Hi, welcome back.
In this lecture, we will learn how to visualize our data that we analyzed from sea is we find that
we created and alluded to it earlier.
So as we learn before that, we first import Banda's as BDM, run the cell to import Bizim Banda's module.
Then we run the cell, then we will read the sea ASV file.
Using Banda's module has the following.
D.F. Equal Dautry underscores ESV between two brackets and two parentheses inside the two brackets.
Airlines underscore data underscore Bertozzi as V, which is name of our file.
So now Banda's read our C is V file and ready to accept Pisin Banda's data frame comment or methods
to apply it on our data and CSV file.
And we will run the common D.F., which is the name of our data frame which will hold our C is V file.
We will run as D.F., which is the name of the data frame holding the data of our C as we file.
After we run, it will return at Returnee's.
The data from sheet, which contains columns and rows of our C is V file, as you see.
Then we will run the following common D.F. Duceppe, which after running it will return the number of
columns and rows in our cities v file, which is one million and sixteen thousand and six hundred twenty
five rows and six columns exist in our data frame.
Holding the C is V file data.
Another comment that we will run, or Massood, which is the F the size as the following, which will
return the number of values in our CSP files, which is six million ninety nine thousand seven hundred
fifty values.
Then we will return the rounded value of the mean of the flow data type sales column, which is one
hundred and six thousand and one hundred eighty four point forty one.
And before the beginning of data visualization, we will first take a sample from our data frame because
our data frame is huge and the name of this symbol is the F one and contains 15 records.
So the this one equals the F that sample between two brackets, 15 and the one brand 15 records, as
you see.
Then we will visualise the first sample, the F one and the following, but may not let that by plot
as p l t cells equal the F one between two square brackets says between two parentheses.
So four months will declare two variable manses, then BLT dot bar manses and sells and BLT y label.
Our Y label will be sales and BLT dot x label.
Our X label will be summer months and finally use so method to show our chart as the following and run
the sale.
We get that the sales in August more than the sales in July, more than the sales in June.
So August is a month to month and sales of tickets in our database airlines.
But to avoid sampling error, we will take a number of samples and visualize the data from these samba's
to get the best results without any sampling error.
Second sample that we will take is as follows.
Equal the F sample between two brackets 30 and runs this sell and visualise the second sample as following
says equal D.F. to between two square brackets and between two parentheses cells and Monson's equal.
They have to between two square brackets and two parentheses months column milty not born between two
brackets, manses and sales variables.
Belcea Dot, XRX between two brackets.
Manses and rotation equal horizontal.
And size equals 10, the size of our chart is 10 and BLT dot y level IT cells and X label as summer
months and BLT dot show and run the sell, we will get about a chart from it.
We can conclude that the July month is the most months in the sales of tickets.
Then August Zengel the seventh Sambell is as the following year.
F three equals the same five hundred between two brackets.
Then we will visualize our third Sambell as the last two sambas.
We will get the following chart, which is showing that August is the most months is in July.
The June.
And the sales of tickets in our database airlines, the first assemble as they have for equality after
Sambal, one thousand between two brackets, and we will visualize the first scramble as the other three
samples that we visualize.
Then we will run the code for the fourth sample visualization as the following by clicking shift and
enter after the visualization to the first sample.
We get that the same result of the first and the third sample visualization that August, the most months
in the sales of tickets.
Then July and June.
Then we will take the fifth sample as the following day at five equal D.F. dot sample between two brackets
five.
Then we visualize the 50 samples that we take as we learned before.
So we get from Zabari chart the same results from the first and third and fourth samples that we visualize
before, which is that August is the most months in the sales of tickets since July is in June.
The last time we take is the second sample as the following day.
F six equals the F Dawsonville between two brackets.
Ten thousand.
Then we visualize the thickest and last sample as the following after the chart of the last segment,
viz. return it.
And if it's used, we get that the same bill, same result as the first and third and fourth and fifth
sample visualisations.
Its results lead us to our final conclusion, which is the most months and series of tickets is August
than July than June.
At this point, we reached the end of this lecture.
I hope you enjoyed this lecture and get all of that.
Thanks for being here.
Thanks for watching.
See you next with you.