And we will come back to the request of our course about the complete introduction to The Science Show
until now, we talked about statistics and I hope you guys have a good understanding of this topic.
Now it's time to get a bit more into action and to learn about some tools that are used in data science
and when we are analyzing data so it can also be used in machine learning as well as deep learning.
So basically one of those tools is no.
So in this class, we are going to have an introduction.
We are not going to practice what it is going to be in the next few classes.
But in this class, we are going to talk about what is no what's the difference between lumpy and lists
and what are the advantages of using numpty in general versus Python lists, of course.
So let's.
So basically non P stands for numerical python.
And what you guys need to understand is that it's a Python library that consists of multidimensional
arrays, objects and a collection of routines for processing those areas.
So basically numpty, as I said, it's it's a library in Python.
So there are many features of them that, well, that he has.
First of all, it will allow you multi will allow you using multidimensional arrays, which is pretty
cool and will fester will will allow you to make faster operations.
So if you need to make faster operations that will allow you to make faster operations, it has the
functions and operators for those areas.
So once again, you will be able to create one dimension, two dimensional, three dimensional areas
and you will have different functions and operators that will be used with those areas too fast to make
it faster for all the calculations.
It's a good alternative for Matlab.
So for the for those of you who know what that is, it's a python alternative for this, for Matlab
in general.
Also, it's a four year transform.
It's well, the four year transforms and shape manipulation, so it will allow you to have shape manipulation.
So when you create your 3D, multitenant, multidimensional areas, you will be able to well, to use
the shapes and control the shape manipulation.
And finally, there is linear algebra and random number generation that is integrated to it.
And with linear algebra, you will be able to work faster your calculations because using multi multidimensional
areas will allow you using linear algebra as well and faster this way your calculations.
So once again, there are plenty of advantages of using them.
So as I said, the first of all would be the area oriented computing.
So those are areas that we are talking about right now.
So this would be one dimensional area, two dimensional areas, and those are three dimensional areas.
So here it's presented very, very simply.
Usually the areas that are way bigger.
So the amount of calculations that is here is well, that you will be able to do by using numpty is
pretty huge versus working with the Python list.
For example, it's the efficient, multidimensional, well, efficient, multidimensional area implementation.
So this is exactly what I was talking about.
So there are it's really, really efficient.
There are multiple multi arrays.
Multidimensional areas are pretty efficient.
So it will allow you, as I said, to make huge calculations in a fast amount of time.
It's really created for scientific computation also.
So as I said, once again, it's four huge calculations.
So it's not like Python list.
You can use Python this once again.
It will not be as efficient as using no faster resolution of operations on entire arrays of data without
writing loops with the use of standardized mathematical functions.
So as we said before, there is a linear algebra and some other functions and operations that are integrated
with the number that will allow you to make huge operations on arrays well, on three dimensional areas.
That will also be really huge.
And this will save you a lot of times when you guys are making huge operations once again, maybe right
now you don't see why you use this once again.
If you guys create huge programs, the use of is is really recommended.
There are different tools that are integrated with no, first of all.
Well, you can read I read that.
You can read, I read that on disk.
You can write, I read that on disk.
And you can work with different memory map files, which is a pretty good, pretty cool as well.
As we said before, there is an integration of fully transform capabilities.
And finally, as we said in the past, our point there is linear algebra and random number generation.
All right, so finally, numpty versus versus Biton list.
So, as I said, the number is a Python library and the three of the three main reasons of why using
nonpeak, the first one will be it will consume less memory since there is less operations to do.
It will go way faster and it will consume way, way less.
Memory has an execution speed that well, this will bring us to the second point that is having an issue
in the execution speed that is way faster than Python list.
So I said it goes way faster than working with Python Lisk, especially if you guys want to work with
the huge operations.
Once again, this is an interesting thing that I found.
So you can compare the speed of Arizona versus lists.
So as you can see, well, the time is in milliseconds, so it's not even one second.
But you still can see that number is at least four to five times faster than the list than Python list.
So once again, there is a huge well, this is a huge difference, especially if you guys work with
huge operations, if you can with numpty, for example, the result of an operation in one second,
it will take you with Python list, maybe like five to 10 seconds to resolve.
So if you have a set of operations, Numpty is really efficient way to be able to resolve all the operations.
And finally, it's just convenient to use.
So it's pretty much it's not well, you have to get used to it to use it properly.
But it's not that complicated to use since it's a Python library, it's already integrated into Python.
So you guys know exactly what what numpty number is, how?
Well, maybe not how it works, but you know exactly how what what it is, why exactly we use it and
why we prefer using numbers instead of python lists.
So all the advantages and the features of this Python library.
So in the next few classes, we are going to practice a little bit.
Writing Numpty functions with Python, of course.
So you will see what it looks like and you will have a certain introduction to this Python library.
So that's it for this class, guys, and see all in our next class.