Hey everyone.
So this video is about creating the data for pandas crystallization and you're gonna clear it up and
you are working on that.
But here this is a separate video in which I'm going to show you that how you can create some of the
data frames and we will use these data frames to realize by using pandas.
So here I have the Jupiter notebook.
First of all import pandas as baby also import seaboard as a S.A. and NIMBY as and b that is it for
importing seaborne is not where we are going to seaborne.
It's just we need the data from that.
Also at this length that is met bloat lab
space in line and difficult.
Now if you remember this dataset that is the data set that I have downloaded randomly This is 2 0 wondered
easy to CSP in the previous videos when we have done this one in by loading the dataset in pandas.
You have the name ECD or CSP.
Now you will get to 0 1 not easy to CSB so be sure about the name and again if you forget where to save
this file that you have downloaded go to computer data the hard drive users Dave whatever your location
is and you can get the location just by writing the WD.
So this is for those who forgot this thing.
Now just copy that one and paste it here that is here.
This is too to wonder wondered easy NTSB.
I have already posted this one so that the one type of data here.
Now let me create few details.
That is first we have the effort and this is going to be simple seaborne dataset that is S.A. don't
load underscore data set and load any one of these like tips.
The most common one that we have used.
After that we have the F2.
This one is going to be simple be read by the CSC matter and define file name that is disparate.
So you need to pass here to 0 1 doped ECB note CSP so extension is also required when you read five
files from your hard drive.
So David go with that one life of print one.
You have this one and if you print the F too you will have despite the standard data set.
And let me know remote not those more separate data types so rated assets that are random datasets that
we are going to create.
First one is x1 and this one is going to be just and B don't random note Randi make sure you are not
writing already and here and I will show you why.
But before that just ran with hundred draws.
And let we have five columns then we have another dataset sorry.
And in the random variable that is and B do random not read sorry friend may we have only 10 draws and
five columns.
Now create these two intra day difference that is DLF three now and then we have D4.
So first day of three and that will be just BD.
Don't get a frame.
I'm going a little fast because you are all expert in these things.
Now just so that you have the data now.
So here we have X1 first and then defined the columns and the columns would be something like we have
a.
And let we have small letters so that it's easy to use them b c b and we have five columns so to be
otherwise you will get data.
Now if you've been DFT you will have dispatched a data frame of hundred rows.
This one is 99 because this is beginning from 0 and five columns and if you did not provide him five
values you will get there Adam.
So make sure you are writing the exact number of columns wheels.
This one is the most prone to these errors.
Now we have the fourth dataset that is these seem like this one data frame.
There we have just x 2 instead of X1.
Then we have columns and we will provide the same columns.
This in command c come on.
There you go.
Now if you print the fourth we will have this kind of data.
Now you can do all the things with only one kind of data.
But I'm showing you here all the kinds of data so that whatever the type you will get in your project
you will easily able to do that.
So thanks for watching.
This is only about creating the data as from the next video.
We will look at how we can you blow these data so see in the next video.