Hey everyone.
So in this video we are going to learn something by which you will get to know that how on are used
for data analyses.
Now you have just learned the data frames and all that but you did not get the idea that how we can
use this bound us to analyze any data.
So that's what something we are going to do here by a topic that is known as data frames.
So goodbye.
So we are going to learn head goodbye.
And let me begin first.
I have an example dictionary that is something a person that is selling fruits may be having a fruit
shop.
Like we have bee stencil bugger.
So Bulger is a fruit seller and Bulger have few items on his shop like we consider first
like that money's app.
But a guy selling Apple and they will use this twice that put I will tell you I have used this one twice.
All right then second thing Bulger is selling orange.
And then again copy that orange also come on and come on.
And then there is also a smuggler.
So he's selling consensus.
So stay away from them.
Now we have a list of items.
This one is not supposed to be there.
So this is the list of items that Bulger is selling.
And now we have and at least here in the dictionary that is 150 days debugger is selling the items like
Bulger have stock all at once on Monday Tuesday then Orange is on Bannister tested and guns on Friday
Saturday and Sunday's awful Bulger.
So here we have days maybe that was OK.
Then we have in days like First Monday then Tuesday then to this day.
After Tuesday we have an escape and then to this day then we have Friday and we have secured
after that.
This is the second thing that we need here now for third column.
Require something like this sales that is we know if it sells because it's in any analyses you will
either just go for sales or go for the ghost or we can see that revenue have generated.
And that's what you analyze in the Ponderosa also also known one like something like to whom I have
sold things.
No.
So here I have sales and that is like on Monday but I sold hundred cage of heifers.
That's something possible.
But he's.
But his brother so other 20 after that on Tuesday he sold 80 gauging then oranges we have.
And one thing I for digital that I have used this twice.
So that's all you have use twice because I have to consider six days here.
So I have done something like Epsilon two days orange on two days and guns on two days so that you will
understand the good buy better then the oranges oranges are more popular than have great demand to bugger
sell 200 kids your oranges on the first day and 100 gauge you next.
Then smuggler.
He sold five cagey.
Not crazy.
Five guns on Friday then pencil tested and that's over dictionaries now get in the beginning and you
go hey this now should be done.
And then this is a dictionary here.
Items that are epaulets drop days and seeds.
Now convert this one into our data frame that is def just do BD door data frame.
Now you are expert in this one.
Then we have B.
And if you print b you will get this.
So this is the list or we get the data from which we are going to analyze here.
But something is known as Group by is a we can see that inbuilt function that do something like if I
want to have the means of my sale the standard deviation of my C Let me do that then you will understand
better.
Like if I consider something like X any variable.
And first I need to go buy this all so I will do something like the F note group by all in small case
letters nothing is in capitalized then I will group according to anything like I want to group according
to items because I have two types of item there and now she's done.
Now if you tried to print x when you will get something like that one because there is not any value.
This one is just I have group this data frame according to the items and what that means is something
like if I want to have the mean of all these since then I will do something like X don't mean
then which type of mean we like it.
I will get according to the item because I have grouped the item according the item down like I have
three items apple orange and guns.
So it will automatically scan for the items that are common like apple apple orange or injured in guns
guns.
If I also add there a third one that is denoting the Saturday and again the guns like if I do the
guns here and then the day will be like does that show maybe have some may
then this is will be like in five.
And then I learned that one I will get into the one that is Sunday.
And now if I perform the mean I will get this thing that is at Pulse the mean of all the [REMOVED] like
hundred on Monday it on Tuesday hundred plus eighty 180 half of that one ninety because they are two
then if I go for oranges three hundred divided by two one 150 then I will go for guns that is twenty
by three six point six because there are three not two because it just take the mean value.
So for me we will add only values and divided by the total number of values.
So here is how you can group the element.
So that's what you can also do for the big scales like if you are working on like thousands of data.
Then according to item you will still do it and you will find that each item is best on sale.
Like if you as a fruit seller like brother and you will get like hey I'm selling thousand apples in
a week five hundred oranges and like 80 burned out or like that one.
So we'll find that this thing is largest in say.
Like here I have orange do orange is something I am selling the most.
So I will go for larger stock of oranges smaller stock of apples and even our most molars like guns
number of guns they so that's how by using these thing you can have these talks and all that and can
work on that.
You can also make a table of like cost the rupees you have made.
You can also consider this one as it BS so that you can also analyze the profit.
There are a few more methods related to that one at this point.
This is clear that how you guys use these goodbye to analyze the data now you will find the fundamentals
like if I want to have some of the items that I just use X notes some I will get one eighty there 20
guns because I have sold 20 guns and 300 oranges that is 200 plus 300.
If you want to had have standard deviation you will get the standard definition.
This is what you can calculate the with formula.
That's what I'm not going to do here.
After that if you need to count that how many of these are so selling like I am selling Apple on 2D
guns on 3D oranges on base here I have taken the example of base but you can work on anything like you
are selling the number of that particular item according to any particular condition or any particular
item with that one.
Something like that.
You can also find the maximum and minimum values like X load maximum you will get there.
This one like Apple I have sold Apple maximum on Tuesday.
So here on these do Apple on Tuesday's hundred.
This is sort of showing me Tuesday a hundred but I have done on Monday.
Guns on Sunday.
Maybe this one is showing the next day and since it's showing maximum says that is hundred of apples
then of guns and you can see two hundred of all ages but it's showing days differently.
Maybe is because of index.
So if you try minimum also you will get the minimum like 80 Apple guns five and hundred oranges.
Also if you need all the information regarding these things like if you want to completely describe
the table then you have a matter that is known as describe.
So you just use X not describe.
And then the apprentice nothing in the barometer because you have already got the item with the item.
And when you return that one you will get something like this one apples selling on two days.
Their mean is this one the standard deviation this one minimum value sold it.
And that one here you have percentages and maximum value under.
And here we have guns all the values of a level for guns oranges.
And this one is like something here horizontally you can make this one vertically also like here you
can just use another method that is transpose because people prefer something like this one apples guns
and oranges they want to compare them and it's easy to comparing vertical lines so that's how you can
also convert that one vertically.
I have to do because that one is horizontally there.
So this is how you can analyze the data by using panels.
And you have some few methods by which you can easily get these things because to analyze any data you
I believe you just need all these things only you either need mean maximum aluminium value some and
you are not going to do something like on value at analyzing any product sales and you are performing
their integration that something maybe.
So this is how you're going to group by the elements.
And I hope you understand that one this one is an important video so I would suggest you to go through
this one again if you do not understand anything there.
So thanks for watching.
And if you still have any doubts then ask me I will help you there.
So see in the next video.