Because each one is independent, they will not interact or “mess up” the other. You can create as many environments as you want. Environments have unique names and can be activated when you need them, allowing you to have ultimate control over the libraries that are installed at any given time.

Python EnvironmentsĪ Python environment is a dedicated directory where specific dependencies can be stored and maintained. Conda allows you to install multiple environments on your computer and to address dependency issues. Dependency conflicts can cause significant issues when working with Python. If they aren’t stored separately, Python will not be able to tell the difference between the two that are both installed in the same location. This means that if you depend on GDAL 2.4.2 in an older project and GDAL 3.0.1 in your brand new project, you will need a way to store these dependencies separately. In Python, regardless of what project you are working on, your computer will store third party libraries in the same location. This is especially true for Python programmers who are not dedicated software engineers! While having a plethora of open source functionality available to the community is one of Python’s greatest strengths, dependency management is a major challenge for many Python programmers. When you require one of these third party libraries in your workflow, they are called dependencies because your workflow depends on them to function. Developers and scientists all over the world are constantly improving and adding to the functionality Python provides by writing new packages. Third party libraries are critical to making Python the great tool it is.

There is a good chance you have used at least one of these libraries such as numpy, matplotlib, or pandas. Most projects written in Python require a certain set of third party libraries that are not in the Python standard library. Explain how to use conda environments to manage your third party libraries.Ĭonda Environments in Python The Third Party Library Issue.Understand how dependency management can play a large role in Python programming.

Creating conda environment