Hi, welcome back.
And this lecture, you will learn how to visualize the data that you from grabbing of it, as we said
before, firstly, we will involve pandas as we then import mateparae the plot as builtin, then we
will run the self as a following.
Secondly, we will use pandas to read the HTML file from our webpage, then bring that data.
And finally, we will restore the important part of that data in a data frame, for example, called
D.F..
So the factual data between two square brackets zero.
Then we will present the data frame by type D.F. and Excel and run that.
So now we will use matplotlib by plot module to visualize that data as the following assign website's
variable to the EFF website column.
Then we do that for the second column.
We want to visualize popularity.
So we have now two variables websites and unique visitors per month and ready to be.
Then if we want to add a great effect to our short, which will be a pie chart, we will use explode
or explosion Caywood.
As the following, but firstly, as fines explode values to a variable called AXP, as we will choose
the first three important values to be.
Four point twenty five exploded far from our pie chart and the rest of values are zeroes.
Then we will write the code for the blood method as the following e x, the equality between two square
brackets, four point twenty five point one in five point twenty five and comma between them, then
comma zero zero zero zero zero zero zero zero zero zero.
So we will complete that 13 13.
Websites, all 16 volumes of our columns as the following by exploded Xeros bellati dot by between two
brackets, unique, underscore, visita, underscore, bear, underscore months and labels Werbe website
schedule will be through, radius will be two and Utterback will be percentage two point one f centage
percentage between to Prentice's and explode equal Yaqubi and DLT Duchow.
From this, could we observe that we have unique visitors per month variable as the values of the bytecode
and the labels for that values will be the website's variable values?
Also that we choose to shadow our pie chart as shadow equal.
True then to percentage of every value of unique visitors for every website we will add up to baked.
World as the following as.
Percentage two point one percent if presented with a sentence.
Lastly, we add explosion to our pie chart by using explode Caywood as we discussed before.
The last episode in the sale of data visualization using matplotlib block by block module as the methods
as you see VLT, Duchow then runs the cell and we get our Bichard.
Please look at this bite chart and write your conclusion and you will compare your conclusion with my
final conclusion.
Our final conclusion will be as the following Zumwalt's autosite, a number of unique visitors as Google
and the most second two sites and a number of unique visitors are Facebook and YouTube.
JavaScript is the most wanted programming language in these tech companies.
Most database management system in these companies is Maria De.
At this point, we reach the end of this lecture.
I hope you enjoyed this lecture and get all of that.
Thank you for being here.
And please don't forget to stop the video and make your observation about the data visualization and
make conclusions that better than the final conclusion or convey our conclusion with my final conclusion
as data analysis.
Final conclusion of report differ from person to person according to data, but not differ.
And in fact, this differ in expressions.
So try to write.
You'll find a conclusion by yourself.
Thanks for watching.
See you next with you.