In this video, we will learn about web scraping in power, so we will have to pass in total and today
we will do the part parfum.
For this, I need to show you one website, Wadlow meters dot info.
And here we have different population collectivize.
For example, if I click China, then we can see.
The population of China.
Right, so here we have different columns and then let me copy this link and if I want to see a population,
for example, India.
So, again, I need to play one by one to see different population in in different cities.
So here you can see we have a population in different cities as well.
So we are Indesit to fetch this information.
And for this, I need to go to the power theory here first.
I need to get data.
And you have to click whip because we need to get the data from the Web and we need to be Stelling and
click, OK?
And now the paperback is extracting the data.
And here you can see we have different data.
Let me submit so here you can see the table one we have Colomban and table two, we have data and population.
And then again, you can see different tables that are available on the website, Overbay has accepted
all the information.
So here you can see in the table six, we have a city name and population, so we are interested in
this table for this.
We need to click.
And this statement, and then we have to transform later.
So here we have data, 40 cities in China, two different cities and their populations.
Let's go to the audience editor here.
And.
If I, um, let me show you the website, if I go to like India or maybe.
So here you can see if I mouth over on India and you can see this is India dash population and the India
hyperlink.
Right.
So if I click USA, then is the US population.
So then what we have to do is let's call the power rate.
And the advance editor and here we just need to replace China.
Possibly want to see the population for the India so you can see India population and then we have a
click then and you will see we are getting all the cities from India and in the same way we can quite
different sitting here like a US based population.
Then we will get the data from the US.
Right.
So let's light one function here.
So it would be as simple.
Country name is taxed because it's a tax information and we need as a table and then we have the right
to say equal and greater, then.
So this is the function, right?
The name and we just need to click on.
Before clicking, then, we just need to replace.
Here they are, the population we have to replace the name, the parameter name, like the word for
the.
What was what is the name of the pyramid of this country name?
I just need to copy and then I have to, because every time this thing is being changed in the adult
population.
So what I have to do is for this, I need to write and ampersand.
And then I have to write the name of the perimeter escorting continent and then again and sign and hear
the wind, right.
OK, so this is the stuff that we need to right here.
And so it is taking we are putting country name as a barometer to the to the you are it.
Right?
So let's click done.
And here you can see we are getting one convenient barometer.
So here I have to, for example, if I want to get data from the U.S. So I just see right here is the
population.
And then I need to click, OK, invoke.
So here you can see this is the population from us and the same like if I was in Pakistan, click,
OK, and here you can see the population of all the different cities.
How many more?
So this is how we can get data from different countries.
So let's do one more thing.
We we just need to delete this invert function cable.
And the latest.
And we need to write one BlackBerry here, too, because we don't need to right every time different
countries or we will make a table that will have different countries inside.
So we need right here like BlackBerry.
And today it was Ed.
And here we have two.
Delete this one.
And then we have right here, for example, one table hash table.
And then when it right died, David.
And they had some.
The Blackhawks and the heroic and write country name.
And then when you tried Koma.
It and then one more calibrated because we need a couple of values here.
So here we have to write the name of the the curious thing.
So it is India.
This population.
So I just need to copy
and then I need to create another.
So this is.
US population and we can create more for Ssempa.
Pakistan population, right?
So here we have created one table that contain three the to India.
India population, the US and Pakistan, and then we just need to click 10.
And the name of the stable we can right here
country list, this is the name of the table and what we can do now, we just need to click Ed column
and here we have to invoke custom function.
And in the custom function.
We need to listen to one one one more thing here.
This is the name of a function, right?
So we have to write a name like a fine
country, and then we just have to click then and again to the country list.
OK, so here we have all the three values and now we need to click invoked function, invoked custom
function.
So here you can write any any name of the column, for example,
combine data and the function query, this is the name of the curious country and then the country name.
This is the table like the country name table.
So that's all.
So let's click, OK?
And here is the information is required to get approval to continue.
And ignore privacy level checks.
Save.
And then we need to wait and here you can see.
They bailed out Ed column country list and we will get one one table that will have one column, the
color name will be continuous and it will contain the information.
And here you can see each so this function F and country will execute for each curious thing.
I see.
So this function has been executed for the Indian population, U.S. population and Pakistan population
and fetch this record city name and some other for every country.
It has received different messages depending on the country.
We need to just need to combine data for the India, US and Pakistan.
We just need to click here and you can click, OK?
So here you can see we are getting data like a country name, and this is a city name and city population,
and here you can see this in India and these are the different cities in India and their population.
And then you can see us.
And here it is the same I mean, different cities in the US and the population in the same way in Pakistan.
Right.
So what we have done here, we have just created one one table that holds all the recorded data that
we need to scrap for Zimbabwe.
We wanted to separate our two countries, Pakistan, US and India, and we don't need to go every time
to the website to change the U.N. And I mean, then we just need to if we need to combine all the data
in in one column, like a country name or the population of Sydney, then we don't need to go to like
for example, here you can say we have different is so far has been used to get data from the US and
then we need to get data from in India, like India and Pakistan in three different tables.
And then we have to apply Mouskouri to to match all the data in the single destination.
Right.
So with the help of some function in poverty, we have to all the stuff in one step.
So this is how we can use custom function to retrieve the data very quickly.
So this is this was the first part.
We will continue to enhance the web scrapping in poverty.
In the next part.