Here.
So now we have the data uploaded here.
And if you can try to bring these fights like that we go for the largest one that is projects.
And if I shoot you down here we will get this fight in which if you scroll down known and they will
you find they are this like maybe this one is one million eleven Lek and seventeen fights.
Rose we can see and 18 columns.
So you're going to have the idea that how many out there.
And this one is 1 million maybe not 11 million but a large number of data and we are going to analyze
all this data here.
You can check any of these files.
They are all nuts like this one.
So first of all if you print here like project though shape and then you will get the number of rows
and number of columns that are present in the particular data fight and you feel I notice there.
This one is the same number that we have seen in that particular fight.
Now we will be in the shape of all these files first to describe and show the data put column ideas
will go for building the shape then we will be in their head and then we will describe that data.
So now let's begin with printing that one.
So first of all I will do something like print and then shape of resources for the first one.
That is not data solicit donations that is the donations data frame is and then provide these notes.
After that here just passed the name donations and then shape the method by which we have printed to
shape.
If you do that money you will get a shape of donation data frame is for like sixty eight thousand for
like forty six like eighty seven thousand eight hundred and eighty four.
That is four million approx four point five then just copy this line maybe have some and based date
for six times for all of these and this one will be donors then we have projects.
So first.
Donors this one projects.
So rude checks.
After that we have these sources
and then schools
and in lust reaches
also teens that one here like this one is going to us this one going to be projects
and this one going to be sold says this sign is schools.
So here we have this other schools what is then after that we have just
shifted down.
We get the data for all these shows and columns.
If you multiply the number of rows with the columns you will find that how many data files are available
in that particular data frame.
And if you sum all these two will get that 68 million data is best and then none of it being the head
of these so that we can have the children chlamydia.
So first of all donations or had shifted and there we go we define face.
After that we have no as low head David Levy that one lump effect in this thing also.
Then we have projects teachers teachers just project resources so projects not head.
Then we have these sources don't head
after that.
We have schools no head and at last we have teachers doped.
Had nobody noticed this data since then.
First one is did donations stop.
This contains a particular project.
And if you notice this one that is projects it also has project writing.
It doesn't have school I.D. and teacher writing and this one has donation ideas and donor.
So few have same ideas like this one also has donor writing but they don't have donation and project
data.
This one at school lady similarly this one has teacher
and after that these particular C slide ideas we have few things here like donation included then donation
amount.
Then donor card sequence and donation to C..
So we have to work with the date and we also have to work with the amount and these ideas.
After that if you notice donors festival is just about the donation received the amount and number of
the donations and then dissipate.
If we talk about donors then it's simple that we are going to have some names their cities and whatever
they have donated.
We have donors.
Ze fine donors is teacher or not.
Then we have projects projects is something that is going to be something like projects type their name.
So here we have these things like project type data says here we have the coast and they are funding
and expiry date.
Here we have project fully funded data.
Here we have project posted date So remember these columns which I'm focusing here so that while we
are working on that one you will get that.
Yes the columns up as and these dolphins hidden resources don't head.
We have a resource item Dec 1 Kitty and as more you can check their price is schools.
We have type.
We are going to have schools state the sun and we are going to have the country here.
After that we have teachers.
We just create teach writing there prefix and a date when their project first posted.
So this is about the data frames that we have.
Now we are going to describe these data frames so we can have a general idea of the things available
in that.
Like first of all we have donations.
So we just do donations don't describe method here and postie pencils shifted on.
It will take little time legals it will provide some calculation there and provide do things like here
we have come the donation amount here.
Then we have the mean of the donation amount.
Standard deviation of that one minimum of debt from that 25 percent 50 percent and 75 percent.
Here we have the maximum value there.
We have done a cop sequence and if you notice on the door no's donations do Ted.
Then we have just these two columns which are actually in digital floating values.
So they are going to have these mathematical values only for that particular columns which are integers
because like it or find like 25 percent or standard means of any string then we have doughnuts and then
we will do describe
this would provide us the basic idea that what these things are in that particular data frame.
This one is again processing them and now we have projects
not describe.
There we go with that one who did ended.
LICHTMAN So here we have the count unique to append frequency for donors.
So that want something different from that one depends on the type of values of a liberal deal.
After that in the projects we again have standard mean 25 50 and 75 percent for that particular fight.
After this we have resources.
Describe we go with that one and we go to attribute error because I have misplaced they soon describe.
There we go then we have schools low describe and have a look at this one again we have a minimum standard
in twenty five seventy five and fifty percent there makes him when we leave home values here if you
notice these values are like something one E plus zero this means the power of that explanation is something
like these zero three this one and in featureless we have just these things so what can we do this one
on teach us.
So if you write here teachers don't describe and then we this.
And when you should on that one you will find again that things that are available for donors because
in donor ourselves we have something like that one so it has counted did the teacher prefix speech it
ideas uniquely them.
How many of them are unique their dates.
And it's something like it had not calculated that particular things like it has calculated the standard
deviation of like any amount or any value it has just counted all the numbers of ideas or prefix available
there or the unique values of a live a live so that's about this one and now we do not have done anything
much important they are just describing and inputting the phys printing the head from now we are going
to have some particular analyses that we're going to do from the very next video.
So thanks for watching.
Tune in next to.