This if the final course in the specialization which builds upon the knowledge learned in Python Programming Essentials, Python Data Representations, and Python Data Analysis. We will learn how to install external packages for use within Python, acquire data from sources on the Web, and then we will clean, process, analyze, and visualize that data. This course will combine the skills learned throughout the specialization to enable you to write interesting, practical, and useful programs. By the end of the course, you will be comfortable installing Python packages, analyzing existing data, and generating visualizations of that data. This course will complete your education as a scripter, enabling you to locate, install, and use Python packages written by others. You will be able to effectively utilize tools and packages that are widely available to amplify your effectiveness and write useful programs.
Project 1 Video
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From the course by Rice University
Python Data Visualization
75 оценки
From the lesson
Week 2
This module will teach you about packages and modules in Python, including how to install packages and how to create your own modules. You will also learn to use the Pygal plotting library.
Meet the Instructors
Scott RixnerProfessor
Computer Science
Joe WarrenProfessor
Computer Science
[MUSIC]
Throughout this class, we're going to use GDP data from The World Bank for
countries around the world.
We will visualize this data in multiple different ways to allow
us to understand the economic health of various countries around the world.
In this first project, we're going to use this historical data to draw line plots
of the data for countries that we select.
Now in order to do this we will use the Pygal plotting library.
But the functions that you need to write for the project are not coupled to Pygal.
We're going to write them in such a way that they should be useful regardless of
what plotting library you use.
In order to extract the correct information from the data and
put it into the correct format that you can then pass to the plotting library.
Finally, you can visualize that data in Pygal
allowing you see the fruits of your labor so to speak.
All right, let's look at the project.
As with all of the projects throughout the specialization,
you will be required to follow a set of coding style guidelines.
Following such guidelines is really important.
It's useful for you to be able to write your code clearly and consistently so
that when others look at it, they can easily understand it.
Frequently that other person who's looking at it is you some time in the future.
When you look back on your code a year later, having followed the clear set of
coding style guidelines will allow you to very easily understand your code and
maybe use it again.
Also, as we have been doing throughout the specialization,
we will be using our machine grading infrastructure called Owltest.
If you go to this particular Owltest page here,
you can submit your project as you work on it.
And the machine grader will both grade the project and
give you feedback allowing you to assess where you are.
Remember though that Owltest does not submit any grades to Coursera.
Once you're done with the project and ready to submit it to Coursera,
you have to go back to the main page and submit to the actual LTI assignment page.
That will register your grade with Coursera.
Now as I said before, we are going to provide
you with GDP data that we were able to acquire from The World Bank.
But we would like our code to be more general so
that it's not tied to that particular data.
And so in order to do so we will create a GDP information dictionary.
And I'll call that a GDP info dictionary
that contains information about the GDP data.
There are seven key value pairs within that GDP info dictionary.
And the first one is the name of the GDP file.
Then we have the separator and
quote characters that are used within the CSV file that is going to hold the data.
And we know that we're going to need that when we actually try
to read the data out of the CSV file using python CSV module.
Then we have the minimum and maximum years that there is data for within the file.
And it's easier to encode that information here in this GDP info
dictionary than trying to read it out of the file itself.
You could potentially do it.
But you'd have to look at all the column names, and decide whether or
not they were years or not, and then sort them and so on.
And so it's a little bit simpler just to encode them directly here.
And then finally, we need the name of the column that is the country_name and
the name of the column that is the country_code.
Now we're going to use exactly the same GDP info dictionary through out
all of the projects in this course so don't be alarmed if you don't use
all of the fields in the GDP info dictionary in this first project.
Don't worry, you'll use them in future projects and so
if you want to see what one of these looks like there is one in the template that we
provide, But you can also see it here directly, okay?
And so you can see the seven fields.
This is what one of these would look like, and in fact, this is what the GDP
info dictionary looks like for the data that we've gotten from The World Bank.
By using this, we can now use other data.
We can test our code on data that's not the full data set.
We can also acquire GDP data from somewhere else and
still be able to use our code without having to re-write it.
There are four parts to this assignment.
The first one is to read a CSV file as a nested dictionary
by creating a function called read_csv_as_nested_dict.
Now if this sounds familiar to you well it should, we've already done this.
We did this in the last class and
if you have that solution feel free to use it here.
So hopefully this is not something you need to write.
If you'd like more practice though, feel free to go ahead and
write it from scratch.
And don't bother looking back at what you wrote before.
See if you can do it much easier this time around.
The second problem that you're going to attack here is to
create a list of GDP data suitable for x,y plotting.
So we're going to write a function called build_plot_values.
And that's going to take a gdpinfo struct and then it's also going to take gdpdata.
This is a single country's GDP data stored in a dictionary whose keys
are strings indicating year.
So it's mapping the stream year to strings indicating the country's corresponding
GDP for that year.
So it maps the year to their GDP, both as strings.
This function is then going to produce a list of tuples
where each tuple has two elements.
The first element being the year and the second element being the GDP.
And we don't want these things to be strings.
Instead, we'd like to have the year be an integer and
the GDP be a floating point value.
We can then take that list, potentially, and
plot it using a our favorite plotting library.
Now, we also want that information to be sorted in order from
the minimum year of the data that's stored in the GDP file up to
the maximum year of the data that's stored in the GDP file.
For our next function,
we want to be able to create a dictionary mapping countries to these XY plot lists.
So we need to create a function called build_plot_dict which takes as input
gdpinfo and country_list where gdpinfo is one of these GDP info structures.
Country_list is a list of strings that are country names.
These are the countries for which we want to create these plot lists and
put them in to the output dictionary.
So this function should return a dictionary whose keys are the country
names that appeared in country_list.
And whose values are XY plot list as returned by the previous function
that we get out of the CSV file, so that we can then have a dictionary
of countries and their GDP values that we can go off and plot.
Finally, we want to be able to create an SVG image of xy_plots for
the yearly GDP data for a set of specified countries.
An SVG image is just a type of image and we will use Pygal in order to do this.
So you're going to write a function called render_xy_plot.
It takes a GDP info dictionary, a country list
which again is the same as the inputs to the function we just talked about.
And then a plot_file, that's the name of the file that you want to output.
You would then use the functions that we've already written.
And you'd use Pygal in order to actually generate one of these
SVG files which you can then look at.
Now, the first three functions that you need to write will be graded by Owltest
and that will contribute to your grade on the project.
This last problem here, well, there's a lot of different ways of creating these
plots and they can be slightly different.
So we're not actually going to machine grade it.
Instead, we would like you to self-assess how well you're able to do
on this project.
If you wrote the functions that are graded by Owltest correctly,
then this function really comes down to using Pygal correctly in order to
plot the data that you are able to get using the previous functions.
And we have provided you with our own SVG files that show the correct output.
So once you've written your function, you should generate your own SVG files and
then you should compare them to ours.
Other than the names on the axes and other small little details,
they should look identical so that you can verify that you're doing the right thing.
I hope that you'll enjoy this part because it shows you that you are able to put
everything together and actually visualize your data.
I do want to emphasize that while we do give you a few hints here,
we have not taught you everything you need to know about Pygal
in order to be able to write this function.
Why?
Is it because we're lazy?
No, it's because reading documentation is important.
I am expecting you to go off and read the Pygal documentation to fill in whatever
holes you have in your knowledge in order to be able to create this line plot.
That's going to be true in the future projects as well.
It is a really important skill to be able to read the documentation
of a new package in order to figure out how to use it.
And so we're asking you to do that here.
That may seem a little bit difficult but practice makes perfect.
We want you to actually go off and do this.
This is a very important skill for a scripter to have.
We're not going to be able to teach you everything so
you need to be able to learn from the documentation.
You can do it.
I really hope you're going to enjoy this project.
You get to actually see the results in a graph.
Also, you're going to put together a lot of the skills that you've been learning
throughout this specialization.
You're going to read data out of a CSV file, process it so
that it's in the right format to do what we need to with it, and
then actually be able to create a plot out of that data.
So you're going to see the results of what you're able to to do and
that's going to be pretty exciting I think.
And also, you can plot the GDP data for whatever country you'd like.
Well, maybe not quite.
It has to be in The World Bank's GDP data, or well,
actually maybe you could go find the GDP data somewhere else if you
wanted to look at a country that isn't actually in there.
Although, most countries are in The World Bank data.
Ultimately, you'll be able to have these pictures,
you'll be able to see what's going on.
And this is really the power of visualizing your data.
And so hopefully you're going to get a real thrill out of being able to see
the results of your work.
All right, go ahead and get started and good luck.
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