager | Pip | State Tool (CLI) | |
| Repository | PyPI | ActiveState Platform Catalog1 | Anaconda Repository1 |
| Source Code Build Tooling | None (third party only) | ActiveState Platform: automated cloud-based builds | Conda-Forge: local, manual builds |
| Native Virtual Environments | No | Yes | Yes |
| Dependency Resolution | No (under development) | Yes – solve and suggest conflict solutions | Yes – solve and warn |
1 – All packages are pulled as source code from PyPI, as well as other sources
ConclusionsExperienced Pythonistas will likely prefer to use Python.org’s Python core, and then manually install all the packages they require from PyPI using pip. For the time being, dependency resolution will still need to be managed manually.New to Python? We’d recommend starting with a pre-built version of Python such as those offered by ActivePython or Anaconda in order to simplify and speed setup. These kinds of “batteries included” Python environments provide everything you need to get started coding right away.
- Since ActiveState provides commercial support, ActivePython is the best choice for those focused on building commercial applications.
- Anaconda is a good choice for those focused on creating non-commercial data science applications since you can take advantage of Anaconda’s proven Python ecosystem for free.
Related Blogs:
ActivePython vs Anaconda: The ActiveState Advantage for Anaconda Users
Unlocking the Power of Data Science & Machine Learning with Python
