We’ve improved that behavior in conda 4.7.10, so that conda will do a better job telling you which spec in the environment is preventing installation of your requested update. Conda’s behavior for a long time now has been to return a message like “All requested specs are already satisfied” - which is frustrating when you know there’s a newer version available. Conflicts with the software in the environment is the main reason why. People sometimes wonder why conda refuses to update a particular package. However, that set of highly constrained packages is also limiting, in that large numbers of constraints impose a harder satisfiability problem for conda to solve. This is good, in that that set of packages has been tested and is known to work. When you have “anaconda-2019.03-p圓7_0” installed, conda is constraining all of the dependencies in that metapackage to exactly the version specified in that metapackage. Why is it important to know about the anaconda metapackage? It is useful for creating environments that have all of the Anaconda Distribution packages in them, and it has strong effects on conda’s solver behavior. Moreover, we have a dedicated QA team that runs integration tests with all of the packages in the installer, so the installers and the metapackages represent known-good collections of packages.
When you install Anaconda Distribution, the anaconda metapackage is installed as part of it. When we build the Anaconda Distribution installers, we first make the anaconda metapackage, and then we use that metapackage to pull all of the other packages into the installer. We call it a metapackage because it doesn’t actually contain any files - just dependencies on other packages. That collection of packages at specific versions is encapsulated in the anaconda metapackage. Each installer release has a version number, which corresponds to a particular collection of packages at specific versions. Those installers have a set of packages associated with them. When you see “Anaconda Distribution,” we’re referring to these installers. sh installer (for MacOS and Linux) and a GUI.
pkg installer (for MacOS), a command-line. This installer may come in the form of a GUI. It includes Python, pandas, scikit-learn, multiple data visualization options, and many other helpful libraries. The Anaconda Distribution is the installer that many people download to get a good start on a Python data science coding environment. Here we hope to clarify two of those - the difference between the Anaconda Distribution and the anaconda metapackage.
There’s our company, Anaconda, Inc., the Anaconda Distribution, the anaconda metapackage, Anaconda Enterprise, and several other, sometimes completely unrelated projects (like Red Hat’s Anaconda). The name “Anaconda” is overloaded in many ways.
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