One of the important aspects and ideas you might have to analyze a lot of detail which we keep getting
from different sensors of course it's going to be huge.
You know it's it's not at the initial stages that you're going to use but maybe in later stages where
we have to analyze a lot of data.
You have to do some numerical calculations.
We want to work on really huge data sets let's say for example if you're going to have around.
You know there's a company which you will see whirlpool or some company which sells washing machines
and air conditioners.
Now washing machines are almost used by almost every person in the country if he if he can think about
in that particular manners or not every person at least if not 60 percent.
Now if you think about 50 percent of the country if they use washing machines then you can say they
can be around they can be around you know laxer billions of years lakhs of washing machines or cars
or washing machines which are they are all across the market now that can be just not just one company
but can be multiple companies of course.
But let's say for example if you want to know if a particular company decides to put up IMT you know
and I own to device basement in the washing machine in such a way that it can monitor data from different
sensors it can keep monitoring data in such a way that it can it can analyze it can put you know it
can figure out how the custom was going to work and how it's not going to happen and lot of data needs
to be analyzed and stuff like that.
If that's the case then you know you want to see lots of data you want to see lots of data happening
on your on the server side.
If that's the case then you would have to go for something Gaulish you need to go for the data analysis
libraries.
So we're going to have a look at them which is quite important so we're going to create a package completely
which we are going to call this data analysis.
I can just say data analysis in Iowa.
So that's what we're trying to do so for data analysis and we're going to have a let's have a let's
have completely you know new how can we do them in tighten.
So let's start up with the first library that we have of which we are going to use for analyzing data.
So we can use the library call less than the library that I'm going to say is the first one that we're
going to use is the number library.
So that's what we're going to use.
So.
Or I can just say using pi so that it doesn't conflict.
Right.
And it's quite easy to remember.
So.
So you have using them by that particular file.
We can close all the other applications which we're not currently working with now coming again once
again today the science indeed is using Python.
Right.
So what exactly is it analysis it is it is a science of managing analyzing large data sets of big data.
Right.
So you need to have the tools transform and model data and Pitre in Python has evolved over the over
the years to give a very effective effective solution in handling different components as well as entering
into the details as fields by providing libraries like NUMP by Skype pipe band and matlab live and stuff
like that Matlab business specialty use whenever you want to plot plot data on graphs or something like
that.
So that's exactly what we are looking at.
And so when we come to know when we come to apply that to understand what about what does not buy by
is an acronym for numeric pipes.
So this is the acronym for pipe.
So numeric pipes in some pipe.
It is an open source extension module.
So pricing This provides you know the library provide so fast pre-compiled functions.
So for doing any mathematical numerical routines also can enrich the programming language in such a
way with two very powerful data structures.
So there is a data structure which we are going to look at which is going to be called as the NUMP by
adding And this particular numpad is so you know it is such a powerful data structure which helps inefficient
computation of multi dimensional arrows and mitrailleuses.
Now you know you see we have already seen other data structures in python in the others.
So in standard python The rest are just standard structures were if you remember structures are less
and less Toppo then you have the dictionary and then you have the set.
So these are a few data structures which are there instead of fighting but none by comes up with the
concept of ID which completely defeats the entire or not defeat.
But it takes uprighted to a completely higher level of how things can work with than in an umpire.
So that's where we're going to deal with.
And that's what we're going to look at.
You can also can if you want a chip in a notebook and work with it so that you can have something written
out there.
So these are a few things that we need to understand.
Right.
OK.
So now let's get into it by hand.
So you have you know this particular implementation of Pi is aimed at very human traces that is also
this model provides a very large library of high level mathematical functions to operate in this Macraes
that you can just have a look at their documentation of it.
So if you have a look at the documentation you can go for the NUMP by documentation and you can have
a look how was their documentation is.
So if you go to talks that Skype right.
Or you can call them by Skype by documentation.
You can just have a look at them by manual and then you have the user guide and you have the reference.
And so it's it's really huge.
So by setting up this is all about the user guide for the reference let's say this is the area of checks
that you can do with the universal functions the routines that are there searchings sorting searching
things statistics set routines polynomials padding addy's input output linear algebra logic functions
financial functions functional programming you know data type routines daytime support functions a lot
of them string operations that fusion routines.
It's it's just so huge that of course we're not going to cover all of them.
We're just going to cover just a few of them and then go on to you know people building third party
applications on them.
Bye bye bye.
You know Skype is built up on them by and then and then you have a and then you have the entire you
know.
So if you want to go for the Skype idle gaiety you can go to your.
And of course you have documentation and you can have a look at how things work.
You know with their entire programs that's about Skype by then we have something called as the Pandurs
which is also quite inflammatory.
So Pandurs is something it also is built on by it's built on them by the panderers is built in such
a way that Pandurs makes use of the data structure that is Adoree and builds its own structures Qala
CDs and data frames which are so in passing you know the entire funda of how things work and that's
that's beautiful to figure out.
So we're going to have a look at do into all of these in detail and as we go ahead we will see how we
can double up on the same.
Okay.
So you have one that is using them by the way if you want or you can open and ship in a not so I'll
just open a Jupiter notebook and I'll teach you how to link them to your particular you know how to
get to your particular in the same particular you know in this same thing How will you link them so
maybe the openness to that book will just have a look it's right OK so maybe if you can just have a
look at when you open up.
Correct.
So you have this tokin which is there.