Hello everyone and welcome to the introduction to my lecture in this lecture we'll briefly discuss what
an umpire is and how to get it installed onto your computer.
None pi or none.
Some people write or pronounce it as a linear algebra library for Python.
And the reason it's so important for data science with Python is that almost all of the other libraries
in the PI data ecosystem rely on PI as one of their main building blocks.
Plays incredibly fast and has bindings to C libraries and we going to learn how to work with higher
res as we continue on this section of the Course as far as getting them pie onto your computer.
It's highly recommended that you install Python using the Anaconda distribution to make sure all the
underlying dependencies such a certain linear algebra libraries all sync up with the use of a con to
install.
That means if you have Anaconda as your python distribution and you install Python onto your computer
using anaconda you can install them pie by just going to your terminal or to your command prompt and
typing Conda install NUMP pi.
Remember that's not it's a python that's just straight into your terminal or your command prompt.
If you do not have the Anaconda distribution you can also use Pippin's stall to install num pi.
Again the top conta install num pi is if you have the Anaconda's distribution and thus the recommended
method to install this library.
If you do not have the Anaconda distribution numpad can also be installed for Pipp install them.
All right.
Let's briefly discuss how we're going to use pi throughout the section of the course.
We're mainly going to be working with numb pire race throughout the course.
The empire is essentially come in two flavors either vectors or matrices.
Vectors are strictly one d arrays and matrices are two the two that missional.
We should also note that a matrix can still only have one row or one column.
OK we're going to talk a lot more about an umpire raise and show you how to work with them in python
in the next lecture for umpires.
So let's go ahead and get started with first if an umpire is in Python.
Thanks and I'll see you at the next lecture.