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Everyone so from this video they are going to do some exciting stuff here.

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Selfless dusk.

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Here we have is create new data by using the data sets.

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We are going to create some new data by using these datasets that we have.

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So let's begin with that one.

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What we are going to do here.

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We are going to define a data set that contains all these files in only one file or we can say in one

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data.

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So we will march all these available data frames here one by one and begin with donations and see which

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one not out of the other five we can merge with this one.

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So here if you notice first of all we have project data and find the one with Project data.

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Go.

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So we will take this one as we can see key and merge these files.

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So first of all here we have our data and demanded multiple use Bebe dot much

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and then provides the file first one is one we have donations.

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Second one is one projects and how we will most them enough.

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If you remember this one how in the outer left right then we have on the vote.

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We can say key according to which we are going to most this one that is project Heidi B's capital and

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I N D also capital them.

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So make sure you are also using the capital votes if there is any capital vote otherwise you will get

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any DNA or find note found later shifted on that one and it will take a little time to most these vice

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and here that we write and then data head and possibly leave to do them so that we can have the idea

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of the columns that are going to be in one

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this one is taking long time.

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So let me just check out the columns by Eva said Lim.

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Now we have the data.

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And the second idea is donation and then we have Donna.

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So check for this one has Donna writing.

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So will most the data the donors the data that we are going to get here is the one that have already

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these columns so data to be really defined something like BD don't much this one is don't know if I

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did on this one I have projected a donation I don't write writing so I will march this data with donors

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and then again in the

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and the key this time is going to be the donor right.

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So here the capital owner.

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And then I'd be again these are capitalism shifted on.

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David go with that one not the one that is going with this one is going to have the I.D. of donations.

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We have this one donations Donna and projects.

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Now let me move to another one that is resources schools and DHL.

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So if you notice Project have also school lately I have noticed this one here.

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And also a teacher writing so we can mostly the data the schools and the teachers by using the teacher

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I.D. and school I.D..

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So we really do hear data three dot much then we will pass data to help and then what we are going to

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miss this one.

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Wait.

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First let me go with the schools that he had discerned how in

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then we have the key on equal to and the one we are going to use here.

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These school lady so escapee done school and then I did this one is not most to know.

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So we are not going to run this one.

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We will run this one after this one completely matches and then we have data for that will be PD don't

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much and then in that run we will provide data three this one is taking time because these fires are

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very large so did burn.

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Now if you print data or head right now you will have project I.D. donation I.D. and on writing.

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Now we have marked this one with the school Lady by using that one with schools.

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So shifted on that one and did it get done let me complete our syntax here.

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What we are going to merge here is the teachers now

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and how it will do enough.

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Then we have a equal to what they're going to use our teacher writing each US based I.D. then we are

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only left with assume resources don't CSC that.

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Here we have this one resources.

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So we are not going to miss this one.

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Because we are not going to require that when you know a complete dataset even this one has a project

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item.

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You can also use this one to most these files but we are not going to do that here.

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So this one is still processing and let it proved learn here.

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So let me pose the video and I will resume after completing these two processing so here.

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These two has done processing.

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And if you've been the head of the data for now you will get this one a complete most file of all these

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data frames and this one create contains all the data of all the five CSP files and just one that is

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resources towards CSC not included in this one.

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After that this one has a number of columns from each of these.

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So let me just print the columns here sort of in the columns who will take a variable equal to data

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for Dot.

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Then we will provide columns and we need their values.

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So values Hale and in which form in a least form shifted and if we have that one.

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And now to print that one just add a here shifted and we have all the columns present in Denver that

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is beginning from project idea donation idea as you can see and who's your daddy donation I lead on

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variety and like this one we have here approximately maybe 30 columns so this is about creating new

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data by merging the available data.

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So thanks for watching.

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We will continue in the next video the analyses.

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Now the questions are going to be something logical.

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Now they are only on data.

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So thanks for watching.

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Soon the next video.