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 2 Video
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Do curso por Rice University
Python Data Visualization
100 classificações
Na lição
Week 3
This module will teach you about Python sets. Sets are used to hold unordered collections of data without duplicates. We will also discuss efficiency.
Conheça os instrutores
Scott RixnerProfessor
Computer Science
Joe WarrenProfessor
Computer Science
[MUSIC]
In the final project in this specialization,
we're going to plot GDP data on a world map.
So for a particular year, we'll be able to visualize the relative economic health
of countries around the world.
Now, a key component of doing this is figuring out how
to correlate the countries within the GDP data
with the countries that the plotting library understands.
So we're going to break this project down into two parts and
look at two different ways of doing that.
In this first part, we are going to use the actual country names.
So Pygal understands certain country names, and
the World Bank records the GDP data with country names as well.
And so we'll try to match those up and plot the data on the world map and
see how well we do.
So let's take at how we can go about doing this.
As always, we expect you to follow the coding style guidelines for
the course, and we are also going to provide you with an Owltest page so
that you can machine grade your code as you work on it.
Now remember, Owltest is different for every project, so you have to follow
the link here on this page to get to the Owltest page for this particular project.
We are also going to be using GDP data again in this project.
So we will again be using a GDP info dictionary to describe the GDP data,
or rather the file within which that GDP data is stored.
You should already be familiar with the GDP info
dictionary structure from the previous project.
This project has three problems within it, so let's take a look at these problems.
For the first problem, we need to create a dictionary that maps
Pygal country codes to World Bank country names.
Now Pygal, like any other plotting library, does not
use country names to indicate which country you're trying to plot data on.
Instead, it uses country codes that are a little less ambiguous, let's say.
So Pygal uses a set of two-letter country codes and so
when you pass data to Pygal asking it to plot on a world map,
you pass it a dictionary where the keys are these two-letter country codes and
the value is the data that you'd like to plot.
In our case, we're going to plot numerical data, which is the GDP data, and
the differences in that numerical data will change the shade of
the color that Pygal uses for that particular country.
So first, we're going to write this function and
we're going to call it reconcile_countries_by_name.
And what we're going to do is take two inputs.
The first is called plot_countries,
which is a dictionary whose keys are the plot library country codes and
the values are the corresponding country name.
Notice I didn't mention Pygal here, right?
We want to write this function such that it'll work with any plot library.
All we really need from the plot library is a dictionary that maps the codes that
that plot library uses to use the names of the countries that those codes
correspond to.
And then our second input is gdp_countries, which is
a dictionary whose keys are the country names that are used in the GDP data.
We don't actually care about the values.
This is very likely the actual GDP information, but
all we need to know is what are the names that are actually used in the GDP data?
This function takes these inputs and
produces a tuple containing a dictionary in a set, so it returns two values.
That dictionary should map country codes from the plot_countries
input to country names from the gdp_countries input.
And so you're going to have to look through the country names that are values
in plot countries and the country names that are keys in GDP countries and
figure out how to make them correspond.
And so everything that appears in both of the inputs
should appear in this dictionary output where I'm mapping the plot country codes
to the country names that are understood in the GDP data.
And then the second return value is just a set, and that set contains all
of the countries codes from plot countries that were not found in GDP countries.
And so I'm going to want to be able to plot those on the map as well and
in different colors so I can show these are all the countries where I couldn't
find any GDP data because I couldn't find the country name in the GDP data that
the plot library was expecting.
In the second problem, we're going to transform GDP data for
a given year into a form suitable for a world map plot.
And so we're going to build a function called build_map_dict_by_name.
This function takes three inputs.
The first one is a gdpinfo directory.
The second one is plot_countries, which is the dictionary
of exactly the form that was expected by the previous function, a dictionary whose
keys are plot library country codes and values of the corresponding country name.
And then finally, a year.
And this is a string which indicates the year for
which we'd like to create this GDP mapping.
And so what we're trying to do here is create a dictionary that maps
the country codes that the plot library understands to the GDP value for
every country that we can find from the GDP data that we have.
We would then be able to pass that off to our plot library like Pygal
in order to actually display this information on a world map.
So this function should return a tuple that has three elements, a dictionary and
two sets.
The dictionary here should map country codes from the plot_country inputs
to the logarithm base ten of the GDP value for that country in the specified year.
Why do we do log base ten?
Well, we want to compress the data a little bit so
that it's easier to see the shading on the final plot.
If we just plotted the GDP data as is, some countries have a significantly higher
GDP than others, and so it would make it very difficult to see any subtle
differences, as the colors would be very strikingly different for
countries with very large GDP than countries with a very small GDP.
Otherwise you wouldn't be able to see the difference among anything.
Now, the second and third output are two sets.
The first set contains all of the country codes from plot countries that were not
found in the GDP data file at all.
And so you might recognize that output that's the same exact
thing that we return from the previous function we just discussed.
The second set contains the country codes from plot countries that were found in
the GDP data.
So the GDP data did have GDP information about that particular country,
but it had no GDP data for the specified year.
So maybe it wasn't recorded in that particular year, and so this function now
has created output that we can use to actually pass to a plot library.
Our final function here is going to create an SVG image of the GDP data plotted on
the world map.
So this function is going to be called render_world_map and
it is going to make use of the functions that we just wrote
to actually produce data that it can then pass to Pygal.
And so the primary function of this particular function here
is to use Pygal to get the data plotted on a world map.
Now as before, we are not machine grading this as the output will have a lot
of different subtle differences depending on how you do it.
And so we have provided you with SVG files that
are the results that you should expect for passing particular inputs.
And so what you should do now is self assess your function by running it,
generating these plots, and comparing them to ours.
They should look basically the same as ours, perhaps with slight differences on
the names of the axes, the titles and the legends, and so on.
But effectively, you should see the same world plot and
you should see the same countries having relative shading in the same way.
As with the last project, we didn't really teach you everything you needed to know
about Pygal in order to accomplish what we're asking you to do, but that's okay.
We've given you hints to push you in the right direction, and from the last
project, you're already familiar with reading the Pygal documentation.
So I am very confident that you'll be able to figure it out.
Hopefully you're really excited to work on this project.
There's something about being able to see the data on a world map that is
really exciting.
If you think about it, if you just had a big table of data,
it would be really hard to really glean any useful information out of it.
But once you can visualize it on a map, all of a sudden it becomes very
easy to see the relative economic health of countries around the world.
And so we're really, really excited to get to that point in the project.
Now, why did we break this project into two parts?
Well, we're actually trying to match countries up between Pygal and
the World Bank by looking at their country name.
And after you complete this project, I strongly encourage you to go off and
look at exactly what's happening here and which countries are not matching up.
And you'll see that, well, maybe country name is not a good way of
matching up countries in the two data sets.
And so the next project is about taking something that's already pretty cool here
and thinking about, how can we make it even better?
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