Let's discuss a few positives of using a package-management tool:

• Anaconda lets us create environments to install libraries and packages. This environment is completely independent of the operating system or admin libraries. This means we can create user-level environments with custom versions of libraries for specific projects, which helps us port the project across operating systems with minimal effort.
• Anaconda can have multiple environments with different versions of Python and supporting libraries. This way, any version mismatch can be avoided and is not affected by existing packages and libraries of the operating system.

• Anaconda comes preloaded with most of necessary packages and libraries for data-science-related tasks, including a highly popular and interactive Python editor called Jupyter Notebook. Throughout this book, we will be using Jupyter Notebook a lot, mostly when we need to interactively code our tasks.