Hey, welcome to today's exercise.
In this exercise, you will be doing this analysis.
Now, yesterday you learned about data analysis, but today you'll be consolidating that knowledge by
doing things yourself.
So let me give you the exact tasks you have to do for this exercise.
First task is to please go to the lecture resources and download the ones that CSV file from there.
And before I go to the next task, let me explain you what this dataset contains.
So I have opened it in a spreadsheet program, and these data are about wine tastings.
So let's take the first row.
What happens here is that a person named Caryn tasted the wine with title in the course of 2013 Voelker,
Bianco, Etna, and they gave a review.
So they left 87 points after tasting this wine.
They also left a description.
The wine has also a name.
It comes from Italy.
And then we have the price.
The price is missing for this particular row.
So some values are missing, empty, that's fine.
And other wines have a price.
So the price for that bottle of wine, the province where the wine comes from, the region.
And yeah, those are the data and some other data here, which we don't really need.
So each row represents a tasting of a particular wine and we have the data for that now.
Your tasks will be to, as I told you, get this CSV file, place it in folder and then open Jupyter
and then you need to.
Answer a few questions about those data.
So you'll be doing data analysis.
But what data analysis is, is that it is a process to turn data into information.
So currently these are simply data.
I mean, we cannot tell what's going on here.
That's why we use data analysis to answer a few questions.
So to give us more information, a better overview of those data.
So the first thing you need to do is, of course, to load the data frame in a variable.
Then the first question you need to answer, or in other words, the first information you need to get
from these data is to find out how many wines have been given a rating of 100 points.
So I showed you that.
So we have this Point column here.
And the ratings range from 0 to 100.
So the highest is 100.
And you need to find out how many ones have been given a rating of 100 points.
So you need to answer that question by using Python.
The next question is what is the name of the most expensive wine?
So the name column is here.
This one here.
So you should make a query to find out what is the name of the wine that has the highest value in the
price column.
The next question is to calculate a new column where you show the ratings in scale from 0 to 5 and float
values are allowed in this column.
So you can have 0.1, for example, 2.3, etc..
So in other words, what you need to do is you need to add a new column in your dataframe, and that
column will have the ratings, you can name it the ratings, for example, and the column will have
the ratings, but in a scale from 0 to 5.
So this has a scale of 0 to 100.
So you will convert this basically into a scale from 0 to 5.
I hope that's clear.
The next question is to create a price histogram for wines that cost less than 100.
So don't create a histogram for all the wines because you have some outliers data which are prices which
are extremely big and they will not create a good histogram.
So you need to only create a histogram for a smaller range.
So in this case you need to create the histogram only for the ones that cost less than 100.
And the last question is to plot the price horizontally against the points vertical.
So X is price points is vertically so.
Price.
This one points this one.
The purpose of this is to find out if there is some relationship between price and the points.
So.
So that helps us as data analysts to answer the question of is ratings increasing as the price increases?
So your task here is to just produce the plot and then you can just think of an opinion.
What do you think if there is a relationship or not?
But the main idea is to produce a plot using the plot method, and here is the tip inside the plot methods.
You can pass a kind equal to scatter arguments to get a plot of points instead of lines.
A scatterplot better represents relationships and a line plot represent a trend across time.
Yeah, so those are the tasks you have for this exercise.
In the next video, I'll show you my code solution.
So either.