Hi Reader,
Soon it will be winter break for my 6-year-old, so this is going to be my last Tuesday Tip of the year! ⛄
If you've ever taken one of my courses, you may have noticed that I frequently recommend the Anaconda distribution of Python.
You might be left wondering:
I'll answer those questions below! 👇
Anaconda is a Python distribution aimed at data scientists that includes 250+ packages (with easy access to 7,500+ additional packages). Its value proposition is that you can download it (for free) and "everything just works." It's available for Mac, Windows, and Linux.
A new Anaconda distribution is released a few times a year. Within each distribution, the versions of the included packages have all been tested to work together.
If you visit the installation page for many data science packages (such as pandas), they recommend Anaconda because it makes installation easy!
conda is an open source package and environment manager that comes with Anaconda.
As a package manager, you can use conda to install, update, and remove packages and their "dependencies" (the packages they depend upon):
As an environment manager, you can use conda to manage virtual environments:
conda has a few huge advantages over other tools:
Miniconda is a Python distribution that only includes Python, conda, their dependencies, and a few other useful packages.
Miniconda is a great choice if you prefer to only install the packages you need, and you're sufficiently familiar with conda. (Here's how to choose between Anaconda and Miniconda.)
Personally, I make extensive use of conda for creating environments and installing packages. And since I'm comfortable with conda, I much prefer Miniconda over Anaconda.
Would you be interested in taking a short course about conda? Reply and let me know! 💌
If you enjoyed this week’s tip, please forward it to a friend! Takes only a few seconds, and it really helps me reach more people!
I'll see you again in January! 👋
- Kevin
P.S. Christmas decorating injuries 🎄
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