Here even now we will continue combining data frames in this video.
So first we will consider the concatenated that thing like when you concatenate two strings.
Just add one after DNA.
Without any combining of anything.
So for that again we need some examples.
So here I have X fun a dictionary X to an endeavor on an x 3 and so on.
And they will be something like here first a then I have 1 1 2 3 1 1 1.
Like this one 5 elements in that.
Now just come on see me dot com.
So do and this one based this one five times the comma three four and five only change the column value
here like this an e this one.
This on C and this one B.
Now again without.
So time will not get wasted here.
Just copy this one again.
Based here and convert this one into doo doo doo doo doo again it could be the sun replace all the
dissonant again and there we go with this fun.
The
and just e now change D also like after e we have f no no.
Hey I also need e now F G.
And this one edge and this one right now again could be this whole command see.
And this time without changing the column value just change the indexes.
That is this one not indexes what the values come on C here.
This one
individual go.
Now we are done with the dictionaries cheaply done here.
Now convert all of these into three data frames.
That is DFA and the F two.
And then the three here the first one will be equal to like Bebe dot de da frame and he'll invent his
bus X1 and then the index and index value will be like one two three four and five.
Just copy this complete one and pasted here and here also this one into x2 this one into equity.
In this case we will not change the index material change the index that is 6 7 8 9 and 0 0 0 5 also.
Now here if you noticed on the about I have X1 and X3 with the same column value here I have the one
in DFT the different index value but the F1 and d F2 with same index value.
That's what you will get here.
I have done that one.
Now if you print the one you will have this one the F2 this one and they have three this one now for
concatenate and concatenation.
We have syntax like Billy don't.
And then we use gon get just gonna get and then pass dependencies.
Now if you press shift return here you will get different objects like Xs join join Adios keys and then
a lot more.
You can have a look at all of them if you want to.
Now here b did not concatenate the one we need to contact me like first I will try for B F1 and Def
to here.
You will get error avoid this happen.
Oh that one required the brackets also.
So here you will get this one.
If you notice them we have only one.
The index values are seen.
That is one two three four five.
And if you notice here we have lost one two three four five.
Then again one two three four five.
But they're separated.
And then we have this one ABC and up to E and equal them.
And that is common in both have this one now in the F2 in extra we have these things different.
The column value.
So here we have e f g h and I.
So this is the concatenation of two data frames in which we have same index values but different column
values.
Now this one is an x is 0.
If you convert this one with x is 1 and this is that is in really by the backwards here because if we
do not require the brackets the Biden will understand this one as X because that then becomes the second
parameter.
So we declare the brackets says all this will assumes to be a 1.
Now if you do that one the energy and the Xs.
So here the elements are contaminated with these y axes or we can see that column x.
So here we have same index one two three four five.
And here we have all the values.
But if you notice here we have this one E and E we are both e are different.
Here we have a large one here there too.
So it's supposed to be three there with single e.
But this thing is concatenation and concatenation is done on strings.
If you remember.
So if you try to add two strings like B plus E you will get the E.
And if you do something like one plus two you will get well instead of three.
So that's what's happening here in concatenation David we be some that by joining concatenation and
merging are all different from each other and their three concepts are not what it seems to be their
working is just like combining the the difference but they're a little different.
So if you do hear now three with is one you will get this one that is five here that the index that
is same on both that I have taken that's why I have taken same index here one only one index and are
only one column seem to show that one.
Now here we have this one.
And if you know geez the access to zero you will get the one like this one the indexes will be in middle
and ABC each just those are common because these columns are common.
So X is zero means we are then doing with this one.
And these are common X is one which means nothing will be common.
All will be separated.
So this is when we have index values different.
So when we have index values different multi columns value will be same billion or divide any X as one
we will get this one without any empty space but with index is zero.
If we have different same indexes but different column values we have this thing.
So that should be focused here.
That's how these things are working so that you will not get confused them if you mode look out at this
one here.
We do not have how in this one
so we are not done with the concatenation and the how that we have done indeed before that is in joining
in that outer left and right are not in this one because the elements would not combine here.
So concatenation is just simple have two or more data frames and you can add that one like this.
And if you do something like this one the F three you will have all the trees concatenated here that
is now if you notice when we have different indexes but same column value the columns will be shared
like here this one is D7 be a tree.
The columns are shared but not with those who have different column value but same indexes.
And if you done the X is equal to one you will get something like this one.
Now the same indexes will be shared but not the columns.
So that's what all about concatenation.
I hope you understand that one and I know you will get a little confused here because of these indexes
different seem different to him but if you focus on this video only once you will get that all properly
just of one concept that is the Xs and index variation or column variation just focus on that one according
to the condition you need to concatenate the data frames and the rest will be very easy.
So thanks for watching and we will continue with merging in the next video.