Hello, everyone.
In this video, we will import our midweek election data and do Python.
So before doing that, let's first import all the important libraries that we need.
First, we will import number with Alia's as and B, we will import pandas with Elías as pilly.
We will import Seabourne with Elías as an and we will also import my blood clip, not by a plot as ability
to execute DUSSEL.
If you remember, you can shift less and that.
It will execute the sale and it will make the next sale as active sale.
If you want to insert a blank cell after your currency, then you can press or you enter.
It will execute your current sale and it will insert an empty cell below your current cell.
So here we will use tender now, importing data as found, our data frame is very easy.
You just have to use read Reed, Underscore CSP, Mentone.
And if you hit shift less stab, you will get all the parameters that we need for this 3D underscores.
Yes, we might hold the first parameter here is file, but.
So first, you have to enter the final part of the file you want to import, since my movie underscored
regression NTSC file is in my default folder.
I don't have to write the whole file, but I can just write the file name.
If you have restored your file in some another directory, you have to write the full file.
But.
And remember to put forward slashes and sort of back slashes while inserting your file.
But.
The second parameter here that I'm going to uses headers equate to zero since my ACSU file have Haddad's
at zero row.
That is the first true.
Consist header.
That's what I have to write, headers equate to zero header.
Since my files I've heard it and zero since the headers are at the first row of my file and indexing,
although that starts at zero in Python.
I can execute this.
Now, to view a sample of our beef data frame, we can just read D.F. Dot Head.
This is my third, which will give us the sample of first five rows of our data frame.
You can see this is over.
They tapped him on the top.
We have all the column headers and on the left we have the indexes there, zero, one, two, three,
four.
And in between, we have all the data, all four data from.
You can see that the last column here is collection.
This is our way, variable or dependent, variable and dress of all the variables are over independent
variable.
Now, to get a quick summary of data types and account of each variable we can use in formatter.
We will write D.F. Dot in full.
And we will execute.
You can see on the top we have that type of data frame, since this is binary data frame.
We are getting pounded our core frame dot data frame.
Then, since our data frame consists five hundred and six entries.
The second goal here is telling us that there are five hundred and six entries and the index range is
from zero to five zero five.
Then we have all the detail columns that we have in our data frame.
We have the cone of columns.
You can see for all the columns except time taken, the count base five zero six.
This means that there are some null values in our time taken, there are empty rows in time taken column
in the later part of this course, we will see how to treat our time taken very well to correct all
the missing values.
Then.
At the last we have the tape of each column, you can see most of our variables are of flawed and end
date.
But there are two variable.
Which is to be available and Jonah, which are object type object, Mings String.
So these two variables are a string type variable.
And in the later part of this course, we will see how to convert this categorical string variables
and to numerical dummy variable in the next video.
We will see how to treat missing values.