Welcome back.
In this section we're going to look at num pie.
And now I'm very excited about this section because num pie is basically the backbone of all data science
machine learning and numerical computing in Python.
And that's actually what num pie stands for in a sense for numerical python.
And what is num pie.
Well in all of machine learning no matter what kind of data that you're working with.
Now this could be a data frame like we saw in the previous section on panders num pi is going to form
the foundation of turning your data into a series of numbers and then what a machine learning algorithm
will do is work out the patterns in those numbers.
So if we refer back to our machine learning modelling framework we've got Step 1 is Problem Definition
Step 2 is Data Evaluation features modelling and experiments.
And if we have a look at what tools we can use in this section we're going to be focused on num pi num
pi comes into the data analysis section.
However as you'll see in the future and what we just reference before is that machine learning although
it's referred to as modelling is essentially taking data turning it into numbers.
And in this case with NUM pi usually the form of a nun pi array which will have plenty of experience
with after this section and then finding patterns in those numbers.
So almost across this entire pipeline num PI will come into play now why num pi or the main reason we
use num pi instead of pure Python.
I mean you can do numerical calculations in Python but usually if we want to do large scale numerical
calculations we refer to num PI because it's fast.
And now the reason why it's fast is because behind the scenes there's optimizations written in C code.
The beautiful thing about the NUM pi package is that you can use these C code optimizations using pure
Python.
That means you don't have to know C to be able to use num pi and Python it is a bit easier to write
than pure C.
So that's the advantage of using num pi.
Now it gets these optimizations in speed through a process called vector ization via a term num pi uses
called broadcasting which is essentially just avoiding writing loops when you're writing calculations
because if you're writing loops to make calculations such as in Python to calculate over a list that
can form some eventual slowdowns if you have a list say of of millions of different numbers which becomes
common practice when we're working on machine learning projects and as we mentioned before num pi is
the backbone of other Python scientific packages now pi actually forms the basis of the pandas package
so what are we going to cover in this section.
Well of course we're always going to be focused on the most useful functions the ones that you can start
using straight away.
We'll have a look at different nameplate data types and attributes of those data types.
Now the main one is an NDA Ray but we'll have a look at that in another video.
We'll look at how to create arrays are raised.
You can think of as a list of numbers.
We'll have a look at how to view arrays and matrices.
Now matrix or a matrices you can imagine as a combination of different arrays will then look at how
to manipulate and compare arrays will then finish up with sorting arrays which is sorting out different
lists of numbers and then some practical use cases of where arrays get used or where num pi can be used
to turn data into lists of numbers so where can you get help if you get stuck.
Well the steps you want to try and follow is to follow along with the code so as we're going through
the video as we're working through our Jupiter notebook through the introduction to num pi lessons you
want to follow along as best you can.
Now if you can't keep pace or if I'm going too fast that's all right.
Feel free to stop it.
Go back practice something on your own but you should be practicing writing the code and running it
as much as possible.
So that's step to try it for yourself.
Try to run the code.
Remember the saying If in doubt run the code if you get stuck on a certain function or a certain thing
that we've covered.
You can try to search for it here you'll probably come across stack overflow or the official name pi
documentation.
Then after that once you've searched for.
If you find something on stack overflow or the NUM pi documentation.
The important thing is to keep trying again enter the code that you find in the Jupiter notebook.
Practice running it.
If in doubt run the code and then finally if you're still stuck don't forget you can always ask for
help.
There's no such thing as a stupid question because if you have a question it's likely someone else has
the same question.
You can ask on stack overflow or in any other resource you can find.
All right with that being said that's an introduction of num PI.
It's the foundation it's the backbone of other data science and scientific packages.
But let's get in in code.
Let's see what it's all about.