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Here.

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So now we have the data uploaded here.

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And if you can try to bring these fights like that we go for the largest one that is projects.

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And if I shoot you down here we will get this fight in which if you scroll down known and they will

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you find they are this like maybe this one is one million eleven Lek and seventeen fights.

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Rose we can see and 18 columns.

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So you're going to have the idea that how many out there.

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And this one is 1 million maybe not 11 million but a large number of data and we are going to analyze

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all this data here.

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You can check any of these files.

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They are all nuts like this one.

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So first of all if you print here like project though shape and then you will get the number of rows

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and number of columns that are present in the particular data fight and you feel I notice there.

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This one is the same number that we have seen in that particular fight.

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Now we will be in the shape of all these files first to describe and show the data put column ideas

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will go for building the shape then we will be in their head and then we will describe that data.

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So now let's begin with printing that one.

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So first of all I will do something like print and then shape of resources for the first one.

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That is not data solicit donations that is the donations data frame is and then provide these notes.

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After that here just passed the name donations and then shape the method by which we have printed to

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shape.

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If you do that money you will get a shape of donation data frame is for like sixty eight thousand for

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like forty six like eighty seven thousand eight hundred and eighty four.

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That is four million approx four point five then just copy this line maybe have some and based date

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for six times for all of these and this one will be donors then we have projects.

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So first.

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Donors this one projects.

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So rude checks.

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After that we have these sources

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and then schools

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and in lust reaches

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also teens that one here like this one is going to us this one going to be projects

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and this one going to be sold says this sign is schools.

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So here we have this other schools what is then after that we have just

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shifted down.

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We get the data for all these shows and columns.

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If you multiply the number of rows with the columns you will find that how many data files are available

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in that particular data frame.

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And if you sum all these two will get that 68 million data is best and then none of it being the head

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of these so that we can have the children chlamydia.

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So first of all donations or had shifted and there we go we define face.

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After that we have no as low head David Levy that one lump effect in this thing also.

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Then we have projects teachers teachers just project resources so projects not head.

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Then we have these sources don't head

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after that.

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We have schools no head and at last we have teachers doped.

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Had nobody noticed this data since then.

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First one is did donations stop.

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This contains a particular project.

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And if you notice this one that is projects it also has project writing.

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It doesn't have school I.D. and teacher writing and this one has donation ideas and donor.

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So few have same ideas like this one also has donor writing but they don't have donation and project

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data.

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This one at school lady similarly this one has teacher

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and after that these particular C slide ideas we have few things here like donation included then donation

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amount.

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Then donor card sequence and donation to C..

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So we have to work with the date and we also have to work with the amount and these ideas.

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After that if you notice donors festival is just about the donation received the amount and number of

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the donations and then dissipate.

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If we talk about donors then it's simple that we are going to have some names their cities and whatever

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they have donated.

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We have donors.

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Ze fine donors is teacher or not.

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Then we have projects projects is something that is going to be something like projects type their name.

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So here we have these things like project type data says here we have the coast and they are funding

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and expiry date.

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Here we have project fully funded data.

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Here we have project posted date So remember these columns which I'm focusing here so that while we

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are working on that one you will get that.

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Yes the columns up as and these dolphins hidden resources don't head.

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We have a resource item Dec 1 Kitty and as more you can check their price is schools.

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We have type.

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We are going to have schools state the sun and we are going to have the country here.

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After that we have teachers.

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We just create teach writing there prefix and a date when their project first posted.

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So this is about the data frames that we have.

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Now we are going to describe these data frames so we can have a general idea of the things available

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in that.

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Like first of all we have donations.

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So we just do donations don't describe method here and postie pencils shifted on.

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It will take little time legals it will provide some calculation there and provide do things like here

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we have come the donation amount here.

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Then we have the mean of the donation amount.

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Standard deviation of that one minimum of debt from that 25 percent 50 percent and 75 percent.

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Here we have the maximum value there.

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We have done a cop sequence and if you notice on the door no's donations do Ted.

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Then we have just these two columns which are actually in digital floating values.

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So they are going to have these mathematical values only for that particular columns which are integers

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because like it or find like 25 percent or standard means of any string then we have doughnuts and then

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we will do describe

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this would provide us the basic idea that what these things are in that particular data frame.

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This one is again processing them and now we have projects

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not describe.

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There we go with that one who did ended.

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LICHTMAN So here we have the count unique to append frequency for donors.

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So that want something different from that one depends on the type of values of a liberal deal.

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After that in the projects we again have standard mean 25 50 and 75 percent for that particular fight.

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After this we have resources.

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Describe we go with that one and we go to attribute error because I have misplaced they soon describe.

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There we go then we have schools low describe and have a look at this one again we have a minimum standard

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in twenty five seventy five and fifty percent there makes him when we leave home values here if you

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notice these values are like something one E plus zero this means the power of that explanation is something

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like these zero three this one and in featureless we have just these things so what can we do this one

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on teach us.

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So if you write here teachers don't describe and then we this.

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And when you should on that one you will find again that things that are available for donors because

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in donor ourselves we have something like that one so it has counted did the teacher prefix speech it

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ideas uniquely them.

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How many of them are unique their dates.

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And it's something like it had not calculated that particular things like it has calculated the standard

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deviation of like any amount or any value it has just counted all the numbers of ideas or prefix available

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there or the unique values of a live a live so that's about this one and now we do not have done anything

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much important they are just describing and inputting the phys printing the head from now we are going

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to have some particular analyses that we're going to do from the very next video.

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So thanks for watching.

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Tune in next to.