Anaconda Repository: Collection of commonly used Python/R packages. The Anaconda distribution is composed of three main components.ġ. (Refer to part two for a specific example) The Anaconda DistributionĪnaconda is marketed as “the easiest way to perform Python/R data science and machine learning on a single machine”. Instead of having to install each and every package to run your code, a colleague can just import the entire environment and run your script with ease. Environments can also facilitate sharing of scripts with co-workers. This is especially useful when working on multiple projects on the same machine, that must each utilize different versions of the same packages. Multiple Python Environments ( dataquest.io)Īs you can see in the picture above, each environment may have a specific installation of Python, and also different versions of each package. Typically, this includes a Python interpreter, and any third party packages that are to be used within the environment. What is a Python Environment?Īt the simplest level, a Python environment is a folder located on your machine that contains everything you will need to run your Python scripts. The topic of this writing will be the Anaconda Distribution, the world’s most popular open-source Python distribution platform. This article is part one of a series dedicated to explaining different methodologies for managing Python environments. This may work just fine for a while, but eventually you are bound to run into issues. If you’re like me, then you probably just pip-installed every package into your base environment. Managing Python environments can be one of the most difficult concepts for a beginner to grasp. One way to organize these packages is through the use of “environments”. py files that contain predefined functions and classes that can make your life a whole lot easier when writing Python code. As you work on more advanced Python projects, you will eventually need to install third party packages.
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