If you’re using Python in the world of data science or scientific computing, you will soon discover that Python has two different packaging systems: pip and Conda. Which raises some questions:
- How are they different?
- What are the tradeoffs between the two?
- Which should you use?
While it’s not possible to answer this question for every situation, in this article you will learn the basic differences, constrained to:
- Python only; Conda has support for other languages but I won’t go into that.
- Linux, including running on Docker, though with some mention of macOS and Windows.
- Focusing on the Conda-Forge package repository; Conda has multiple package repositories, or “channels”.
By the end you should understand why Conda exists, when you might want to use it, and the tradeoffs between choosing each one.
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