Now, let us go ahead and give you different details about the date of this, so let me have this data
from the witness age of my fiancee, this education before violence, all of these problems present.
So the first thing what we can do is we can check the different type of problems which has.
So we can say the problem is.
This gives us the list of different problems that have arisen and an important function is D.F. Thought
Info, which gives us top level information about the.
So here we can see that there are four thousand five hundred ten rows of data and.
These are different from.
In full, this is the age.
Then this is.
The job is the marital status, the education level, the default, the violence, housing details,
loans, deals, all the biggies, these other details related to it, other things.
What we can check is.
Is the describe, which gives us a summary of the numerical problems, so out of the numerical column,
we can see that this is the each column which has counts as forty five thousand two hundred and eleven.
The mean age is is going to be the standard deviation is often the minimum age is 18, the maximum age
is ninety five.
And you can see that the maximum value, which is seventy five percent, has a value as what, eight,
which means that there is a huge gap between the seventy five percent and the maximum value.
So we can consider getting rid of the outlier values in the H.
So these are a few insights which we can get from these volumes and bring out those structures, apart
from that, we can take different vibes of the volume so we can take the detailed.
This gives us the details of the volumes so we can see that each is individual job is object here,
these object data VIPs means that these are strings and you can see that we have these values.
So other values we can check if a particular value is numeric or North American needs and or take care
of that.
So here it seems that all the values at that, but in case if we have a column that should actually
be numeric in nature and is in strength, then we can change the date of the column itself.
So these are different things which we can do.
Then we can check the shape of the data frame using the cheap.
So this is the shape of the data frame.
There are 17 volumes and these many number of those.
Now, if you want to filter out some data from this particular data frame, then there are several ways
of doing it.
One is using a location so we can see if.
Got a lock, and inside this, we can give the location values, so let us see, I want to view of the
first row so we one and I want to see the second column.
So it has the value of single.
We can see the same thing using the left hand.
So you can see this value is single now let us see.
We want to have a sequence of values so I can see it from let's say I want to see the fourth row and
I want to see the values in the fifth column.
So I can do it this way.
So using I is not really suggested.
So instead of using I love, we actually use the location function because I love me because of having
this dependency and confusion while working the data frames.
So instead of using I love to actually work with different Farlam names itself.
Let us try to filter out.
So for filtering out these, we can use.
There and then we give the column name usually, so the week now this gives us the column now later
said they don't want to get only one column, but multiple columns.
In that case, we will have to pass Mr..
So we have the next 12 minutes on this show, then the next column, which I want to do is.
So I can get these quotes now.
I think what we can do is see we don't want these columns.
I don't want all your details of data, but we want to put these out on the basis of a particular condition.
So in that case, what we can do is we will have to use clock.
So what we do is.
He can simply see the dot look.
Inside this, they have to be, which we are fudging.
So we can give the condition.
So let's say we want to get the condition as see the.
Now.
We have to give the conditions in the down bracket and then we will in one condition at every point.
Now, what is the condition which I want to have?
Let us see.
I want to have age where the age of the person is more than 75.
OK, so for that I can simply see D.F..
I'm against cloning, so I h.
I want people I want this each week, seventy 75.
Now, on this, I get on the data and the INS has indicated that 70 five now legacy, I want the but
also being so in that case, I will give under the conditions.
So let us say I want on condition you OK?
And I don't want to give under the condition which is C. having education as primary.
So I would say the.
Hi, the name.
Education.
And this education value has to be private.
So I say, is he to.
Brian.
OK, so.
Like this, I can get the details that age is good enough.
Seventy five, education is primary.
I'm like this.
I can keep on adding different conditions.
Now, once I'm adding condition, legacy, I want to have these conditions also.
And I also want to I don't know the columns which I want to have.
So what I can do is just against these conditions.
So I have these given these conditions now after these conditions, I just give a comma and after all
my all, which I wouldn't have.
So I have an agent of education.
So let me give each an education so I can give each.
They give education and let me give.
So I can get it out and I can get more education based on the criteria which I have, if I.
Now, let's see, we want to have these conditions with just fine now in case we want to have any negative
condition.
So in that case, we can simply put all negation sign in front of this.
So just to get in front of this so it will give everything apart from this particular condition.
Now, other thing, what we can do is let's see if we want to drop a particular column, so in case
we want to drop a particular column, then what we can do is we can save the ABC
and save this function.
They can give the list of columns which we want to.
So I get to look at the list of each of.
So I give you the quote.
Often not the axis, which I want to get to this, so I have easy access to one, this is what you want.
So I can simply say this.
So once I have this, it will remove those two columns from the plane.
Now, one thing you notice here is if we again run now, if we again run this war and the.
So.
If you check, the head still has the goal of open while it has been for you, so it will actually make
this change, what we have to do is if you are going in this way, then we will have the same
sort of thing over and over again this way.
Then it will just show you what it will be very different.
So this is one that will be liberal.
And another way we will be the this.
That we can write in the equal to.
So what we can do is we can simply type of in place equal to do in front of it.
So we can simply say Obama.
In equal to then I think from this now out of the way of doing this is we can simply delete.
So what we can do is we can simply see then and then we can give the name.
I'm there and then we can give the qualifying age, so then it will delete the goal of age from this.
So if we see the again.
So now the D.F. will not have the goal of each and another thing, what we can do is we can delete the
entire data frame by then.
I'm just simply saying I like this.
So these are a few basic operations which we have.
Apart from this, there are several other operations we would have to do.
These are operations which will allow us to basically analyze the data initially and view the detail
properly.
But we will have to do a lot more on top of these on top of these dolphins.
So we will see the entire process, which we will be following for data transformation and data cleaning.
So let us go to that.
I'm going to see this in the next session, how we will work with actual data and what are the things
you need to take care of?
What are the things you need to consider while analyzing and modifying your data?
That is something we will look after this session.