Halaal, before going ahead in this session, let's have a quick recap of what we have done below in
this water project since the very first session, we have collect this data using this baby logic.
After what we have done, we have had some derived attributes from data because once we have that much
that much data, which is exactly this one.
So from this data, you can come up with some different different insights with respect like these days
with respect to some minerals month out and lots of analysis, you can buff on the data.
That's what I have.
First, some kind of did I have to attribute some of this data?
So in this session, we have this assignment.
The very first one is we have to perform analysis of journey by week.
This you will visualize over here you have a feature, which is exactly what we did.
You have to perform analysis on this feature.
So for this what I am going to do very close, I have to exit this video.
So I'm going to say for V.T. and on this, if I'm going to call this value underscore counselor, now
you will see with respect to each and every day you have some value.
It means on Thursday you have that much trust.
You on Friday, you have that much with respect to each and every day.
You have some value assigned to it.
Now, what do you have to do?
You have to simply plot this data.
So far, this is what I'm going to do.
I'm basically going to use some advance validation library, which is exactly plotline.
And if you are not very much aware about plot, the plot is basically that advanced level with the additional
library that is highly used.
Whenever you have to create some deployment level visual for this.
What I'm going to do, I'm just going to say encode plot, lead, dot express, which is exactly a submodular
plot.
And if you haven't installed floridly, you can install using PIP install this plot.
So I'm just going to install using this one and I have already installed, so I'm not going to install
it.
So using this you can simply import this plot.
So just executed after what I have to do in this plot, I have something which is exactly which is exactly
my backload because BA plot over here will definitely help us a lot.
So I'm going to say dorkbot and if I will just step, you will get the documentation of this and that
function on X what you need, or Y-axis, what is it.
Color and all these different different types of custom parameters that you can play with that let's
say on Access's.
I just need the index of this.
So if I'm going to call this index over here.
So it will exactly return me my this index.
So I'm just going to copy this entire code.
And on this X I'm basically going to paste over here once I will do all the stuff on Y axis.
I just need value with respect to this.
So if I'm going to remove this and again, I'm going to execute it, it will return me my value.
So here I'm going to say on Y X is I have to simply pass this data.
So on Y axis, I just need this one just to copy paste.
And if I'm going to execute it, it will take some couple of seconds, but it will be done in some beautiful
results.
Now you will see over here how much user friendly this visual is.
If you are going to hover your mouse on this bar chart, you will see with respect to 30, you have
that much of those.
With respect to Friday, you have that Munster's similarly with respect to each and every day.
You have some values assigned to it.
So if you have to conclude this visualization, then you can see a rush is definitely highest on Tosti.
So that's the type of conclusion you can fetch from this visit.
So just go ahead with our next statement, which is exactly.
We have to perform analysis of Jurnee by hour.
So if let's say if on this D.F. of our if I'm going to create its histogram basically using this histo
function.
So here, if I'm going to pass mine this hour, which is exactly this one, and if I'm going to execute
it, it will be done with this beautiful histogram.
If I have to conclude from this histogram that I can see, it definitely peaks during evening time when
people are logging off from the work.
Let's say I have to perform this analysis with respect to each and every month.
So for this, it means I need I need each and every month very first.
So if on this let's say if on DFAS month, if I'm going to call this unique or.
So it will exactly ridgen my each and every month.
And if.
On this month, I'm going to say for I in this department, not unique, it means I have to fetch each
and every month here.
I'm just going to paste and just I have to accept each and every month.
So far, this is what I'm going to do.
Let's say on each and every subclause I'm going to visualize my visitation with respect to each and
every month.
Four days I'm going to say BLT Dot subplot.
And here let's say I need a matrix of three, comma two, because I have six months and here I'm going
to say it is nothing but I plus one because by default I will be zero.
So here, let me show you what I think over here.
Yeah.
So if I am going to say we're here for let me copy this entire thing.
Let me again paste over here.
Yeah.
And on this, if I'm going to call this enumerate.
So I'm just going to call enumerate over here.
So if I'm going to call this enum rate, it will exactly return me some index.
So if I have to access this, I'm going to say for I come a month in, so I will basically that close
to my index and month that I'm going to access from this one for I come a month in this one.
And if I'm going to print, let's say I and UpToDate if I'm going to print my month so you will see
over here, it will exactly that.
And this is debt zero, which is exactly my index nine, which is exactly my month.
First is exactly my index five is exactly my month.
So here I'm just going to copy this entire code.
Let me copy this code and let me just paste over here.
So once you have all these stuff, you will access each and every eye, which is exactly my index.
Once you have each and every index, then you have each and every month and whatever month you have
very close.
You need a separate data frame of that month.
So here I am basically going to define a filter as deals all month if it goes to month.
So this is exact my filter.
I have to just pass this filter in my dear data stream and what our data stream I have on this data
frame I have to access this hour, which is exactly this tool.
And once I have this hour, I'm just going to create its histogram.
That's it.
Let's say I have to create my own will decide on which I have to display this visual for this.
I'm just going to say BLT, Dot, Fagre, and here I have to mention my own site.
Let's say I want a window of forty cross twenty trollope's, whatever you want, so just executed.
It will take some couple of seconds but it will exactly return me my six.
ViSalus with respect to each and every month with respect to rush of each and every month.
Now you will realize over here this is exactly your rush with respect to your nine month, with respect
to a five month and all these things.
So if you have to conclude from this vision, you will see almost in each and every month you have your
peak time in your evening hours.
So that's all about the session.
Hopefully you will love my way of explaining this complex thing in the easiest way.
So I hope you will love a lot.
Thank you.
Have a nice day.
Keep learning.
Keep growing.
Keep practicing.