Everyone so from this video they are going to do some exciting stuff here.
Selfless dusk.
Here we have is create new data by using the data sets.
We are going to create some new data by using these datasets that we have.
So let's begin with that one.
What we are going to do here.
We are going to define a data set that contains all these files in only one file or we can say in one
data.
So we will march all these available data frames here one by one and begin with donations and see which
one not out of the other five we can merge with this one.
So here if you notice first of all we have project data and find the one with Project data.
Go.
So we will take this one as we can see key and merge these files.
So first of all here we have our data and demanded multiple use Bebe dot much
and then provides the file first one is one we have donations.
Second one is one projects and how we will most them enough.
If you remember this one how in the outer left right then we have on the vote.
We can say key according to which we are going to most this one that is project Heidi B's capital and
I N D also capital them.
So make sure you are also using the capital votes if there is any capital vote otherwise you will get
any DNA or find note found later shifted on that one and it will take a little time to most these vice
and here that we write and then data head and possibly leave to do them so that we can have the idea
of the columns that are going to be in one
this one is taking long time.
So let me just check out the columns by Eva said Lim.
Now we have the data.
And the second idea is donation and then we have Donna.
So check for this one has Donna writing.
So will most the data the donors the data that we are going to get here is the one that have already
these columns so data to be really defined something like BD don't much this one is don't know if I
did on this one I have projected a donation I don't write writing so I will march this data with donors
and then again in the
and the key this time is going to be the donor right.
So here the capital owner.
And then I'd be again these are capitalism shifted on.
David go with that one not the one that is going with this one is going to have the I.D. of donations.
We have this one donations Donna and projects.
Now let me move to another one that is resources schools and DHL.
So if you notice Project have also school lately I have noticed this one here.
And also a teacher writing so we can mostly the data the schools and the teachers by using the teacher
I.D. and school I.D..
So we really do hear data three dot much then we will pass data to help and then what we are going to
miss this one.
Wait.
First let me go with the schools that he had discerned how in
then we have the key on equal to and the one we are going to use here.
These school lady so escapee done school and then I did this one is not most to know.
So we are not going to run this one.
We will run this one after this one completely matches and then we have data for that will be PD don't
much and then in that run we will provide data three this one is taking time because these fires are
very large so did burn.
Now if you print data or head right now you will have project I.D. donation I.D. and on writing.
Now we have marked this one with the school Lady by using that one with schools.
So shifted on that one and did it get done let me complete our syntax here.
What we are going to merge here is the teachers now
and how it will do enough.
Then we have a equal to what they're going to use our teacher writing each US based I.D. then we are
only left with assume resources don't CSC that.
Here we have this one resources.
So we are not going to miss this one.
Because we are not going to require that when you know a complete dataset even this one has a project
item.
You can also use this one to most these files but we are not going to do that here.
So this one is still processing and let it proved learn here.
So let me pose the video and I will resume after completing these two processing so here.
These two has done processing.
And if you've been the head of the data for now you will get this one a complete most file of all these
data frames and this one create contains all the data of all the five CSP files and just one that is
resources towards CSC not included in this one.
After that this one has a number of columns from each of these.
So let me just print the columns here sort of in the columns who will take a variable equal to data
for Dot.
Then we will provide columns and we need their values.
So values Hale and in which form in a least form shifted and if we have that one.
And now to print that one just add a here shifted and we have all the columns present in Denver that
is beginning from project idea donation idea as you can see and who's your daddy donation I lead on
variety and like this one we have here approximately maybe 30 columns so this is about creating new
data by merging the available data.
So thanks for watching.
We will continue in the next video the analyses.
Now the questions are going to be something logical.
Now they are only on data.
So thanks for watching.
Soon the next video.